A pollution detection method and system based on an environmental consultation knowledge base

Abstract

This invention relates to the field of pollutant detection technology, and more particularly to a pollutant detection method and system based on an environmental consulting knowledge base. The method includes: receiving basic information about the project to be tested, inputting it into a pre-built environmental consulting knowledge base for matching, and outputting a preliminary detection plan; generating a final detection plan after verification using historical cases; performing on-site sampling and in-situ testing, linking the knowledge base to complete real-time quality control early warning and on-site calibration, and obtaining a calibrated in-situ detection dataset; performing laboratory testing on environmental samples and performing three-level calibration to obtain a valid pollutant detection dataset; performing compliance analysis on the valid detection data, and outputting the detection results. This invention effectively solves the core problems of fragmented processes and lack of unified knowledge support in existing detection technologies, overcomes the shortcomings of manual reliance, insufficient quality control and calibration, low data accuracy, and poor analysis efficiency, and achieves intelligent standardization of the entire detection process, significantly improving detection efficiency and data accuracy.

Description

Technical Field

[0001] This invention relates to the field of pollutant detection technology, and in particular to a pollutant detection method and system based on an environmental consulting knowledge base. Background Technology

[0002] With the increasing emphasis on environmental protection in my country, environmental pollutant testing has become a core foundation for environmental supervision, pollution control, and corporate compliance. Currently, in routine pollutant testing, the processes of developing testing protocols, on-site sampling and testing, laboratory data calibration, and compliance assessment of testing data operate independently, lacking a unified knowledge support system.

[0003] In existing environmental pollutant detection technologies, the detection process is typically divided into four independent stages: protocol development, on-site sampling and testing, laboratory analysis, and result output. Each stage employs a manually-driven work model, lacking a unified knowledge support system: detection protocols are formulated by technicians based on personal experience, failing to achieve intelligent matching and optimization based on standardized regulations and historical case data; during on-site sampling and in-situ testing, quality control and early warning systems and data calibration rely on manual judgment and fixed instrument parameters, unable to dynamically adjust in real time based on environmental conditions and historical data; laboratory data calibration is only performed on the instrument itself, without fully considering complex factors such as environmental matrix interference; and the final compliance analysis and report generation also require manual comparison with standards, resulting in low efficiency and inconsistent standard implementation. Summary of the Invention

[0004] This invention provides a pollutant detection method based on an environmental consulting knowledge base, which can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Receive basic information about the project to be tested, input a pre-built environmental consulting knowledge base for matching, and output a preliminary testing plan; The testing plan was optimized by combining data from the environmental consulting knowledge base and the final testing plan was generated after being verified by historical cases. Based on the final detection scheme, on-site sampling and in-situ detection are carried out, and the knowledge base is linked to complete real-time quality control early warning and on-site calibration to obtain the calibrated in-situ detection dataset. Laboratory testing of environmental samples was conducted, and a three-level calibration was performed based on the aforementioned environmental advisory knowledge base to obtain an effective dataset of pollutant detection. Based on the aforementioned environmental consulting knowledge base, compliance analysis is performed on the valid testing data, and the testing results are output.

[0006] Furthermore, the three-level calibration includes: Based on the standard substance traceability data and historical calibration dataset in the environmental consulting knowledge base, the systematic errors of the testing instruments are corrected. Based on at least one quality control rule in the environmental consulting knowledge base, random measurement errors are corrected and abnormal detection data are removed; Based on the pollutant standard characteristic spectrum and environmental matrix interference factor database in the environmental consulting knowledge base, the detection interference caused by the complex matrix of the sample is corrected.

[0007] Furthermore, the basic information of the item to be tested must at least match the regulatory and standard data, pollutant characteristic data, and regional control data in the environmental consulting knowledge base.

[0008] Furthermore, the testing plan is optimized by combining data from the environmental consulting knowledge base, and the final testing plan is generated after verification through historical cases, including: Based on the core features of the project to be detected, adaptive weight coefficients are assigned to different data sources, and at least one dimension of the preliminary detection scheme is optimized and adjusted. The optimized detection scheme was matched with historical cases in the environmental consulting knowledge base in multiple dimensions. The final customized testing solution is generated by weighting the success rate of the historical cases.

[0009] Furthermore, the linked knowledge base completes on-site calibration, including: Simultaneously collect real-time environmental parameters and in-situ detection raw data from the site, and transmit them to the environmental consulting knowledge base in real time; The pre-built dynamic mapping model of environmental parameter calibration coefficients in the environmental consulting knowledge base is invoked to generate an adaptive calibration coefficient matrix based on multi-dimensional environmental parameters on site. The adaptive calibration coefficient matrix is ​​used to perform multi-factor cross-calibration on the in-situ detection data; The calibrated data is then compared with the historical calibration dataset under the same conditions in the environmental consulting knowledge base to generate the final calibrated in-situ detection dataset.

[0010] Furthermore, the dynamic mapping model is a nonlinear mapping model trained using a machine learning algorithm based on the historical calibration dataset in the environmental consulting knowledge base.

[0011] Furthermore, compliance analysis is conducted on valid testing data based on the environmental consulting knowledge base, including: Extract the detection values ​​of each pollutant and the corresponding detection conditions from the effective detection dataset; The system invokes the hierarchical compliance judgment rule base and pollutant correlation analysis model in the environmental consulting knowledge base to complete multi-dimensional compliance judgment; Based on the assessment results, environmental risk is classified, and a compliance analysis report containing clues to the source of the exceedances is generated.

