Industrial sewage treatment method and system based on big data and Internet of Things

Abstract

The invention discloses an industrial sewage treatment method and system based on big data and Internet of Things, and relates to the technical field of industrial sewage treatment. The method comprises the following steps: setting a historical data cycle and a unit detection water volume, and collecting chemical data corresponding to the unit detection water volume and a historical cycle chemical database of the chemical data corresponding to the historical data cycle; constructing a chemical data comparison table corresponding to the chemical data based on the big data; according to the chemical data comparison table, classifying chemical data corresponding to the historical cycle chemical database to obtain a chemical data category pool; setting a category pool processing mechanism, and processing the chemical data category pool according to the category pool processing mechanism to construct a category pool processing model; acquiring a real-time histogram corresponding to the chemical data category pool based on the category pool processing model; monitoring the chemical data of the historical cycle chemical database in real time according to the real-time histogram to obtain an anomaly generation type; the comprehensiveness of industrial sewage treatment is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial wastewater treatment, and in particular to an industrial wastewater treatment method and system based on big data and the Internet of Things. Background Art

[0002] Industrial wastewater includes production wastewater, production sewage and cooling water. It refers to wastewater and waste liquid generated during industrial production, which contains industrial production materials, intermediate products, by-products and pollutants generated during the production process that are lost with water. Industrial wastewater is of various types and has complex components. For example, electrolytic salt industry wastewater contains mercury, heavy metal smelting industry wastewater contains lead, cadmium and other metals, electroplating industry wastewater contains cyanide, chromium and other heavy metals, petroleum refining industry wastewater contains phenol, and pesticide manufacturing industry wastewater contains various pesticides. Since industrial wastewater often contains a variety of toxic substances, polluting the environment is very harmful to human health, and industrial wastewater needs to be treated; due to the wide variety of industrial wastewater types and the complexity of its components; there are still many defects in the existing industrial wastewater treatment system based on big data. For example, the existing technology usually only collects the pollutant component data in industrial wastewater for the treatment of industrial wastewater, such as acidic wastewater, alkaline wastewater, cyanide-containing wastewater, chromium-containing wastewater, cadmium-containing wastewater, mercury-containing wastewater, phenol-containing wastewater, formaldehyde-containing wastewater, oil-containing wastewater, sulfur-containing wastewater, organic phosphorus-containing wastewater and radioactive wastewater; and then directly collects the pollutant components of industrial wastewater according to the pollution. The industrial wastewater is treated based on the chemical composition data of the pollutants; in this process, only the pollutant composition data of the industrial wastewater is considered, and the industrial processing products of the industrial wastewater and the chemical composition data of the pollutants are not considered; therefore, the treatment of industrial wastewater based on only the pollutant composition data is too one-sided and not comprehensive enough, and it is impossible to judge whether the chemical composition forms the pollution component data due to excessive concentration, resulting in the omission of the detection of the pollution component data of the industrial wastewater, and thus making it impossible to accurately perform industrial wastewater treatment processes on the industrial wastewater; therefore, in order to solve the above technical problems, the present invention provides an industrial wastewater treatment method and system based on big data and the Internet of Things. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides an industrial wastewater treatment method and system based on big data and the Internet of Things; The purpose of the present invention can be achieved through the following technical solution: A method for treating industrial wastewater based on big data and the Internet of Things, characterized in that the method comprises the following steps: Step S1: Setting the historical data period and the unit detection water volume, collecting all chemical data corresponding to the unit detection water volume; and then obtaining a historical period chemical database of chemical data corresponding to the historical data period; Step S2: constructing a chemical data comparison table corresponding to the chemical data based on the big data; classifying the chemical data corresponding to the historical period chemical database according to the chemical data comparison table to obtain a chemical data category pool; Step S3: Setting a category pool processing mechanism, processing the chemical data category pool according to the category pool processing mechanism to construct a category pool processing model; obtaining a real-time histogram corresponding to the chemical data category pool based on the category pool processing model; Step S4: monitoring the chemical data of the historical period chemical database in real time according to the real-time histogram to obtain the abnormality generation type.

