Sewage component detection method and system and electronic equipment

Abstract

The invention discloses a sewage component detection method and system and electronic equipment, and relates to the technical field of sewage detection.The sewage component detection method comprises the steps that a historical sewage detection result set is obtained and clustered, and Q historical pollutants and Q historical pollutant concentration sets are obtained; performing centralized screening on the Q historical pollutant concentration sets, and determining pollutant concentrations in Q historical sets; sewage component combination neighborhoods are constructed, and Q historical sewage component combination neighborhoods are obtained; traversing the Q historical sewage component combination neighborhoods to perform a sewage component detection test to obtain Q test historical sewage detection result neighborhoods; and performing interference calibration historical pollutant authentication to obtain M interference calibration historical pollutants, and inputting the M interference calibration historical pollutants into a pre-constructed sewage component detection scheme identifier to obtain a target sewage component detection scheme. The technical problem that interference pollutants are not accurately identified in sewage component detection in the prior art is solved, and the technical effect of improving the sewage component detection accuracy is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage detection, and particularly relates to a method, a system and an electronic device for detecting the components of sewage. Background Art

[0002] With the acceleration of the urbanization process, sewage treatment has become an important part of environmental protection. There are a wide variety of pollutants in sewage, and the concentration changes are complex, which makes the analysis and detection of sewage components face great challenges. Traditional methods for detecting sewage components usually identify pollutants through conventional chemical analysis means, but this method often has certain limitations. Especially when facing sewage with complex components, it is unable to effectively cope with the influence of interfering pollutants on the detection results. The existence of interfering pollutants may lead to inaccurate detection results, thereby affecting the effect of sewage treatment and the implementation of environmental protection measures. Summary of the Invention

[0003] This application provides a method, a system and an electronic device for detecting the components of sewage, which are used to solve the technical problem that the prior art is inaccurate in identifying interfering pollutants in the detection of sewage components.

[0004] In view of the above problems, this application provides a method, a system and an electronic device for detecting the components of sewage.

[0005] In the first aspect of this application, a method for detecting the components of sewage is provided. The method includes: Obtain a set of historical sewage detection results at the sewage outlet of the target city; cluster the set of historical sewage component detection results based on the types of pollutants to obtain Q historical pollutants and Q sets of historical pollutant concentrations, where Q is a positive integer; conduct a centralized screening on the Q sets of historical pollutant concentrations to determine Q historical centralized pollutant concentrations; construct a neighborhood of sewage component combinations for the Q historical pollutants according to the Q historical centralized pollutant concentrations to obtain Q historical sewage component combination neighborhoods, where each historical sewage component combination includes at least one calibrated historical pollutant and one combined historical pollutant, as well as the corresponding historical centralized pollutant concentrations respectively; traverse the Q historical sewage component combination neighborhoods to conduct sewage component detection tests to obtain Q neighborhoods of test historical sewage detection results; conduct certification of interfering calibrated historical pollutants based on the Q historical sewage component combination neighborhoods and the Q neighborhoods of test historical sewage detection results to obtain M interfering calibrated historical pollutants, where M is a positive integer less than or equal to Q; use the M interfering calibrated historical pollutants as preprocessing items and input them into a pre-constructed sewage component detection scheme identifier to obtain a target sewage component detection scheme.

[0006] In the second aspect of this application, a system for detecting the components of sewage is provided. The system includes: A data acquisition module for acquiring a set of historical sewage detection results of sewage outlets in a target city; a clustering module for clustering the set of historical sewage component detection results based on pollutant types to obtain Q historical pollutants and Q sets of historical pollutant concentrations, where Q is a positive integer; a centralized screening module for centrally screening the Q sets of historical pollutant concentrations to determine Q historical centralized pollutant concentrations; a neighborhood construction module for constructing a neighborhood of sewage component combinations for the Q historical pollutants according to the Q historical centralized pollutant concentrations to obtain Q historical sewage component combination neighborhoods, where each historical sewage component combination includes at least one calibrated historical pollutant and one combined historical pollutant, and the corresponding historical centralized pollutant concentrations respectively; a detection test module for traversing the Q historical sewage component combination neighborhoods to conduct sewage component detection tests to obtain Q test historical sewage detection result neighborhoods; a pollutant certification module for conducting interference calibration historical pollutant certification based on the Q historical sewage component combination neighborhoods and the Q test historical sewage detection result neighborhoods to obtain M interference calibration historical pollutants, where M is a positive integer less than or equal to Q; an identification module for using the M interference calibration historical pollutants as preprocessing items and inputting them into a pre-constructed sewage component detection scheme identifier to obtain a target sewage component detection scheme.