[0012] Furthermore, both on-site and laboratory calibration of the test data were completed based on the aforementioned environmental consulting knowledge base.

[0013] A pollutant detection system based on an environmental consulting knowledge base, the system comprising: The preliminary solution output module receives basic information about the project to be tested, inputs a pre-built environmental consulting knowledge base for matching, and outputs a preliminary testing solution. The final solution output module optimizes the testing plan by combining data from the environmental consulting knowledge base and generates the final testing plan after verification by historical cases. The in-situ dataset acquisition module, based on the final detection scheme, performs on-site sampling and in-situ detection, and links with the knowledge base to complete real-time quality control early warning and on-site calibration, thereby obtaining the calibrated in-situ detection dataset; The effective dataset acquisition module performs laboratory testing on environmental samples and performs three-level calibration based on the environmental consultation knowledge base to obtain an effective detection dataset of pollutants. The test result generation module performs compliance analysis on the valid test data based on the environmental consulting knowledge base and outputs the test results.

[0014] Furthermore, the final solution output module includes: The weight allocation unit assigns adaptive weight coefficients to different data sources based on the core features of the item to be detected, and optimizes and adjusts at least one dimension of the preliminary detection scheme. The similarity matching unit performs multi-dimensional similarity matching between the optimized detection scheme and historical cases in the environmental consulting knowledge base; The weighted verification unit performs weighted verification based on the success rate of the historical cases, and generates a final customized detection solution that passes the verification.

[0015] The technical solution of this invention can achieve the following technical effects: The environmental consulting knowledge base automatically generates preliminary testing plans, replacing the traditional manual experience-based approach. This eliminates quality fluctuations caused by differences in personnel expertise, ensuring the uniformity and standardization of testing plans. The preliminary plans are optimized using knowledge base data and validated through historical cases to generate the final plan. This allows for full utilization of past successful experiences, accurate matching of project characteristics, avoidance of redundant testing and missed detection of characteristic pollutants, and improved plan relevance and feasibility. During on-site sampling and in-situ testing, the knowledge base is linked to enable real-time quality control and early warning, as well as dynamic on-site calibration. This promptly corrects non-standard operations and automatically adjusts calibration parameters based on on-site environmental conditions, effectively improving the accuracy and reliability of in-situ testing data. Three-level calibration based on the environmental consulting knowledge base not only corrects for systematic errors in the instruments but also effectively eliminates interference from complex factors such as the environmental matrix, significantly improving the detection accuracy of low-concentration pollutants. The knowledge base automatically completes compliance analysis, replacing the tedious process of manually comparing standards one by one, greatly improving analysis efficiency while ensuring consistency in compliance judgment standards across different projects and personnel. By establishing a unified environmental consulting knowledge base that runs through the entire testing process, data silos between different stages are broken down, knowledge is accumulated and reused, and a continuously optimized testing system is formed, providing intelligent support for environmental testing work.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a pollutant detection method based on an environmental consulting knowledge base. Figure 2 A flowchart illustrating the process of generating the final testing plan; Figure 3 This is a flowchart illustrating the process of conducting compliance analysis on valid test data. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] Example 1: like Figure 1 As shown, this application provides a pollutant detection method based on an environmental consulting knowledge base, the method comprising: S1: Receive basic information about the project to be tested, input a pre-built environmental consulting knowledge base for matching, and output a preliminary testing plan; Specifically, the system first receives basic information about the project to be tested from the client. This information typically includes the industry category, type of testing medium, administrative region, testing purpose, client's specific requirements, and basic project scale parameters. For example, for a groundwater pollution hazard investigation and testing project for an electroplating company, this basic information is used as input parameters and fed into a pre-built environmental consulting knowledge base. This knowledge base is a structured database that pre-integrates relevant environmental testing regulations and standards, industry pollution characteristics, standard testing methods, and historical project data. It then calls upon the corresponding current national and local environmental standards database, industry characteristic pollutant database, and standard testing method database within the knowledge base, through pre-defined multidimensional... The system uses multi-dimensional correlation matching rules for automatic matching. These rules simultaneously filter data based on multiple dimensions, such as industry type, testing medium, and regional control requirements. The system then selects mandatory and optional indicators, corresponding standard testing methods, basic requirements for sampling point layout, and sampling frequency requirements that match the characteristics of the project. Mandatory indicators are pollutants that must be tested according to relevant laws and standards. Optional indicators are pollutants that can be selectively tested based on the specific characteristics of the project. The basic requirements for sampling point layout are the specifications for determining the sample collection location. The sampling frequency requirements are the specified number of sample collections and the time interval. The final output is a preliminary testing plan that includes all of the above core elements.

[0022] S2: Optimize the testing plan by combining data from the environmental consulting knowledge base, and generate the final testing plan after verification by historical cases; Specifically, the preliminary testing plan is optimized and adjusted by combining the latest regulatory updates, regional special control requirements, and historical testing data of similar projects in the environmental consulting knowledge base. Optimization and adjustment refer to supplementing missing characteristic pollutants, removing unnecessary redundant testing indicators, and adjusting unreasonable sampling points and sampling frequencies according to the latest requirements. After optimization, successful historical cases from the same industry, testing media, and testing purposes over the past three years are retrieved from the environmental consulting knowledge base for cross-validation. Historical case verification compares the optimized plan with previously validated and effective plans to confirm the rationality and feasibility of the plan. After successful verification, a final testing plan containing a complete testing process, quality control requirements, and safety precautions is generated.

