A system and method for collecting ammonia in air

Abstract

The present invention relates to the field of gas collection and detection technology. The present invention provides a system and method for collecting ammonia in the air, which aims to solve the problem of reduced accuracy of data processing and analysis due to the influence of the natural environment in the prior art. The system includes data acquisition, preprocessing, collection, data analysis and recycling decision generation modules. The preprocessing module eliminates environmental interference, the collection module intelligently matches the collection method, the data analysis module performs statistical and time series analysis, predicts the change of ammonia concentration and outputs a spatial distribution map, and the recycling decision generation unit uses a machine learning algorithm to correct the data, build a comprehensive evaluation model, output comprehensive evaluation results including concentration, change trend, spatial distribution, recycling cost and environmental impact, and give recycling activity priority sorting strategy recommendations, thereby improving the scientificity and accuracy of ammonia collection and recycling.

Description

Technical Field

[0001] The present invention relates to the technical field of gas collection and detection, and in particular to a system and method for collecting ammonia in the air. Background Art

[0002] In recent years, with the enhancement of environmental awareness and the continuous advancement of industrial technology, ammonia collection systems, as an important part of environmental monitoring and industrial waste gas treatment, have developed rapidly. Early ammonia collection technology mainly relied on manual sampling and simple chemical analysis methods, which had problems of low sampling efficiency and poor accuracy. However, with the continuous advancement of sensor technology, automatic control technology and data analysis technology, modern ammonia collection systems have realized a fully automated process from sampling to analysis, improving collection efficiency and accuracy. At the same time, the system also integrates remote monitoring, data storage and report generation functions, providing users with a more convenient and efficient use experience.

[0003] The use of an ammonia collection system usually includes the following steps: First, the user needs to select a suitable sampling point and sampling time according to actual needs, and set the corresponding sampling condition parameters (such as sampling flow rate and sampling duration). Then, the ammonia in the air is collected into a specific container through the system's built-in sampling tube or sampler. Next, the system will pre-treat the collected ammonia, such as solvent elution and impurity removal, to optimize the subsequent collection method effect. After the pre-treatment is completed, the system will collect ammonia according to the preset collection method (such as chemical absorption and condensation capture). Finally, the collected ammonia will be quantitatively analyzed by a suitable analysis method to determine its concentration, and the analysis results will be stored in the system database to generate a detailed analysis report for the user's reference.

[0004] Although the ammonia collection system has made significant progress, there are still the following technical pain points that need to be solved. First, environmental interference factors in the sampling process have a great impact on the accuracy of the collection. For example, changes in temperature, humidity, and wind speed all have an impact on the sampling results. Since the collected data is affected by the natural environment, the accuracy of the ammonia collection system for data processing and analysis is reduced. In response to the technical pain points, the present invention provides a collection system and method for ammonia in the air. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a system and method for collecting ammonia in the air, which solves the problem that the accuracy of data processing and analysis by the ammonia collection system decreases due to the collected data being affected by the natural environment.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] In a first aspect, the present invention provides a system for collecting ammonia in air, comprising:

[0008] A data acquisition module is used to acquire basic data of ammonia gas collection points, including location data of ammonia gas collection points, environmental parameters of sampling points and sampling time;

[0009] A preprocessing module is used to receive parameter information of an adopted device and a data preprocessing standard, wherein the parameter information of the adopted device includes a sampling flow rate, a sampling duration, a sampling flow rate error, a measurement range, and a measurement sensitivity, and preprocess the basic data of the ammonia collection point according to the parameter information of the adopted device and the data preprocessing standard to obtain the preprocessed basic data of the ammonia collection point;

[0010] The acquisition module is used to extract data features from the pre-processed basic data of the ammonia collection points, obtain the characteristics of the pre-processed basic data of the ammonia collection points, match the characteristics of the pre-processed basic data of the ammonia collection points in the preset acquisition method knowledge base, obtain the collection method corresponding to ammonia, and provide real-time feedback on the collection progress and status during the collection process for monitoring by the backend control end;

[0011] The data analysis module is used to perform statistical analysis on the pre-processed data, calculate the average concentration, maximum value, minimum value and standard deviation of ammonia, use the time series analysis method to perform data processing and analysis on the change trend of ammonia concentration over time, and obtain the prediction result of ammonia concentration change. According to the location data of ammonia collection points in the prediction result of ammonia concentration change, the spatial distribution map of ammonia concentration is output. The spatial distribution map of ammonia concentration is used to identify high-concentration areas and low-concentration areas;

[0012] The recycling decision generation unit is used to use a machine learning algorithm to correct the ammonia concentration data according to the collected environmental parameters to obtain calibrated data, and use the real-time feedback data in the collection process and identify high-concentration areas and low-concentration areas to build a comprehensive evaluation model. The calibrated data is substituted into the comprehensive evaluation model to output a comprehensive evaluation result, which includes ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact. The comprehensive evaluation result is matched in a preset strategy library to obtain a recycling activity priority ranking strategy recommendation.

[0013] Furthermore, in the system for collecting ammonia in air of the present invention, the collection module is also used for:

[0014] Extracting ammonia data features from the pre-processed basic data of ammonia collection points, the ammonia data features include ammonia concentration level, environmental parameters and sampling time characteristics;

[0015] The preset collection method knowledge base includes the method name, applicable conditions, collection efficiency and collection cost of the ammonia collection method;

[0016] Update the knowledge base at a preset time period, preliminarily match the standardized feature data with the collection methods in the knowledge base, and match a set of applicable collection methods;

[0017] According to the collection efficiency, cost budget and environmental impact, the collection method set that is initially matched is screened, and the screened collection methods are comprehensively evaluated to obtain a comprehensive evaluation result. Based on the comprehensive evaluation result, the collection method that meets the characteristics and requirements of the current ammonia collection point is determined.