[0007] In a third aspect of the present application, there is provided an electronic device, including: a memory for storing executable instructions; a processor for implementing a sewage component detection method provided by the present application when executing the executable instructions stored in the memory.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: This application obtains a set of historical sewage detection results of the target urban sewage outlet; clusters the set of historical sewage composition detection results based on pollutant types to obtain Q historical pollutants and Q sets of historical pollutant concentrations, where Q is a positive integer; conducts a centralized screening on the Q sets of historical pollutant concentrations to determine Q historical centralized pollutant concentrations; constructs a neighborhood of sewage composition combinations for the Q historical pollutants according to the Q historical centralized pollutant concentrations to obtain Q historical neighborhoods of sewage composition combinations, where each historical sewage composition combination includes at least one calibrated historical pollutant and one combined historical pollutant, as well as the corresponding historical centralized pollutant concentrations; traverses the Q historical neighborhoods of sewage composition combinations to conduct sewage composition detection experiments to obtain Q experimental historical sewage detection result neighborhoods; conducts interference calibrated historical pollutant authentication based on the Q historical neighborhoods of sewage composition combinations and the Q experimental historical sewage detection result neighborhoods to obtain M interference calibrated historical pollutants, where M is a positive integer less than or equal to Q; inputs the M interference calibrated historical pollutants as preprocessing items into a pre-constructed sewage composition detection scheme recognizer to obtain a target sewage composition detection scheme. The present invention solves the technical problem in the prior art of inaccurate identification of interfering pollutants in sewage composition detection. Through the optimization processes of historical pollutant concentration clustering, centralized screening, neighborhood construction, and interference pollutant authentication, the technical effect of improving the accuracy of sewage composition detection is achieved. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0010] Figure 1 It is a schematic flowchart of a sewage composition detection method provided by an embodiment of this application; Figure 2 It is a schematic structural diagram of a sewage composition detection system provided by an embodiment of this application; Figure 3 It is a schematic structural diagram of an exemplary electronic device of this application.

[0011] Description of the reference numerals: data acquisition module 11, clustering module 12, centralized screening module 13, neighborhood construction module 14, detection experiment module 15, pollutant authentication module 16, recognition module 17, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. Detailed Embodiments

[0012] The present application provides a method, a system and an electronic device for detecting the components of sewage, aiming to solve the technical problem of inaccurate identification of interfering pollutants in the analysis of sewage components in the prior art. Through the processes of historical pollutant concentration clustering, centralized screening, neighborhood construction, and optimization of interfering pollutant certification, the technical effect of improving the accuracy of sewage component detection is achieved.

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0014] It should be noted that any variations of the terms "including" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0015] Embodiment 1, as Figure 1 shown, the present application provides a method for detecting the components of sewage, and the method includes: Step S100: Obtain a set of historical sewage detection results of the sewage outlet of the target city.

[0016] In the embodiment of the present application, to obtain a set of historical sewage detection results of the sewage outlet of the target city, first receive the identification information of the sewage outlet of the target city input by the user, which may be the number of the sewage outlet, and then query the data in the historical database according to the number of the sewage outlet of the target city to obtain a set of historical sewage detection results of the sewage outlet of the target city. The historical database stores the historical detection results of each sewage outlet, and these data include information such as the types, concentrations, sampling times, and detection methods of multiple pollutants. At the same time, the historical detection results of each sewage outlet stored in the historical database are marked with the number of the corresponding sewage outlet.

[0017] Through the above process, a set of historical sewage detection results of the sewage outlet of the target city is obtained. The set of historical sewage detection results includes the types of pollutants at the sewage outlet and the concentration values of each pollutant, etc.

[0018] Step S200: Cluster the set of historical sewage component detection results based on the pollutant types to obtain Q historical pollutants and Q sets of historical pollutant concentrations, where Q is a positive integer.

[0019] In the embodiment of the present application, the historical sewage composition detection result set is clustered based on the pollutant type, that is, through a retrieval method, according to the pollutant type in each historical sewage detection record, the pollutant data of the same type are aggregated together. The pollutant types include ammonia nitrogen, chemical oxygen demand (COD), heavy metals, suspended solids, etc. Through the retrieval process, the data of all the same pollutants are concentrated into the same group. The retrieval process mainly classifies the data according to the pollutant name to ensure that the detection results of each pollutant type are grouped together.

[0020] Then, for each aggregated pollutant type, the concentration data of the pollutant in the historical sewage composition detection result set are extracted to form the pollutant concentration set. Each pollutant concentration set contains the concentration values of the pollutant at different sampling time periods, and these concentration values reflect the change trend and concentration level of the pollutant at different time points. Through such a process, a historical concentration data set is obtained.

[0021] Finally, through the above steps, Q historical pollutants and Q historical pollutant concentration sets are obtained, where Q represents the number of different pollutant types, and Q is a positive integer.

[0022] Step S300: Conduct a centralized screening on the Q historical pollutant concentration sets to determine the Q historical centralized pollutant concentrations.

[0023] In the embodiment of the present application, when conducting a centralized screening on the Q historical pollutant concentration sets, first, the mean value of each historical pollutant concentration set is calculated respectively as the preliminary representative concentration value of the pollutant. Then, taking these mean values as indexes, the concentration data are centrally screened according to a preset screening step to eliminate the extreme values with large fluctuations. Next, based on the calculation of the neighborhood density, the distribution of each concentration value in the data is evaluated to determine the effective screening direction. If the neighborhood density of the iterative historical concentration is greater than or equal to the neighborhood density of the mean value, iterate along this direction until the preset number of iterations is reached, and finally obtain the stable historical centralized pollutant concentration; if the neighborhood density is relatively low, add this direction to the taboo iteration direction table to avoid continuing to iterate along this direction, and iterate in other directions until the historical centralized pollutant concentration is finally determined.

[0024] Through the above process, Q historical centralized pollutant concentrations are obtained.

[0025] Furthermore, in the method provided by the application embodiment, when conducting a centralized screening on the Q historical pollutant concentration sets to determine the Q historical centralized pollutant concentrations, it further includes: Calculate the means of the Q sets of historical pollutant concentrations respectively to obtain Q historical pollutant concentration means; using the Q historical pollutant concentration means as indices, perform centralized screening on the Q sets of historical pollutant concentrations according to a preset centralized screening step size and an arbitrary direction to obtain Q iterative historical pollutant concentrations; respectively construct neighborhoods of the Q historical pollutant concentration means and neighborhoods of the Q iterative historical pollutant concentrations based on the preset centralized screening step size; when the neighborhood densities of the neighborhoods of the Q iterative historical pollutant concentrations are respectively greater than or equal to the neighborhood densities of the neighborhoods of the Q historical pollutant concentration means, use the direction from the Q historical pollutant concentration means to the Q iterative historical pollutant concentrations as the iterative direction, and combine the preset centralized screening step size to perform iteration on the Q iterative historical pollutant concentrations in the Q sets of historical pollutant concentrations until the preset number of iterations is satisfied, and use the historical pollutant concentration obtained in the last iteration as the Q historical centralized pollutant concentrations.

