Intelligent sewage treatment system and method based on data analysis

Abstract

The invention discloses an intelligent sewage treatment system and method based on data analysis, and belongs to the technical field of sewage treatment.The intelligent sewage treatment system comprises a sewage treatment equipment monitoring module for obtaining associated data generated by sewage equipment in the sewage treatment process; the sewage monitoring module is used for acquiring water quality parameters in the sewage treatment process; the data processing terminal is used for generating a sewage treatment condition value according to the acquired associated data and the water quality parameters, so that the sewage treatment equipment is correspondingly optimized and adjusted. According to the method, the data generated in the working process of the sewage treatment equipment and the water quality parameters of the sewage in the sewage treatment process are obtained and comprehensively analyzed to generate the sewage treatment condition value, and whether the currently set parameters of the sewage treatment equipment are qualified or not is judged according to the sewage treatment condition value; and if the sewage is unqualified, a corresponding optimization adjustment strategy is generated to carry out corresponding optimization treatment, so that the sewage treatment efficiency can be ensured and the operation cost can be effectively saved during sewage treatment.

Description

Technical Field

[0001] The present invention belongs to the technical field of sewage treatment, and specifically relates to an intelligent sewage treatment system and method based on data analysis. Background Art

[0002] As water resources become increasingly scarce, more and more cities are beginning to pay attention to the reuse of treated sewage. Recycled water after deep treatment can be used for agricultural irrigation, landscape water, industrial cooling, etc. to alleviate the problem of water shortage.

[0003] Existing sewage treatment generally involves purifying sewage through sewage treatment equipment, and discharging it only when the water quality meets the discharge standard or reuse standard. Otherwise, the substandard sewage will be transported back to the source for secondary treatment. Although this method can ensure that the final sewage discharge meets the standard, it cannot guarantee high treatment efficiency and cost optimization. For example, if the water quality meets the standard only after multiple treatments, it will result in low treatment efficiency. On the contrary, if the water quality meets the standard in a shorter period of time, although it is efficient, it will increase the energy consumption in the sewage treatment process, and will cause the treatment equipment to work under high load, requiring additional operating costs. Summary of the invention

[0004] The purpose of the present invention is to provide a smart sewage treatment system and method based on data analysis to solve the problems faced in the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A smart sewage treatment system based on data analysis, the treatment system comprising:

[0007] A sewage treatment equipment monitoring module, which is used to obtain relevant data generated by sewage equipment during sewage treatment;

[0008] A sewage monitoring module, which is used to obtain water quality parameters of various detection indicators during sewage treatment;

[0009] A data processing terminal, which is connected to the sewage treatment equipment monitoring module and the sewage monitoring module, and is used to perform comprehensive processing and analysis based on the acquired associated data and water quality parameters, thereby generating a sewage treatment status value, and determining whether to generate an optimization adjustment strategy based on the sewage treatment status value;

[0010] The optimization decision module performs corresponding optimization adjustments on the sewage treatment equipment according to the generated optimization adjustment strategy.

[0011] Furthermore, the method for the data processing terminal to generate the optimization adjustment strategy is:

[0012] According to the sewage treatment process, the sewage treatment process is divided into multiple treatment steps, and the time ΔT for the water quality in each treatment step to reach the qualified water quality and the treatment consumption M generated by the sewage treatment equipment during this time are obtained.

[0013] By formula Obtain the sewage treatment status value W a ;

[0014] The obtained sewage treatment status value W a The threshold range of the sewage treatment condition is set To compare:

[0015] when When , no optimization adjustment strategy is generated;

[0016] Otherwise, generate an optimization adjustment strategy;

[0017] Where m is the total number of associated indicators in the processing step, and i∈[1,m], Q i is the processing consumption generated by the i-th associated indicator, τ i is the weight coefficient of the processing consumption generated by the i-th associated indicator, ΔT st and M st are respectively preset reference values, and α1 and α2 are respectively preset proportional coefficients.

[0018] Furthermore, the method by which the optimization decision module performs corresponding optimization adjustment on the sewage treatment equipment according to the generated optimization adjustment strategy is:

[0019] The optimization adjustment strategy includes a first adjustment instruction and a second adjustment instruction;

[0020] when When , a first adjustment instruction is generated;

[0021] when When , a second adjustment instruction is generated.