[0018] Furthermore, in the system for collecting ammonia in air of the present invention, the data analysis module is also used for:

[0019] The average concentration of ammonia in the pre-processed data is calculated using statistical methods, the maximum and minimum values ​​of ammonia in the pre-processed data are screened out, and the standard deviation of ammonia concentration is calculated to evaluate the dispersion or volatility of ammonia concentration;

[0020] Combine the preprocessed ammonia concentration data with the corresponding sampling time to construct time series data;

[0021] The pre-processed ammonia concentration data is fitted using a preset time series analysis model to capture the changing trend of ammonia concentration over time. The ammonia concentration is predicted based on the fitted model and the prediction results are output. The prediction results include the changing trend and concentration value of ammonia concentration within the expected time range.

[0022] Furthermore, in the system for collecting ammonia in air of the present invention, the recycling decision generating unit is further used for:

[0023] Using the calibrated data, real-time feedback data, and the identified high-concentration and low-concentration area information, a comprehensive assessment model is constructed, and the calibrated data is substituted into the constructed comprehensive assessment model for model calculation;

[0024] Output comprehensive evaluation results based on the calculation results of the comprehensive evaluation model, which include ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact information;

[0025] A preset recycling activity strategy library includes information on recycling activities;

[0026] The comprehensive evaluation results are matched with the strategies in the strategy library. Based on the matching results, the recycling activities are prioritized and priority strategy recommendations are output.

[0027] In a second aspect, the present invention provides a method for collecting ammonia in air, which is applied to the system for collecting ammonia in air as described above, comprising:

[0028] Step S101, obtaining basic data of ammonia gas collection points, the basic data of ammonia gas collection points including location data of ammonia gas collection points, environmental parameters of sampling points and sampling time;

[0029] Step S102, receiving parameter information of the adopted device and a data preprocessing standard, wherein the parameter information of the adopted device includes a sampling flow rate, a sampling duration, a sampling flow rate error, a measurement range, and a measurement sensitivity, and preprocessing the basic data of the ammonia collection point according to the parameter information of the adopted device and the data preprocessing standard to obtain the preprocessed basic data of the ammonia collection point;

[0030] Step S103, extracting data features from the pre-processed basic data of the ammonia collection points to obtain characteristics of the pre-processed basic data of the ammonia collection points, matching the characteristics of the pre-processed basic data of the ammonia collection points in a preset collection method knowledge base to obtain a collection method corresponding to ammonia, and providing real-time feedback on the collection progress and status during the collection process for monitoring by the backend control end;

[0031] Step S104, statistically analyzing the preprocessed data, calculating the average concentration, maximum value, minimum value and standard deviation of ammonia, using a time series analysis method to perform data processing and analysis on the change trend of ammonia concentration over time, obtaining a prediction result of ammonia concentration change, and outputting a spatial distribution map of ammonia concentration according to the position data of ammonia collection points in the prediction result of ammonia concentration change, the spatial distribution map of ammonia concentration is used to identify high concentration areas and low concentration areas;

[0032] Step S105, using a machine learning algorithm to correct the ammonia concentration data according to the collected environmental parameters to obtain calibrated data, using real-time feedback data during the collection process and identifying high-concentration areas and low-concentration areas, to build a comprehensive evaluation model, substitute the calibrated data into the comprehensive evaluation model, and output a comprehensive evaluation result, which includes ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact. The comprehensive evaluation result is matched in a preset strategy library to obtain a recycling activity priority ranking strategy recommendation.

[0033] Furthermore, the method for collecting ammonia in the air of the present invention, step S103, comprises:

[0034] Extracting ammonia data features from the pre-processed basic data of ammonia collection points, the ammonia data features include ammonia concentration level, environmental parameters and sampling time characteristics;

[0035] The preset collection method knowledge base includes the method name, applicable conditions, collection efficiency and collection cost of the ammonia collection method;

[0036] Update the knowledge base at a preset time period, preliminarily match the standardized feature data with the collection methods in the knowledge base, and match a set of applicable collection methods;

[0037] According to the collection efficiency, cost budget and environmental impact, the collection method set that is initially matched is screened, and the screened collection methods are comprehensively evaluated to obtain a comprehensive evaluation result. Based on the comprehensive evaluation result, the collection method that meets the characteristics and requirements of the current ammonia collection point is determined.

[0038] Furthermore, the method for collecting ammonia in air according to the present invention, step S104, comprises:

[0039] The average concentration of ammonia in the pre-processed data is calculated using statistical methods, the maximum and minimum values ​​of ammonia in the pre-processed data are screened out, and the standard deviation of ammonia concentration is calculated to evaluate the dispersion or volatility of ammonia concentration;

[0040] Combine the preprocessed ammonia concentration data with the corresponding sampling time to construct time series data;

[0041] The pre-processed ammonia concentration data is fitted using a preset time series analysis model to capture the changing trend of ammonia concentration over time. The ammonia concentration is predicted based on the fitted model and the prediction results are output. The prediction results include the changing trend and concentration value of ammonia concentration within the expected time range.

[0042] Furthermore, the method for collecting ammonia in the air of the present invention, step S105, comprises:

[0043] Using the calibrated data, real-time feedback data, and the identified high-concentration and low-concentration area information, a comprehensive assessment model is constructed, and the calibrated data is substituted into the constructed comprehensive assessment model for model calculation;

[0044] Output comprehensive evaluation results based on the calculation results of the comprehensive evaluation model, which include ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact information;

[0045] A preset recycling activity strategy library includes information on recycling activities;

[0046] The comprehensive evaluation results are matched with the strategies in the strategy library. Based on the matching results, the recycling activities are prioritized and priority strategy recommendations are output.

