A method for constructing a pollution fingerprint of a drainage household, a pollution tracing method and device for drainage

Abstract

The application discloses a drainage pollution fingerprint construction method and a drainage pollution tracing method and device, and relates to the technical field of water resource analysis. Since the correlation coefficient and the significance level between various pollution indexes of various source drainage users are determined by statistical analysis of water quality data, the candidate fingerprint set is determined through correlation analysis, then the industry characteristic analysis is performed on the candidate fingerprint set to determine the industry characteristic fingerprint set of each type of source drainage user, and finally, the target fingerprint is screened from the industry characteristic fingerprint set according to the detection mode of each pollution index, so that the finally determined target fingerprint set has the advantages of statistical significance, clear industry differentiation degree and on-site operability. In the subsequent process of analyzing the to-be-tested water quality data, the industry type of the to-be-tested drainage user can be determined according to the target fingerprint of each type of source drainage user, so as to accurately identify the industry attribute of the drainage user with excessive drainage pollution, and the pollution tracing is realized.

Description

Technical Field

[0001] This application relates to the field of water resource analysis technology, and in particular to a method for constructing pollution fingerprints of wastewater users, a method and device for tracing the source of wastewater pollution. Background Technology

[0002] Urban drainage systems are an important infrastructure for urban water environment protection. The sources of drainage pollution they connect to include residential areas, catering service establishments, auto repair shops, medical and health institutions, as well as commercial complexes and public facilities (integrated drainage users).

[0003] In related technologies, the concentration values ​​of physicochemical indicators such as chemical oxygen demand, ammonia nitrogen, and total phosphorus are usually detected and compared with preset emission standard limits to determine whether there is excessive discharge pollution.

[0004] However, when faced with complex urban drainage systems involving multiple industries, the relevant technologies cannot accurately identify the industry attributes of drainage users whose drainage pollution exceeds the standards, making it difficult to trace the source of pollution. Summary of the Invention

[0005] This application provides a method for constructing pollution fingerprints of wastewater users, a method and apparatus for tracing the source of wastewater pollution, so as to at least solve the problem in related technologies that it is difficult to accurately identify the industry attributes of wastewater users whose wastewater pollution exceeds the standard, and thus difficult to achieve pollution source tracing.

[0006] This application provides a method for constructing pollution fingerprints of wastewater users, including: Obtain water quality data from multiple sources of wastewater discharge; For any type of source wastewater user, based on the water quality data, calculate the correlation coefficient and significance level between each pollution indicator; wherein, the correlation coefficient is the Spearman rank correlation coefficient; Based on the correlation coefficient and significance level between the pollution indicators, a combination of multiple pollution indicators that meet the preset strong correlation threshold is used as a candidate fingerprint set for this type of source wastewater user. A horizontal comparison is performed between candidate fingerprint sets of various source wastewater users to screen industry-specific fingerprint sets with industry characteristics of wastewater users from each candidate fingerprint set; For any type of source wastewater discharger, industry feature fingerprints that meet preset test conditions are selected from the industry feature fingerprint set of that type of source wastewater discharger to form the target fingerprint set corresponding to that type of source wastewater discharger; wherein, each target fingerprint in the target fingerprint set includes at least a combination of pollution indicators and a correlation type, or includes a discrimination mode composed of multiple combinations of pollution indicators and correlation types.

[0007] Optionally, based on the correlation coefficients and significance levels among the pollution indicators, a combination of multiple pollution indicators that meet a preset strong correlation threshold is used as a candidate fingerprint set for this type of source wastewater discharger, including: Traverse all combinations of pollution indicators. When the correlation coefficient of any combination of pollution indicators is not less than a preset coefficient threshold and the significance level is lower than a preset level threshold, determine that the combination of pollution indicators meets the preset strong correlation threshold. By summarizing all combinations of pollution indicators that meet the preset strong correlation threshold, a candidate fingerprint set for this type of source wastewater user is obtained.

[0008] Optionally, the horizontal comparison between candidate fingerprint sets of various source wastewater users to screen industry-specific fingerprint sets with industry characteristics of wastewater users from each candidate fingerprint set includes: For any candidate fingerprint in the candidate fingerprint set, calculate the frequency of its occurrence in the candidate fingerprint sets of various types of source wastewater users; Based on the frequency of occurrence, candidate fingerprints that appear only in the candidate fingerprint set of a single type of source wastewater discharger are used as the industry-specific fingerprints of that single type of source wastewater discharger. The industry-specific fingerprint set with characteristics of the drainage user industry includes the industry-specific fingerprint.

[0009] Optionally, the method further includes: Candidate fingerprints that appear in the same candidate fingerprint clusters in both types of source wastewater users will be used as auxiliary fingerprints. Based on the differences in industry characteristics between the two types of source wastewater users, auxiliary indicators for the target type of source wastewater users are determined. The auxiliary indicators are added to the fingerprint to be assisted to obtain the industry-specific fingerprint of the target type of source drainage user.

[0010] Optionally, for any type of source wastewater discharger, the step of filtering industry feature fingerprints that meet preset test conditions from the industry feature fingerprint set of that type of source wastewater discharger to form a target fingerprint set corresponding to that type of source wastewater discharger includes: For any type of source wastewater discharger, the industry characteristic fingerprint set of that type of source wastewater discharger is traversed to determine the combination of pollution indicators in each industry characteristic fingerprint. Analysis of the detection methods for contamination index combinations in the characteristic fingerprints of various industries; If the detection method of any of the pollution index combinations meets the preset test conditions, the industry characteristic fingerprint corresponding to the pollution index combination is included in the target fingerprint set corresponding to the source wastewater user. Specifically, when the indicator detection method is implemented based on a portable device or the test time of the indicator detection method is lower than a preset time threshold, the indicator detection method is determined to meet the preset test conditions.

[0011] Optionally, the target fingerprints of residential wastewater dischargers should include at least a strong positive correlation between suspended solids and chemical oxygen demand and a strong positive correlation between ammonia nitrogen and total phosphorus. The target fingerprints of catering-related wastewater dischargers include at least a strong positive correlation network between suspended solids, chemical oxygen demand (COD), and animal and vegetable oils; a strong positive correlation combination between COD and anionic surfactants; and a strong positive correlation between ammonia nitrogen and total phosphorus. The target fingerprints of wastewater dischargers from automotive repair sources include at least a strong positive correlation network between chemical oxygen demand (COD), total phosphorus, and anionic surfactants, and a high correlation network between suspended solids and COD / total phosphorus / ammonia nitrogen. The target fingerprints of medical wastewater dischargers include at least a strong negative correlation between total residual chlorine and suspended solids / ammonia nitrogen, and a strong positive correlation between suspended solids, chemical oxygen demand, and ammonia nitrogen.

[0012] This application also provides a method for tracing the source of wastewater pollution, including: Obtain the target fingerprint sets of various source wastewater users pre-constructed using any of the above-mentioned wastewater user pollution fingerprint construction methods; Obtain the water quality data of the wastewater discharger to be tested; Based on the correlation relationships between various pollution indicators characterized by the water quality data to be tested, determine the set of correlation relationships to be tested; Determine whether the set of related relationships to be tested contains any target fingerprint in the target fingerprint set of any type of source wastewater user; If the set of relationships to be tested contains the target fingerprint of any type of source wastewater user, the industry type of that type of source wastewater user shall be taken as the industry type of the wastewater user to be tested. If the combination of pollution indicators and the corresponding relationship type in the set of relationships to be tested are the same as the combination of pollution indicators and the relationship type in any target fingerprint, then the set of relationships to be tested is determined to contain the target fingerprint.