[0026] In the embodiments of the present application, first, calculate the means of the Q sets of historical pollutant concentrations respectively to obtain Q historical pollutant concentration means. Specifically, by calculating the means of all data points in the Q sets of historical pollutant concentrations respectively, the average concentration value of each pollutant is obtained, and Q historical pollutant concentration means are obtained.

[0027] Next, using the Q historical pollutant concentration means as indices, perform centralized screening on the Q sets of historical pollutant concentrations according to a preset centralized screening step size and an arbitrary direction. The preset centralized screening step size refers to adjusting the mean of each pollutant upward and downward by a preset step size on the basis of the mean of each pollutant to obtain a new concentration value, which is called an iterative historical pollutant concentration. Through this process, Q iterative historical pollutant concentrations are obtained.

[0028] Subsequently, construct neighborhoods of the Q historical pollutant concentration means and neighborhoods of the Q iterative historical pollutant concentrations respectively based on the preset centralized screening step size. Specifically, for each historical pollutant concentration mean, perform addition and subtraction operations according to the preset step size to obtain a range, which is the neighborhood of the mean value of this concentration value. Similarly, for each iterative historical pollutant concentration, also generate the neighborhood of the iterative historical pollutant concentration by adding and subtracting the preset screening step size. Through this process, neighborhoods of the Q historical pollutant concentration means and neighborhoods of the Q iterative historical pollutant concentrations are constructed.

[0029] Next, calculate the neighborhood density of the Q neighborhood of the iterative historical pollutant concentrations and the neighborhood density of the Q neighborhood of the mean historical pollutant concentrations respectively. The calculation of the neighborhood density is carried out by evaluating the distribution of each concentration value within its neighborhood range. Specifically, the neighborhood density refers to the occurrence frequency of a certain pollutant concentration within the neighborhood. When calculating, each concentration value in the set of historical pollutant concentrations is compared with its corresponding neighborhood range, the number of concentration data within the neighborhood is counted, and then divided by twice the preset centralized screening step. Through this process, the neighborhood density of the Q neighborhood of the iterative historical pollutant concentrations and the neighborhood density of the Q neighborhood of the mean historical pollutant concentrations are obtained.

[0030] Subsequently, compare the neighborhood density of the Q neighborhood of the iterative historical pollutant concentrations and the neighborhood density of the Q neighborhood of the mean historical pollutant concentrations respectively. When the neighborhood density of the Q neighborhood of the iterative historical pollutant concentrations is greater than or equal to the neighborhood density of the Q neighborhood of the mean historical pollutant concentrations respectively, the direction from the Q mean historical pollutant concentrations to the Q iterative historical pollutant concentrations is taken as the iterative direction, and combined with the preset centralized screening step, in the set of Q historical pollutant concentrations, iterate on the Q iterative historical pollutant concentrations until the preset number of iterations is satisfied, and the historical pollutant concentration obtained in the last iteration is taken as the Q historical centralized pollutant concentrations. In this step, by comparing the neighborhood density of each iterative historical pollutant concentration with the neighborhood density of the mean historical pollutant concentration, it is determined whether to iterate along this direction. If the iterative neighborhood density is greater than or equal to the mean neighborhood density, it is considered that this direction is valid, and continue to iterate along this direction until the preset number of iterations is reached. The iterative process gradually optimizes the pollutant concentration value, and finally obtains a stable historical centralized pollutant concentration.

[0031] Through these steps, the Q historical centralized pollutant concentrations are finally determined.

[0032] Furthermore, the method provided by the application embodiment further includes: When the neighborhood density of the Q neighborhood of the iterative historical pollutant concentrations is less than the neighborhood density of the Q neighborhood of the mean historical pollutant concentrations respectively, add the direction from the Q mean historical pollutant concentrations to the Q iterative historical pollutant concentrations into the tabu iterative direction table, and combined with the preset centralized screening step, in the set of Q historical pollutant concentrations, iterate on the Q mean historical pollutant concentrations according to the directions except those in the tabu iterative direction table until the preset number of iterations is satisfied, and the historical pollutant concentration obtained in the last iteration is taken as the Q historical centralized pollutant concentrations, where the tabu iterative direction table includes a preset tabu number of iterations.

[0033] In an embodiment of the present application, when the neighborhood densities of the Q iterative historical pollutant concentration neighborhoods are respectively less than the neighborhood densities of the Q historical pollutant concentration mean neighborhoods, it indicates that the concentration change in this direction is not common in the historical data or is sparsely distributed in the data. Therefore, it is avoided to continue iterating in this direction. To achieve this, the directions from the Q historical pollutant concentration means to the Q iterative historical pollutant concentrations are added to the tabu iteration direction table. The tabu iteration direction table is a set that records all ineffective iteration directions, and through this table, it is avoided to repeatedly execute those unrepresentative directions in the subsequent iteration process.