[0047] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0048] The present invention receives equipment parameter information and data preprocessing standards through a preprocessing module, preprocesses the basic data of ammonia collection points, effectively eliminates the interference of natural environmental factors on data collection, and improves the accuracy of subsequent data processing. The acquisition module matches the data characteristics after preprocessing in the preset acquisition method knowledge base, combines the acquisition efficiency, cost budget and environmental influencing factors, and determines the optimal acquisition method after comprehensive evaluation, thus realizing intelligent and efficient acquisition decision-making. The data analysis module uses statistical methods and time series analysis to comprehensively process the ammonia concentration data, calculate key indicators and predict future trends, and outputs the spatial distribution map of ammonia concentration, providing strong data support for decision-making.

[0049] The recycling decision generation unit corrects the ammonia concentration data through machine learning algorithms, constructs a comprehensive evaluation model, comprehensively considers ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact factors, outputs comprehensive evaluation results, and matches them with the preset strategy library to provide scientific recycling activity priority ranking strategy recommendations.

[0050] The data collection system and method of the present invention improve the overall performance of the system, including the accuracy of data collection, the scientific nature of analysis and the rationality of decision-making, through the collaborative work of various modules, and provide a comprehensive and efficient solution for the monitoring and recovery of ammonia in the air. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0052] Figure 1 A schematic diagram of a method for collecting ammonia in the air provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0054] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0055] The present invention provides a system for collecting ammonia in air, comprising:

[0056] In a first aspect, the present invention provides a system for collecting ammonia in air, comprising:

[0057] A data acquisition module is used to acquire basic data of ammonia gas collection points, including location data of ammonia gas collection points, environmental parameters of sampling points and sampling time;

[0058] The data acquisition module is a component of the ammonia collection system in the present invention, and its main function is to obtain basic data of ammonia collection points. The basic data includes but is not limited to the following aspects:

[0059] The location data of ammonia collection points records the geographical location information of ammonia collection points, which is crucial for subsequent data analysis and recycling decisions. Location data helps to determine the regional scope of ammonia pollution, so as to carry out targeted governance.

[0060] The environmental parameters of the sampling point cover a variety of environmental factors at the sampling point, such as temperature, humidity, atmospheric pressure, wind speed, and wind direction. Environmental parameters have a significant impact on the collection and concentration analysis of ammonia, so they need to be accurately recorded during the data acquisition stage.

[0061] The sampling time records the specific time point or time period when ammonia gas is collected. The accuracy of the sampling time is of great significance for analyzing the changing pattern of ammonia gas concentration and evaluating the environmental impact.

[0062] Through the data acquisition module, the present invention can comprehensively and accurately collect the basic data of the ammonia collection point, providing a solid foundation for the subsequent data preprocessing, feature extraction, knowledge base matching, data analysis and recycling decision-making steps. The accuracy and completeness of the data are crucial to improving the accuracy of the ammonia collection system for data processing and analysis.

[0063] A preprocessing module is used to receive parameter information of an adopted device and a data preprocessing standard, wherein the parameter information of the adopted device includes a sampling flow rate, a sampling duration, a sampling flow rate error, a measurement range, and a measurement sensitivity, and preprocess the basic data of the ammonia collection point according to the parameter information of the adopted device and the data preprocessing standard to obtain the preprocessed basic data of the ammonia collection point;

[0064] The meaning of the data preprocessing standard in the technical solution of the present invention is as follows: the data preprocessing standard refers to the rules or criteria that need to be followed in the data preprocessing process, which is used to guide how to clean, convert, and standardize the raw data. In the system for collecting ammonia in the air, due to the influence of various natural environmental factors such as temperature, humidity, atmospheric pressure, and wind speed during the ammonia collection process, the collected raw data may have noise, outliers, or inconsistency problems. The purpose of the data preprocessing standard is to eliminate or reduce the problems in the raw data through standardized processing operations, thereby improving the quality of the data and providing strong support for subsequent data feature extraction, knowledge base matching, data analysis, and recycling decision-making steps. The data preprocessing standard is a preset setting including data cleaning rules (such as removing invalid data and processing missing values), data conversion rules (such as converting data into a unified format and dimension), and data standardization rules (such as scaling data to a specific range). By following the data preprocessing standard, it can be ensured that the preprocessed data is more accurate, reliable, and easy to analyze later, thereby improving the performance of the entire ammonia collection system.

[0065] The data preprocessing standard is an indispensable part of the collection system for ammonia in the air. The data preprocessing standard is used to improve the data quality and the accuracy of subsequent data processing.

[0066] The preprocessing module in the present invention is responsible for receiving parameter information of the adopted equipment and data preprocessing standards, and preprocessing the basic data of the ammonia collection point according to the information, so as to improve the accuracy and reliability of subsequent data processing.

[0067] Specifically, the parameter information of the sampling device received by the preprocessing module includes the key parameters of sampling flow, sampling time, sampling flow error, measurement range, and measurement sensitivity. Parameters are crucial to understanding the significant impact of changes in environmental conditions during the collection process on ammonia concentration. For example, changes in temperature affect the diffusion rate and sampling volume of ammonia, while changes in pressure affect the flow state and sampling efficiency of the gas in the sampling device, thereby affecting the detection of ammonia concentration.

[0068] After receiving the equipment parameter information, the preprocessing module will perform a series of processing operations on the basic data of the ammonia collection point according to the preset data preprocessing standards. The operations include but are not limited to data cleaning, data conversion, and data standardization, aiming to eliminate or reduce noise, outliers, and inconsistency problems in the data, while improving the uniformity of data format and dimension.

[0069] Through the processing of the preprocessing module, the basic data of the ammonia collection point will become more accurate, reliable and easy to analyze later. The preprocessed data not only improves the data quality, but also provides strong support for the subsequent feature extraction, knowledge base matching, data analysis and recycling decision-making steps.

[0070] In summary, the preprocessing module is an indispensable part of the present invention. It effectively preprocesses the basic data of the ammonia collection point by receiving equipment parameter information and following the data preprocessing standard, thereby improving the accuracy and reliability of the entire collection system.