[0004] Furthermore, the process of the collection unit detecting all chemical data corresponding to the water volume includes: Obtain all the industrial wastewater outlets of the enterprise and number them; and set up automatic sampling units at the industrial wastewater outlets. The automatic sampling units include unit detection water volume and collection period, and are used to collect chemical data corresponding to the unit detection water volume of each industrial wastewater outlet according to the collection period. The chemical data includes chemical composition and chemical concentration, and is stored in the cloud database.

[0005] Furthermore, the process of obtaining a historical period chemical database of chemical data corresponding to the historical data period includes: The historical data period includes a short time period, a medium time period, and a long time period; the historical collection period corresponding to the historical data period is set to be weekly, monthly, and annual respectively; the chemical data of the industrial sewage outlet corresponding to the historical data period is collected based on the cloud database according to the historical collection period, and the corresponding historical collection time point is obtained, recorded as year-month-day; and the chemical data corresponding to the short time period, the medium time period, and the long time period are connected with the historical collection time point to generate a corresponding chemical data set; The chemical data sets corresponding to the historical collection periods are respectively integrated to generate the historical period chemical database corresponding to the historical data period, and mapped with the corresponding numbers.

[0006] Furthermore, the process of constructing a chemical data comparison table corresponding to chemical data based on big data includes: Based on big data, the water pollutant emission standard table and property change table corresponding to industrial wastewater are obtained; the water pollutant emission standard table includes pollutant chemical components and pollutant chemical concentrations; the property change table includes property chemical components and property change concentration thresholds; and then the water pollutant emission standard table and property change table are integrated to form a chemical data comparison table corresponding to the chemical data.

[0007] Furthermore, the process of obtaining the chemical data category pool includes: Match the chemical data set corresponding to the historical period chemical database with the corresponding chemical data comparison table according to the chemical composition, and determine whether the corresponding chemical concentration is qualified; If the chemical composition matches the pollutant chemical composition in the water pollutant discharge standard table, and the corresponding chemical concentration exceeds the corresponding pollutant chemical concentration, the corresponding chemical concentration fails to meet the requirements, and the corresponding chemical dataset is marked as a pollutant-unqualified chemical dataset; If the chemical composition matches the property chemical composition in the property change table, and the corresponding chemical concentration exceeds the corresponding property change concentration threshold, the corresponding chemical concentration is unqualified, and the corresponding chemical dataset is marked as a property unqualified chemical dataset; Otherwise, no action is taken; The chemical data set corresponding to the historical period chemical database is classified according to the pollution unqualified chemical data set and the property unqualified chemical data set to obtain a chemical data category pool corresponding to the historical period chemical database; the chemical data category pool includes a pollution unqualified category pool and a property unqualified category pool.

[0008] Furthermore, the process of building a category pool processing model includes: The category pool processing mechanism is used to generate corresponding columns of the pollution unqualified chemical data set and the property unqualified chemical data set corresponding to the pollution unqualified category pool and the property unqualified category pool according to the corresponding historical collection time points, thereby forming a histogram corresponding to the pollution unqualified category pool and the property unqualified category pool; Obtain the processing parameters corresponding to each column of the histogram according to the chemical composition, and mark them on the corresponding columns to form processing parameter nodes; Set the pollution degree weight parameter corresponding to the chemical composition and the periodic variation coefficient corresponding to the historical data period; obtain the pre-pollution rate corresponding to the pollution unqualified category pool and the property unqualified category pool based on the pollution degree weight parameter and the periodic variation coefficient; That is, the specific formula is: ; Wherein, P represents the pre-pollution rate; The values ​​are a, b, and c, which represent the periodic variation coefficients corresponding to the short time period, medium time period, and long time period, respectively; i represents the number corresponding to the chemical component, and i=1, 2, 3, ..., j, and j is a positive integer; w i It is represented by the pollution degree weight parameter corresponding to the i-th numbered chemical component; It is expressed as the ratio of the chemical concentration corresponding to the i-th numbered chemical component to the corresponding pollution chemical concentration or property change concentration threshold in the chemical data comparison table; The pre-contamination rate is sent to the corresponding bar chart for display to build a category pool processing model.

