A carbon source precise dosing system and method based on an automatic control logic algorithm

Abstract

The present invention relates to the technical field of sewage treatment, and discloses a precise carbon source dosing system and method based on an automatic control logic algorithm, including: a data processing module for constructing a data chart corresponding to sewage data, optimizing the sewage data to obtain target sewage data; a carbon source quantity calculation module for analyzing the organic matter components and component concentrations in the area to be treated and calculating the carbon source supplement quantity in the area to be treated; a water quality analysis module for collecting the equipment parameters corresponding to the carbon source dosing equipment, analyzing the dosing correlation relationship when the carbon source dosing equipment is managed in the area to be treated, and analyzing the water quality evolution trend in the area to be treated; a scheme generation module for calculating the carbon source dosing quantity and carbon source dosing cycle corresponding to the carbon source dosing equipment, and generating a carbon source dosing scheme for the area to be treated according to the carbon source dosing quantity and carbon source dosing cycle. The present invention aims to improve the utilization rate of the dosed carbon source.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and particularly to a carbon source precise dosing system and method based on an automatic control logic algorithm. Background Art

[0002] The problems of water environmental pollution and water eutrophication are becoming increasingly serious. Nitrogen is one of the main factors causing water eutrophication. In order to curb the eutrophication of surface water bodies, more stringent control is imposed on the total nitrogen index of the effluent based on the Class-A first-level discharge standard. The advanced treatment of total nitrogen has become a major trend in water pollution control.

[0003] At present, the core of the denitrification process in most sewage treatment plants in China is heterotrophic denitrification, that is, using biodegradable COD in sewage as a carbon source to complete the biological denitrification process. Most sewage treatment plants currently adopt the method of manually dosing carbon sources at a constant amount to improve the denitrification ability of the system. However, due to the large fluctuations in the influent water quality and quantity of sewage treatment plants, the constant dosing often exceeds the actual demand. On the one hand, it leads to an increase in the cost of chemicals, and on the other hand, it also brings the risk of carbon source breakthrough, resulting in the problem of excessive effluent COD, thus reducing the utilization rate of the dosed carbon source. Summary of the Invention

[0004] The present invention provides a carbon source precise dosing system and method based on an automatic control logic algorithm, and its main purpose is to improve the utilization rate of the dosed carbon source.

[0005] To achieve the above object, a carbon source precise dosing system based on an automatic control logic algorithm provided by the present invention includes:

[0006] A data processing module, configured to collect regional sewage data of a to-be-treated area, construct a data chart corresponding to the sewage data, and optimize the sewage data according to the data chart to obtain target sewage data;

[0007] A carbon source quantity calculation module, configured to analyze the organic matter components and component concentrations in the to-be-treated area according to the target sewage data, and calculate the carbon source supplement quantity of the to-be-treated area according to the component content;

[0008] A water quality analysis module, configured to dispatch the carbon source dosing equipment in the to-be-treated area, collect the equipment parameters corresponding to the carbon source dosing equipment, analyze the dosing correlation relationship when the carbon source dosing equipment is managed in the to-be-treated area according to the equipment parameters, and analyze the water quality evolution trend of the to-be-treated area according to the target sewage data.

[0009] A scenario generation module, configured to combine the water quality evolution trend, the dosing association relationship, and the carbon source replenishment amount, and use a preset automatic control logic algorithm to calculate the carbon source dosing amount and the carbon source dosing cycle corresponding to the carbon source dosing device, and generate a carbon source dosing plan for the area to be treated according to the carbon source dosing amount and the carbon source dosing cycle.

[0010] Optionally, the optimizing the regional sewage data according to the data chart to obtain target sewage data includes:

[0011] Identifying the data point coordinates in the data chart, and calculating the average density value corresponding to the regional sewage data according to the data point coordinates;

[0012] According to the average density value, performing rejection processing on the discrete data in the regional sewage data to obtain rejected sewage data;

[0013] Performing normalization processing on the rejected sewage data to obtain target sewage data.

[0014] Optionally, the performing normalization processing on the rejected sewage data to obtain target sewage data includes:

[0015] Performing normalization processing on the rejected sewage data through the following formula:

[0016]

[0017] where A represents the target sewage data, B max represents the upper bound of the normalization processing of the rejected sewage data, B min represents the lower bound of the normalization processing of the rejected sewage data, D a represents the a-th data in the rejected sewage data, D max represents the maximum value in the rejected sewage data, D min represents the minimum value in the rejected sewage data.