[0071] The acquisition module is used to extract data features from the pre-processed basic data of the ammonia collection points, obtain the characteristics of the pre-processed basic data of the ammonia collection points, match the characteristics of the pre-processed basic data of the ammonia collection points in the preset acquisition method knowledge base, obtain the collection method corresponding to ammonia, and provide real-time feedback on the collection progress and status during the collection process for monitoring by the backend control end;

[0072] The collection module is responsible for in-depth processing of the basic data of the ammonia collection points after pre-processing to determine the most suitable ammonia collection method, and provide real-time feedback on the progress and status during the collection process so that the backend control end can monitor and manage it.

[0073] First, the acquisition module extracts data features from the pre-processed basic data of the ammonia collection point. This step aims to mine key information from complex data sets, such as ammonia concentration levels, environmental parameters (such as temperature, humidity, wind speed), and sampling time characteristics. The feature data can fully reflect the actual situation of the collection point and provide an important basis for subsequent data analysis and collection method selection.

[0074] Next, the acquisition module will match the extracted data features with the preset acquisition method knowledge base. The knowledge base is a database containing a variety of ammonia collection methods and their applicable conditions, collection efficiency, and collection cost information. By comparing the data features with the information in the knowledge base, the acquisition module can filter out a collection method set that matches the characteristics of the current collection point.

[0075] After obtaining a set of applicable collection methods, the collection module will further conduct a comprehensive evaluation based on collection efficiency, cost budget, and environmental impact factors. By weighing various factors, the collection module can determine the optimal collection method under the current collection point characteristics and requirements.

[0076] During the collection process, the collection module will also provide real-time feedback on the collection progress and status information. The information includes but is not limited to the amount of samples collected, the remaining collection time, and the equipment operation status. By transmitting the information to the backend control terminal, users can monitor the collection process in real time and improve the smooth progress of the collection activities.

[0077] In short, the collection module realizes the intelligent management of ammonia collection points through data feature extraction, knowledge base matching, comprehensive evaluation and real-time feedback steps. It can not only improve the accuracy and reliability of collected data, but also improve collection efficiency, reduce collection costs, and reduce the impact on the environment.

[0078] The data analysis module is used to perform statistical analysis on the pre-processed data, calculate the average concentration, maximum value, minimum value and standard deviation of ammonia, use the time series analysis method to perform data processing and analysis on the change trend of ammonia concentration over time, and obtain the prediction result of ammonia concentration change. According to the location data of ammonia collection points in the prediction result of ammonia concentration change, the spatial distribution map of ammonia concentration is output. The spatial distribution map of ammonia concentration is used to identify high-concentration areas and low-concentration areas;

[0079] The data analysis module is responsible for conducting in-depth statistical analysis on the pre-processed data in the present invention to reveal the variation pattern of ammonia concentration and its spatial distribution characteristics.

[0080] The data analysis module will first perform basic statistical analysis on the pre-processed data and calculate the average concentration, maximum value, minimum value and standard deviation of ammonia. The indicators can reflect the overall level, fluctuation range and dispersion of ammonia concentration, providing a basis for subsequent data processing and analysis.

[0081] In addition to basic statistical analysis, the data analysis module also uses time series analysis methods to conduct in-depth research on the changing trend of ammonia concentration over time. Time series analysis is a powerful data processing tool that can help us capture the periodicity, trend and seasonal characteristics in the data, so as to more accurately predict future changing trends. In the present invention, the data analysis module constructs time series data and uses a preset time series analysis model to fit and predict ammonia concentration data. Through this step, we can obtain the ammonia concentration change trend and concentration value prediction results in the future period of time, providing a basis for subsequent recycling decisions.

[0082] In addition, the data analysis module will output a spatial distribution map of ammonia concentration based on the location data in the ammonia concentration change prediction results. This map can intuitively show the distribution of ammonia concentration in different areas and help us identify high-concentration areas and low-concentration areas. This is crucial for subsequent recycling and utilization decisions, because we can determine priority areas for treatment and take targeted measures based on the spatial distribution map.

[0083] In summary, the data analysis module conducts in-depth processing and analysis of the pre-processed data through statistical analysis, time series analysis, and spatial distribution map drawing. It not only reveals the variation pattern of ammonia concentration and its spatial distribution characteristics, but also provides strong support for subsequent recycling and utilization decisions.

[0084] The recycling decision generation unit is used to use a machine learning algorithm to correct the ammonia concentration data according to the collected environmental parameters to obtain calibrated data, and use the real-time feedback data in the collection process and identify high-concentration areas and low-concentration areas to build a comprehensive evaluation model. The calibrated data is substituted into the comprehensive evaluation model to output a comprehensive evaluation result, which includes ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact. The comprehensive evaluation result is matched in a preset strategy library to obtain a recycling activity priority ranking strategy recommendation.

[0085] The recycling decision generation unit plays a vital role in the present invention. By correcting the collected ammonia concentration data, building a comprehensive evaluation model and outputting the comprehensive evaluation results, it provides a scientific decision-making basis for subsequent recycling activities.

[0086] First, the decision generation unit uses a machine learning algorithm to correct the collected ammonia concentration data. Since changes in environmental parameters (such as temperature, humidity, atmospheric pressure, and wind speed) will affect ammonia concentration, there are errors in the directly collected data. Through the machine learning algorithm, the recycling decision generation unit can identify the influence of environmental parameters on ammonia concentration and correct the original data to obtain more accurate and reliable ammonia concentration data.

[0087] Next, the decision-making unit uses the real-time feedback data from the collection process and the information of the identified high-concentration and low-concentration areas to build a comprehensive evaluation model. This model comprehensively considers multiple factors such as ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact, and can comprehensively evaluate the ammonia recycling potential in different areas.

[0088] After building the comprehensive evaluation model, the recycling decision generation unit will substitute the calibrated data into the model for calculation and output the comprehensive evaluation results. The results include not only the specific value of ammonia concentration, but also the change trend, spatial distribution characteristics, recycling cost estimation and environmental impact assessment information, providing users with a comprehensive and detailed decision-making reference.