[0023] S3: Based on the final detection scheme, conduct on-site sampling and in-situ detection, link the knowledge base to complete real-time quality control early warning and on-site calibration, and obtain the calibrated in-situ detection dataset; Specifically, on-site testing personnel strictly followed the final testing plan to conduct environmental sample collection and in-situ testing. On-site sampling refers to collecting representative environmental media samples such as water, air, and soil at designated locations. In-situ testing refers to directly using portable testing instruments to measure pollutant concentrations in real time at the sample collection site. During the testing process, on-site operation data, instrument operating status data, and raw in-situ testing data are synchronized in real time to the environmental consulting knowledge base via mobile terminals. The knowledge base continuously analyzes and judges all uploaded data according to preset quality control rules. Real-time quality control early warning means that when non-compliance with specifications is detected, such as sampling point deviation from the specified location, sampling duration not meeting standard requirements, insufficient instrument power, or abnormal operating parameters, prompt information is immediately pushed to the on-site mobile terminal along with corresponding corrective measures. At the same time, based on the real-time uploaded on-site environmental condition data, including key factors affecting test results such as temperature, humidity, air pressure, and wind speed, the knowledge base automatically calls the corresponding environmental calibration parameters to dynamically correct the raw in-situ testing data. On-site calibration refers to eliminating the impact of on-site environmental fluctuations on the measurement accuracy of portable testing instruments, ensuring that the test data can accurately reflect the actual pollution situation on-site. After all in-situ detection data are calibrated, they are automatically organized into a calibrated in-situ detection dataset and stored uniformly.

[0024] S4: Conduct laboratory testing on environmental samples, perform three-level calibration based on the environmental consulting knowledge base, and obtain an effective dataset of pollutant detection. Specifically, environmental samples collected on-site are packaged, labeled, and transported to the laboratory via a complete cold chain according to standardized requirements. Laboratory personnel then rigorously perform quantitative analysis on the samples using calibrated analytical instruments, strictly adhering to the standard testing methods specified in the final testing plan, to obtain the laboratory's raw test data. A three-level calibration is then performed based on the environmental consulting knowledge base. This three-level calibration process involves sequentially conducting calibration based on instrument calibration data, standard substance data, and relevant environmental matrix data stored in the knowledge base. After calibration, abnormal data is automatically removed, and a valid dataset of pollutant detections is compiled and simultaneously stored in the environmental consulting knowledge base.

[0025] S5: Conduct compliance analysis on valid testing data based on the environmental consulting knowledge base and output the testing results.

[0026] Specifically, the calibrated and valid test dataset is imported into the environmental consulting knowledge base. The currently valid national, industry, and local pollutant emission standards in the knowledge base are then invoked. These pollutant emission standards are the permissible emission concentrations or total limits for pollutants set by the state or local governments to control environmental pollution. A compliance analysis is then conducted, which involves comparing the actual test values ​​of each pollutant with the limits specified in the corresponding standards one by one to determine whether each indicator meets the standard requirements. The test values ​​of the indicators that exceed the standards and the differences from the standard limits are then statistically analyzed. After all the analysis work is completed, the test results, which include basic information on the test items, valid test data for each pollutant, and compliance judgment conclusions, are output.

[0027] As a preferred embodiment of the above, the three-level calibration includes: Based on the source data of standard substances and historical calibration datasets in the environmental consulting knowledge base, the systematic errors of the testing instruments are corrected. Based on at least one quality control rule in the environmental consulting knowledge base, correct random measurement errors and remove abnormal detection data; Based on the pollutant standard characteristic spectrum and environmental matrix interference factor database in the environmental consulting knowledge base, the detection interference caused by the complex matrix of the sample is corrected.

[0028] Specifically, based on the reference material traceability data and historical calibration datasets in the environmental consulting knowledge base, the systematic errors of the testing instruments are corrected. Reference material traceability data refers to the concentration data of reference materials that have been valued by national metrology institutions and have a complete value transfer chain. Historical calibration datasets refer to the error variation data obtained from multiple past calibrations of the same instrument. By comparing the current instrument blank value and reference material measurement value with the corresponding data in the knowledge base, the drift error and inherent systematic bias generated by long-term instrument operation are calculated and corrected. Next, based on at least one quality control rule in the environmental consulting knowledge base, random measurement errors are corrected and abnormal detection data is eliminated. Quality control rules include commonly used quality control rules such as parallel sample relative deviation control, blank sample value control, and spiked recovery rate control. By judging the dispersion of multiple measurements of the same sample, the detection value range of blank samples, and the recovery rate range of spiked samples, abnormal data caused by operational errors and accidental instrument fluctuations are identified and eliminated. Simultaneously, the effective measurement values ​​are averaged. To reduce the impact of random errors, and addressing complex detection scenarios involving multiple pollutants and varying concentration gradients in samples, this method uses pollutant standard characteristic maps and an environmental matrix interference factor database from an environmental consulting knowledge base to correct detection interference caused by the complex matrix. The pollutant standard characteristic map refers to the standard response maps of single-component and multi-component mixtures of pure pollutants under corresponding detection methods. The environmental matrix interference factor database contains response characteristics and quantified interference coefficient data of common interfering substances in different types of environmental media and at different pollutant concentration ranges. The correction method involves: first, locating characteristic peaks and splitting peak areas in the sample detection map to distinguish the independent response signals of different pollutants; then, comparing the split maps of each pollutant with the corresponding standard characteristic maps one by one to determine the pollutant type and initial detection concentration; finally, using the interference coefficients from the environmental matrix interference factor database for the corresponding medium and concentration range, and employing a weighted subtraction method to independently correct the initial concentration of each pollutant. A specific example is the correction of Cr in groundwater samples. 6+ When coexisting with nitrates, first pass through the characteristic peak wavelength (Cr). 6+ (540 nm for Cr, nitrate for nitrate for 220 nm) The response peaks of the two substances were separated to obtain Cr. 6+ The initial detection concentration was 0.52 mg / L; then, the concentration of nitrates against Cr was retrieved from the groundwater matrix interference factor database. 6+ The interference coefficient is 0.08, and after correction, Cr 6+ Concentration = 0.52 mg / L × (1 - 0.08) = 0.48 mg / L, accurately eliminating the interference of coexisting substances and complex matrices on the detection results.