[0013] This application also provides a device for constructing a wastewater user's pollution fingerprint, comprising: The acquisition module is used to acquire water quality data from various sources of wastewater discharge. The calculation module is used to calculate the correlation coefficient and significance level between various pollution indicators for any type of source wastewater user, based on the water quality data; wherein the correlation coefficient is the Spearman rank correlation coefficient. The first screening module is used to combine multiple pollution indicators that meet a preset strong correlation threshold as a candidate fingerprint set for the source wastewater users based on the correlation coefficient and significance level between the pollution indicators. The second screening module is used to perform a horizontal comparison between candidate fingerprint sets of various source drainage users, so as to screen industry feature fingerprint sets with drainage user industry characteristics from each candidate fingerprint set. The third screening module is used to select industry feature fingerprints that meet preset test conditions from the industry feature fingerprint set of any type of source wastewater discharger, thereby forming the target fingerprint set corresponding to that type of source wastewater discharger; wherein, each target fingerprint in the target fingerprint set includes at least a combination of pollution indicators and a correlation type, or includes a discrimination mode composed of multiple combinations of pollution indicators and correlation types.

[0014] Optional, the first filtering module is specifically used for: Traverse all combinations of pollution indicators. When the correlation coefficient of any combination of pollution indicators is not less than a preset coefficient threshold and the significance level is lower than a preset level threshold, determine that the combination of pollution indicators meets the preset strong correlation threshold. By summarizing all combinations of pollution indicators that meet the preset strong correlation threshold, a candidate fingerprint set for this type of source wastewater user is obtained.

[0015] Optional, the second filtering module is specifically used for: For any candidate fingerprint in the candidate fingerprint set, calculate the frequency of its occurrence in the candidate fingerprint sets of various types of source wastewater users; Based on the frequency of occurrence, candidate fingerprints that appear only in the candidate fingerprint set of a single type of source wastewater discharger are used as the industry-specific fingerprints of that single type of source wastewater discharger. The industry-specific fingerprint set with characteristics of the drainage user industry includes the industry-specific fingerprint.

[0016] Optionally, the second filtering module is also used for: Candidate fingerprints that appear in the same candidate fingerprint clusters in both types of source wastewater users will be used as auxiliary fingerprints. Based on the differences in industry characteristics between the two types of source wastewater users, auxiliary indicators for the target type of source wastewater users are determined. The auxiliary indicators are added to the fingerprint to be assisted to obtain the industry-specific fingerprint of the target type of source drainage user.

[0017] Optionally, the third filtering module is specifically used for: For any type of source wastewater discharger, the industry characteristic fingerprint set of that type of source wastewater discharger is traversed to determine the combination of pollution indicators in each industry characteristic fingerprint. Analysis of the detection methods for contamination index combinations in the characteristic fingerprints of various industries; If the detection method of any of the pollution index combinations meets the preset test conditions, the industry characteristic fingerprint corresponding to the pollution index combination is included in the target fingerprint set corresponding to the source wastewater user. Specifically, when the indicator detection method is implemented based on a portable device or the test time of the indicator detection method is lower than a preset time threshold, the indicator detection method is determined to meet the preset test conditions.

[0018] Optionally, the target fingerprints of residential wastewater dischargers should include at least a strong positive correlation between suspended solids and chemical oxygen demand and a strong positive correlation between ammonia nitrogen and total phosphorus. The target fingerprints of catering-related wastewater dischargers include at least a strong positive correlation network between suspended solids, chemical oxygen demand (COD), and animal and vegetable oils; a strong positive correlation combination between COD and anionic surfactants; and a strong positive correlation between ammonia nitrogen and total phosphorus. The target fingerprints of wastewater dischargers from automotive repair sources include at least a strong positive correlation network between chemical oxygen demand (COD), total phosphorus, and anionic surfactants, and a high correlation network between suspended solids and COD / total phosphorus / ammonia nitrogen. The target fingerprints of medical wastewater dischargers include at least a strong negative correlation between total residual chlorine and suspended solids / ammonia nitrogen, and a strong positive correlation between suspended solids, chemical oxygen demand, and ammonia nitrogen.

[0019] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described methods for constructing pollution fingerprints of wastewater users or the steps of any of the above-described methods for tracing the source of wastewater pollution.

[0020] This application also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of any of the above-described methods for constructing pollution fingerprints of wastewater users or the steps of any of the above-described methods for tracing the source of wastewater pollution.

[0021] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above-described methods for constructing pollution fingerprints of wastewater users or the steps of any of the above-described methods for tracing the source of wastewater pollution.

[0022] This application utilizes statistical analysis of water quality data to determine the correlation coefficients and significance levels among various pollution indicators for different types of wastewater dischargers. Correlation analysis is then used to identify candidate fingerprint sets for each type of discharger. Furthermore, industry-specific fingerprint analysis is conducted on these candidate fingerprint sets to determine industry-specific fingerprint sets for each type of discharger. Finally, based on the detection method for each pollution indicator, target fingerprints are selected from these industry-specific fingerprint sets for each type of discharger. This results in target fingerprint sets that possess advantages such as statistical significance, clear industry differentiation, and on-site operability. In subsequent analysis of the water quality data, the industry type of the discharger can be determined by comparing the correlations between the pollution indicators represented by the data with the matching results between the target fingerprints of different dischargers. This allows for accurate identification of the industry attributes of dischargers exceeding pollution standards, thus achieving pollution source tracing. Attached Figure Description

[0023] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating the method for constructing pollution fingerprints of wastewater users provided in this application embodiment; Figure 2 A schematic diagram of the correlation coefficient matrix of an exemplary residential drainage user provided for embodiments of this application; Figure 3 A flowchart illustrating an exemplary method for constructing a wastewater user's pollution fingerprint, provided for embodiments of this application; Figure 4 A flowchart illustrating the drainage pollution source tracing method provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of the wastewater user pollution fingerprint construction device provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0026] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0027] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] This application provides a method for constructing pollution fingerprints for wastewater dischargers, used to determine target fingerprints for wastewater dischargers in different industries. During subsequent wastewater pollution detection, the industry attribute of the wastewater discharger currently exceeding pollution standards can be determined based on the target fingerprints corresponding to different industry types. The execution subject of this application embodiment is an electronic device, such as a server, desktop computer, laptop computer, tablet computer, and other electronic devices that can be used for water quality data analysis.

[0029] like Figure 1 The diagram shown is a flowchart illustrating the wastewater user pollution fingerprint construction method provided in this application embodiment. The method includes: Step 101: Obtain water quality data from multiple sources of wastewater discharge.

[0030] Specifically, multiple water quality samples can be taken from various sources of wastewater to test physicochemical indicators including pH value, suspended solids (SS), chemical oxygen demand (COD), ammonia nitrogen, total phosphorus (TP), anionic surfactants, animal and vegetable oils, and total residual chlorine, thus generating water quality data for various sources of wastewater.