[0089] Finally, the unit matches the comprehensive evaluation results with the preset strategy library. The strategy library contains detailed information on various recycling activities and their applicable conditions, such as activity name, cost, and environmental impact. By matching the comprehensive evaluation results with the information in the strategy library, the recycling decision generation unit can recommend the best recycling activity plan for users and sort them by priority. The strategy recommendations not only help improve the recycling efficiency of ammonia, but also minimize the cost and environmental impact of the recycling process.

[0090] In summary, the recycling and utilization decision generation unit provides scientific and comprehensive decision support for the recycling and utilization of ammonia in the air through data correction, model construction, result output and strategy matching steps.

[0091] Specifically, the system for collecting ammonia in air of the present invention, the collection module is also used for:

[0092] Extracting ammonia data features from the pre-processed basic data of ammonia collection points, the ammonia data features include ammonia concentration level, environmental parameters and sampling time characteristics;

[0093] The preset collection method knowledge base includes the method name, applicable conditions, collection efficiency and collection cost of the ammonia collection method;

[0094] Update the knowledge base at a preset time period, preliminarily match the standardized feature data with the collection methods in the knowledge base, and match a set of applicable collection methods;

[0095] According to the collection efficiency, cost budget and environmental impact, the collection method set that is initially matched is screened, and the screened collection methods are comprehensively evaluated to obtain a comprehensive evaluation result. Based on the comprehensive evaluation result, the collection method that meets the characteristics and requirements of the current ammonia collection point is determined.

[0096] Specifically, the collection module of the air ammonia collection system of the present invention also has the following functions and characteristics:

[0097] From the pre-processed basic data of the ammonia collection point, the collection module extracts key ammonia data features. These data features include ammonia concentration levels, environmental parameters (such as temperature, humidity, wind speed), and sampling time characteristics.

[0098] The collection module has a built-in knowledge base of preset collection methods. This knowledge base contains information on a variety of ammonia collection methods, including:

[0099] Method Name: The official name of each collection method.

[0100] Applicable conditions: The specific environmental conditions or ammonia concentration range applicable to each collection method.

[0101] Collection efficiency: The efficiency or ability of each collection method to work under specific conditions.

[0102] Collection cost: The cost budget required to implement each collection method.

[0103] The knowledge base is updated regularly. In order to ensure the timeliness and accuracy of the knowledge base, the acquisition module will update the knowledge base according to the preset time period. The update content includes new acquisition methods, corrections or optimizations to existing methods.

[0104] After extracting the features of ammonia data, the collection module will initially match the standardized feature data with the collection methods in the knowledge base. This step aims to screen out a collection of collection methods that are applicable to the characteristics and requirements of the current collection point.

[0105] For the initially matched collection method set, the collection module will further screen based on collection efficiency, cost budget and environmental impact factors. The screened collection methods will be comprehensively evaluated, including but not limited to the feasibility of the method, cost-effectiveness ratio and environmental impact.

[0106] Based on the results of the comprehensive evaluation, the collection module will determine the optimal collection method based on the current characteristics and requirements of the ammonia collection point. This method will be used for subsequent ammonia collection operations, and the collection progress and status will be fed back in real time during the collection process so that the backend control end can monitor and manage it.

[0107] Through the above steps, the collection module can intelligently select the ammonia collection method that best suits the characteristics and needs of the current collection point, improve collection efficiency and accuracy, and reduce collection costs and impact on the environment.

[0108] Specifically, the data analysis module of the system for collecting ammonia in air of the present invention is also used for:

[0109] The average concentration of ammonia in the pre-processed data is calculated using statistical methods, the maximum and minimum values ​​of ammonia in the pre-processed data are screened out, and the standard deviation of ammonia concentration is calculated to evaluate the dispersion or volatility of ammonia concentration;

[0110] Combine the preprocessed ammonia concentration data with the corresponding sampling time to construct time series data;

[0111] The pre-processed ammonia concentration data is fitted using a preset time series analysis model to capture the changing trend of ammonia concentration over time. The ammonia concentration is predicted based on the fitted model and the prediction results are output. The prediction results include the changing trend and concentration value of ammonia concentration within the expected time range.

[0112] Specifically, in the system for collecting ammonia in air according to the present invention, the data analysis module also has the following core functions and steps:

[0113] Statistical calculation of ammonia concentration index:

[0114] Average concentration calculation: The data analysis module uses statistical methods to calculate the average concentration of ammonia in the preprocessed data. The average concentration reflects the overall level of ammonia in a specific time period or spatial range.

[0115] Maximum and minimum value screening: The module will screen the maximum and minimum values ​​of ammonia from the preprocessed data. These two extreme values ​​help to understand the fluctuation range and extreme conditions of ammonia concentration.

[0116] Standard Deviation Calculation: By calculating the standard deviation of ammonia concentration, the data analysis module can evaluate the dispersion or volatility of ammonia concentration. The larger the standard deviation, the greater the fluctuation of ammonia concentration at different time points or different locations.

[0117] Constructing time series data, the data analysis module combines the preprocessed ammonia concentration data with the corresponding sampling time to construct time series data. Time series data is a data set arranged in chronological order, which can reflect the change pattern of ammonia concentration over time.

[0118] Preset model fitting: Using the preset time series analysis model, the data analysis module fits the preprocessed ammonia concentration time series data. The fitting process aims to find the mathematical model that can best describe the trend of ammonia concentration over time.

[0119] Trend Capture: Through model fitting, the data analysis module can capture the trend of ammonia concentration over time. This trend includes long-term growth, cyclical fluctuations or random changes.

[0120] Ammonia concentration prediction: Based on the fitted time series analysis model, the data analysis module can predict the ammonia concentration in the future. The prediction results include the ammonia concentration change trend and specific concentration values ​​within the expected time range. These prediction results are of great significance for formulating effective ammonia monitoring and control strategies.