[0022] Furthermore, when the first adjustment instruction is generated, the processing procedure is divided into multiple processing sections according to the order of the processing steps in the processing procedure, and the water quality parameter variation curve R in each processing section is obtained. x (t);

[0023] By formula The deviation coefficient V is obtained;

[0024] According to the deviation coefficient of each processing section, sort them from large to small, and according to the sorting result, give priority to adjusting the time period with the larger deviation coefficient;

[0025] Among them, t1 is the time when the water quality parameters in the treatment section reach the standard. The standard curve of water quality parameters changing with time in the treatment section set by the system, t2 is the standard time when the water quality parameters in the treatment section reach qualified water quality.

[0026] Further, when generating the second adjustment instruction, firstly obtain the temperature variation curve T of the processing device during the process. emp (t), the pressure variation curve P(t) of the working pump of the processing equipment, and the working current variation curve I(t) of the processing equipment;

[0027] Combining formulas To determine whether there is any abnormality in the sewage treatment equipment;

[0028] When D>1, it means that the sewage treatment equipment is abnormal, and a corresponding alarm is generated to remind the management personnel to repair the sewage treatment equipment;

[0029] Otherwise, according to the same processing method when generating the first adjustment instruction, the deviation coefficients of various processes are obtained, and they are sorted from large to small. According to the sorting result, the time period with the larger deviation coefficient is adjusted first;

[0030] Among them, D st is the judgment threshold, D is the judgment coefficient, P0(t) and I0(t) are respectively the standard curves of the temperature of the processing equipment, the pressure of the working pump, and the working current of the processing equipment changing with time, which are preset by the system.

[0031] Furthermore, the data terminal also warns of potential problems in various treatment processes based on the acquired sewage treatment status values:

[0032] When no optimization adjustment strategy is generated, the sewage treatment status value of each treatment process under n times is obtained. Thus constructing the sewage treatment status value W a The curve equation f(x, W a );

[0033] By formula The potential risk factor H is obtained;

[0034] When H>H st When the processing step is considered to have potential problems, a corresponding early warning signal is generated;

[0035] in, and j∈[1,n], H stis the preset risk factor threshold, maxK is the maximum slope value, minK is the minimum slope value, β1 and β2 are proportional coefficients, f0(x,W a ) is the standard curve equation of the preset sewage treatment status value and treatment times.

[0036] A smart sewage treatment method based on data analysis, wherein the treatment method is controlled and executed by a smart sewage treatment system based on data analysis.

[0037] Beneficial effects of the present invention:

[0038] The present invention generates a sewage treatment status value by acquiring data generated by the sewage treatment equipment during its operation and water quality parameters of the water during the sewage treatment process for comprehensive analysis. The sewage treatment status value is used to determine whether the various parameters currently set for the sewage treatment equipment are qualified. If they are unqualified, a corresponding optimization adjustment strategy is generated for corresponding optimization processing. In this way, the efficiency of sewage treatment can be guaranteed while the operating cost can be effectively saved during sewage treatment, thereby better achieving energy conservation, emission reduction and sustainable development.

[0039] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] In one embodiment, a smart sewage treatment system based on data analysis is disclosed, such as Figure 1 As shown, the processing system mainly includes the following modules:

[0044] The sewage treatment equipment monitoring module is used to obtain the associated data generated by the sewage treatment equipment during the sewage treatment process, including but not limited to the temperature value of the sewage treatment equipment, the pressure value of the working pump, the working current value of the treatment equipment, the sewage treatment time, and the consumption generated by each associated indicator;

[0045] Sewage monitoring module: The sewage monitoring module is used to obtain water quality parameters of various detection indicators in the sewage treatment process;

[0046] A data processing terminal, which is connected to the sewage treatment equipment monitoring module and the sewage monitoring module, and is used to perform comprehensive processing and analysis based on the acquired associated data and water quality parameters, thereby generating a sewage treatment status value, and determining whether to generate an optimization adjustment strategy based on the sewage treatment status value;

[0047] The optimization decision module makes corresponding optimization adjustments to the sewage treatment equipment according to the generated optimization adjustment strategy.