[0031] For example, taking multiple types of source wastewater users, including five categories—general wastewater users (mainly residential areas), catering wastewater users, auto repair wastewater users, medical wastewater users, and comprehensive wastewater users (commercial complexes, enterprises, and institutions)—sampling points can be set up separately to collect wastewater samples in multiple time periods and batches. Taking a city's drainage system as an example, 112 valid samples were obtained from general wastewater users, 197 from catering wastewater users, 25 from auto repair wastewater users, 43 from medical wastewater users, and 278 from comprehensive wastewater users. The tested indicators included pH, SS, COD, ammonia nitrogen, TP, anionic surfactants, animal and vegetable oils, and total residual chlorine. Basic statistics such as the mean, median, standard deviation, quartiles, and coefficient of variation for each indicator for each type of wastewater user were calculated. Statistical analysis reveals the following preliminary pollution characteristics among various wastewater dischargers: catering wastewater dischargers had the highest COD and ammonia nitrogen exceedance rates (38.38% and 42.78%, respectively); general wastewater dischargers had the highest average SS and TP values ​​(118.53 mg / L and 4.85 mg / L, respectively); auto repair wastewater dischargers showed significant fluctuations in various indicators; medical wastewater dischargers had generally low concentrations of various indicators but detected total residual chlorine; and comprehensive wastewater dischargers had indicators between those of general wastewater dischargers and catering wastewater dischargers.

[0032] Step 102: For any type of source wastewater user, calculate the correlation coefficient and significance level between various pollution indicators based on water quality data.

[0033] The correlation coefficient is the Spearman rank correlation coefficient.

[0034] It should be noted that the correlation coefficient ranges from -1 to 1. A positive value indicates that the two indicators have the same trend (positive correlation), while a negative value indicates that they have opposite trends (negative correlation). The larger the absolute value, the stronger the correlation. The significance level measures the probability that the obtained correlation is caused by random error; the smaller the value, the higher the statistical reliability of the correlation.

[0035] Step 103: Based on the correlation coefficient and significance level between various pollution indicators, a combination of multiple pollution indicators that meet the preset strong correlation threshold is used as a candidate fingerprint set for this type of source wastewater user.

[0036] Specifically, the correlation between various pollution indicators can be analyzed based on their correlation coefficients and significance levels. The correlation can be divided into two types: positive correlation and negative correlation. Multiple pollution indicators with strong correlation (meeting the preset strong correlation threshold) can be combined as a candidate fingerprint set for the source wastewater discharger. For example, the pollution indicator of suspended solids-chemical oxygen demand can be combined as a candidate fingerprint to obtain a candidate fingerprint set composed of multiple candidate fingerprints.

[0037] Step 104: Perform a horizontal comparison between candidate fingerprint sets of various source wastewater users to screen industry-specific fingerprint sets with industry characteristics of wastewater users from each candidate fingerprint set.

[0038] It should be noted that horizontal comparison is a cross-category comparison of candidate fingerprint sets of various source drainage users, while industry feature fingerprint set is a set of indicator combinations with industry differentiation capabilities.

[0039] Step 105: For any type of source wastewater discharger, screen the industry feature fingerprints that meet the preset test conditions from the industry feature fingerprint set of that type of source wastewater discharger to form the target fingerprint set corresponding to that type of source wastewater discharger.

[0040] Each target fingerprint in the target fingerprint set includes at least a combination of pollution indicators and a correlation type, or a discrimination pattern composed of multiple combinations of pollution indicators and correlation types.

[0041] Specifically, for each type of wastewater discharger's industry-specific fingerprint set, the detection methods of all indicators within the combination can be verified one by one. Finally, combinations of indicators that can be measured using portable on-site testing equipment or conventional rapid measurement methods are retained, while combinations of indicators that rely on large-scale laboratory analytical equipment are eliminated. The selected combinations are then identified as the target fingerprint corresponding to that type of wastewater discharger. The standard expression for the target fingerprint is "pollution indicator combination + correlation type." The discrimination mode includes a biphasic mode where positive and negative correlations coexist.

[0042] Based on the above embodiments, as an implementable approach, in one embodiment, multiple pollution indicators that meet a preset strong correlation threshold are combined as a candidate fingerprint set for this type of source wastewater discharger, according to the correlation coefficient and significance level between various pollution indicators, including: Step 1031: Traverse all combinations of pollution indicators. When the correlation coefficient of any combination of pollution indicators is not less than the preset coefficient threshold and the significance level is lower than the preset level threshold, determine that the combination of pollution indicators meets the preset strong correlation threshold. Step 1032: Summarize all combinations of pollution indicators that meet the preset strong correlation threshold to obtain the candidate fingerprint set of this type of source wastewater user.

[0043] The correlation coefficients include the Spearman rank correlation coefficient. The Spearman method is a non-parametric statistical method that does not have strict requirements on the data distribution pattern and is suitable for situations where wastewater quality data fluctuates greatly and outliers exist. Through correlation calculation, the correlation coefficient matrix between each pair of water quality indicators within each type of wastewater user is obtained. For example... Figure 2 The diagram shown is an exemplary correlation coefficient matrix of residential drainage users provided in this application embodiment. The size of the bubble represents the absolute value of the Spearman rank correlation coefficient, with blue indicating positive correlation and red indicating negative correlation.

[0044] For example, the preset coefficient threshold can be set to 0.65, the preset level threshold can be set to 0.05, and the preset strong correlation threshold can satisfy the condition r. s ≥0.65 and p<0.05, r s The correlation coefficient is represented by r, and p represents the significance level. Only the combination of indicators that meets this condition is retained for the next level of screening, while r is eliminated. s A weak correlation (p < 0.65 or p ≥ 0.05) or a combination of no statistically significant correlations. Here, r is used. s ≥0.65 is used as the strong correlation threshold, and p<0.05 is used as the significance threshold (in actual screening, the highly significant combination with p<0.01 is preferred to enhance statistical reliability).

[0045] The preset coefficient threshold of 0.65 and the preset level threshold of 0.05 are optimal implementation methods based on the statistical distribution characteristics of typical urban drainage data. In practical applications, due to differences in drainage habits, pipe network conditions, and sampling seasons in different regions, the above thresholds can fluctuate within the range of ±0.10 (correlation coefficient) and ±0.02 (significance level), ensuring that the selected combinations are statistically significant while reserving tolerance for reasonable fluctuations under different cities and sampling conditions. The candidate fingerprint set obtained through correlation screening ensures that the combinations entering subsequent stages have an intrinsic material basis and can reflect the synchronous change patterns of emissions from the same pollution source or the same type of chemical / biological processes.

[0046] Based on the above embodiments, as an implementable approach, in one embodiment, a horizontal comparison is performed between candidate fingerprint sets of various source wastewater users to screen industry-specific fingerprint sets with wastewater user industry characteristics from each candidate fingerprint set, including: Step 1041: For any candidate fingerprint in the candidate fingerprint set, count the frequency of occurrence of that candidate fingerprint in the candidate fingerprint sets of various types of source wastewater users. Step 1042: Based on the frequency of occurrence, select candidate fingerprints that appear only in the candidate fingerprint set of a single type of source wastewater user as the industry-specific fingerprints of that single type of source wastewater user.

[0047] Among them, the industry-specific fingerprint set with characteristics of the drainage user industry includes industry-specific fingerprints.