[0018] Optionally, the calculating the carbon source replenishment amount of the area to be treated according to the component content includes:

[0019] Scheduling the historical water volume data of the area to be treated, and extracting the regional water consumption and the regional carbon source density from the water volume data;

[0020] The regional water consumption includes the previous water consumption and the current water consumption, and obtaining the control index requirements corresponding to the area to be treated;

[0021] According to the control requirements, determining the control index concentration in the area to be treated;

[0022] Calculate the carbon source supplement amount of the area to be treated in combination with the control index concentration, the regional carbon source density, the previous water consumption, and the current water consumption.

[0023] Optionally, the calculating the carbon source supplement amount of the area to be treated in combination with the control index concentration, the regional carbon source density, the previous water consumption, and the current water consumption includes:

[0024] Calculate the carbon source supplement amount of the area to be treated through the following formula:

[0025]

[0026] where E represents the carbon source supplement amount of the area to be treated, F t represents the current water consumption, F t-1 represents the previous water consumption, α represents the carbon-nitrogen ratio of the area to be treated, G t represents the nitrate nitrogen concentration of the area to be treated, C represents the control index concentration, and ρ represents the regional carbon source density.

[0027] Optionally, the analyzing the dosing correlation relationship of the carbon source dosing device when managing in the area to be treated according to the device parameters includes:

[0028] Calculate the parameter weight value corresponding to the device parameters, and extract the key device parameters from the device parameters according to the parameter weight value;

[0029] Analyze the device function attributes corresponding to the carbon source dosing device according to the key device parameters;

[0030] Determine the device dynamic behavior corresponding to the carbon source dosing device according to the device function attributes;

[0031] Query the water quality indicators corresponding to the area to be treated, and calculate the correlation coefficient between the device dynamic behavior and the water quality indicators;

[0032] Analyze the dosing correlation relationship of the carbon source dosing device when managing in the area to be treated according to the correlation coefficient.

[0033] Optionally, the calculating the correlation coefficient between the device dynamic behavior and the water quality indicators includes:

[0034] Calculate the correlation coefficient between the device dynamic behavior and the water quality indicators through the following formula:

[0035]

[0036] where H represents the correlation coefficient between the device dynamic behavior and the water quality indicators, g represents the number of behaviors of the device dynamic behavior, Mi represents the eigenvalue corresponding to the i-th behavior in the dynamic behavior of the device, N j represents the eigenvalue corresponding to the water quality index, i represents the serial number of the eigenvalue of the device dynamic behavior, and j represents the serial number of the eigenvalue of the water quality index.

[0037] Optionally, analyzing the water quality evolution trend of the area to be treated according to the target sewage data includes:

[0038] Performing feature extraction on the target sewage data to obtain data features, and calculating the feature gain value corresponding to the data features;

[0039] According to the feature gain value, extracting key features from the data features, and analyzing the feature variables corresponding to the key features;

[0040] Identifying the numerical data in the target sewage data, and constructing a water quality time series graph of the area to be treated according to the feature variables and the numerical data;

[0041] Calculating the image slope of the water quality time series graph, and analyzing the water quality evolution trend of the area to be treated according to the image slope.

[0042] Optionally, combining the water quality evolution trend, the dosing correlation relationship and the carbon source supplement amount, and calculating the carbon source dosing amount and carbon source dosing period corresponding to the carbon source dosing device by using a preset automatic control logic algorithm, includes:

[0043] Identifying the evolution nodes in the water quality evolution trend, and calculating the change rate corresponding to the evolution nodes;

[0044] According to the change rate, calculating the carbon source dosing period corresponding to the carbon source dosing device by using the periodic function in the automatic control logic algorithm;

[0045] According to the dosing correlation relationship, calculating the carbon source dosing amount corresponding to the carbon source dosing device by using the control strategy function in the automatic control logic algorithm.