[0029] As a preferred embodiment of the above, the basic information of the item to be tested must at least match the regulatory and standard data, pollutant characteristic data, and regional control data in the environmental consulting knowledge base.

[0030] Specifically, the basic information of the project to be tested is matched with the environmental consulting knowledge base in multiple dimensions. The matching objects not only include three core data categories: regulatory standards data, pollutant characteristic data, and regional control data, but can also be extended to adapt other relevant categories of data within the knowledge base. By matching with regulatory standards data, applicable environmental standards and testing specifications at all levels are identified based on the project's industry, medium, and testing purpose. By matching with pollutant characteristic data, corresponding characteristic pollutants are selected based on industry production characteristics. By matching with regional control data, specific control policies and special limit requirements of the project location are aligned. Furthermore, testing method data, historical data from similar projects, project scale, and commission requirements can be matched simultaneously, comprehensively adapting aspects such as testing methods, sampling layout, and testing frequency. Based on the comprehensive multi-dimensional matching results, complete data support is provided for accurately generating a preliminary testing plan.

[0031] As a preferred embodiment of the above, such as Figure 2 As shown, step S2 involves optimizing the testing plan based on data from the environmental consulting knowledge base. After verification using historical cases, a final testing plan is generated, including: S21: Based on the core features of the project to be detected, assign adaptive weight coefficients to different data sources and optimize and adjust at least one dimension of the preliminary detection scheme; S22: Perform multi-dimensional similarity matching between the optimized detection plan and historical cases in the environmental consulting knowledge base; S23: Perform weighted verification based on the success rate of implementation of historical cases to generate a final customized testing solution that has passed the verification.

[0032] Specifically, the core characteristics of the project to be tested are first comprehensively extracted, including the sub-sector of the industry to which the project belongs, the specific type of testing medium, the administrative region and regional control level of the project, and the purpose of testing. The purpose of testing includes categories such as routine monitoring, environmental impact assessment acceptance, and emergency testing. The above qualitative core characteristics are transformed into quantitative weight coefficients through a quantitative assignment-normalization calculation model. The specific implementation plan is as follows: Quantitative assignment of features: Industry sub-sector, testing medium, regional control level, and testing purpose are set as four core features, and assigned quantitative scores of 0-10 points according to the closeness of their correlation with the testing project. The higher the correlation, the higher the score; Weight normalization calculation: The weight coefficient of a single data source = the score of the feature / the total score of the four features, and the sum of all weight coefficients is 1; Purpose priority adaptation: The feature score weight is automatically adjusted according to the type of testing purpose, with emergency testing > environmental impact assessment acceptance > routine monitoring. The scoring rules are automatically matched, and then adaptive weight coefficients are assigned to different data sources in the environmental consulting knowledge base based on these core characteristics. For example, in an emergency surface water monitoring project in a chemical industrial park, four characteristics are quantitatively assigned as follows: industry segmentation (chemical) 10 points, monitoring medium (surface water) 9 points, regional control level (key control area) 10 points, and monitoring purpose (emergency monitoring) 10 points, for a total score of 39 points. The corresponding data source weighting coefficients are: industry characteristic pollutant data 10 / 39 ≈ 0.26, medium testing standard data 9 / 39 ≈ 0.23, regional emergency control data 10 / 39 ≈ 0.26, and emergency rapid testing method data 10 / 39 ≈ 0.26, achieving a precise conversion from qualitative characteristics to quantitative weights. For instance, in the emergency monitoring project, the weighting coefficients for regional emergency control data and rapid testing method data are higher than those for conventional regulatory standard data, while the weighting coefficients for historical project data are lower than those for emergency-specific data. After allocation, at least one dimension of the preliminary detection plan is optimized and adjusted according to the weight and priority of each data source. The optimization and adjustment is not a simple modification, but rather a targeted correction based on the core requirements of the high-weight data source, including the addition or reduction of detection indicators, the reasonable layout of sampling points, the adjustment of sampling frequency, and the adaptation of execution standards, to ensure that the optimized plan meets the actual needs of the project.