[0048] Specifically, for each independent candidate fingerprint composed of indicator combinations and corresponding correlation types in the total candidate fingerprint set of multiple types of source wastewater users, we can check its occurrence in the candidate fingerprint sets of other multiple types of wastewater users one by one, count the occurrence frequency of the candidate fingerprint, and then calculate the occurrence frequency. For example, for the COD-vegetable and animal oil strongly positive correlation combination in the candidate fingerprint set of catering wastewater users, it was found that it did not appear in the candidate fingerprint sets of residential, auto repair, and medical wastewater users, so its occurrence frequency is 1, and the occurrence frequency is 1 / total number of categories. That is, when designing five types of source wastewater users, the occurrence frequency is 20%.

[0049] Furthermore, the frequency of occurrence is characterized as the candidate fingerprint that appears only in the candidate fingerprint set of a single type of source wastewater user, and is used as the industry-specific fingerprint of a single type of source wastewater user.

[0050] Specifically, in one embodiment, candidate fingerprints that appear in the candidate fingerprint sets of the two types of source wastewater users can be used as fingerprints to be assisted; based on the differences in industry characteristics between the two types of source wastewater users, auxiliary indicators for the target type of source wastewater users can be determined; and auxiliary indicators can be added to the fingerprints to be assisted to obtain the industry-specific fingerprints of the target type of source wastewater users.

[0051] Specifically, some candidate fingerprints, although appearing in both categories of wastewater dischargers, can be distinguished by auxiliary indicators (such as the concentration range of animal and vegetable oils, the concentration level of anionic surfactants, and the presence or absence of total residual chlorine). Combinations that generally overlap and lack distinctiveness in three or more categories of wastewater dischargers are eliminated to ensure the industry-specific identification capability of pollution fingerprints.

[0052] For example, both the candidate fingerprint sets of catering and general wastewater dischargers show a strong positive correlation between "COD-anionic surfactant" and "COD-anionic surfactant," which alone cannot distinguish between them. In this case, adding "animal and vegetable oil concentration" as an auxiliary indicator to this combination is beneficial. Since the median concentration of animal and vegetable oil in catering wastewater dischargers is typically higher than 50 mg / L, while the median concentration in general wastewater dischargers is lower than 10 mg / L, adding this auxiliary indicator clearly distinguishes the "COD-anionic surfactant-animal and vegetable oil (high concentration)" fingerprint of catering wastewater dischargers from the same basic combination of general wastewater dischargers. The specific category and concentration threshold of the added auxiliary indicator can be determined based on the industry-specific differences in the actual sampling data.

[0053] Based on the above embodiments, as an implementable approach, in one embodiment, for any type of source wastewater discharger, industry feature fingerprints that meet preset test conditions are screened from the industry feature fingerprint set of that type of source wastewater discharger to constitute a target fingerprint set corresponding to that type of source wastewater discharger, including: Step 1051: For any type of source wastewater discharger, traverse the industry characteristic fingerprint set of that type of source wastewater discharger to determine the combination of pollution indicators in each industry characteristic fingerprint. Step 1052: Analyze the detection methods of the pollutant index combinations in the characteristic fingerprints of various industries; Step 1053: If the detection methods of any combination of pollution indicators meet the preset test conditions, the industry characteristic fingerprint corresponding to the combination of pollution indicators is included in the target fingerprint set corresponding to the source wastewater user.

[0054] Specifically, when the indicator detection method is implemented based on a portable device or the test time of the indicator detection method is lower than the preset time threshold, the indicator detection method is determined to meet the preset test conditions.

[0055] Specifically, by analyzing the detection methods of pollution indicators involved in the characteristic fingerprints of various industries, it can be determined whether the industry characteristic fingerprints meet the preset testing conditions. After screening, combinations of indicators that can be detected by portable on-site testing equipment or conventional rapid determination methods (with testing time less than the preset time threshold) are retained, such as COD (potassium dichromate rapid digestion spectrophotometry), SS (portable turbidity-SS conversion method), ammonia nitrogen (salicylic acid spectrophotometry or Nessler's reagent colorimetric method), TP (potassium persulfate digestion-molybdenum antimony spectrophotometry), anionic surfactants (methylene blue spectrophotometry), animal and vegetable oils (infrared spectrophotometric oil determination method), and total residual chlorine (portable residual chlorine meter), etc. Combinations of indicators such as heavy metals and trace organic matter that require complex pretreatment and analysis using large laboratory instruments (such as inductively coupled plasma mass spectrometry, gas chromatography-mass spectrometry, etc.) are eliminated.

[0056] The final combination of indicators retained after the three-level progressive screening process constitutes the pollution fingerprint of this type of wastewater discharger. Each indicator fingerprint is expressed in the format of "indicator combination + correlation type," clearly indicating the industry activity attributes and mechanistic meaning it reflects.

[0057] Among them, the target fingerprint of residential wastewater dischargers includes at least a strong positive correlation between suspended solids and chemical oxygen demand (COD) and a strong positive correlation between ammonia nitrogen and total phosphorus. Residential wastewater dischargers are primarily engaged in daily residential activities, with core wastewater discharge behaviors including bathing (using phosphorus-containing detergents), toilet flushing (discharging urine and feces, containing ammonia nitrogen and organic matter), and laundry. Therefore, the pollution fingerprint of general wastewater dischargers (residential wastewater dischargers) consists of a combination of two sets of indicators: Fingerprint A – Particulate Organic Pollution Fingerprint: Composed of a strong positive correlation between SS and COD. This combination is statistically associated with the discharge behavior of organic matter attached to particulate matter and emitted simultaneously, originating from domestic waste such as feces and food scraps. SS acts as a good carrier of COD. Key identification points: The two show a stable and strong positive correlation, with medium to high concentration levels (average SS approximately 118 mg / L, average COD approximately 388 mg / L).

[0058] Fingerprint B – Nutrient-Associated Fingerprint: Composed of a strong positive correlation between ammonia nitrogen and total phosphorus (TP). This combination reflects the synchronous occurrence of human urinary metabolites (ammonia nitrogen) and phosphorus-containing detergents (TP) in domestic wastewater. Key identification points: The two show a stable positive correlation, with an average ammonia nitrogen level of approximately 44 mg / L and an average TP level of approximately 4.9 mg / L, matching the typical characteristics of domestic wastewater.

[0059] It should be noted that the SS-COD combination also exists among catering wastewater users (r s =0.78), but catering wastewater dischargers form a unique "SS-COD-animal and vegetable oil" triangular network due to their high concentration of animal and vegetable oils, while general wastewater dischargers (residential source wastewater dischargers) lack high concentrations of animal and vegetable oils (the concentration of animal and vegetable oils in general wastewater dischargers is significantly lower than that in catering wastewater dischargers), which can effectively distinguish them. Ammonia nitrogen-TP combination also exists in catering wastewater dischargers (r s =0.69), but the exceedance rates and magnitudes of ammonia nitrogen and TP in catering wastewater were significantly higher, and were also correlated with oil-related indicators, showing a clear difference. The two combinations mentioned above did not exhibit the same single combination pattern in automotive repair wastewater (showing multiple strong correlation networks) and medical wastewater (coexisting positive and negative correlations), providing a basis for differentiation. The two combinations for general wastewater users passed the second-level screening.