[0046] A carbon source precise dosing method based on an automatic control logic algorithm, characterized in that the method includes:

[0047] Collecting the regional sewage data of the area to be treated, constructing a data chart corresponding to the sewage data, and performing optimization processing on the sewage data according to the data chart to obtain target sewage data;

[0048] According to the target sewage data, analyzing the organic matter components and component concentrations in the area to be treated, and calculating the carbon source supplement amount of the area to be treated according to the component content;

[0049] Dispatch the carbon source dosing equipment in the area to be processed, collect the equipment parameters corresponding to the carbon source dosing equipment, analyze the dosing correlation relationship when the carbon source dosing equipment is managed in the area to be processed according to the equipment parameters, and analyze the water quality evolution trend of the area to be processed according to the target sewage data;

[0050] Combined with the water quality evolution trend, the dosing correlation relationship and the carbon source supplement amount, use a preset automatic control logic algorithm to calculate the carbon source dosing amount and carbon source dosing cycle corresponding to the carbon source dosing equipment, and generate a carbon source dosing plan for the area to be processed according to the carbon source dosing amount and the carbon source dosing cycle.

[0051] By optimizing the sewage data, the present invention can remove abnormal data in the sewage data, thereby improving the overall quality of the data and the reliability of the results of subsequent data analysis and processing. According to the target sewage data, the present invention analyzes the organic matter components and component concentrations in the area to be processed, and can obtain the composition of the organic matter and the content of the components in the sewage of the area to be processed, so as to facilitate improving the calculation accuracy of the subsequent carbon source supplement amount. By collecting the key parameters corresponding to the carbon source dosing equipment, the present invention can obtain the representative equipment information of the carbon source dosing equipment, so as to accurately analyze the dynamic behavior corresponding to the carbon source dosing equipment, thereby providing a basis for the subsequent analysis of the correlation relationship between the dynamic behavior and the water quality index. By combining the water quality evolution trend, the dosing correlation relationship and the carbon source supplement amount, the present invention uses a preset automatic control logic algorithm to calculate the carbon source dosing amount and carbon source dosing cycle corresponding to the carbon source dosing equipment, so as to improve the accuracy of carbon source dosing in the area to be processed, and further improve the utilization efficiency of the carbon source. Therefore, a carbon source precise dosing system and method based on an automatic control logic algorithm provided by an embodiment of the present invention can improve the utilization rate of the dosed carbon source. Brief Description of the Drawings

[0052] Figure 1 It is a functional module diagram of a carbon source precise dosing system based on an automatic control logic algorithm provided by an embodiment of the present invention;

[0053] Figure 2 It is a flowchart of a carbon source precise dosing method based on an automatic control logic algorithm provided by an embodiment of the present invention.

[0054] The realization, functional characteristics and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiments

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] In addition, the sequence of steps in the following method embodiments is only an example and is not strictly limited.

[0057] In fact, the server device deployed by the carbon source precise dosing system based on the automatic control logic algorithm may be composed of one or more devices. The above carbon source precise dosing system based on the automatic control logic algorithm can be implemented as: a service instance, a virtual machine, or a hardware device. For example, the carbon source precise dosing system based on the automatic control logic algorithm can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, the live broadcast service system can be understood as a software deployed on a cloud node for providing the carbon source precise dosing service based on the automatic control logic algorithm to each client. Alternatively, the carbon source precise dosing system based on the automatic control logic algorithm can also be implemented as a virtual machine deployed on one or more devices in a cloud node. An application software for managing each client is installed in the virtual machine. Or, the carbon source precise dosing system based on the automatic control logic algorithm can also be implemented as a server composed of many identical or different types of hardware devices, and one or more hardware devices are set to provide the carbon source precise dosing service based on the automatic control logic algorithm to each client.

[0058] In terms of implementation form, the carbon source precise dosing system based on the automatic control logic algorithm and the client adapt to each other. That is, if the carbon source precise dosing system based on the automatic control logic algorithm is an application installed on a cloud service platform, then the client is a client that establishes a communication connection with this application; or if the carbon source precise dosing system based on the automatic control logic algorithm is implemented as a website, then the client is implemented as a web page; or if the carbon source precise dosing system based on the automatic control logic algorithm is implemented as a cloud service platform, then the client is implemented as a small program in an instant messaging application.

[0059] Refer to Figure 1 As shown, it is a functional module diagram of the carbon source precise dosing system provided by an embodiment of the present invention.

[0060] The carbon source precise dosing system 100 based on the self - control logic algorithm of the present invention can be set in a cloud server. In terms of implementation form, it can be used as one or more service devices, or can be installed as an application on the cloud (such as the server of a live service operator, a server cluster, etc.), or can also be developed into a website. According to the functions achieved, the carbon source precise dosing system 100 based on the self - control logic algorithm includes a data processing module 101, a carbon source quantity calculation module 102, a water quality analysis module 103, and a solution generation module 104.