[0009] Furthermore, the process of obtaining a real-time histogram corresponding to the chemical data category pool includes: Based on the construction of the category pool treatment model, the treatment process parameters corresponding to the pollution unqualified histogram and the property unqualified histogram of the historical period chemical database are obtained in real time and integrated to generate the corresponding process parameters to be updated, and then displayed in the corresponding histogram to generate a real-time histogram.

[0010] Furthermore, the process of obtaining the type of exception generation includes: In real time, the industrial wastewater treatment process parameters corresponding to the industrial wastewater are updated according to the process parameters to be updated, and all chemical data corresponding to the industrial wastewater are re-obtained, and then a real-time histogram is obtained based on the category pool treatment model, and so on; According to the real-time histogram, the pre-pollution rate is connected in real time to generate a pre-pollution rate curve chart to determine the change trend of the pre-pollution rate; When the changing trends corresponding to the short-term, medium-term and long-term periods are flat or rising, the abnormal types corresponding to the industrial sewage outlet are abnormal production shifts, abnormal seasons and abnormal equipment respectively; otherwise, no action is taken.

[0011] Furthermore, the following modules are included: Data acquisition module, used to set the historical data cycle and unit detection water volume, and collect all chemical data corresponding to the unit detection water volume; A historical period module is used to obtain a historical period chemical database of chemical data corresponding to the historical data period; The comparison table module is used to construct a chemical data comparison table corresponding to chemical data based on big data; A classification module is used to classify the chemical data corresponding to the historical period chemical database according to the chemical data comparison table to obtain a chemical data category pool; The processing module is used to set the category pool processing mechanism, process the chemical data category pool according to the category pool processing mechanism, and build a category pool processing model; A real-time update module is used to obtain a real-time histogram corresponding to the chemical data category pool based on the category pool processing model; The anomaly type module is used to monitor the chemical data of the historical period chemical database in real time based on the real-time histogram to obtain the anomaly generation type.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention sets a historical data period and a unit test water volume, collects chemical data corresponding to the unit test water volume, and a historical period chemical database of chemical data corresponding to the historical data period; sets three different historical data periods and performs analysis in three time dimensions, which not only avoids the incompleteness of single time dimension analysis, but also improves the one-by-one analysis of factors affecting data fluctuations, thereby improving the comprehensiveness of industrial wastewater treatment; 2. Establish a category pool processing mechanism, and construct a category pool processing model by processing the chemical data category pool according to the category pool processing mechanism. Obtain a real-time histogram corresponding to the chemical data category pool based on the category pool processing model. Monitor the chemical data of the historical period chemical database in real time based on the real-time histogram to obtain the type of anomalies. Effectively achieve an intuitive mapping of "data-countermeasures" and intuitively obtain the real-time and convenient dual information of "what exceeds the standard + how to deal with it." BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 Flow chart of the method of the present invention.

[0015] Figure 2 This is a system principle diagram of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] like Figure 1 As shown, a method for treating industrial wastewater based on big data and the Internet of Things comprises the following steps: Step S1: Setting the historical data period and the unit detection water volume, collecting all chemical data corresponding to the unit detection water volume; and then obtaining a historical period chemical database of chemical data corresponding to the historical data period; Step S2: constructing a chemical data comparison table corresponding to the chemical data based on the big data; classifying the chemical data corresponding to the historical period chemical database according to the chemical data comparison table to obtain a chemical data category pool; Step S3: Setting a category pool processing mechanism, processing the chemical data category pool according to the category pool processing mechanism to construct a category pool processing model; obtaining a real-time histogram corresponding to the chemical data category pool based on the category pool processing model; Step S4: monitoring the chemical data of the historical period chemical database in real time according to the real-time histogram to obtain the abnormality generation type.

[0018] It should be further explained that the process of collecting chemical data corresponding to the water volume detected at each industrial wastewater outlet unit includes: Obtain all industrial wastewater outlets of the enterprise and number them, denoted as i, where i is a positive integer; and obtain chemical data corresponding to each industrial wastewater outlet; the chemical data includes chemical composition and chemical concentration; An automatic sampling unit is set at the industrial sewage outlet. The automatic sampling unit includes a unit detection water volume and a collection period. It is used to collect chemical data corresponding to the unit detection water volume of each industrial sewage outlet according to the collection period and store it in the cloud database.