[0033] The optimized testing plan (S21 version) was used as the matching benchmark. Multi-dimensional similarity matching was conducted with complete and traceable historical testing cases archived in the environmental consulting knowledge base. The matching process employed a two-layer weighted similarity calculation logic combining precise matching and fuzzy matching. The core dimensions were divided into precise matching and fuzzy matching dimensions: Precise matching dimensions: industry type, pollutant medium, and testing business type, judged by complete string consistency; a successful match scores 1 point, a failed match scores 0 points. Fuzzy matching dimensions: regional control requirements and testing scale, calculated using interval quantification and deviation ratios; similarity values ​​range from 0 to 1 point. Matching was performed one by one from the above core dimensions. The total similarity = (sum of scores for precise matching dimensions ÷ total number of precise matching dimensions) × 0.6 + (sum of scores for fuzzy matching dimensions ÷ total number of fuzzy matching dimensions) × 0.4. This fuzzy matching logic is used to filter out similar historical cases with similar scenarios and consistent core needs, avoiding omissions in matching due to differences in case details. The preset total similarity threshold is 0.7. Historical cases with a total similarity ≥ 0.7 are judged as qualified matches. Finally, several groups of historical cases with similarity reaching the preset threshold are selected as the verification basis.

[0034] For example, the items to be tested are chemical industry, surface water, emergency testing, key control area, and medium-sized scale; In the precise matching dimension: the industry type (chemical), pollutant medium (surface water), and testing business type (emergency testing) are all completely consistent, with a score of 3 / 3 = 1.0; In the fuzzy matching dimension: the regional control requirements (key control area) score is 1.0, the testing scale (medium-sized) has a deviation of 0.1, with a score of 0.9, and the average score of the fuzzy dimension is 0.95; the total similarity = 1.0 × 0.6 + 0.95 × 0.4 = 0.98 ≥ 0.7, which is considered a qualified match.

[0035] The actual implementation data of each selected historical case is retrieved, and the implementation success rate of each case is extracted as the key point. The implementation success rate refers to the probability that the test results meet the standard requirements and pass the review smoothly after the case solution is implemented. Then, according to the similarity between each historical case and the current project, a corresponding weighting coefficient is assigned to the implementation success rate of each case. The higher the similarity, the larger the weighting coefficient, and the higher the verification weight of the current scheme. Subsequently, the individual weighting coefficient is determined based on the total similarity between historical cases and the project to be tested. The optimized testing scheme is broken down into five evaluation dimensions: testing indicator setting, sampling point layout, testing frequency arrangement, quality control measure configuration, and implementation standard selection. Each dimension is scored independently, with a scoring range of 0 to 10. The scores of each dimension are multiplied by their corresponding weighting coefficients and then summed to obtain the overall score of the scheme. Preferably, the content of dimensions with an overall score greater than or equal to eight is directly retained; the content of dimensions with an overall score between five and eight is optimized and improved according to the knowledge base standards; and the content of dimensions with an overall score less than five is directly eliminated. This completes a comprehensive rationality verification and evaluation. Through weighted calculation, the optimized testing scheme undergoes a comprehensive rationality verification and evaluation. In a specific embodiment, the emergency testing scheme for a chemical industrial park scores nine points in all five dimensions, and the weighting coefficient for each dimension is 0.2. The overall score of the scheme is equal to nine multiplied by 0.2 and summed five times, resulting in nine points, which is greater than or equal to eight points. Therefore, all content of the scheme passes verification. The sampling point layout dimension of a certain scheme received a score of four, with a weighting coefficient of 0.2. Since the score of this dimension was lower than the threshold, the sampling points were redeployed and evaluated again.

[0036] As a preferred embodiment of the above, the linkage knowledge base completes the on-site calibration, including: Simultaneously collect real-time environmental parameters and in-situ detection raw data from the site, and transmit them to the environmental consulting knowledge base in real time; The system calls upon a pre-built dynamic mapping model of environmental parameter calibration coefficients in the environmental consulting knowledge base to generate an adaptive calibration coefficient matrix based on multi-dimensional environmental parameters on site. An adaptive calibration coefficient matrix is ​​used to perform multi-factor cross-calibration on the in-situ detection data; The calibrated data is then compared with the historical calibration dataset under the same conditions in the environmental consulting knowledge base to generate the final calibrated in-situ detection dataset.

[0037] Specifically, firstly, while conducting in-situ testing, on-site testing personnel simultaneously collect real-time environmental parameters and raw in-situ testing data. The real-time environmental parameters include key environmental factors that affect the accuracy of in-situ testing, such as temperature, humidity, air pressure, and wind speed. The raw in-situ testing data consists of basic testing data, such as pollutant concentrations measured in real-time by portable testing instruments. After collection, both types of data are transmitted to the environmental consulting knowledge base in real-time and synchronously via mobile terminals to ensure the timeliness and integrity of data transmission, providing an accurate data foundation for subsequent calibration work.

[0038] Subsequently, the system automatically invokes a pre-built dynamic mapping model for environmental parameter calibration coefficients from the environmental consulting knowledge base. This model is an adaptive model trained based on a large amount of historical field environmental parameters, corresponding detection data, and calibration results, capable of dynamically outputting appropriate calibration coefficients according to different environmental conditions. After receiving the transmitted multi-dimensional field environmental parameters, the model automatically generates an adaptive calibration coefficient matrix through preset algorithm logic, combining the influence weights of each environmental parameter on the detection results. This matrix contains calibration coefficients corresponding to different environmental factors, which can be specifically matched to the error correction needs of various in-situ detection indicators.