[0034] Next, in combination with the preset screening step size, in the set of Q historical pollutant concentrations, the Q historical pollutant concentration means are iterated in the directions except those in the tabu iteration direction table until the preset number of iterations is satisfied. In this process, based on each historical pollutant concentration mean and the preset screening step size, the concentration adjustment is continued in the effective directions not included in the tabu iteration direction table. Iterate until the preset number of iterations is satisfied to ensure the stability and accuracy of each pollutant concentration.

[0035] Finally, the historical pollutant concentrations obtained in the last iteration are used as the pollutant concentrations in the Q historical sets. This step means that through continuous iteration and adjustment, the finally obtained concentration values are the most representative concentration levels of each pollutant in the historical data, reflecting the central tendency of the pollutants. Using this result as the pollutant concentrations in the Q historical sets provides accurate basic data for subsequent sewage component analysis and detection scheme optimization.

[0036] In addition, if during the iteration process, all directions are added to the tabu iteration direction table, it means that no effective concentration results are generated in all possible directions or all directions do not meet the requirements. At this time, there is no further effective direction to continue the iteration. Therefore, the Q historical pollutant concentration means are used as the pollutant concentrations in the Q historical sets.

[0037] Step S400: Construct Q historical sewage component combination neighborhoods for the Q historical pollutants according to the Q pollutant concentrations in the historical sets, where each historical sewage component combination includes at least one calibrated historical pollutant and one combined historical pollutant, and the corresponding historical set pollutant concentrations respectively.

[0038] In an embodiment of the present application, when constructing the Q historical sewage component combination neighborhoods for the Q historical pollutants according to the Q pollutant concentrations in the historical sets, first each historical pollutant is respectively used as a calibrated historical pollutant, and then for the remaining historical pollutants, at least one is randomly selected as a combined historical pollutant. By randomly selecting the combined historical pollutants, different pollutant combinations are constructed.

[0039] Next, construct the neighborhood of historical sewage component combinations. Each neighborhood of historical sewage component combinations includes multiple historical sewage component combinations. Each historical sewage component combination contains at least one fixed calibrated historical pollutant and one randomly selected combined historical pollutant, and the corresponding historical centralized pollutant concentration is calibrated for the pollutant concentration in each historical sewage component combination.

[0040] Through the above steps, Q neighborhoods of historical sewage component combinations are finally obtained. Each neighborhood of historical sewage component combinations contains multiple sewage component combinations. Each combination consists of a fixed calibrated historical pollutant and a randomly selected combined historical pollutant, and each pollutant corresponds to its historical centralized pollutant concentration.

[0041] Step S500: Traverse the Q neighborhoods of historical sewage component combinations to conduct sewage component detection tests, and obtain Q neighborhoods of test historical sewage detection results.

[0042] In the embodiment of the present application, by traversing the Q neighborhoods of historical sewage component combinations to conduct sewage component detection tests, at this stage, for each neighborhood of historical sewage component combinations, the corresponding detection instrument is used with actual sewage samples to detect the sewage components. The detection methods include chemical analysis, spectral analysis or other laboratory techniques, which are used to measure the actual concentrations of various pollutants in the sewage. These test data can reflect the concentrations and distributions of pollutants in the actual sewage, providing more accurate component analysis results.

[0043] Then, construct the neighborhood of test historical sewage detection results according to the detection test results. Each detection result will generate a corresponding concentration value, and these values reflect the actual concentrations of sewage components under a given pollutant combination. By summarizing these detection results, Q neighborhoods of test historical sewage detection results are obtained.

[0044] Furthermore, in the method provided by the embodiment of the application, traversing the Q neighborhoods of historical sewage component combinations to conduct sewage component detection tests and obtaining Q neighborhoods of test historical sewage detection results further includes: Obtain the usage duration of the instrument for conducting the sewage component detection test to obtain a set of instrument usage durations; perform asynchronous periodic calibration on the instrument according to the set of instrument usage durations.

[0045] In the embodiment of the present application, the usage duration of the instrument is first obtained. During the sewage component detection test, the usage duration of each instrument is recorded. The usage duration of the instrument refers to the cumulative time from the start of using the instrument for detection to the current moment. This step is completed through the timer built into the instrument or a usage recording system to ensure accurate tracking of the usage time of each instrument. Finally, a set of instrument usage durations is obtained, which contains the usage duration data of all relevant instruments.

[0046] Next, asynchronous periodic calibration of the instruments is performed according to the set of instrument usage durations. In this step, it is determined whether calibration is required based on the usage duration of each instrument. Generally, as the usage duration of the instrument increases, there may be some accuracy drifts in the instrument, especially after long-term continuous use. To ensure the accuracy and reliability of the measurement data, it is necessary to perform zero calibration on the instrument. Zero calibration means restoring the measurement system of the instrument to its initial state to ensure that it can accurately reflect the components of the sample. The trigger condition for calibration is usually a long usage duration, such as exceeding a certain time threshold (e.g., 100 hours, 500 hours, etc.). Once this condition is met, the instrument enters the calibration mode and restores its accuracy through a zeroing operation.

[0047] Finally, the calibration process is asynchronous, that is, different instruments are calibrated independently according to their respective usage durations without the need for synchronous operation. This asynchronous calibration method can ensure that each instrument is adjusted in a timely manner during its use without affecting the normal operation of other instruments.

[0048] Through the foregoing process, the asynchronous periodic calibration of the instruments is completed.

[0049] Step S600: Perform interference calibration historical pollutant authentication based on Q historical sewage component combination neighborhoods and Q test historical sewage detection result neighborhoods to obtain M interference calibration historical pollutants, where M is a positive integer less than or equal to Q.