[0019] It should be further explained that the process of obtaining the historical period chemical database corresponding to the chemical data of the historical data period includes: The historical data period includes a short time period, a medium time period, and a long time period; the historical collection periods corresponding to the historical data periods are set to be weekly, monthly, and annual respectively; chemical data of the industrial sewage outlet corresponding to the historical data period is collected based on the cloud database according to the historical collection period, and the corresponding historical collection time points are obtained, recorded as year-month-day; the chemical data corresponding to the short time period, the medium time period, and the long time period are connected with the historical collection time points to generate corresponding chemical data sets, namely, a first chemical data set, a second chemical data set, and a third chemical data set; Integrating the first chemical data set, the second chemical data set, and the third chemical data set to generate a historical period chemical database corresponding to the historical data period, and mapping them with the corresponding numbers; the historical period chemical database includes a historical period chemical data weekly database, a historical period chemical data monthly database, and a historical period chemical data annual database; In the above embodiment, it should be further explained that the historical data period is used to collect the historical period chemical database corresponding to each industrial sewage outlet in the cloud database; for example, when the historical data period is a short time period, and the historical collection period corresponding to the short time period is weekly, it means that the chemical data corresponding to the short time period is collected with weekly as the historical collection period, and the historical collection time is obtained as May 2, 2023, then the year-month-day is 2023-5-2; and then the historical period chemical data weekly database corresponding to the short time period is obtained; setting three different historical data periods and performing analysis in three time dimensions can not only avoid the incompleteness of single time dimension analysis, but also improve the one-by-one analysis of the factors affecting data fluctuations, thereby improving the comprehensiveness of industrial sewage treatment. It should be further explained that the process of constructing a chemical data comparison table corresponding to chemical data based on big data includes: Based on big data, the water pollutant emission standard table and property change table corresponding to industrial wastewater are obtained; the water pollutant emission standard table includes pollutant chemical components and pollutant chemical concentrations; the property change table includes property chemical components and property change concentration thresholds; and then the water pollutant emission standard table and property change table are integrated to form a chemical data comparison table corresponding to the chemical data.

[0020] In the above embodiment, it needs to be further explained that the water pollutant emission standard table is used to represent the emission standard table corresponding to the pollutants in industrial wastewater; the polluting chemical components are used to represent specific chemical components in industrial wastewater that cause harm to the ecological environment, human health or other organisms, and have properties such as toxicity and harmfulness; including but not limited to heavy metal ions (Hg², Cd², Pb²), toxic organic matter (polycyclic aromatic hydrocarbons, phenols, pesticides), pathogens (bacteria, viruses), etc.; the polluting chemical concentration is used to represent the emission standard concentration corresponding to the polluting chemical components; the property change table is used to indicate that if the concentration in industrial wastewater is low and degradable, it does not belong to the polluting chemical component; if the concentration is high, the property changes to form a chemical component table corresponding to the polluting chemical component, for example, glucose, starch, sucrose, etc. in carbohydrates; as well as proteins and amino acids, alcohols, fatty acids, etc.; the property change concentration threshold is used to represent the concentration threshold at which the property corresponding to the property chemical component changes.

[0021] It should be further explained that the process of classifying the chemical data corresponding to the historical period chemical database according to the chemical data comparison table to obtain the chemical data category pool includes: Match the chemical data set corresponding to the historical period chemical database with the corresponding chemical data comparison table according to the chemical composition, and determine whether the corresponding chemical concentration is qualified; If the chemical composition matches the pollutant chemical composition in the water pollutant discharge standard table, and the corresponding chemical concentration exceeds the corresponding pollutant chemical concentration, the corresponding chemical concentration fails to meet the requirements, and the corresponding chemical dataset is marked as a polluted unqualified chemical dataset; otherwise, the corresponding chemical concentration meets the requirements, and the corresponding chemical dataset is marked as a polluted qualified chemical dataset; If the chemical composition matches the property chemical composition in the property change table, and the corresponding chemical concentration exceeds the corresponding property change concentration threshold, the corresponding chemical concentration is unqualified, and the corresponding chemical dataset is marked as a property unqualified chemical dataset; otherwise, the corresponding chemical concentration is qualified, and the corresponding chemical dataset is marked as a property qualified chemical dataset; The chemical components that do not exist in the chemical data comparison table are marked as non-polluting chemical components, and the corresponding chemical data sets are marked as non-polluting chemical data sets; The chemical data sets corresponding to the historical period chemical data weekly database, the historical period chemical data monthly database, and the historical period chemical data annual database are classified according to the pollution-unqualified chemical data set, the pollution-qualified chemical data set, the property-unqualified chemical data set, the property-qualified chemical data set, and the pollution-free chemical data set to obtain the chemical data category pools corresponding to the historical period chemical data weekly database, the historical period chemical data monthly database, and the historical period chemical data annual database; the chemical data category pools include the pollution-unqualified category pool, the pollution-qualified category pool, the property-unqualified category pool, the property-qualified category pool, and the pollution-free category pool.