[0039] Next, the raw data from in-situ detection were recorded in a standardized numerical format, with the following sequence: pollutant number, concentration value, detection time, ambient temperature, ambient humidity, atmospheric pressure, and instrument number. All data were quantifiable numerical data. Changes in ambient temperature, humidity, and air pressure directly alter the optical response and electrochemical sensitivity of the portable detection instrument, thereby changing the concentration detection value and having a clear impact on the final result. A generated adaptive calibration coefficient matrix was used to perform multi-factor cross-calibration on the raw data from in-situ detection. This multi-factor cross-calibration is not a single-dimensional error correction, but rather a comprehensive calculation and correction of each set of raw data from in-situ detection, combining calibration coefficients corresponding to multiple environmental factors such as temperature, humidity, and air pressure. The calibration calculation uses a multi-coefficient multiplication method; the calibrated concentration equals the original concentration multiplied by the temperature calibration coefficient, humidity calibration coefficient, and air pressure calibration coefficient. This comprehensively eliminates the deviations caused by the combined effects of multiple environmental factors, ensuring the scientific rigor and comprehensiveness of the calibration process. For example, the original data for in-situ detection is: pollutant number 01, concentration value 0.50 mg / L, detection time 14 hours 30 minutes, ambient temperature 25 degrees Celsius, ambient humidity 60%, atmospheric pressure 101 kPa, and instrument number A03. The adaptive calibration coefficient matrix provides a temperature calibration coefficient of 1.02, a humidity calibration coefficient of 0.99, and an atmospheric pressure calibration coefficient of 1.00. After calibration, the concentration is equal to 0.50 multiplied by 1.02 multiplied by 0.9 multiplied by 1.00, resulting in 0.50 mg / L. Multi-factor coupling correction eliminates detection bias caused by environmental fluctuations.

[0040] Finally, the data after multi-factor cross-calibration is compared with the historical calibration dataset under the same conditions in the environmental consulting knowledge base for consistency verification. The historical calibration dataset under the same conditions refers to the collection of valid calibration data retained after calibration was completed under conditions consistent with the current site environmental parameters, testing items, and testing instruments. By comparing the current calibration data with this dataset, it is determined whether the current calibration result is within a reasonable error range. If the verification passes, the calibrated data is organized and archived to generate the final calibrated in-situ testing dataset; if the verification fails, the calibration coefficient matrix is ​​regenerated and recalibrated until the consistency verification is passed.

[0041] As a preferred embodiment of the above, the dynamic mapping model is a nonlinear mapping model trained by a machine learning algorithm based on historical calibration datasets in the environmental consulting knowledge base.

[0042] Specifically, a massive historical calibration dataset is collected from the environmental consulting knowledge base. This dataset covers raw instrument test data, error correction records, and corresponding calibration results under different field conditions and environmental parameters. The collected historical calibration dataset is used as the basic training sample, and machine learning algorithms are introduced for feature mining and model training. The algorithm automatically mines the inherent correlation between various environmental parameters and detection errors, fitting the complex nonlinear relationship between them, thus overcoming the limitations of traditional fixed linear conversion methods. The trained nonlinear mapping model can autonomously identify the influence weight of different environmental factors on the detection values, without the need for manually setting uniform calibration formulas and correction standards in advance.

[0043] As a preferred embodiment of the above, such as Figure 3 As shown, compliance analysis is performed on valid testing data based on the environmental consulting knowledge base, including: A10: Extract the detection values ​​of each pollutant and the corresponding detection conditions from the valid detection dataset; A20: Call upon the graded compliance judgment rule base and pollutant correlation analysis model in the environmental consulting knowledge base to complete multi-dimensional compliance judgment; A30: Based on the judgment results, conduct environmental risk classification and generate a compliance analysis report containing clues to the source of the exceedance.

[0044] Specifically, the actual detection values ​​of various pollutants are extracted one by one from the completed valid detection dataset. At the same time, the detection conditions corresponding to the detection process are extracted. The detection conditions cover key information such as on-site environmental conditions, sampling time period, type of environmental medium being tested, and detection execution method. All basic information is completely retained for subsequent analysis.

[0045] Subsequently, the tiered compliance judgment rule base and pollutant correlation analysis model stored within the environmental consulting knowledge base were retrieved. The tiered compliance judgment rule base includes environmental standard provisions at all levels, industry-specific control requirements, and locally limited control indicators. The pollutant correlation analysis model can uncover the mutual influence and correlation patterns among different pollutants. Based on the standard constraints of the rule base and the correlation deduction of the analysis model, judgment rules are established at multiple levels, including indicator limit compliance, industry adaptation and standardization, regional control matching, and pollutant mutual influence. Based on these rules, each item in the valid test dataset is judged, and the judgment results at each level jointly determine the final compliance conclusion, completing a comprehensive and multi-dimensional compliance judgment. At the indicator limit compliance level, the test values ​​of each pollutant in the dataset are compared one by one with the national and local standard limits in the knowledge base. If the test value is not higher than the standard limit, it is considered compliant. This level directly determines whether a single pollutant is compliant. At the industry adaptation and standardization level, the testing methods, sampling procedures, and sample pretreatment methods are checked to see if they comply with the testing guidelines of the relevant industry. If all are compliant, it is considered compliant at this level. At the regional control matching level, the test results are compared with the specific control limits and special requirements of the project location. Meeting the regional control requirements constitutes compliance at this level. At the pollutant interaction level, pollutant association thresholds are called from the knowledge base. If the ratio of the concentrations of associated pollutants is within the threshold range, compliance at this level is achieved. If all four levels meet the requirements, the overall test data is deemed compliant; if any level fails to meet the requirements, the overall test data is deemed non-compliant.