[0048] Through the above technical scheme, the present application first obtains the associated data generated by the sewage treatment equipment during the sewage treatment process through the sewage treatment equipment monitoring module, including the temperature value of the sewage treatment equipment, the pressure value of the working pump, the working current value of the treatment equipment, the sewage treatment time, and the consumption generated by each associated indicator, etc., and then monitors the changes in water quality parameters during the sewage treatment process through the sewage monitoring module, so as to analyze and process through the data processing terminal, generate a sewage treatment status value, and judge whether it is necessary to generate an optimization adjustment strategy for the sewage treatment equipment according to the university of the sewage treatment status value. After determining the generation of the optimization adjustment strategy, the sewage treatment equipment is optimized and adjusted accordingly. In this way, the present application generates a sewage treatment status value by obtaining the data generated by the sewage treatment equipment during the working process and the water quality parameters of the water quality during the sewage treatment process for comprehensive analysis, and judges whether the various parameters currently set by the sewage treatment equipment are qualified according to the size of the sewage treatment status value. If they are unqualified, the corresponding optimization adjustment strategy is generated for corresponding optimization processing, so that the efficiency of sewage treatment can be guaranteed and the operating cost can be effectively saved during sewage treatment, so as to better achieve energy conservation, emission reduction and sustainable development.

[0049] As an implementation mode of the present invention, a method for a data processing terminal to generate an optimization adjustment strategy is as follows:

[0050] According to the sewage treatment process, the sewage treatment process is divided into multiple treatment steps, and the time ΔT for the water quality in each treatment step to reach the qualified water quality and the treatment consumption M generated by the sewage treatment equipment during this time are obtained.

[0051] By formula Obtain the sewage treatment status value W a ;

[0052] The obtained sewage treatment status value W a The threshold range of the sewage treatment condition is set To compare:

[0053] when When , no optimization adjustment strategy is generated;

[0054] Otherwise, generate an optimization adjustment strategy;

[0055] Where m is the total number of associated indicators in the processing step, and i∈[1,m], Q i is the processing consumption generated by the i-th associated indicator, τ i is the weight coefficient of the processing consumption generated by the i-th associated indicator, ΔT st and M st are respectively preset reference values, and α1 and α2 are respectively preset proportional coefficients.

[0056] Through the above technical scheme, this embodiment provides a specific method for generating an optimization adjustment strategy. First, according to the sewage treatment process, the sewage treatment process is divided into multiple treatment processes. The qualified water quality standard in each treatment process is determined from big data, and the water quality change is monitored in real time. After the standard is reached, the time ΔT when the water quality reaches the qualified water quality is recorded. At the same time, the treatment consumption M generated by the sewage treatment equipment during this time process can be the consumption of the reagents invested in each treatment work. In the same treatment process, multiple reagents may be invested. Therefore, through the formula The treatment consumption of the sewage treatment equipment during this time is obtained, and then the formula is used to calculate Obtain the sewage treatment status value W a ; Generally speaking, when sewage is treated, its treatment time and consumption during treatment need to be in a moderate range. If the sewage treatment time is too long or the consumption is too much, it will not only cause low treatment efficiency, but also waste various treatment agents. On the contrary, if the sewage treatment time is too short or the consumption is too little, it means that the sewage treatment equipment needs to bear a higher workload and higher energy consumption in a short time, which will increase the operating cost. Therefore, the sewage treatment status value W is obtained. a The threshold range of the sewage treatment condition is set Compare: When When , it means that the sewage treatment is within the set reasonable range, indicating that the sewage treatment situation is normal, and no optimization adjustment strategy is generated; otherwise, it means that the sewage treatment situation is abnormal, and an optimization adjustment strategy is generated to make corresponding adjustments to the sewage treatment equipment.

[0057] In the above technical solution, the weight coefficient τ of the processing consumption generated by each associated indicator is i , the respective preset proportional coefficients α1 and α2 can be determined based on empirical data, and the standard threshold interval of the sewage treatment condition is set Respective preset reference value ΔT st and M st It can be determined based on the relevant historical data in the big data, which needs no further description here.