[0060] Among them, the target fingerprint of catering source wastewater users includes at least a strong positive correlation network between suspended solids, chemical oxygen demand (COD), and animal and vegetable oils; a strong positive correlation combination between COD and anionic surfactants; and a strong positive correlation between ammonia nitrogen and total phosphorus. Catering wastewater users' core activities are food cooking, processing, and dishwashing. Their discharged wastewater is rich in grease, food residue, and detergent components, resulting in a complex pollution structure and a highly correlated synergistic pollution network of multiple pollutants. Therefore, the pollution fingerprint of catering source wastewater users consists of a combination of three sets of indicators: Fingerprint A – The Core Fingerprint of Oil Pollution: Composed of a strong positive correlation network among three indicators: SS (Self-Contamination), COD (Chemical Oxygen Demand), and animal and vegetable oils. This network forms a stable triangular positive correlation, revealing the physicochemical behavior of oils after emulsification, primarily adsorbing onto suspended particulate matter, thus simultaneously increasing COD. Key Identification Points: All three indicators increase simultaneously; animal and vegetable oil concentration is the key differentiating indicator. The strong positive correlation between COD and animal and vegetable oils is industry-specific for catering wastewater users and is absent in general wastewater users, automotive repair wastewater users, medical wastewater users, and integrated wastewater users. Other industries lack the material basis for this strong correlation due to the absence of high-concentration animal and vegetable oil emissions. Therefore, this combination can serve as the key fingerprint for identifying catering wastewater users.

[0061] Fingerprint B – Detergent Pollution Fingerprint: Characterized by a strong positive correlation between COD and anionic surfactants. This combination is observed in food service wastewater treatment plants along with the use of synthetic detergents such as dishwashing liquid. Anionic surfactants contribute a portion of the COD. Key Identification Points: The concentration of anionic surfactants is significantly higher than that of general wastewater treatment plants and combined wastewater treatment plants, and can be used as a supplementary distinguishing indicator.

[0062] Fingerprint C – Catering Nutritional Fingerprint: Composed of a strong positive correlation between ammonia nitrogen and total phosphorus (TP). Similar to general wastewater discharge, catering wastewater shows a correlation between ammonia nitrogen and TP. However, both TP and ammonia nitrogen in catering wastewater originate from protein decomposition during food washing (animal blood, bone and meat scraps, etc.) and are contributed by some detergents. Therefore, the high concentrations and fluctuations in catering wastewater discharge are greater than in general wastewater discharge. Combined with oil-related indicators, this can effectively differentiate the two.

[0063] It should be noted that the COD-animal and vegetable oil combination (r s =0.69) showed a strong correlation only in the catering industry sample. Other industries had extremely low or undetectable concentrations of animal and vegetable oils, lacking the material basis for this strong correlation; this combination is exclusive to catering wastewater users. The SS-COD-animal and vegetable oil three-indicator linkage network is only fully presented in the catering industry. The ammonia nitrogen-TP combination also appears in general wastewater users, but when combined with the grease triple fingerprint and detergent indicators, it can form a comprehensive multi-dimensional identification model for the catering industry. COD-anionic surfactant (r s The correlation (r = 0.62) also exists among integrated wastewater users. s =0.65), but the two types of wastewater users can be distinguished by the absolute concentration level of anionic surfactants (the catering industry is significantly higher). The catering wastewater user combination passed the second-level screening.

[0064] Among them, the target fingerprint of automotive repair source wastewater users includes at least a strong positive correlation network between chemical oxygen demand (COD), total phosphorus, and anionic surfactants, and a strong positive correlation network between suspended solids and COD / total phosphorus / ammonia nitrogen. Automotive repair wastewater users' core activities are automotive mechanical repair, maintenance, and cleaning; wastewater mainly originates from parts grinding and cleaning, lubricant changes, and vehicle exterior washing. Therefore, the pollution fingerprint of automotive repair source wastewater users consists of a combination of two sets of indicators: Fingerprint A – Characteristic Fingerprint of Cleaning Agent Contamination: This fingerprint is characterized by a strong, simultaneous positive correlation between three indicators: COD, TP, and anionic surfactants. This dense positive correlation points to the emission of industrial machine wastewater containing phosphorus and surfactants, as well as cleaning agents and degreasers, all originating from the same source and released simultaneously. Key Identification Points: This strong, combined correlation of the three indicators is unique to automotive repair wastewater users. General wastewater users and integrated wastewater users do not exhibit this simultaneous strong correlation combination, and other industries lack the material basis for this combination. Therefore, this combination can serve as a key fingerprint for identifying automotive repair wastewater users.

[0065] Fingerprint B – SS Pollution Diffusion Fingerprint: This fingerprint is composed of a strong positive correlation network between SS and COD, TP, and ammonia nitrogen. SS, as a strong adsorption carrier for various pollutants, forms a dense synergistic network, reflecting the strong adsorption effect of metal and inorganic solid particles generated during automotive repair and polishing on organic matter, phosphorus, and ammonia nitrogen. Key identification points: SS generally shows a strong positive correlation with multiple indicators, with the SS-COD correlation being the strongest across all industries, constituting a highly characteristic feature of the automotive repair industry.

[0066] It should be noted that the correlation between SS and COD is as high as 0.94, the highest among all industry types, constituting an important reference for identifying wastewater from automotive repair. A strong positive correlation exists between COD, TP, and anionic surfactants in the automotive repair industry, directly corresponding to the use of phosphorus-containing and surfactant-containing cleaning agents. General wastewater users and integrated wastewater users do not exhibit this simultaneous strong correlation combination of the three indicators; this is the second original identification result of this application's embodiments. SS is generally strongly positively correlated with multiple indicators (all r... s The dense network pattern (>0.8) is unique to the auto repair industry. Auto repair drainage user combinations pass the second-level screening.

[0067] Among them, the target fingerprint of medical source wastewater dischargers includes at least a strong negative correlation between total residual chlorine and suspended solids / ammonia nitrogen, and a strong positive correlation between suspended solids, chemical oxygen demand, and ammonia nitrogen. Medical wastewater dischargers include hospitals, clinics, and community health service centers, whose wastewater generally undergoes chlorine disinfection treatment before discharge. The strong oxidizing effect of disinfectants alters the correlation patterns between conventional pollution indicators, forming a unique fingerprint structure with both positive and negative correlations. Therefore, the pollution fingerprint of medical source wastewater dischargers consists of a combination of two sets of indicators: Fingerprint A – Disinfection Inhibition Fingerprint: Composed of a strong negative correlation between "Total Residual Chlorine - SS" and "Total Residual Chlorine - Ammonia Nitrogen". This strong negative correlation is statistically stable and its formation may be related to the addition of chlorine-containing disinfectants. Regardless of its exact chemical mechanism, this statistical relationship is unique and highly valuable for industry identification among medical wastewater dischargers. Wastewater from other industries generally contains no residual chlorine or only trace amounts, making this strong negative correlation impossible. Key Identification Points: The presence of total residual chlorine is a crucial and unique characteristic of medical wastewater dischargers. Wastewater from other industries generally contains no residual chlorine or only trace amounts, making this strong negative correlation impossible. This combination of strong negative correlations is the most unique fingerprint feature across all industries, thus serving as a key fingerprint for identifying medical wastewater dischargers.

[0068] Fingerprint B – Background Fingerprint of Medical Wastewater: Composed of a strong positive correlation between SS, COD, and ammonia nitrogen. This positive correlation exists in background wastewater that has not been adequately disinfected or has been diluted with water, reflecting the routine discharge of human excrement and organic waste during medical activities. Key Identification Points: Coexisting with the disinfection inhibition fingerprint, it forms a biphasic pattern where "the undisinfected portion retains a positive correlation, while the disinfected portion becomes negatively correlated." This biphasic pattern is unique to medical wastewater dischargers; no other type of discharger possesses this characteristic. Therefore, this biphasic pattern can serve as a key fingerprint for identifying medical wastewater dischargers.