[0050] In the embodiment of the present application, when performing interference calibration historical pollutant authentication based on Q historical sewage component combination neighborhoods and Q test historical sewage detection result neighborhoods, analysis is performed through a preset detection loss analysis function to obtain Q historical sewage component combination detection loss amount neighborhoods. Next, loss authentication is performed on the Q historical sewage component combination detection loss amount neighborhoods according to a preset detection loss amount threshold. As long as there is a loss amount greater than the preset detection loss amount threshold in any historical sewage component combination detection loss amount neighborhood, the calibration historical pollutant corresponding to the historical sewage component combination detection loss amount neighborhood is used as an interference calibration historical pollutant. Through this process, M interference calibration historical pollutants are obtained. Among them, M is a positive integer less than or equal to Q, and M represents the number of finally selected interference calibration historical pollutants.

[0051] Further, the method provided by the application embodiment further includes: Performing a detection loss analysis on the Q historical sewage test result neighborhoods based on the historical concentrated pollutant concentrations within the Q historical sewage composition combination neighborhoods to obtain Q historical sewage composition combination detection loss amount neighborhoods; performing loss certification on the Q historical sewage composition combination detection loss amount neighborhoods according to a preset detection loss amount threshold to obtain M interference-calibrated historical pollutants.

[0052] In the embodiment of the present application, first, a detection loss analysis is performed on the Q historical sewage test result neighborhoods based on the historical concentrated pollutant concentrations within the Q historical sewage composition combination neighborhoods. By using a detection loss analysis function, the loss amount of each sewage composition combination is calculated according to the difference between the pollutant concentration in the historical sewage composition combination and the corresponding test result. Through this process, Q historical sewage composition combination detection loss amount neighborhoods are obtained.

[0053] Next, loss certification is performed on the Q historical sewage composition combination detection loss amount neighborhoods according to a preset detection loss amount threshold. In this step, the preset detection loss amount threshold is used to screen the detection loss amount of each historical sewage composition combination in the Q historical sewage composition combination detection loss amount neighborhoods. If the detection loss amount of a certain historical sewage composition combination is greater than the preset threshold, it indicates that a large error has occurred during the detection of this pollutant combination, so it is considered that there are interfering pollutants. At this time, the calibrated historical pollutants corresponding to the detection loss amount neighborhood of this historical sewage composition combination are regarded as interference-calibrated historical pollutants.

[0054] Finally, through this process, M interference-calibrated historical pollutants are obtained, where M is a positive integer less than or equal to Q. M represents the number of pollutants that are confirmed to interfere with the sewage composition test results after loss certification.

[0055] Further, in the method provided by the application embodiment, performing a detection loss analysis on the Q historical sewage test result neighborhoods based on the historical concentrated pollutant concentrations within the Q historical sewage composition combination neighborhoods to obtain Q historical sewage composition combination detection loss amount neighborhoods further includes: Obtaining a detection loss analysis function, where the detection loss analysis function is: ; Wherein, is the detection loss amount of the historical sewage composition combination, is the total number of combined historical pollutants in the historical sewage composition combination, is the historical concentrated pollutant concentration of the th combined historical pollutant in the historical sewage composition combination, is the concentration of the historical pollutant in the th combined historical pollutant in the historical sewage composition for the corresponding test historical sewage test result, is the historical concentrated pollutant concentration of the calibrated historical pollutant in the historical sewage composition, is the concentration of the test historical pollutant in the corresponding test historical sewage test result for the calibrated historical pollutant in the historical sewage composition, is the coefficient for balancing the detection loss of the calibrated historical pollutant and the combined historical pollutant; the historical concentrated pollutant concentrations within the neighborhood of the Q historical sewage composition combinations and the corresponding neighborhoods of the Q test historical sewage test results are respectively input into the detection loss analysis function to obtain the neighborhood of the detection loss amounts of the Q historical sewage composition combinations.

[0056] In the embodiment of the present application, the detection loss analysis function is as follows: ; wherein, is the detection loss amount of the historical sewage composition combination, is the total number of combined historical pollutants in the historical sewage composition combination, is the historical concentrated pollutant concentration of the th combined historical pollutant in the historical sewage composition combination, is the th combined historical pollutant in the historical sewage composition combination, and is the concentration of the test historical pollutant in the corresponding test historical sewage test result, is the historical concentrated pollutant concentration of the calibrated historical pollutant in the historical sewage composition, is the concentration of the test historical pollutant in the corresponding test historical sewage test result for the calibrated historical pollutant in the historical sewage composition, is the coefficient for balancing the detection loss of the calibrated historical pollutant and the combined historical pollutant, and is preset by technical experts.

[0057] By respectively inputting the historical concentrated pollutant concentrations within the neighborhood of the Q historical sewage composition combinations and the corresponding neighborhoods of the Q test historical sewage test results into the detection loss analysis function. Specifically, the historical concentration data of each pollutant in the neighborhood of the historical sewage composition combination and the actual test concentration data in the neighborhood of the test historical sewage test result are input into the detection loss analysis function, and the neighborhood of the detection loss amounts of the Q historical sewage composition combinations, that is, the detection loss values of each composition combination, are calculated through this function.

[0058] Step S700: Use the M interference-calibrated historical pollutants as preprocessing items and input them into a pre-constructed sewage composition detection scheme identifier to obtain a target sewage composition detection scheme.

[0059] In an embodiment of the present application, by using M interference-calibrated historical pollutants as preprocessing items and inputting them into a pre-constructed sewage component detection scheme recognizer, the sewage component detection scheme recognizer analyzes the relationship between the interfering pollutants and the sewage component detection scheme through the learned mapping relationship, and generates a target sewage component detection scheme.