[0121] In summary, the data analysis module conducts comprehensive and in-depth processing and analysis of the pre-treated ammonia concentration data through statistical calculation, time series data construction, time series analysis, and ammonia concentration prediction steps. The analysis results provide strong data support for subsequent recycling decisions and help improve the accuracy of ammonia collection and recycling.

[0122] Specifically, the system for collecting ammonia in air of the present invention, the recycling decision generating unit is also used for:

[0123] Using the calibrated data, real-time feedback data, and the identified high-concentration and low-concentration area information, a comprehensive assessment model is constructed, and the calibrated data is substituted into the constructed comprehensive assessment model for model calculation;

[0124] Output comprehensive evaluation results based on the calculation results of the comprehensive evaluation model, which include ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact information;

[0125] A preset recycling activity strategy library includes information on recycling activities;

[0126] The comprehensive evaluation results are matched with the strategies in the strategy library. Based on the matching results, the recycling activities are prioritized and priority strategy recommendations are output.

[0127] Constructing a comprehensive evaluation model:

[0128] Data input: The recycling decision generation unit first receives the calibrated ammonia concentration data, real-time feedback data, and the high-concentration area and low-concentration area information identified by the data analysis module.

[0129] Model construction: Based on the above input data, the decision generation unit will construct a comprehensive evaluation model. The model aims to comprehensively consider multiple factors such as ammonia concentration, change trend, spatial distribution, recovery cost and environmental impact to comprehensively evaluate the potential and feasibility of ammonia recovery and utilization.

[0130] Model operation: The calibrated data is substituted into the constructed comprehensive evaluation model for operation. The model operation process will process and analyze the input data based on the preset algorithm and logic to obtain a comprehensive evaluation result.

[0131] Output comprehensive evaluation results: After the model operation is completed, the recycling decision generation unit will output comprehensive evaluation results. These results include information on ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact, providing users with comprehensive and detailed decision-making references.

[0132] Preset recycling activity strategy library: A recycling activity strategy library is also preset in the system. This strategy library contains detailed information on various recycling activities, such as activity name, applicable conditions, cost, and environmental impact. This information provides guidance and basis for subsequent recycling activities.

[0133] Strategy matching and prioritization: The recycling decision generation unit matches the comprehensive evaluation results with the strategies in the strategy library. By comparing the comprehensive evaluation results with the information in the strategy library, the decision generation unit can recommend the recycling activity plan that best suits the current situation to the user.

[0134] Based on the matching results, the decision generation unit will also prioritize the recycling activities. The ranking is based on recycling benefits, cost-effectiveness, and environmental impact. Finally, the decision generation unit will output priority ranking strategy recommendations to provide a reference for users to formulate specific recycling plans.

[0135] In summary, the recycling and utilization decision generation unit provides scientific and comprehensive decision support for the recycling and utilization of ammonia in the air by constructing a comprehensive evaluation model, performing model operations, outputting comprehensive evaluation results, and strategy matching and priority sorting steps.

[0136] In a second aspect, the present invention provides a method for collecting ammonia in air, which is applied to the system for collecting ammonia in air, comprising:

[0137] Step S101, obtaining basic data of ammonia gas collection points, the basic data of ammonia gas collection points including location data of ammonia gas collection points, environmental parameters of sampling points and sampling time;

[0138] Step S102, receiving parameter information of the adopted device and a data preprocessing standard, wherein the parameter information of the adopted device includes a sampling flow rate, a sampling duration, a sampling flow rate error, a measurement range, and a measurement sensitivity, and preprocessing the basic data of the ammonia collection point according to the parameter information of the adopted device and the data preprocessing standard to obtain the preprocessed basic data of the ammonia collection point;

[0139] Step S103, extracting data features from the pre-processed basic data of the ammonia collection points to obtain characteristics of the pre-processed basic data of the ammonia collection points, matching the characteristics of the pre-processed basic data of the ammonia collection points in a preset collection method knowledge base to obtain a collection method corresponding to ammonia, and providing real-time feedback on the collection progress and status during the collection process for monitoring by the backend control end;

[0140] Step S104, statistically analyzing the preprocessed data, calculating the average concentration, maximum value, minimum value and standard deviation of ammonia, using a time series analysis method to perform data processing and analysis on the change trend of ammonia concentration over time, obtaining a prediction result of ammonia concentration change, and outputting a spatial distribution map of ammonia concentration according to the position data of ammonia collection points in the prediction result of ammonia concentration change, the spatial distribution map of ammonia concentration is used to identify high concentration areas and low concentration areas;

[0141] Step S105, using a machine learning algorithm to correct the ammonia concentration data according to the collected environmental parameters to obtain calibrated data, using real-time feedback data during the collection process and identifying high-concentration areas and low-concentration areas, to build a comprehensive evaluation model, substitute the calibrated data into the comprehensive evaluation model, and output a comprehensive evaluation result, which includes ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact. The comprehensive evaluation result is matched in a preset strategy library to obtain a recycling activity priority ranking strategy recommendation.

[0142] Specifically, the method for collecting ammonia in the air provided by the present invention, step S103, comprises:

[0143] Extracting ammonia data features from the pre-processed basic data of ammonia collection points, the ammonia data features include ammonia concentration level, environmental parameters and sampling time characteristics;

[0144] The preset collection method knowledge base includes the method name, applicable conditions, collection efficiency and collection cost of the ammonia collection method;

[0145] Update the knowledge base at a preset time period, preliminarily match the standardized feature data with the collection methods in the knowledge base, and match a set of applicable collection methods;

[0146] According to the collection efficiency, cost budget and environmental impact, the collection method set that is initially matched is screened, and the screened collection methods are comprehensively evaluated to obtain a comprehensive evaluation result. Based on the comprehensive evaluation result, the collection method that meets the characteristics and requirements of the current ammonia collection point is determined.