[0069] It should be noted that there is a strong negative correlation between total residual chlorine and SS in medical wastewater dischargers (r s =-0.84) and a strong negative correlation between total residual chlorine and ammonia nitrogen (r s =-0.84) is the most unique fingerprint feature across all industries. Other wastewater dischargers have virtually no residual chlorine or only trace amounts, making it impossible to form this strong negative correlation. Simultaneously, medical wastewater dischargers exhibit a biphasic pattern of "coexistence of positive correlation (background wastewater) and negative correlation (disinfection interference)," a feature absent in other dischargers. This is the third original identification achievement of this application's embodiments. The medical wastewater discharger combination passed the second-level screening.

[0070] Among them, the target fingerprint of comprehensive source wastewater users includes at least a strong positive correlation between suspended solids and chemical oxygen demand (COD) and a strong positive correlation between COD and anionic surfactants. Comprehensive wastewater users are a collective term for multi-functional wastewater units such as commercial complexes, office buildings, schools, and enterprises, whose wastewater is a mixture of domestic sewage and commercial facility wastewater. Therefore, the fingerprint of comprehensive source wastewater users consists of a combination of two sets of indicators: Fingerprint A – Basic fingerprint of municipal wastewater: Composed of a strong positive correlation between SS and COD. This combination retains the core characteristics of domestic wastewater from general wastewater users, but due to the dilution from commercial wastewater, the correlation is slightly lower than that of general wastewater users, reflecting the mixing and dilution effect.

[0071] Fingerprint B – Detergent Contamination Fingerprint: Composed of a strong positive correlation between COD and anionic surfactants. This combination indicates the contamination of wastewater from cleaning in commercial areas and public sanitation facilities, providing a basis for distinguishing it from purely residential wastewater. Key identification points: The presence of both the basic municipal wastewater fingerprint and the detergent contamination fingerprint reflects a mixed pattern of multiple sources.

[0072] It should be noted that the SS-COD combination (r s =0.75) retains the core characteristics of domestic sewage from general wastewater users, but due to dilution from commercial wastewater, the correlation coefficient is higher than that of general wastewater users (r s =0.80) is slightly lower, and the two can be distinguished by the order of magnitude of the correlation and the concentration level of auxiliary indicators. COD-anionic surfactant (r s =0.65) was present in integrated wastewater users but not significant in general wastewater users, reflecting the mixing characteristics of commercial laundry wastewater. The integrated wastewater user group passed the second-level screening.

[0073] After the above progressive screening, the pollution fingerprints of the five types of source wastewater users were finally identified as shown in Table 1 below: Table 1

[0074] For example, such as Figure 3 The diagram illustrates an exemplary method for constructing pollution fingerprints for wastewater sources according to an embodiment of this application. First, water quality sampling data is acquired from five categories of wastewater sources: residential, catering, auto repair, medical, and general. Data cleaning yields water quality data for multiple categories of wastewater sources. Correlation analysis is performed on this data to determine the correlation coefficients and significance levels among various pollution indicators for each category. Target fingerprints for each category are determined through a three-tiered progressive screening process based on correlation, specificity, and measurability. Specifically, correlation screening identifies candidate fingerprint sets for each category, specificity screening identifies industry-specific fingerprint sets, and measurability screening determines the final target fingerprint.

[0075] The wastewater source pollution fingerprint construction method provided in this application determines the correlation coefficients and significance levels between various pollution indicators of different types of wastewater sources through statistical analysis of water quality data. Then, through correlation analysis, a candidate fingerprint set for each type of wastewater source is determined. Next, industry-specific fingerprint analysis is performed on the candidate fingerprint sets for each type of wastewater source to determine its industry-specific fingerprint set. Finally, target fingerprints are selected from the industry-specific fingerprint sets for each type of wastewater source based on the detection method for each pollution indicator. This results in target fingerprints that are scientifically sound, clearly distinguishable by industry, and highly operable on-site. In subsequent analysis of the water quality data to be tested, the industry type of the wastewater source can be determined based on the target correlations between the pollution indicators represented by the water quality data and the matching between the target fingerprints of different types of wastewater sources. This accurately identifies the industry attributes of wastewater sources exceeding pollution standards, thus achieving pollution source tracing.

[0076] Furthermore, this application's embodiments break through the existing technical approaches of single-index threshold comparison, feature tracing, and spectral fingerprinting. For the first time, it defines industry pollution fingerprints from the dimension of "statistical correlation" between conventional water quality indicators, establishing a three-level progressive screening system of "correlation—specificity—measurability," providing a novel technical approach for industry identification of wastewater users. It is highly scientific and provides stable identification. The use of Spearman's rank correlation analysis has no strict requirements on data distribution, making it suitable for the characteristics of large fluctuations in wastewater quality and numerous outliers. The three-level progressive screening converges layer by layer from three dimensions: statistics, industry characteristics, and practicality, ensuring that the extracted fingerprint combinations are both significant and discriminative. s The threshold for p is set using an interval (r) s (≥0.65, p<0.05) to allow for reasonable fluctuations under different cities and sampling conditions. Clear industry differentiation. The five types of wastewater discharger pollution fingerprints each have unique indicator combination patterns and mechanistic bases, especially the "COD-vegetable and animal oil" exclusive combination for the catering industry, the "COD-TP-anionic surfactant" three-indicator homologous combination for the auto repair industry, and the "positive and negative dual-phase coexistence" pattern for the medical industry, exhibiting high industry specificity. Strong on-site operability. Indicators retained through the third-level measurability screening can all be detected within half an hour using portable equipment or rapid on-site measurement methods, significantly reducing application threshold and cost, making them suitable for grassroots drainage inspections and rapid source tracing scenarios.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0078] This application provides a method for tracing the source of wastewater pollution. During wastewater pollution detection, this method determines the industry attribute of wastewater users whose wastewater pollution exceeds standards based on target fingerprints corresponding to different industry types. The execution subject of this application is an electronic device, such as a server, desktop computer, laptop computer, tablet computer, or other electronic devices that can be used for water quality data analysis.

[0079] like Figure 4 The diagram shown is a flowchart illustrating the wastewater pollution source tracing method provided in this embodiment of the application. The method includes: Step 401: Obtain the target fingerprint set of various source wastewater users pre-constructed using the aforementioned wastewater user pollution fingerprint construction method; Step 402: Obtain the water quality data of the wastewater discharger to be tested; Step 403: Determine the set of correlation relationships to be measured based on the correlation relationships between various pollution indicators characterized by the water quality data to be measured; Step 404: Determine whether the set of related relations to be tested contains any target fingerprint in the target fingerprint set of any type of source wastewater user; Step 405: If the set of related relations to be tested contains the target fingerprint of any type of source wastewater user, then the industry type of that type of source wastewater user shall be used as the industry type of the wastewater user to be tested.

[0080] If the combination of pollution indicators and the corresponding relationship type in the correlation set to be tested are the same as the combination of pollution indicators and the relationship type in any target fingerprint, then it is determined that the correlation set to be tested contains the target fingerprint.