[0061] In the embodiments of the present invention, in the tracking of the carbon source precise dosing based on the self - control logic algorithm, each of the above - mentioned modules can be independently implemented and called with other modules. Here, the call can be understood as that a certain module can be connected to multiple modules of another type and provide corresponding services for the multiple modules it is connected to. In the carbon source precise dosing system based on the self - control logic algorithm provided by the embodiments of the present invention, without modifying the program code, the applicable scope of the carbon source precise dosing architecture based on the self - control logic algorithm can be adjusted by adding modules and directly calling them, realizing cluster - type horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the carbon source precise dosing system based on the self - control logic algorithm. In practical applications, the above - mentioned modules can be set in the same device or different devices, or can also be set in virtual devices, such as service instances in a cloud server.

[0062] Next, specific embodiments are combined to illustrate each component and the specific working process of the carbon source precise dosing system based on the self - control logic algorithm respectively.

[0063] The data processing module 101 is used to collect the regional sewage data of the area to be processed, construct a data chart corresponding to the regional sewage data, and optimize the regional sewage data according to the data chart to obtain target sewage data.

[0064] By optimizing the sewage data, the present invention can remove abnormal data in the sewage data, thereby improving the overall quality of the data and the reliability of the subsequent data analysis and processing results. Among them, the regional sewage data is data about the sewage discharge situation in the area to be processed, the data chart is a visual table of the regional sewage data, and the target sewage data is the data obtained after removing abnormal data in the sewage data. Optionally, collecting the regional sewage data of the area to be processed can be achieved through sensors, such as water quality sensors, and constructing the data chart corresponding to the regional sewage data can be achieved through visio mapping tools.

[0065] As an embodiment of the present invention, optimizing the regional sewage data according to the data chart to obtain target sewage data includes: identifying the data point coordinates in the data chart, calculating the average density value corresponding to the regional sewage data according to the data point coordinates, removing discrete data in the regional sewage data according to the average density value to obtain removed sewage data, and normalizing the removed sewage data to obtain target sewage data.

[0066] Among them, the data point coordinates are the coordinate values corresponding to the regional sewage data in the data chart, the average density value is the distribution of the regional sewage data in the data chart, and the removed sewage data is the data obtained after removing discrete data in the regional sewage data.

[0067] Optionally, the data point coordinates in the data chart can be implemented through an identification tool, the identification tool is compiled by a scripting language, calculating the average density value corresponding to the regional sewage data can be achieved by calculating the distance values between the data point coordinates, calculating the average of the distance values, and obtaining the average density value according to the average value. Removing discrete data in the regional sewage data can be implemented through a removal function, and the removal function is compiled by a programming language.

[0068] Optionally, as an alternative embodiment of the present invention, normalizing the removed sewage data to obtain target sewage data includes:

[0069] Normalizing the removed sewage data through the following formula:

[0070]

[0071] Among them, A represents the target sewage data, B max represents the upper bound of the normalization process of the removed sewage data, B min represents the lower bound of the normalization process of the removed sewage data, D a represents the a-th data in the removed sewage data, D max represents the maximum value in the removed sewage data, D min represents the minimum value in the removed sewage data.

[0072] The carbon source quantity calculation module 102 is used to analyze the organic matter components and component concentrations in the area to be treated according to the target sewage data, and calculate the carbon source supplement quantity of the area to be treated according to the component content.

[0073] Based on the target sewage data, the present invention analyzes the organic matter components and component concentrations in the area to be treated, and can obtain the composition of the organic matter and the content of the components in the sewage of the area to be treated, thereby facilitating the improvement of the calculation accuracy of the subsequent carbon source supplement amount. Among them, the organic matter components are the composition of the organic matter in the sewage in the area to be treated, and the component concentration represents the content of the organic matter components. Optionally, analyzing the organic matter components in the area to be treated can be achieved through gas chromatography-mass spectrometry technology, and analyzing the component concentration of the organic matter components in the area to be treated can be achieved through fluorescence spectroscopy. By measuring the fluorescence intensity and emission spectrum of the organic matter components at different excitation light wavelengths, there is a positive correlation between the fluorescence intensity of the organic matter and its concentration, that is, the higher the concentration, the stronger the fluorescence intensity. By measuring the fluorescence intensity of standard products with different concentrations, a standard curve between the fluorescence intensity and the concentration can be established. Then, by measuring the fluorescence intensity of the sample to be tested, the concentration of the organic matter in the sample to be tested can be inferred according to the standard curve.