[0147] Specifically, the method for collecting ammonia in the air provided by the present invention, step S104, comprises:

[0148] The average concentration of ammonia in the pre-processed data is calculated using statistical methods, the maximum and minimum values ​​of ammonia in the pre-processed data are screened out, and the standard deviation of ammonia concentration is calculated to evaluate the dispersion or volatility of ammonia concentration;

[0149] Combine the preprocessed ammonia concentration data with the corresponding sampling time to construct time series data;

[0150] The pre-processed ammonia concentration data is fitted using a preset time series analysis model to capture the changing trend of ammonia concentration over time. The ammonia concentration is predicted based on the fitted model and the prediction results are output. The prediction results include the changing trend and concentration value of ammonia concentration within the expected time range.

[0151] Specifically, the method for collecting ammonia in the air provided by the present invention, step S105, comprises:

[0152] Using the calibrated data, real-time feedback data, and the identified high-concentration and low-concentration area information, a comprehensive assessment model is constructed, and the calibrated data is substituted into the constructed comprehensive assessment model for model calculation;

[0153] Output comprehensive evaluation results based on the calculation results of the comprehensive evaluation model, which include ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact information;

[0154] A preset recycling activity strategy library includes information on recycling activities;

[0155] The comprehensive evaluation results are matched with the strategies in the strategy library. Based on the matching results, the recycling activities are prioritized and priority strategy recommendations are output.

[0156] The present invention solves the problem that the accuracy of data processing and analysis by the ammonia collection system decreases due to the influence of the natural environment on the collected data through the following technical solutions:

[0157] The data preprocessing module receives the parameter information of the equipment (including sampling flow, sampling time, sampling flow error, measurement range and measurement sensitivity) and the data preprocessing standard, and preprocesses the basic data of the ammonia collection point. This step can eliminate or reduce the impact of natural environmental factors on the collected data and improve the accuracy of subsequent data processing. In the acquisition module, data features are extracted from the preprocessed basic data of the ammonia collection point, including ammonia concentration level, environmental parameters and sampling time characteristics. The features are matched in the preset acquisition method knowledge base to determine the applicable acquisition method. The regular update of the knowledge base ensures the timeliness and accuracy of the matching results, thereby adapting to the data acquisition needs under different environmental conditions.

[0158] The data analysis module uses statistical methods to calculate the average concentration, maximum value, minimum value and standard deviation of ammonia to evaluate the discreteness or volatility of ammonia concentration. At the same time, the time series analysis method is used to fit and predict the change trend of ammonia concentration over time to obtain the prediction result of ammonia concentration change. This method can capture the dynamic changes of ammonia concentration and improve the accuracy of data analysis.

[0159] The recycling decision generation unit uses machine learning algorithms to correct ammonia concentration data based on collected environmental parameters to obtain calibrated data. Combined with real-time feedback data and information on identified high-concentration and low-concentration areas, a comprehensive evaluation model is constructed to output comprehensive evaluation results including ammonia concentration, change trends, spatial distribution, recycling costs, and environmental impacts. This step not only improves the accuracy of the data, but also comprehensively considers environmental and economic factors, providing a scientific basis for recycling activities.

[0160] The comprehensive evaluation results are matched in the preset strategy library to obtain the recycling activity priority ranking strategy recommendations. The strategy library contains detailed information on various recycling activities and their applicable conditions, which improves the pertinence and feasibility of the strategy recommendations.

[0161] In summary, the present invention effectively solves the problem of decreased accuracy of data processing and analysis due to the influence of the natural environment on the collected data through multiple steps of data preprocessing, feature extraction and knowledge base matching, statistical analysis and time series analysis, data correction and comprehensive evaluation, and strategy library matching and priority sorting.

[0162] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if the modifications and variations of the present invention fall within the scope of the claims of the present invention and the equivalent technologies thereof, the present invention is also intended to include the modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A system for collecting ammonia in air, characterized in that: include: A data acquisition module is used to acquire basic data of ammonia gas collection points, including location data of ammonia gas collection points, environmental parameters of sampling points and sampling time; A preprocessing module is used to receive parameter information of an adopted device and a data preprocessing standard, wherein the parameter information of the adopted device includes a sampling flow rate, a sampling duration, a sampling flow rate error, a measurement range, and a measurement sensitivity, and preprocess the basic data of the ammonia collection point according to the parameter information of the adopted device and the data preprocessing standard to obtain the preprocessed basic data of the ammonia collection point; The acquisition module is used to extract data features from the pre-processed basic data of the ammonia collection points, obtain the characteristics of the pre-processed basic data of the ammonia collection points, match the characteristics of the pre-processed basic data of the ammonia collection points in the preset acquisition method knowledge base, obtain the collection method corresponding to ammonia, and provide real-time feedback on the collection progress and status during the collection process for monitoring by the backend control end; The data analysis module is used to perform statistical analysis on the pre-processed data, calculate the average concentration, maximum value, minimum value and standard deviation of ammonia, use the time series analysis method to perform data processing and analysis on the change trend of ammonia concentration over time, and obtain the prediction result of ammonia concentration change. According to the location data of ammonia collection points in the prediction result of ammonia concentration change, the spatial distribution map of ammonia concentration is output. The spatial distribution map of ammonia concentration is used to identify high-concentration areas and low-concentration areas; The recycling decision generation unit is used to use a machine learning algorithm to correct the ammonia concentration data according to the collected environmental parameters to obtain calibrated data, and use the real-time feedback data in the collection process and identify high-concentration areas and low-concentration areas to build a comprehensive evaluation model. The calibrated data is substituted into the comprehensive evaluation model to output a comprehensive evaluation result, which includes ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact. The comprehensive evaluation result is matched in a preset strategy library to obtain a recycling activity priority ranking strategy recommendation.