[0081] Specifically, based on the water quality data to be tested, Spearman's rank correlation analysis, consistent with the fingerprint construction stage, is used to calculate the correlation coefficients and significance levels between various indicators. Relationship combinations that meet the strong correlation threshold are selected to form a set of correlation relationships to be tested. This set of correlation relationships is then compared with pre-constructed target fingerprints of various source wastewater users. If the set of correlation relationships contains the target fingerprint of a certain type of wastewater user (e.g., a strong positive correlation between COD and animal / vegetable oils for catering, or a strong positive correlation network between COD, TP, and anionic surfactants for auto repair), then the wastewater user is directly identified as belonging to that industry type. This solves the technical problem of traditional methods being unable to locate the source of pollution in mixed-source scenarios of drainage systems, and possesses high identification efficiency and accuracy. In the embodiments of this application, "strong positive correlation," "strong negative correlation," and "strong positive correlation network" all refer to statistical relationships that satisfy rs ≥ 0.65 and p < 0.05.

[0082] For a strongly positively correlated network consisting of three or more pollution indicators, all combinations of indicators in the network must simultaneously meet the strong correlation threshold to be considered a match. Furthermore, if multiple industry types are matched simultaneously, the one with the most matched fingerprints is used as the judgment result; if none are matched, it is determined to be unidentifiable or not belonging to a known industry type.

[0083] Embodiments of this application also provide a wastewater user contamination fingerprint construction apparatus for performing the wastewater user contamination fingerprint construction method provided in the above embodiments.

[0084] like Figure 5 The diagram shown is a structural schematic of a wastewater user pollution fingerprint construction device provided in an embodiment of this application. The wastewater user pollution fingerprint construction device 50 includes: an acquisition module 501, a calculation module 502, a first screening module 503, a second screening module 504, and a third screening module 505.

[0085] The system comprises the following modules: an acquisition module for acquiring water quality data from multiple types of wastewater dischargers; a calculation module for calculating the correlation coefficient and significance level between various pollution indicators based on the water quality data for any type of wastewater discharger, wherein the correlation coefficient is the Spearman rank correlation coefficient; a first screening module for combining multiple pollution indicators that meet a preset strong correlation threshold based on the correlation coefficient and significance level between the pollution indicators, serving as a candidate fingerprint set for that type of wastewater discharger; a second screening module for performing a horizontal comparison between the candidate fingerprint sets of various types of wastewater dischargers to screen for industry-specific fingerprint sets with industry characteristics of the discharger; and a third screening module for selecting industry-specific fingerprints that meet preset test conditions from the industry-specific fingerprint set of any type of wastewater discharger, forming the target fingerprint set corresponding to that type of wastewater discharger, wherein each target fingerprint in the target fingerprint set includes at least a combination of pollution indicators and a correlation type, or includes a discrimination pattern composed of multiple combinations of pollution indicators and correlation types.

[0086] Specifically, in one embodiment, the first screening module is specifically used for: Traverse all combinations of pollution indicators. When the correlation coefficient of any combination of pollution indicators is not less than the preset coefficient threshold and the significance level is lower than the preset level threshold, determine that the combination of pollution indicators meets the preset strong correlation threshold. By summarizing all combinations of pollution indicators that meet the preset strong correlation threshold, a candidate fingerprint set for this type of source wastewater user is obtained.

[0087] Specifically, in one embodiment, the second filtering module is specifically used for: For any candidate fingerprint in the candidate fingerprint set, calculate the frequency of its occurrence in the candidate fingerprint sets of various types of source wastewater users; Based on the frequency of occurrence, candidate fingerprints that appear only in the candidate fingerprint set of a single type of source wastewater discharger are used as industry-specific fingerprints for that single type of source wastewater discharger. Among them, the industry-specific fingerprint set with characteristics of the drainage user industry includes industry-specific fingerprints.

[0088] Specifically, in one embodiment, the second screening module is further configured to: Candidate fingerprints that appear in the same candidate fingerprint clusters in both types of source wastewater users will be used as auxiliary fingerprints. Based on the differences in industry characteristics between the two types of source wastewater users, auxiliary indicators for the target type of source wastewater users are determined. Add auxiliary indicators to the fingerprint to be assisted to obtain the industry-specific fingerprint of the target type of source drainage users.

[0089] Specifically, in one embodiment, the third filtering module is specifically used for: For any type of source wastewater discharger, the industry characteristic fingerprint set of that type of source wastewater discharger is traversed to determine the combination of pollution indicators in each industry characteristic fingerprint. Analysis of the detection methods for contamination index combinations in the characteristic fingerprints of various industries; If the detection methods of any combination of pollution indicators meet the preset test conditions, the industry characteristic fingerprint corresponding to the combination of pollution indicators will be included in the target fingerprint set corresponding to the source wastewater user. Specifically, when the indicator detection method is implemented based on a portable device or the test time of the indicator detection method is lower than the preset time threshold, the indicator detection method is determined to meet the preset test conditions.

[0090] Specifically, in one embodiment, the target fingerprint of residential source wastewater users includes at least a strong positive correlation between suspended solids and chemical oxygen demand and a strong positive correlation between ammonia nitrogen and total phosphorus; The target fingerprints of catering-related wastewater dischargers include at least a strong positive correlation network between suspended solids, chemical oxygen demand (COD), and animal and vegetable oils; a strong positive correlation combination between COD and anionic surfactants; and a strong positive correlation between ammonia nitrogen and total phosphorus. The target fingerprints of wastewater dischargers from automotive repair sources include at least a strong positive correlation network between chemical oxygen demand (COD), total phosphorus, and anionic surfactants, and a high correlation network between suspended solids and COD / total phosphorus / ammonia nitrogen. The target fingerprint of medical source wastewater users includes at least a strong negative correlation between total residual chlorine and suspended solids / ammonia nitrogen, and a strong positive correlation between suspended solids, chemical oxygen demand, and ammonia nitrogen. For descriptions of the features in the embodiments corresponding to the wastewater user pollution fingerprint construction device, please refer to the relevant descriptions in the embodiments corresponding to the wastewater user pollution fingerprint construction method; these will not be repeated here.

[0091] Embodiments of this application also provide an electronic device, such as... Figure 6 The diagram shown is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. It includes a processor 10 and a memory 20. The memory 20 stores a computer program. The processor 10 is configured to run the computer program to execute the steps in any of the above embodiments of the wastewater pollution fingerprint construction method or the steps in any of the above embodiments of the wastewater pollution source tracing method.

[0092] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above embodiments of the wastewater pollution fingerprint construction method or the steps in any of the above embodiments of the wastewater pollution source tracing method when it runs.

[0093] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0094] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the wastewater pollution fingerprint construction method or the steps in any of the above embodiments of the wastewater pollution source tracing method.

[0095] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps in any of the above embodiments of the wastewater pollution fingerprint construction method or the steps in any of the above embodiments of the wastewater pollution source tracing method.

[0096] Any of the components, modules, units, parts, methods, and operations described herein can be implemented using software, firmware, hardware (e.g., fixed logic circuitry), manual processing, or any combination thereof. Alternatively or additionally, any functionality described herein can be executed at least in part by one or more hardware logic components, such as, but not limited to, a central processing unit (CPU), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), an application-specific standard product (ASSP), a system-on-a-chip (SoC), a complex programmable logic device (CPLD), a microprocessor (MCU), etc. The terms "system," "computing device," or "apparatus" as used herein encompass various means, devices, and machines for processing data, including, for example, one or more programmable processors, computers, SoCs, or combinations thereof. The apparatus may also include code that creates an execution environment for the computer program in question, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or one or more combinations thereof. The aforementioned computer program (also known as a program, software, software application, app, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, object, or other unit suitable for a computing environment.