[0022] It should be further explained that the process of setting up a category pool processing mechanism and processing the chemical data category pool according to the category pool processing mechanism to construct a category pool processing model includes: The category pool processing mechanism is used to generate corresponding columns of the pollution unqualified chemical data set and the property unqualified chemical data set corresponding to the pollution unqualified category pool and the property unqualified category pool according to the corresponding historical collection time points, thereby forming a histogram corresponding to the pollution unqualified category pool and the property unqualified category pool; the histogram includes a pollution unqualified histogram and a property unqualified histogram; Obtain the processing parameters corresponding to each column of the histogram according to the chemical composition, and mark them on the corresponding columns to form processing parameter nodes; Set the pollution degree weight parameter corresponding to the chemical composition and the periodic variation coefficient corresponding to the historical data period; obtain the pre-pollution rate corresponding to the pollution unqualified category pool and the property unqualified category pool based on the pollution degree weight parameter and the periodic variation coefficient; That is, the specific formula is: ; Wherein, P represents the pre-pollution rate; The values ​​are a, b, and c, which represent the periodic variation coefficients corresponding to the short time period, medium time period, and long time period, respectively; i represents the number corresponding to the chemical component, and i=1, 2, 3, ..., j, and j is a positive integer; w i It is represented by the pollution degree weight parameter corresponding to the i-th numbered chemical component; It is expressed as the ratio of the chemical concentration corresponding to the i-th numbered chemical component to the corresponding pollution chemical concentration or property change concentration threshold in the chemical data comparison table; The pre-contamination rate is sent to the corresponding bar chart for display to build a category pool processing model.

[0023] In the above embodiment, it should be further explained that if the pollution failure histogram corresponds to a heavy metal exceeding the standard column display, the corresponding column is directly marked with the dosage of the chelating agent to be added, etc.; thus achieving an intuitive mapping of "data-countermeasures" and allowing management personnel to intuitively obtain the dual information of "what exceeds the standard + how to deal with it".

[0024] It should be further explained that the process of obtaining a real-time histogram corresponding to a chemical data category pool based on the category pool processing model includes: Based on the construction of the category pool treatment model, the treatment process parameters corresponding to the pollution unqualified histogram and the property unqualified histogram of the historical period chemical database are obtained in real time and integrated to generate the corresponding process parameters to be updated, and then displayed in the corresponding histogram to generate a real-time histogram.

[0025] It should be further explained that the process of monitoring the chemical data of the historical period chemical database in real time based on the real-time histogram to obtain the type of abnormality generation includes: In real time, the industrial wastewater treatment process parameters corresponding to the industrial wastewater are updated according to the process parameters to be updated, and all chemical data corresponding to the industrial wastewater are re-obtained, and then a real-time histogram is obtained based on the category pool treatment model, and so on; According to the real-time histogram, the pre-pollution rate is connected in real time to generate a pre-pollution rate curve chart to determine the change trend of the pre-pollution rate; If the change trend corresponding to the short-term period is a flat trend or an upward trend, the abnormality type corresponding to the industrial sewage outlet is an abnormal production shift; otherwise, no action is taken; If the change trend corresponding to the middle period is a flat trend or an upward trend, the abnormality type corresponding to the industrial sewage outlet is abnormal season; otherwise, no action is taken; If the change trend corresponding to the long period of time is a flat trend or an upward trend, the abnormality type corresponding to the industrial sewage outlet is abnormal equipment; otherwise, no processing is performed.