[0074] As an embodiment of the present invention, calculating the carbon source supplement amount of the area to be treated according to the component content includes: scheduling the historical water volume data of the area to be treated, extracting the regional water consumption and regional carbon source density of the area to be treated from the water volume data, where the regional water consumption includes the previous water consumption and the current water consumption, obtaining the control index requirements corresponding to the area to be treated, determining the control index concentration in the area to be treated according to the control requirements, and combining the control index concentration, the regional carbon source density, the previous water consumption and the current water consumption to calculate the carbon source supplement amount of the area to be treated.

[0075] Among them, the historical water volume data is the water-related data before the area to be treated, the regional water consumption is the amount of water used when treating pollutants in the area to be treated, the regional carbon source density represents the concentration of all organic carbon in the area to be treated, the control index requirements are the control standards corresponding to the various indicators of the area to be treated, and the control index concentration is the standard reference value corresponding to each indicator.

[0076] Optionally, scheduling the historical water volume data of the area to be treated can be achieved through a round-robin scheduling algorithm, extracting the regional water consumption and regional carbon source density of the area to be treated from the water volume data can be achieved through the left function, and the control index requirements can be obtained through the management file in the area to be treated.

[0077] Optionally, as an alternative embodiment of the present invention, combining the control index concentration, the regional carbon source density, the previous water consumption and the current water consumption to calculate the carbon source supplement amount of the area to be treated includes:

[0078] Calculate the carbon source supplement amount of the area to be treated through the following formula:

[0079]

[0080] Wherein, E represents the carbon source supplement amount of the area to be treated, F t represents the current water consumption, F t-1 represents the previous water consumption, α represents the carbon-nitrogen ratio of the area to be treated, G t represents the nitrate nitrogen concentration of the area to be treated, C represents the control index concentration, and ρ represents the regional carbon source density.

[0081] The water quality analysis module 103 is used to schedule the carbon source delivery equipment in the area to be treated, collect the equipment parameters corresponding to the carbon source delivery equipment, analyze the delivery correlation relationship when the carbon source delivery equipment is managed in the area to be treated according to the equipment parameters, and analyze the water quality evolution trend of the area to be treated according to the target sewage data.

[0082] By collecting the key parameters corresponding to the carbon source delivery equipment, the present invention can obtain the representative equipment information of the carbon source delivery equipment, so as to accurately analyze the dynamic behavior corresponding to the carbon source delivery equipment, thereby providing a basis for the subsequent analysis of the correlation relationship between the dynamic behavior and the water quality index. Among them, the carbon source delivery equipment is the equipment used to put carbon source substances into the area to be treated, the equipment parameters are the equipment information corresponding to the carbon source delivery equipment, and the delivery correlation relationship represents the corresponding correlation degree when the carbon source delivery equipment conducts sewage treatment in the area to be treated. Optionally, scheduling the carbon source delivery equipment in the area to be treated can be achieved through an equipment manager, and collecting the equipment parameters corresponding to the carbon source delivery equipment can be achieved through a data recorder. The data recorder includes a temperature recorder, a humidity recorder, a pressure recorder, etc.

[0083] As an embodiment of the present invention, analyzing the delivery correlation relationship when the carbon source delivery equipment is managed in the area to be treated according to the equipment parameters includes: calculating the parameter weight values corresponding to the equipment parameters, extracting key equipment parameters from the equipment parameters according to the parameter weight values, analyzing the equipment function attributes corresponding to the carbon source delivery equipment according to the key equipment parameters, determining the equipment dynamic behavior corresponding to the carbon source delivery equipment according to the equipment function attributes, querying the water quality index corresponding to the area to be treated, calculating the correlation coefficient between the equipment dynamic behavior and the water quality index, and analyzing the delivery correlation relationship when the carbon source delivery equipment is managed in the area to be treated according to the correlation coefficient.

[0084] Among them, the parameter weight represents the importance degree corresponding to the device parameter, the key device parameter is a representative parameter among the device parameters, the device function attribute is the device function corresponding to the carbon source dosing device, the device dynamic behavior is the operating state and behavior corresponding to the carbon source dosing device, the water quality index is the water quality treatment item corresponding to the pollution treatment in the area to be treated, and the correlation coefficient represents the correlation degree between the device dynamic behavior and the water quality index.