[0060] Further, in the method provided by the embodiment of the application, using the M interference-calibrated historical pollutants as preprocessing items and inputting them into a pre-constructed sewage component detection scheme recognizer to obtain a target sewage component detection scheme further includes: Obtaining a plurality of sample interference-calibrated historical pollutant sets and a plurality of sample sewage component detection schemes as training data; using the training data to perform supervised training on a framework constructed based on a feedforward neural network to learn the mapping relationship between the interference-calibrated historical pollutant set and the sewage component detection scheme until the training converges, and obtaining the trained sewage component detection scheme recognizer.

[0061] In an embodiment of the present application, first, a plurality of sample interference-calibrated historical pollutant sets and a plurality of sample sewage component detection schemes are obtained from a historical database, and these data will be used as training data for the learning of the model. The interference-calibrated historical pollutant set includes pollutants that are identified as likely to interfere with the detection results in sewage detection, while the sewage component detection scheme is the corresponding pollutant detection method and scheme. Through these sample data, the influence of pollutants on sewage component detection can be learned.

[0062] Next, the training data is used to perform supervised training on a framework constructed based on a feedforward neural network. A feedforward neural network is a common deep learning model that can learn through the mapping relationship between input and output data. During the training process, the input data is the interference-calibrated historical pollutant set, and the output data is the corresponding sewage component detection scheme. By continuously adjusting the parameters inside the network, the neural network learns the complex mapping relationship between the interfering pollutants and the sewage component detection scheme. During the training process, the neural network adjusts its weights according to the feedback error until the training converges. Training convergence means that the neural network has achieved optimal performance on the given training data, and the error no longer decreases significantly. At this time, the model has strong prediction ability and can accurately output the corresponding sewage component detection scheme according to a new interference-calibrated historical pollutant set.

[0063] Through this series of steps, a trained sewage component detection scheme recognizer is finally obtained.

[0064] In an embodiment of the present application, in summary, the embodiment of the present application has at least the following technical effects: This application obtains a set of historical sewage detection results of a target urban sewage outlet; clusters the set of historical sewage component detection results based on pollutant types to obtain Q historical pollutants and Q sets of historical pollutant concentrations, where Q is a positive integer; conducts a centralized screening on the Q sets of historical pollutant concentrations to determine Q historical centralized pollutant concentrations; constructs a neighborhood of sewage component combinations for the Q historical pollutants according to the Q historical centralized pollutant concentrations to obtain Q historical sewage component combination neighborhoods, where each historical sewage component combination includes at least one calibrated historical pollutant and one combined historical pollutant, as well as the corresponding historical centralized pollutant concentrations; traverses the Q historical sewage component combination neighborhoods to conduct sewage component detection tests to obtain Q experimental historical sewage detection result neighborhoods; conducts interference-calibrated historical pollutant authentication based on the Q historical sewage component combination neighborhoods and the Q experimental historical sewage detection result neighborhoods to obtain M interference-calibrated historical pollutants, where M is a positive integer less than or equal to Q; uses the M interference-calibrated historical pollutants as preprocessing items and inputs them into a pre-constructed sewage component detection scheme identifier to obtain a target sewage component detection scheme. The present invention solves the technical problem of inaccurate identification of interfering pollutants in the prior art in sewage component detection, and achieves the technical effect of improving the accuracy of sewage component detection through the optimization processes of historical pollutant concentration clustering, centralized screening, neighborhood construction, and interference pollutant authentication.

[0065] Embodiment 2, based on the same inventive concept as a sewage component detection method in the foregoing embodiment, as Figure 2 shown, this application provides a sewage component detection system. The system in the embodiments of this application and the method embodiments are based on the same inventive concept. Among them, the system includes: A data acquisition module 11 for acquiring a set of historical sewage detection results of sewage outlets in a target city; a clustering module 12 for clustering the set of historical sewage composition detection results based on pollutant types to obtain Q historical pollutants and Q sets of historical pollutant concentrations, where Q is a positive integer; a centralized screening module 13 for centrally screening the Q sets of historical pollutant concentrations to determine Q historical centralized pollutant concentrations; a neighborhood construction module 14 for constructing a neighborhood of sewage composition combinations for the Q historical pollutants according to the Q historical centralized pollutant concentrations to obtain Q historical sewage composition combination neighborhoods, where each historical sewage composition combination includes at least one calibrated historical pollutant and one combined historical pollutant, and the corresponding historical centralized pollutant concentrations respectively; a detection test module 15 for traversing the Q historical sewage composition combination neighborhoods to conduct sewage composition detection tests to obtain Q test historical sewage detection result neighborhoods; a pollutant authentication module 16 for performing interference calibration historical pollutant authentication based on the Q historical sewage composition combination neighborhoods and the Q test historical sewage detection result neighborhoods to obtain M interference calibration historical pollutants, where M is a positive integer less than or equal to Q; an identification module 17 for using the M interference calibration historical pollutants as preprocessing items and inputting them into a pre-constructed sewage composition detection scheme identifier to obtain a target sewage composition detection scheme.