[0046] Finally, based on the results of multi-dimensional compliance assessments, an overall environmental risk level was determined according to pre-defined risk classification standards. Simultaneously, combining the characteristics of the testing data, on-site operating conditions, and historical data patterns from similar projects, various potential causes behind abnormal pollutant values ​​were identified and compiled, forming a complete source tracing framework for exceeding standards. Specific characteristics of the testing data include instantaneous changes in pollutant concentration, continuous exceedances, abnormal peak shapes, and uneven spatiotemporal distribution. Specific on-site operating conditions include fluctuations in production load, the start-up and shutdown status of pollution control facilities, the amount of raw and auxiliary materials used, production process operating parameters, and pollutant emission periods. Specific historical data patterns from similar projects include historical exceedance records, related operating conditions, typical cause types, and rectification verification data. During the source tracing process, data characteristics were first matched with on-site operating conditions in real time, and then compared with historical exceedance causes from similar projects to identify potential causes such as pollution emissions, pollution control failures, process fluctuations, and matrix interference. For example, a wastewater testing project showed persistently high COD levels, characterized by concentrations continuously exceeding the limit. The on-site operating condition was that the pollution control facility was shut down. Historical data showed that the probability of COD exceeding the limit under similar conditions was 90%. Therefore, the shutdown of the pollution control facility was identified as the core cause of the exceedance, forming a complete and verifiable source tracing clue. As a preferred embodiment, both on-site and laboratory calibration of the testing data were performed based on the aforementioned environmental consulting knowledge base.

[0047] Specifically, the on-site calibration and laboratory calibration processes throughout the entire environmental testing process are uniformly supported by an environmental consulting knowledge base to achieve standardized data support and intelligent calibration management.

[0048] During the on-site calibration, the environmental parameters collected on-site and the raw data from in-situ testing are connected to the environmental consulting knowledge base in real time. Relying on the dynamic mapping model, various calibration coefficients, and historical calibration data resources built into the knowledge base, the correction of on-site environmental factor interference and the real-time error correction of the testing instruments are completed, realizing online dynamic calibration of in-situ testing data.

[0049] During laboratory calibration, after the environmental samples are tested by laboratory instruments to obtain raw data, the source information of standard substances, multi-level quality control rules and environmental matrix interference factor data stored in the environmental consulting knowledge base are also called up to correct various detection errors layer by layer according to the set calibration process.

[0050] On-site calibration and laboratory calibration share the same environmental consulting knowledge base data resources and algorithm models, maintaining consistent calibration standards and logic throughout the process. This avoids calibration scale deviations in different testing stages and ensures the accuracy, comparability, and traceability of data throughout the entire process from on-site in-situ testing to laboratory analysis and testing.

[0051] Example 2: Based on the same inventive concept as the pollutant detection method based on an environmental consulting knowledge base in the foregoing embodiments, the present invention also provides a pollutant detection system based on an environmental consulting knowledge base, comprising: The preliminary solution output module receives basic information about the project to be tested, inputs a pre-built environmental consulting knowledge base for matching, and outputs a preliminary testing solution. The final solution output module optimizes the testing plan by combining data from the environmental consulting knowledge base and generates the final testing plan after verification by historical cases. The in-situ dataset acquisition module, based on the final detection scheme, performs on-site sampling and in-situ detection, and links with the knowledge base to complete real-time quality control early warning and on-site calibration, thereby obtaining the calibrated in-situ detection dataset; The effective dataset acquisition module performs laboratory testing on environmental samples, performs three-level calibration based on the environmental consulting knowledge base, and obtains an effective dataset of pollutant detection. The test result generation module performs compliance analysis on valid test data based on the environmental consulting knowledge base and outputs the test results.

[0052] The testing system described above in this invention can effectively realize the pollutant detection method based on the environmental consulting knowledge base, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0053] As a preferred embodiment of the above, the final solution output module includes: The weight allocation unit assigns adaptive weight coefficients to different data sources based on the core features of the item to be detected, and optimizes and adjusts at least one dimension of the preliminary detection scheme. The similarity matching unit performs multi-dimensional similarity matching between the optimized detection scheme and historical cases in the environmental consulting knowledge base; The weighted verification unit performs weighted verification based on the success rate of historical cases, generating a final customized testing solution that passes the verification.