[0085] Optionally, calculating the parameter weight corresponding to the device parameter can be implemented by a weight calculator. The weight calculator is compiled by JAVA language. Extracting the key device parameter from the device parameters can be achieved by the above-mentioned left function. The device performance of the carbon source dosing device can be analyzed through the key device parameter. According to the device performance, the device function attribute corresponding to the carbon source dosing device can be obtained. The operation mode, working parameters, response time, and dynamic state of the carbon source dosing device can be determined through the device function attribute, so as to obtain the device dynamic behavior corresponding to the carbon source dosing device. Query the correlation relationship corresponding to the correlation coefficient in the preset relationship table, so as to obtain the dosing correlation relationship when the carbon source dosing device is managed in the area to be treated.

[0086] Optionally, as an optional embodiment of the present invention, calculating the correlation coefficient between the device dynamic behavior and the water quality index includes:

[0087] Calculate the correlation coefficient between the device dynamic behavior and the water quality index through the following formula:

[0088]

[0089] Among them, H represents the correlation coefficient between the device dynamic behavior and the water quality index, g represents the number of behaviors of the device dynamic behavior, M i represents the eigenvalue corresponding to the i-th behavior in the device dynamic behavior, N j represents the eigenvalue corresponding to the water quality index, i represents the eigenvalue serial number of the device dynamic behavior, and j represents the eigenvalue serial number of the water quality index.

[0090] The present invention analyzes the water quality evolution trend of the area to be treated according to the target sewage data, so as to understand the water quality change situation of the area to be treated, thereby facilitating the subsequent calculation of the carbon source dosing amount and the carbon source dosing cycle. Among them, the water quality evolution trend is the water quality change situation of the area to be treated.

[0091] As an embodiment of the present invention, analyzing the water quality evolution trend of the area to be treated according to the target sewage data includes: extracting features from the target sewage data to obtain data features, calculating the feature gain value corresponding to the data features, extracting key features from the data features according to the feature gain value, analyzing the feature variables corresponding to the key features, identifying numerical data in the target sewage data, constructing a water quality time series graph of the area to be treated according to the feature variables and the numerical data, calculating the image slope of the water quality time series graph, and analyzing the water quality evolution trend of the area to be treated according to the image slope.

[0092] Among them, the data feature is the data representation in the target sewage data, the feature gain value represents the importance corresponding to the data feature, the feature variable is the independent variable corresponding to the key feature, the water quality time series graph is a visual graph of the water quality of the area to be treated, and the image slope represents the degree of inclination of the broken line of the image of the water quality time series graph.

[0093] Optionally, feature extraction of the target sewage data can be achieved through linear discriminant analysis, calculation of the feature gain value corresponding to the data features can be achieved through the Shannon entropy algorithm, analysis of the feature variables corresponding to the key features can be achieved through principal component analysis, identification of numerical data in the target sewage data can be achieved through OCR recognition technology, construction of the water quality time series graph of the area to be treated can be achieved through the above-mentioned Visio drawing tool, calculation of the image slope of the water quality time series graph can be achieved through a slope calculator, the slope calculator is compiled by a scripting language, analyzing the slope trend of the image slope, and determining the water quality evolution trend of the area to be treated according to the slope trend.

[0094] The scheme generation module 104 combines the water quality evolution trend, the dosing correlation relationship and the carbon source supplement amount, and calculates the carbon source dosing amount and the carbon source dosing cycle corresponding to the carbon source dosing device by using a preset automatic control logic algorithm. According to the carbon source dosing amount and the carbon source dosing cycle, a carbon source dosing scheme for the area to be treated is generated.

[0095] The present invention calculates the carbon source dosage and carbon source dosing cycle corresponding to the carbon source dosing device by combining the water quality evolution trend, the dosing correlation relationship, and the carbon source supplement amount, using a preset automatic control logic algorithm, so as to improve the accuracy of carbon source dosing in the area to be treated, and further improve the utilization efficiency of the carbon source. Among them, the automatic control logic algorithm is flexibly configured and adjusted according to the actual situation and requirements, and complex control logic and decision-making are realized through simple rule and condition combinations. The carbon source dosage is the dosing quantity corresponding to the carbon source dosing device, the carbon source dosing cycle is the dosing duration corresponding to the carbon source dosing device, and the carbon source dosing plan is the dosing method for the area to be treated.