[0097] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0098] The above provides a detailed description of a method for constructing pollution fingerprints for wastewater users, a method for tracing the source of wastewater pollution, and an apparatus for this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only intended to help understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for constructing a pollution fingerprint of a wastewater user, characterized in that, include: Obtain water quality data from multiple sources of wastewater discharge; For any type of source wastewater user, based on the water quality data, calculate the correlation coefficient and significance level between each pollution indicator; wherein, the correlation coefficient is the Spearman rank correlation coefficient; Based on the correlation coefficient and significance level between the pollution indicators, a combination of multiple pollution indicators that meet the preset strong correlation threshold is used as a candidate fingerprint set for this type of source wastewater user. A horizontal comparison is performed between candidate fingerprint sets of various source wastewater users to screen industry-specific fingerprint sets with industry characteristics of wastewater users from each candidate fingerprint set; For any type of source wastewater discharger, industry feature fingerprints that meet preset test conditions are selected from the industry feature fingerprint set of that type of source wastewater discharger to form the target fingerprint set corresponding to that type of source wastewater discharger; wherein, each target fingerprint in the target fingerprint set includes at least a combination of pollution indicators and a correlation type, or includes a discrimination mode composed of multiple combinations of pollution indicators and correlation types.

2. The method for constructing a wastewater discharger pollution fingerprint according to claim 1, characterized in that, The step involves combining multiple pollution indicators that meet a preset strong correlation threshold, based on the correlation coefficient and significance level among the various pollution indicators, to form a candidate fingerprint set for this type of source wastewater discharger. This includes: Traverse all combinations of pollution indicators. When the correlation coefficient of any combination of pollution indicators is not less than a preset coefficient threshold and the significance level is lower than a preset level threshold, determine that the combination of pollution indicators satisfies the preset strong correlation threshold. By summarizing all combinations of pollution indicators that meet the preset strong correlation threshold, a candidate fingerprint set for this type of source wastewater user is obtained.

3. The method for constructing a wastewater discharger pollution fingerprint according to claim 1, characterized in that, The horizontal comparison between candidate fingerprint sets of various source wastewater users, in order to screen industry-specific fingerprint sets with industry characteristics of wastewater users from each candidate fingerprint set, includes: For any candidate fingerprint in the candidate fingerprint set, calculate the frequency of its occurrence in the candidate fingerprint sets of various types of source wastewater users; Based on the frequency of occurrence, candidate fingerprints that appear only in the candidate fingerprint set of a single type of source wastewater discharger are used as the industry-specific fingerprints of that single type of source wastewater discharger. The industry-specific fingerprint set with characteristics of the drainage user industry includes the industry-specific fingerprint.

4. The method for constructing a wastewater discharger pollution fingerprint according to claim 3, characterized in that, The method further includes: Candidate fingerprints that appear in the same candidate fingerprint clusters in both types of source wastewater users will be used as auxiliary fingerprints. Based on the differences in industry characteristics between the two types of source wastewater users, auxiliary indicators for the target type of source wastewater users are determined. The auxiliary indicators are added to the fingerprint to be assisted to obtain the industry-specific fingerprint of the target type of source drainage user.

5. The method for constructing a wastewater discharger pollution fingerprint according to claim 1, characterized in that, For any type of source wastewater discharger, the process involves filtering industry feature fingerprints that meet preset test conditions from the industry feature fingerprint set of that type of source wastewater discharger to form the target fingerprint set corresponding to that type of source wastewater discharger, including: For any type of source wastewater discharger, the industry characteristic fingerprint set of that type of source wastewater discharger is traversed to determine the combination of pollution indicators in each industry characteristic fingerprint. Analysis of the detection methods for contamination index combinations in the characteristic fingerprints of various industries; If the detection method of any of the pollution index combinations meets the preset test conditions, the industry characteristic fingerprint corresponding to the pollution index combination is included in the target fingerprint set corresponding to the source wastewater user. Specifically, when the indicator detection method is implemented based on a portable device or the test time of the indicator detection method is lower than a preset time threshold, the indicator detection method is determined to meet the preset test conditions.

6. The method for constructing a wastewater discharger pollution fingerprint according to claim 1, characterized in that, The target fingerprints of residential wastewater dischargers include at least a strong positive correlation between suspended solids and chemical oxygen demand and a strong positive correlation between ammonia nitrogen and total phosphorus. The target fingerprints of catering-related wastewater dischargers include at least a strong positive correlation network between suspended solids, chemical oxygen demand (COD), and animal and vegetable oils; a strong positive correlation between COD and anionic surfactants; and a strong positive correlation between ammonia nitrogen and total phosphorus. The target fingerprints of wastewater dischargers from automotive repair sources include at least a strong positive correlation network between chemical oxygen demand (COD), total phosphorus, and anionic surfactants, and a high correlation network between suspended solids and COD / total phosphorus / ammonia nitrogen. The target fingerprints of medical wastewater dischargers include at least a strong negative correlation between total residual chlorine and suspended solids / ammonia nitrogen, and a strong positive correlation between suspended solids, chemical oxygen demand, and ammonia nitrogen.

7. A method for tracing the source of wastewater pollution, characterized in that, include: Obtain target fingerprint sets of various source wastewater users pre-constructed using the wastewater user pollution fingerprint construction method according to any one of claims 1 to 6; Obtain the water quality data of the wastewater discharger to be tested; Based on the correlation relationships between various pollution indicators characterized by the water quality data to be tested, determine the set of correlation relationships to be tested; Determine whether the set of related relationships to be tested contains any target fingerprint in the target fingerprint set of any type of source wastewater user; If the set of relationships to be tested contains the target fingerprint of any type of source wastewater user, the industry type of that type of source wastewater user shall be taken as the industry type of the wastewater user to be tested. If the combination of pollution indicators and the corresponding relationship type in the set of relationships to be tested are the same as the combination of pollution indicators and the relationship type in any target fingerprint, then the set of relationships to be tested is determined to contain the target fingerprint.

8. A device for constructing fingerprints of wastewater users, characterized in that, include: The acquisition module is used to acquire water quality data from various sources of wastewater discharge. The calculation module is used to calculate the correlation coefficient and significance level between various pollution indicators for any type of source wastewater user, based on the water quality data; wherein the correlation coefficient is the Spearman rank correlation coefficient. The first screening module is used to combine multiple pollution indicators that meet a preset strong correlation threshold as a candidate fingerprint set for the source wastewater users based on the correlation coefficient and significance level between the pollution indicators. The second screening module is used to perform a horizontal comparison between candidate fingerprint sets of various source drainage users, so as to screen industry feature fingerprint sets with drainage user industry characteristics from each candidate fingerprint set. The third screening module is used to select industry feature fingerprints that meet preset test conditions from the industry feature fingerprint set of any type of source wastewater discharger, thereby forming the target fingerprint set corresponding to that type of source wastewater discharger; wherein, each target fingerprint in the target fingerprint set includes at least a combination of pollution indicators and a correlation type, or includes a discrimination mode composed of multiple combinations of pollution indicators and correlation types.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the wastewater pollution fingerprint construction method as described in any one of claims 1 to 6 or the wastewater pollution source tracing method as described in claim 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the wastewater pollution fingerprint construction method as described in any one of claims 1 to 6 or the steps of the wastewater pollution source tracing method as described in claim 7.

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