[0058] As an implementation mode of the present invention, the method in which the optimization decision module performs corresponding optimization adjustment on the sewage treatment equipment according to the generated optimization adjustment strategy is as follows:

[0059] The optimization adjustment strategy includes a first adjustment instruction and a second adjustment instruction. When the first adjustment instruction is generated, When , a second adjustment instruction is generated;

[0060] When the first adjustment instruction is generated, the processing procedure is divided into multiple processing sections according to the order of the processing steps in the processing procedure, and the water quality parameter change curve R in each processing section is obtained. x (t);

[0061] By formula The deviation coefficient V is obtained;

[0062] According to the deviation coefficient of each processing section, sort them from large to small, and according to the sorting result, give priority to adjusting the time period with the larger deviation coefficient;

[0063] Among them, t1 is the time when the water quality parameters in the treatment section reach the standard. The standard curve of the water quality parameters changing with time in the treatment section set by the system, t2 is the standard time when the water quality parameters in the treatment section reach qualified water quality;

[0064] When generating the second adjustment instruction, first obtain the temperature change curve T of the processing device during this process. emp (t), the pressure variation curve P(t) of the working pump of the processing equipment, and the working current variation curve I(t) of the processing equipment;

[0065] Combining formulas To determine whether there is any abnormality in the sewage treatment equipment;

[0066] When D>1, it means that the sewage treatment equipment is abnormal, and a corresponding alarm is generated to remind the management personnel to repair the sewage treatment equipment;

[0067] Otherwise, according to the same processing method when generating the first adjustment instruction, the deviation coefficients of various processes are obtained, and they are sorted from large to small. According to the sorting result, the time period with the larger deviation coefficient is adjusted first;

[0068] Among them, D st is the judgment threshold, D is the judgment coefficient, P0(t) and I0(t) are respectively the standard curves of the temperature of the processing equipment, the pressure of the working pump, and the working current of the processing equipment changing with time, which are preset by the system.

[0069] Through the above technical solution, this embodiment provides a method for the optimization decision module to perform corresponding optimization adjustment on the sewage treatment equipment according to the generated optimization adjustment strategy. First, the optimization adjustment strategy includes a first adjustment instruction and a second adjustment instruction. When , it means that the sewage treatment time is too short and the amount of sewage input is too small, then the first adjustment instruction is generated. When the amount of medicine is too much, the second adjustment instruction is generated. When the first adjustment instruction is generated, it means that the sewage treatment equipment is working under high load. At this time, the workload can be appropriately reduced. Since there are multiple processing steps in the same treatment process, the parameters of each processing step are also different. Therefore, according to the different order of the processing steps in the treatment process, the treatment process is divided into multiple treatment sections, and the water quality parameter change curve R in each treatment section is obtained. x (t), through the formula The deviation coefficient V is obtained, and the change of the water quality parameters obtained in the treatment section is compared with the standard water quality parameters in the treatment section. If the difference is larger, it means that the change of the water quality parameters in this time period is more obvious, and the parameters in the treatment section can be optimized and adjusted accordingly. For example, the amount of reagent treatment can be appropriately increased or the load of the sewage treatment equipment can be reduced in the treatment section to reduce the operating cost, and the treatment equipment can also be protected accordingly; when the second adjustment instruction is generated, it means that the sewage treatment efficiency is low at this time. First of all, it should be considered whether the treatment equipment itself has a fault that causes low treatment efficiency. Therefore, the temperature change curve T of the treatment equipment in this process is obtained. emp (t), the pressure variation curve P(t) of the working pump of the processing equipment, and the working current variation curve I(t) of the processing equipment, and combined with the formula To determine whether the sewage treatment equipment is abnormal, the formula It indicates the difference between the temperature change of the sewage treatment equipment and the standard temperature change during the treatment process. The formula It indicates the difference between the pressure change of the working pump of the sewage treatment equipment and the standard pressure change during operation. The formula It indicates the difference between the current change of the sewage treatment equipment during operation and the standard current transformation. The larger the value, the greater the possibility of equipment failure. Therefore, it is compared with the judgment threshold to obtain the judgment coefficient. When D>1, it means that the sewage treatment equipment is abnormal at this time, and a corresponding alarm is generated to remind the management personnel to repair the sewage treatment equipment. Otherwise, it means that the low efficiency of sewage treatment is not caused by the sewage treatment equipment itself. The same processing method is used when the first adjustment instruction is generated to obtain the deviation coefficient of each treatment, and sort them from large to small. According to the sorting results, the time period with the larger deviation coefficient is adjusted first. For example, when the deviation coefficient of a certain treatment end is too large, it means that it is far from the standard. In this case, the load of the sewage treatment equipment can be increased in this treatment section, or the amount of reagent added can be increased to ensure the efficiency of sewage treatment.