2. The system for collecting ammonia in air according to claim 1, characterized in that: The acquisition module is also used for: Extracting ammonia data features from the pre-processed basic data of ammonia collection points, the ammonia data features include ammonia concentration level, environmental parameters and sampling time characteristics; The preset collection method knowledge base includes the method name, applicable conditions, collection efficiency and collection cost of the ammonia collection method; Update the knowledge base at a preset time period, preliminarily match the standardized feature data with the collection methods in the knowledge base, and match a set of applicable collection methods; According to the collection efficiency, cost budget and environmental impact, the collection method set that is initially matched is screened, and the screened collection methods are comprehensively evaluated to obtain a comprehensive evaluation result. Based on the comprehensive evaluation result, the collection method that meets the characteristics and requirements of the current ammonia collection point is determined.

3. The system for collecting ammonia in air according to claim 1, characterized in that: The data analysis module is also used for: The average concentration of ammonia in the pre-processed data is calculated using statistical methods, the maximum and minimum values ​​of ammonia in the pre-processed data are screened out, and the standard deviation of ammonia concentration is calculated to evaluate the dispersion or volatility of ammonia concentration; Combine the preprocessed ammonia concentration data with the corresponding sampling time to construct time series data; The pre-processed ammonia concentration data is fitted using a preset time series analysis model to capture the changing trend of ammonia concentration over time. The ammonia concentration is predicted based on the fitted model and the prediction results are output. The prediction results include the changing trend and concentration value of ammonia concentration within the expected time range.

4. The system for collecting ammonia in air according to claim 1, characterized in that: The recycling decision generating unit is further used for: Using the calibrated data, real-time feedback data, and the identified high-concentration and low-concentration area information, a comprehensive assessment model is constructed, and the calibrated data is substituted into the constructed comprehensive assessment model for model calculation; Output comprehensive evaluation results based on the calculation results of the comprehensive evaluation model, which include ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact information; A preset recycling activity strategy library includes information on recycling activities; The comprehensive evaluation results are matched with the strategies in the strategy library. Based on the matching results, the recycling activities are prioritized and priority strategy recommendations are output.

5. A method for collecting ammonia in air, applied to the system for collecting ammonia in air as claimed in any one of claims 1 to 4, characterized in that: include: Step S101, obtaining basic data of ammonia gas collection points, the basic data of ammonia gas collection points including location data of ammonia gas collection points, environmental parameters of sampling points and sampling time; Step S102, receiving parameter information of the adopted device and a data preprocessing standard, wherein the parameter information of the adopted device includes a sampling flow rate, a sampling duration, a sampling flow rate error, a measurement range, and a measurement sensitivity, and preprocessing the basic data of the ammonia collection point according to the parameter information of the adopted device and the data preprocessing standard to obtain the preprocessed basic data of the ammonia collection point; Step S103, extracting data features from the pre-processed basic data of the ammonia collection points to obtain characteristics of the pre-processed basic data of the ammonia collection points, matching the characteristics of the pre-processed basic data of the ammonia collection points in a preset collection method knowledge base to obtain a collection method corresponding to ammonia, and providing real-time feedback on the collection progress and status during the collection process for monitoring by the backend control end; Step S104, statistically analyzing the preprocessed data, calculating the average concentration, maximum value, minimum value and standard deviation of ammonia, using a time series analysis method to perform data processing and analysis on the change trend of ammonia concentration over time, obtaining a prediction result of ammonia concentration change, and outputting a spatial distribution map of ammonia concentration according to the position data of ammonia collection points in the prediction result of ammonia concentration change, the spatial distribution map of ammonia concentration is used to identify high concentration areas and low concentration areas; Step S105, using a machine learning algorithm to correct the ammonia concentration data according to the collected environmental parameters to obtain calibrated data, using real-time feedback data during the collection process and identifying high-concentration areas and low-concentration areas, to build a comprehensive evaluation model, substitute the calibrated data into the comprehensive evaluation model, and output a comprehensive evaluation result, which includes ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact. The comprehensive evaluation result is matched in a preset strategy library to obtain a recycling activity priority ranking strategy recommendation.

6. The method for collecting ammonia in air according to claim 5, characterized in that: The step S103 includes: Extracting ammonia data features from the pre-processed basic data of ammonia collection points, the ammonia data features include ammonia concentration level, environmental parameters and sampling time characteristics; The preset collection method knowledge base includes the method name, applicable conditions, collection efficiency and collection cost of the ammonia collection method; Update the knowledge base at a preset time period, preliminarily match the standardized feature data with the collection methods in the knowledge base, and match a set of applicable collection methods; According to the collection efficiency, cost budget and environmental impact, the collection method set that is initially matched is screened, and the screened collection methods are comprehensively evaluated to obtain a comprehensive evaluation result. Based on the comprehensive evaluation result, the collection method that meets the characteristics and requirements of the current ammonia collection point is determined.

7. The method for collecting ammonia in air according to claim 5, characterized in that: The step S104 includes: The average concentration of ammonia in the pre-processed data is calculated using statistical methods, the maximum and minimum values ​​of ammonia in the pre-processed data are screened out, and the standard deviation of ammonia concentration is calculated to evaluate the dispersion or volatility of ammonia concentration; Combine the preprocessed ammonia concentration data with the corresponding sampling time to construct time series data; The pre-processed ammonia concentration data is fitted using a preset time series analysis model to capture the changing trend of ammonia concentration over time. The ammonia concentration is predicted based on the fitted model and the prediction results are output. The prediction results include the changing trend and concentration value of ammonia concentration within the expected time range.

8. The method for collecting ammonia in air according to claim 5, characterized in that: The step S105 includes: Using the calibrated data, real-time feedback data, and the identified high-concentration and low-concentration area information, a comprehensive assessment model is constructed, and the calibrated data is substituted into the constructed comprehensive assessment model for model calculation; Output comprehensive evaluation results based on the calculation results of the comprehensive evaluation model, which include ammonia concentration, change trend, spatial distribution, recycling cost and environmental impact information; A preset recycling activity strategy library includes information on recycling activities; The comprehensive evaluation results are matched with the strategies in the strategy library. Based on the matching results, the recycling activities are prioritized and priority strategy recommendations are output.

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