[0026] like Figure 2As shown, the present invention also provides a system for industrial wastewater treatment based on big data and the Internet of Things, comprising the following modules: Data acquisition module, used to set the historical data cycle and unit detection water volume, and collect all chemical data corresponding to the unit detection water volume; A historical period module is used to obtain a historical period chemical database of chemical data corresponding to the historical data period; The comparison table module is used to construct a chemical data comparison table corresponding to chemical data based on big data; A classification module is used to classify the chemical data corresponding to the historical period chemical database according to the chemical data comparison table to obtain a chemical data category pool; The processing module is used to set the category pool processing mechanism, process the chemical data category pool according to the category pool processing mechanism, and build a category pool processing model; A real-time update module is used to obtain a real-time histogram corresponding to the chemical data category pool based on the category pool processing model; The anomaly type module is used to monitor the chemical data of the historical period chemical database in real time based on the real-time histogram to obtain the anomaly generation type.

[0027] The features and exemplary embodiments of various aspects of the present application are described in detail above. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The above description of the embodiments is merely to provide a better understanding of the present application by showing examples of the present application.

[0028] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for treating industrial wastewater based on big data and the Internet of Things, characterized in that: The method comprises the following steps: Step S1: Setting the historical data period and the unit detection water volume, collecting all chemical data corresponding to the unit detection water volume; and then obtaining a historical period chemical database of chemical data corresponding to the historical data period; Step S2: constructing a chemical data comparison table corresponding to the chemical data based on the big data; classifying the chemical data corresponding to the historical period chemical database according to the chemical data comparison table to obtain a chemical data category pool; Step S3: Setting a category pool processing mechanism, processing the chemical data category pool according to the category pool processing mechanism to construct a category pool processing model; obtaining a real-time histogram corresponding to the chemical data category pool based on the category pool processing model; Step S4: monitoring the chemical data of the historical period chemical database in real time according to the real-time histogram to obtain the abnormality generation type.

2. The industrial wastewater treatment method based on big data and the Internet of Things according to claim 1, characterized in that: The process of collecting all chemical data corresponding to the water volume detected by the collection unit includes: Obtain all the industrial wastewater outlets of the enterprise and number them; and set up automatic sampling units at the industrial wastewater outlets. The automatic sampling units include unit detection water volume and collection period, and are used to collect chemical data corresponding to the unit detection water volume of each industrial wastewater outlet according to the collection period. The chemical data includes chemical composition and chemical concentration, and is stored in the cloud database.

3. The industrial wastewater treatment method based on big data and the Internet of Things according to claim 2, characterized in that: The process of obtaining a historical period chemical database of chemical data corresponding to a historical data period includes: The historical data period includes a short time period, a medium time period, and a long time period; the historical collection period corresponding to the historical data period is set to be weekly, monthly, and annual respectively; the chemical data of the industrial sewage outlet corresponding to the historical data period is collected based on the cloud database according to the historical collection period, and the corresponding historical collection time point is obtained, recorded as year-month-day; and the chemical data corresponding to the short time period, the medium time period, and the long time period are connected with the historical collection time point to generate a corresponding chemical data set; The chemical data sets corresponding to the historical collection periods are respectively integrated to generate the historical period chemical database corresponding to the historical data period, and mapped with the corresponding numbers.

4. The industrial wastewater treatment method based on big data and the Internet of Things according to claim 3, characterized in that: The process of constructing a chemical data comparison table corresponding to chemical data based on big data includes: Based on big data, the water pollutant emission standard table and property change table corresponding to industrial wastewater are obtained; the water pollutant emission standard table includes pollutant chemical components and pollutant chemical concentrations; the property change table includes property chemical components and property change concentration thresholds; and then the water pollutant emission standard table and property change table are integrated to form a chemical data comparison table corresponding to the chemical data.