[0066] Furthermore, the system is also used to implement the following functions: Calculate the means of the Q sets of historical pollutant concentrations respectively to obtain Q historical pollutant concentration means; use the Q historical pollutant concentration means as indexes, and centrally screen the Q sets of historical pollutant concentrations in any direction according to a preset centralized screening step to obtain Q iterative historical pollutant concentrations; construct neighborhoods of the Q historical pollutant concentration means and neighborhoods of the Q iterative historical pollutant concentrations respectively based on the preset centralized screening step; when the neighborhood densities of the Q iterative historical pollutant concentration neighborhoods are respectively greater than or equal to the neighborhood densities of the Q historical pollutant concentration mean neighborhoods, use the direction from the Q historical pollutant concentration means to the Q iterative historical pollutant concentrations as the iterative direction, and combine the preset centralized screening step to perform iteration on the Q iterative historical pollutant concentrations in the Q sets of historical pollutant concentrations until a preset number of iterations is satisfied, and use the historical pollutant concentrations obtained in the last iteration as the Q historical centralized pollutant concentrations.

[0067] Furthermore, the system is also used to implement the following functions: When the neighborhood densities of the Q neighborhood of iterative historical pollutant concentrations are respectively less than the neighborhood densities of the Q neighborhood of historical pollutant concentration means, add the directions from the Q historical pollutant concentration means to the Q iterative historical pollutant concentrations into the tabu iterative direction table, and combine the preset centralized screening step size to iterate the Q historical pollutant concentration means in the Q historical pollutant concentration set according to the directions except those in the tabu iterative direction table until the preset number of iterations is satisfied, and take the historical pollutant concentration obtained in the last iteration as the Q historical centralized pollutant concentrations, where the tabu iterative direction table contains the preset tabu iterative number of times.

[0068] Further, the system is also used to implement the following functions: Based on the historical centralized pollutant concentrations in the Q neighborhood of historical sewage component combinations, perform detection loss analysis on the Q neighborhood of test historical sewage detection results to obtain the Q neighborhood of historical sewage component combination detection loss amounts; perform loss certification on the Q neighborhood of historical sewage component combination detection loss amounts according to the preset detection loss amount threshold to obtain M interference-calibrated historical pollutants.

[0069] Further, the system is also used to implement the following functions: Obtain a detection loss analysis function, where the detection loss analysis function is: ; Where is the historical sewage component combination detection loss amount, is the total number of combined historical pollutants in the historical sewage component combination, is the historical centralized pollutant concentration of the th combined historical pollutant in the historical sewage component combination, is the test historical pollutant concentration of the th combined historical pollutant in the corresponding test historical sewage detection result, is the historical centralized pollutant concentration of the calibrated historical pollutant in the historical sewage component combination, is the test historical pollutant concentration of the calibrated historical pollutant in the corresponding test historical sewage detection result, is the coefficient for balancing the detection loss situations of the calibrated historical pollutants and the combined historical pollutants; input the historical centralized pollutant concentrations in the Q neighborhood of historical sewage component combinations and the corresponding Q neighborhood of test historical sewage detection results into the detection loss analysis function respectively to obtain the Q neighborhood of historical sewage component combination detection loss amounts.

[0070] Further, the system is also used to implement the following functions: Obtain the usage duration of the instrument for sewage component detection tests to obtain a set of instrument usage durations; perform asynchronous periodic calibration on the instrument according to the set of instrument usage durations.

[0071] Further, the system is also used to implement the following functions: Obtain a set of historical pollutants for sample interference calibration and a plurality of sample sewage component detection schemes as training data; use the training data to perform supervised training on a framework constructed based on a feedforward neural network to learn the mapping relationship between the set of historical pollutants for sample interference calibration and the sewage component detection schemes until the training converges, and obtain the trained sewage component detection scheme recognizer.

[0072] Embodiment 3, based on the inventive concept of a sewage component detection method in the foregoing embodiments, the present application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of any one of the methods in the foregoing Embodiment 1.

[0073] Figure 3 It is a schematic structural diagram of an exemplary electronic device of the present application. In Figure 3 , the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges. Bus 300 connects various circuits including one or more processors represented by processor 302 and a memory represented by memory 304 together. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, which provides a unit for communicating with various other devices on a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0074] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The foregoing are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0076] This specification and the drawings are merely exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for detecting sewage components, characterized in that: The method comprises: Obtain a set of historical sewage detection results from sewage outlets in the target city; Clustering the historical sewage component detection result set based on pollutant type to obtain Q historical pollutants and Q historical pollutant concentration sets, where Q is a positive integer; Centrally screen Q historical pollutant concentration sets to determine Q historical concentrated pollutant concentrations; Constructing sewage component combination neighborhoods for Q historical pollutants according to Q historical concentrated pollutant concentrations, and obtaining Q historical sewage component combination neighborhoods, wherein each historical sewage component combination includes at least one calibrated historical pollutant and one combined historical pollutant, as well as the corresponding historical concentrated pollutant concentrations; Traversing the Q historical sewage component combination neighborhoods to perform sewage component detection tests, and obtaining Q experimental historical sewage detection result neighborhoods; Based on Q historical sewage component combination neighborhoods and Q experimental historical sewage detection result neighborhoods, interference calibration historical pollutants are authenticated to obtain M interference calibration historical pollutants, where M is a positive integer less than or equal to Q; The M interference calibration historical pollutants are used as preprocessing items and input into a pre-built sewage component detection scheme identifier to obtain a target sewage component detection scheme.