[0054] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0055] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A pollutant detection method based on an environmental consulting knowledge base, characterized in that, The method includes: Receive basic information about the project to be tested, input a pre-built environmental consulting knowledge base for matching, and output a preliminary testing plan; The testing plan was optimized by combining data from the environmental consulting knowledge base and the final testing plan was generated after being verified by historical cases. Based on the final detection scheme, on-site sampling and in-situ detection are performed. A knowledge base is used to complete real-time quality control early warning and on-site calibration, resulting in a calibrated in-situ detection dataset. The on-site calibration, performed via the knowledge base, includes: Simultaneously collect real-time environmental parameters and in-situ detection raw data from the site, and transmit them to the environmental consulting knowledge base in real time; The pre-built dynamic mapping model of environmental parameter calibration coefficients in the environmental consulting knowledge base is invoked to generate an adaptive calibration coefficient matrix based on multi-dimensional environmental parameters on site. The adaptive calibration coefficient matrix is ​​used to perform multi-factor cross-calibration on the in-situ detection data; The calibrated data is then compared with the historical calibration dataset under the same conditions in the environmental consulting knowledge base to generate the final calibrated in-situ detection dataset. Laboratory testing of environmental samples was conducted, and a three-level calibration was performed based on the aforementioned environmental advisory knowledge base to obtain an effective dataset of pollutant detection. Based on the aforementioned environmental consulting knowledge base, compliance analysis is performed on the valid testing data, and the testing results are output.

2. The pollutant detection method based on an environmental consulting knowledge base according to claim 1, characterized in that, The three-level calibration includes: Based on the standard substance traceability data and historical calibration dataset in the environmental consulting knowledge base, the systematic errors of the testing instruments are corrected. Based on at least one quality control rule in the environmental consulting knowledge base, random measurement errors are corrected and abnormal detection data are removed; Based on the pollutant standard characteristic spectrum and environmental matrix interference factor database in the environmental consulting knowledge base, the detection interference caused by the complex matrix of the sample is corrected.

3. The pollutant detection method based on an environmental consulting knowledge base according to claim 1, characterized in that, The basic information of the items to be tested must at least match the regulatory and standard data, pollutant characteristic data, and regional control data in the environmental consulting knowledge base.

4. The pollutant detection method based on an environmental consulting knowledge base according to claim 1, characterized in that, The testing plan was optimized by combining data from the environmental consulting knowledge base and validated through historical cases to generate the final testing plan, which includes: Based on the core features of the project to be detected, adaptive weight coefficients are assigned to different data sources, and at least one dimension of the preliminary detection scheme is optimized and adjusted. The optimized detection scheme was matched with historical cases in the environmental consulting knowledge base in multiple dimensions. The final customized testing solution is generated by weighting the success rate of the historical cases.

5. The pollutant detection method based on an environmental consulting knowledge base according to claim 1, characterized in that, The dynamic mapping model is a nonlinear mapping model trained using machine learning algorithms based on the historical calibration dataset in the environmental consulting knowledge base.

6. The pollutant detection method based on an environmental consulting knowledge base according to claim 1, characterized in that, Compliance analysis of valid testing data based on an environmental consulting knowledge base includes: Extract the detection values ​​of each pollutant and the corresponding detection conditions from the effective detection dataset; The system invokes the hierarchical compliance judgment rule base and pollutant correlation analysis model in the environmental consulting knowledge base to complete multi-dimensional compliance judgment; Based on the assessment results, environmental risk is classified, and a compliance analysis report containing clues to the source of the exceedances is generated.

7. The pollutant detection method based on an environmental consulting knowledge base according to claim 1, characterized in that, Both on-site and laboratory calibrations of the test data were completed based on the aforementioned environmental consulting knowledge base.

8. A pollutant detection system based on an environmental consulting knowledge base, characterized in that: The system includes: The preliminary solution output module receives basic information about the project to be tested, inputs a pre-built environmental consulting knowledge base for matching, and outputs a preliminary testing solution. The final solution output module optimizes the testing plan by combining data from the environmental consulting knowledge base and generates the final testing plan after verification by historical cases. The in-situ dataset acquisition module, based on the final detection scheme, performs on-site sampling and in-situ detection, and, in conjunction with a knowledge base, completes real-time quality control early warning and on-site calibration to obtain a calibrated in-situ detection dataset. The on-site calibration, performed in conjunction with the knowledge base, includes: Simultaneously collect real-time environmental parameters and in-situ detection raw data from the site, and transmit them to the environmental consulting knowledge base in real time; The pre-built dynamic mapping model of environmental parameter calibration coefficients in the environmental consulting knowledge base is invoked to generate an adaptive calibration coefficient matrix based on multi-dimensional environmental parameters on site. The adaptive calibration coefficient matrix is ​​used to perform multi-factor cross-calibration on the in-situ detection data; The calibrated data is then compared with the historical calibration dataset under the same conditions in the environmental consulting knowledge base to generate the final calibrated in-situ detection dataset. The effective dataset acquisition module performs laboratory testing on environmental samples and performs three-level calibration based on the environmental consultation knowledge base to obtain an effective detection dataset of pollutants. The test result generation module performs compliance analysis on the valid test data based on the environmental consulting knowledge base and outputs the test results.

9. The pollutant detection system based on an environmental consulting knowledge base according to claim 8, characterized in that, The final solution output module includes: The weight allocation unit assigns adaptive weight coefficients to different data sources based on the core features of the item to be detected, and optimizes and adjusts at least one dimension of the preliminary detection scheme. The similarity matching unit performs multi-dimensional similarity matching between the optimized detection scheme and historical cases in the environmental consulting knowledge base; The weighted verification unit performs weighted verification based on the success rate of the historical cases, and generates a final customized detection solution that passes the verification.

Images