[0070] In the above technical solution, the system sets a standard curve of water quality parameters changing with time in the treatment section. The standard time t2 when the water quality parameters in the treatment section reach qualified water quality can be selected according to the relevant historical data in the big data, and the judgment threshold D st , the system preset temperature of the processing equipment, the pressure of the working pump, and the standard curve of the working current of the processing equipment changing with time P0(t) and I0(t) can be determined based on the historical data of the sewage treatment equipment and empirical data, which will not be described in detail here.

[0071] As an implementation mode of the present invention, the data terminal also issues early warnings for potential problems in various treatment processes based on the acquired sewage treatment status values:

[0072] When no optimization adjustment strategy is generated, the sewage treatment status value of each treatment process under n times is obtained. Thus constructing the sewage treatment status value W a The curve equation f(x, W a );

[0073] By formula The potential risk factor H is obtained;

[0074] When H>H st When the processing step is considered to have potential problems, a corresponding early warning signal is generated;

[0075] in, and j∈[1,n], H stis the preset risk factor threshold, maxK is the maximum slope value, minK is the minimum slope value, β1 and β2 are proportional coefficients, f0(x,W a ) is the standard curve equation of the preset sewage treatment status value and treatment times.

[0076] Through the above technical solution, this embodiment also provides a method for early warning of potential system problems. Since the above optimization and adjustment are all performed after the problem occurs, it is impossible to give a warning in time. Therefore, when the optimization and adjustment strategy is not generated, the present embodiment obtains the sewage treatment status value W of each treatment process under n times. aj , thereby constructing the sewage treatment status value W a The curve equation f(x, W a ). Then, through the formula The potential risk coefficient H is obtained, and early warning analysis is performed based on the size of the potential risk coefficient. The formula It represents the fluctuation of the sewage treatment status value of the treatment process within n times. The larger its value is, the worse the stability of the sewage treatment status value obtained each time is, indicating that the possibility of potential problems in the treatment process is high. The formula β1*|maxK|+β2*|minK| represents the maximum slope and minimum slope changes of the proposed curve equation. The larger its value is, the more likely it is to suddenly increase or decrease, indicating that the possibility of potential problems in the treatment process is high. The formula It indicates the difference between the cumulative value of sewage treatment status and the standard cumulative value. If the difference is larger, it means that there is a high possibility of potential problems in the treatment process. Therefore, the formula Conduct a comprehensive analysis to obtain the potential risk factor, and then compare it with the preset risk threshold factor H st Compare, when H>H st If it is considered that there is a potential problem in the treatment process, a corresponding warning signal will be generated to remind the operation management personnel to carry out corresponding maintenance management. In this way, the potential problems in each treatment process can be analyzed and judged, so as to give early warning to ensure the stability of sewage treatment effect.

[0077] In the above technical solution, the preset standard curve equation f0(x, W a ), proportional coefficients β1 and β2, and the preset risk factor threshold H st All of them can be determined based on the historical data of the sewage treatment equipment and empirical data, so I will not elaborate on them here.

[0078] A smart sewage treatment method based on data analysis, which is controlled and executed by using the above-mentioned smart sewage treatment system based on data analysis.

[0079] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A smart sewage treatment system based on data analysis, characterized in that: The processing system comprises: A sewage treatment equipment monitoring module, which is used to obtain relevant data generated by sewage equipment during sewage treatment; A sewage monitoring module, which is used to obtain water quality parameters of various detection indicators during sewage treatment; A data processing terminal, which is connected to the sewage treatment equipment monitoring module and the sewage monitoring module, and is used to perform comprehensive processing and analysis based on the acquired associated data and water quality parameters, thereby generating a sewage treatment status value, and determining whether to generate an optimization adjustment strategy based on the sewage treatment status value; The optimization decision module performs corresponding optimization adjustments on the sewage treatment equipment according to the generated optimization adjustment strategy.