[0096] As an embodiment of the present invention, the combination of the water quality evolution trend, the dosing correlation relationship, and the carbon source supplement amount, and the use of a preset automatic control logic algorithm to calculate the carbon source dosage and carbon source dosing cycle corresponding to the carbon source dosing device includes: identifying the evolution nodes in the water quality evolution trend, calculating the change rate corresponding to the evolution nodes, and according to the change rate, using the periodic function in the automatic control logic algorithm to calculate the carbon source dosing cycle corresponding to the carbon source dosing device, and according to the dosing correlation relationship, using the control strategy function in the automatic control logic algorithm to calculate the carbon source dosage corresponding to the carbon source dosing device.

[0097] Among them, the evolution node is the change turning point in the water quality evolution trend, the change rate represents the change speed between the evolution nodes, the periodic function is compiled by a programming language, and the control strategy function is a function used to calculate the dosing quantity of the carbon source dosing device, such as an exponential growth function.

[0098] Optionally, identifying the evolution nodes in the water quality evolution trend can be achieved through statistical analysis, and calculating the change rate corresponding to the evolution nodes can be achieved through the difference method. The specific steps are as follows: for each evolution node, calculate the difference (i.e., the change amount) between its adjacent two nodes, divide each difference by the time interval between the adjacent nodes to obtain the change rate, according to the change rate, query the dosing speed corresponding to the carbon source dosing device and the water flow speed of the area to be treated, combine the dosing speed, the water flow speed, and the change rate, use the periodic function in the automatic control logic algorithm to calculate the carbon source dosing cycle corresponding to the carbon source dosing device, according to the dosing correlation relationship, use the control strategy function in the automatic control logic algorithm to calculate the relationship linear value corresponding to the carbon source dosing device, according to the relationship linear value, determine the dosing ratio of the carbon source dosing device, and multiply the carbon source supplement amount by the dosing ratio to obtain the carbon source dosage corresponding to the carbon source dosing device.

[0099] By optimizing the sewage data, the present invention can remove abnormal data from the sewage data, thereby improving the overall quality of the data and enhancing the reliability of the results of subsequent data analysis and processing. According to the target sewage data, the present invention analyzes the organic components and component concentrations in the area to be treated, and can obtain the composition of the organic matter and the content of the components in the sewage in the area to be treated, so as to facilitate improving the calculation accuracy of the subsequent carbon source supplement amount. By collecting the key parameters corresponding to the carbon source dosing equipment, the present invention can obtain the representative equipment information of the carbon source dosing equipment, so as to accurately analyze the dynamic behavior corresponding to the carbon source dosing equipment, thereby providing a basis for the subsequent analysis of the correlation between the dynamic behavior and the water quality index. By combining the water quality evolution trend, the dosing correlation relationship and the carbon source supplement amount, the present invention calculates the carbon source dosing amount and the carbon source dosing cycle corresponding to the carbon source dosing equipment by using a preset automatic control logic algorithm, so as to facilitate improving the accuracy of carbon source dosing in the area to be treated, and further improving the utilization efficiency of the carbon source. Therefore, a carbon source precise dosing method based on an automatic control logic algorithm provided by an embodiment of the present invention can improve the utilization rate of the dosed carbon source.

[0100] Refer to Figure 2 As shown, it is a flowchart of a carbon source precise dosing method based on an automatic control logic algorithm provided by an embodiment of the present invention. In this embodiment, the carbon source precise dosing method based on an automatic control logic algorithm includes:

[0101] Collect the regional sewage data of the area to be treated, construct a data chart corresponding to the sewage data, and optimize the sewage data according to the data chart to obtain target sewage data;

[0102] According to the target sewage data, analyze the organic components and component concentrations in the area to be treated, and calculate the carbon source supplement amount of the area to be treated according to the component content;

[0103] Dispatch the carbon source dosing equipment in the area to be treated, collect the equipment parameters corresponding to the carbon source dosing equipment, analyze the dosing correlation relationship when the carbon source dosing equipment is managed in the area to be treated according to the equipment parameters, and analyze the water quality evolution trend of the area to be treated according to the target sewage data;

[0104] Combine the water quality evolution trend, the dosing correlation relationship and the carbon source supplement amount, calculate the carbon source dosing amount and the carbon source dosing cycle corresponding to the carbon source dosing equipment by using a preset automatic control logic algorithm, and generate a carbon source dosing plan for the area to be treated according to the carbon source dosing amount and the carbon source dosing cycle.