5. The industrial wastewater treatment method based on big data and the Internet of Things according to claim 4, characterized in that: The process of obtaining a chemical data category pool includes: Match the chemical data set corresponding to the historical period chemical database with the corresponding chemical data comparison table according to the chemical composition, and determine whether the corresponding chemical concentration is qualified; If the chemical composition matches the pollutant chemical composition in the water pollutant discharge standard table, and the corresponding chemical concentration exceeds the corresponding pollutant chemical concentration, the corresponding chemical concentration fails to meet the requirements, and the corresponding chemical dataset is marked as a pollutant-unqualified chemical dataset; If the chemical composition matches the property chemical composition in the property change table, and the corresponding chemical concentration exceeds the corresponding property change concentration threshold, the corresponding chemical concentration is unqualified, and the corresponding chemical dataset is marked as a property unqualified chemical dataset; Otherwise, no action is taken; The chemical data set corresponding to the historical period chemical database is classified according to the pollution unqualified chemical data set and the property unqualified chemical data set to obtain a chemical data category pool corresponding to the historical period chemical database; the chemical data category pool includes a pollution unqualified category pool and a property unqualified category pool.

6. The industrial wastewater treatment method based on big data and the Internet of Things according to claim 5, characterized in that: The process of building a category pooling model includes: The category pool processing mechanism is used to generate corresponding columns of the pollution unqualified chemical data set and the property unqualified chemical data set corresponding to the pollution unqualified category pool and the property unqualified category pool according to the corresponding historical collection time points, thereby forming a histogram corresponding to the pollution unqualified category pool and the property unqualified category pool; Obtain the processing parameters corresponding to each column of the histogram according to the chemical composition, and mark them on the corresponding columns to form processing parameter nodes; Set the pollution degree weight parameter corresponding to the chemical composition, and set the periodic variation coefficient corresponding to the historical data period; obtain the pre-pollution rate corresponding to the pollution unqualified category pool and the property unqualified category pool according to the pollution degree weight parameter and the periodic variation coefficient; send the pre-pollution rate to the corresponding bar chart for display to build a category pool processing model.

7. The industrial wastewater treatment method based on big data and the Internet of Things according to claim 6, characterized in that: The process of obtaining a real-time histogram corresponding to a chemical data category pool includes: Based on the construction of the category pool treatment model, the treatment process parameters corresponding to the pollution unqualified histogram and the property unqualified histogram of the historical period chemical database are obtained in real time and integrated to generate the corresponding process parameters to be updated, and then displayed in the corresponding histogram to generate a real-time histogram.

8. The industrial wastewater treatment method based on big data and the Internet of Things according to claim 7, characterized in that: The process of obtaining the type of exception generated includes: In real time, the industrial wastewater treatment process parameters corresponding to the industrial wastewater are updated according to the process parameters to be updated, and all chemical data corresponding to the industrial wastewater are re-obtained, and then a real-time histogram is obtained based on the category pool treatment model, and so on; According to the real-time histogram, the pre-pollution rate is connected in real time to generate a pre-pollution rate curve chart to determine the change trend of the pre-pollution rate; When the change trends corresponding to the short-term, medium-term, and long-term periods are flat or rising, the abnormal types corresponding to the industrial sewage outlet are abnormal production shifts, abnormal seasons, and abnormal equipment. Otherwise, no action is taken.

9. An industrial wastewater treatment system based on big data and the Internet of Things, applied to the industrial wastewater treatment method based on big data and the Internet of Things as claimed in any one of claims 1 to 8, characterized in that: Includes the following modules: Data acquisition module, used to set the historical data cycle and unit detection water volume, and collect all chemical data corresponding to the unit detection water volume; A historical period module is used to obtain a historical period chemical database of chemical data corresponding to the historical data period; The comparison table module is used to construct a chemical data comparison table corresponding to chemical data based on big data; A classification module is used to classify the chemical data corresponding to the historical period chemical database according to the chemical data comparison table to obtain a chemical data category pool; The processing module is used to set the category pool processing mechanism, process the chemical data category pool according to the category pool processing mechanism, and build a category pool processing model; A real-time update module is used to obtain a real-time histogram corresponding to the chemical data category pool based on the category pool processing model; The anomaly type module is used to monitor the chemical data of the historical period chemical database in real time based on the real-time histogram to obtain the anomaly generation type.

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