2. According to claim 1, a smart sewage treatment system based on data analysis is characterized in that: The method for the data processing terminal to generate an optimization adjustment strategy is: According to the sewage treatment process, the sewage treatment process is divided into multiple treatment steps, and the time ΔT for the water quality in each treatment step to reach the qualified water quality and the treatment consumption M generated by the sewage treatment equipment during this time are obtained. By formula Obtain the sewage treatment status value W a ; The obtained sewage treatment status value W a The threshold range of the sewage treatment condition is set To compare: when When , no optimization adjustment strategy is generated; Otherwise, generate an optimization adjustment strategy; Where m is the total number of associated indicators in the processing step, and i∈[1,m], Qi is the processing consumption generated by the i-th associated indicator, τ i is the weight coefficient of the processing consumption generated by the i-th associated indicator, ΔT st and M st are respectively preset reference values, and α1 and α2 are respectively preset proportional coefficients.

3. According to claim 2, a smart sewage treatment system based on data analysis is characterized in that: The method by which the optimization decision module performs corresponding optimization adjustment on the sewage treatment equipment according to the generated optimization adjustment strategy is: The optimization adjustment strategy includes a first adjustment instruction and a second adjustment instruction; when When , a first adjustment instruction is generated; when When , a second adjustment instruction is generated.

4. The intelligent sewage treatment system based on data analysis according to claim 3 is characterized in that: When the first adjustment instruction is generated, the processing procedure is divided into multiple processing sections according to the order of the processing steps in the processing procedure, and the water quality parameter change curve R in each processing section is obtained. x (t); By formula The deviation coefficient V is obtained; According to the deviation coefficient of each processing section, sort them from large to small, and according to the sorting result, give priority to adjusting the time period with the larger deviation coefficient; Among them, t1 is the time when the water quality parameters in the treatment section reach the standard. The standard curve of water quality parameters changing with time in the treatment section set by the system, t2 is the standard time when the water quality parameters in the treatment section reach qualified water quality.

5. The intelligent sewage treatment system based on data analysis according to claim 4 is characterized in that: When generating the second adjustment instruction, first obtain the temperature variation curve T of the processing device during the process. emp (t), the pressure variation curve P(t) of the working pump of the processing equipment, and the working current variation curve I(t) of the processing equipment; Combining formulas To determine whether there is any abnormality in the sewage treatment equipment; When D>1, it means that the sewage treatment equipment is abnormal, and a corresponding alarm is generated to remind the management personnel to repair the sewage treatment equipment; Otherwise, according to the same processing method when generating the first adjustment instruction, the deviation coefficients of various processes are obtained, and they are sorted from large to small. According to the sorting result, the time period with the larger deviation coefficient is adjusted first; Among them, D st is the judgment threshold, D is the judgment coefficient, P0(t) and I0(t) are respectively the standard curves of the temperature of the processing equipment, the pressure of the working pump, and the working current of the processing equipment changing with time which are preset in the system.

6. The intelligent sewage treatment system based on data analysis according to claim 5 is characterized in that: The data terminal also warns of potential problems in various treatment processes based on the acquired sewage treatment status values: When no optimization adjustment strategy is generated, the sewage treatment status value of each treatment process under n times is obtained. Thus constructing the sewage treatment status value W a The curve equation f(x, W a ); By formula The potential risk factor H is obtained; When H>H st When the processing step is considered to have potential problems, a corresponding early warning signal is generated; in, and j∈[1,n], H st is the preset risk factor threshold, maxK is the maximum slope value, minK is the minimum slope value, β1 and β2 are proportional coefficients, f0(x,W a ) is the standard curve equation of the preset sewage treatment status value and treatment times.

7. The intelligent sewage treatment method based on data analysis according to claim 6 is characterized in that: The treatment method is controlled and executed by the smart sewage treatment system based on data analysis as described in any of claims 1-6.

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