2. A method for detecting sewage components as claimed in claim 1, characterized in that: The Q historical pollutant concentration sets are centrally screened to determine Q historical concentrated pollutant concentrations, including: Calculating the means of the Q historical pollutant concentration sets respectively to obtain Q historical pollutant concentration means; Taking the mean values ​​of the Q historical pollutant concentrations as an index, centrally screening the Q historical pollutant concentration sets according to a preset central screening step and in an arbitrary direction to obtain Q iterative historical pollutant concentrations; Constructing the Q historical pollutant concentration mean neighborhoods and the Q iterative historical pollutant concentration neighborhoods based on the preset centralized screening step, respectively; When the neighborhood densities of the Q iterative historical pollutant concentration neighborhoods are respectively greater than or equal to the neighborhood densities of the Q historical pollutant concentration mean neighborhoods, the direction from the Q historical pollutant concentration means to the Q iterative historical pollutant concentrations is used as the iteration direction, and combined with the preset centralized screening step, the Q iterative historical pollutant concentrations are iterated in the Q historical pollutant concentration sets until the preset number of iterations is met, and the historical pollutant concentration obtained in the last iteration is used as the Q historical centralized pollutant concentrations.

3. A method for detecting sewage components as claimed in claim 2, characterized in that: When the neighborhood densities of the Q iterative historical pollutant concentration neighborhoods are respectively less than the neighborhood densities of the Q historical pollutant concentration mean neighborhoods, the directions from the Q historical pollutant concentration means to the Q iterative historical pollutant concentrations are added to the taboo iteration direction table, and combined with the preset centralized screening step, the Q historical pollutant concentration means are iterated in the Q historical pollutant concentration sets in the directions except those in the taboo iteration direction table until the preset number of iterations is met, and the historical pollutant concentrations obtained in the last iteration are used as the Q historical centralized pollutant concentrations, wherein the taboo iteration direction table includes a preset number of taboo iterations.

4. A method for detecting sewage components as claimed in claim 1, characterized in that: include: Based on the historical concentrated pollutant concentrations in the Q historical sewage component combination neighborhoods, a detection loss analysis is performed on the Q experimental historical sewage detection result neighborhoods to obtain Q historical sewage component combination detection loss amount neighborhoods; According to a preset detection loss threshold, loss certification is performed on the Q historical sewage component combination detection loss neighborhoods to obtain M interference-calibrated historical pollutants.

5. A method for detecting sewage components as claimed in claim 4, characterized in that: Based on the historical concentrated pollutant concentrations in the Q historical sewage component combination neighborhoods, the Q experimental historical sewage detection result neighborhoods are subjected to detection loss analysis to obtain Q historical sewage component combination detection loss amount neighborhoods, including: A detection loss analysis function is obtained, wherein the detection loss analysis function is: ； in, Detection of losses for historical sewage composition combinations, is the total amount of combined historical pollutants in the historical sewage component combination, It is the first in the historical sewage composition The historical concentrated pollutant concentration of the combined historical pollutants, It is the first in the historical sewage composition The concentration of the experimental historical pollutants in the corresponding experimental historical sewage test results for the combined historical pollutants. The historical concentrated pollutant concentrations for the historical pollutants in the historical sewage component combination, The concentration of the test historical pollutants in the test results of the corresponding test historical sewage is used to calibrate the historical pollutants in the historical sewage component combination. Coefficients for balancing the detection losses of the calibration historical pollutants and the combined historical pollutants; The historical concentrated pollutant concentrations within the Q historical sewage component combination neighborhoods and the corresponding Q experimental historical sewage detection result neighborhoods are respectively input into the detection loss analysis function to obtain the Q historical sewage component combination detection loss amount neighborhoods.

6. A method for detecting wastewater components as claimed in claim 1, characterized in that: Traversing the Q historical sewage component combination neighborhoods to perform sewage component detection tests, Q experimental historical sewage detection result neighborhoods are obtained, including: Obtain the usage time of the instrument for the sewage component detection test, and obtain the instrument usage time set; Asynchronous periodic calibration is performed on the instruments respectively according to the instrument usage time sets.

7. A method for detecting sewage components as claimed in claim 1, characterized in that: The M interference calibration historical pollutants are used as preprocessing items and input into a pre-built sewage component detection scheme identifier to obtain a target sewage component detection scheme, including: Acquire multiple sample interference calibration historical pollutant sets and multiple sample sewage component detection schemes as training data; The training data is used to perform supervised training on a framework built based on a feedforward neural network to learn the mapping relationship between the interference calibration historical pollutant set and the sewage component detection scheme until the training converges, thereby obtaining the trained sewage component detection scheme identifier.

8. A sewage component detection system, characterized in that: The system comprises: A data acquisition module, used to obtain a set of historical sewage detection results of sewage outlets in target cities; A clustering module, used to cluster the historical sewage component detection result set based on the pollutant type, and obtain Q historical pollutants and Q historical pollutant concentration sets, where Q is a positive integer; A centralized screening module is used to centrally screen Q historical pollutant concentration sets to determine Q historical concentrated pollutant concentrations; A neighborhood construction module is used to construct a sewage component combination neighborhood for Q historical pollutants according to the Q historical concentrated pollutant concentrations, and obtain Q historical sewage component combination neighborhoods, wherein each historical sewage component combination includes at least one calibrated historical pollutant and one combined historical pollutant, and the corresponding historical concentrated pollutant concentrations; A detection test module is used to traverse the Q historical sewage component combination neighborhoods to perform sewage component detection tests and obtain Q experimental historical sewage detection result neighborhoods; A pollutant authentication module is used to authenticate interference-calibrated historical pollutants based on Q historical sewage component combination neighborhoods and Q experimental historical sewage detection result neighborhoods, and obtain M interference-calibrated historical pollutants, where M is a positive integer less than or equal to Q; The identification module is used to take the M interference calibration historical pollutants as pre-processing items, input them into a pre-built sewage component detection scheme identifier, and obtain a target sewage component detection scheme.

9. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; The processor is used to implement a sewage component detection method according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.

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