[0105] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0106] In addition, each functional module in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a hardware plus software functional module.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for precise carbon source dosing based on an automatic control logic algorithm, characterized in that, specifically including: Collecting regional sewage data of the area to be treated, constructing a data chart corresponding to the sewage data, and optimizing the sewage data according to the data chart to obtain target sewage data; According to the target sewage data, analyzing the organic matter components and component concentrations in the area to be treated, and calculating the carbon source supplement amount of the area to be treated according to the component concentration. Among them, calculating the carbon source supplement amount of the area to be treated according to the component concentration includes: Scheduling the historical water volume data of the area to be treated, and extracting the regional water consumption and regional carbon source density of the area to be treated from the water volume data; The regional water consumption includes the previous water consumption and the current water consumption, and obtaining the control index requirements corresponding to the area to be treated; Determining the control index concentration in the area to be treated according to the control index requirements; Combining the control index concentration, regional carbon source density, previous water consumption and current water consumption, and calculating the carbon source supplement amount of the area to be treated; Scheduling the carbon source dosing equipment in the area to be treated, collecting the equipment parameters corresponding to the carbon source dosing equipment, analyzing the dosing correlation relationship when the carbon source dosing equipment is managed in the area to be treated according to the equipment parameters, and analyzing the water quality evolution trend in the area to be treated according to the target sewage data; Combining the water quality evolution trend, dosing correlation relationship and carbon source supplement amount, using a preset automatic control logic algorithm to calculate the carbon source dosing amount and carbon source dosing cycle corresponding to the carbon source dosing equipment, and generating a carbon source dosing plan for the area to be treated according to the carbon source dosing amount and carbon source dosing cycle; Analyzing the water quality evolution trend in the area to be treated according to the target sewage data, including: Performing feature extraction on the target sewage data to obtain data features, and calculating the feature gain value corresponding to the data features; Extracting key features from the data features according to the feature gain value, and analyzing the feature variables corresponding to the key features; Identifying the numerical data in the target sewage data, and constructing a water quality time series graph of the area to be treated according to the feature variables and numerical data; Calculating the image slope of the water quality time series graph, and analyzing the water quality evolution trend in the area to be treated according to the image slope; Combining the water quality evolution trend, dosing correlation relationship and carbon source supplement amount, using a preset automatic control logic algorithm to calculate the carbon source dosing amount and carbon source dosing cycle corresponding to the carbon source dosing equipment, including: Identifying the evolution nodes in the water quality evolution trend, and calculating the change rate corresponding to the evolution nodes; Calculating the carbon source dosing cycle corresponding to the carbon source dosing equipment by using the periodic function in the automatic control logic algorithm according to the change rate; Calculating the carbon source dosing amount corresponding to the carbon source dosing equipment by using the control strategy function in the automatic control logic algorithm according to the dosing correlation relationship.

2. A method for precise carbon source dosing based on an automatic control logic algorithm according to claim 1, characterized in that, Optimizing the regional sewage data according to the data chart to obtain target sewage data, including: Identifying the data point coordinates in the data chart, and calculating the average density value corresponding to the regional sewage data according to the data point coordinates; Performing rejection processing on the discrete data in the regional sewage data according to the average density value to obtain the rejected sewage data; Normalize the sewage data after rejection to obtain the target sewage data.

3. A method for precise carbon source dosing based on an automatic control logic algorithm as described in claim 2, characterized in that normalizing the sewage data after rejection to obtain the target sewage data, including: normalizing the sewage data after rejection through the following formula: Among them, A represents the target sewage data, B max represents the upper bound of the normalized treatment of the sewage data after exclusion, B min represents the lower bound of the normalized treatment of the sewage data after exclusion, D a represents the a-th data in the sewage data after exclusion, D max represents the maximum value in the sewage data after exclusion, D min represents the minimum value in the sewage data after exclusion.

4. A method for precise carbon source dosing based on an automatic control logic algorithm as described in claim 1, characterized in that analyze the dosing correlation relationship when the carbon source dosing device is managed in the area to be treated according to the device parameters, including: calculate the parameter weight value corresponding to the device parameters, and extract the key device parameters from the device parameters according to the parameter weight value; analyze the device function attributes corresponding to the carbon source dosing device according to the key device parameters; determine the device dynamic behavior corresponding to the carbon source dosing device according to the device function attributes; query the water quality index corresponding to the area to be treated, and calculate the correlation coefficient between the device dynamic behavior and the water quality index; analyze the dosing correlation relationship when the carbon source dosing device is managed in the area to be treated according to the correlation coefficient.

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