Intelligent control method and system for sewage treatment equipment based on big data

Abstract

The invention is suitable for the technical field of sewage treatment, and provides a sewage treatment equipment intelligent control method and system based on big data. The method comprises the following steps: determining a plurality of reference effect nodes through effect identification, and screening historical data of a plurality of nodes; obtaining pre-detection data, comparing the pre-detection data with historical data of a plurality of nodes, and calculating a plurality of consistent comparison values; arranging and comparing, selecting trial control parameters, and adjusting a photocatalytic angle; and when the treatment is unqualified, the trial control parameters are reselected. Multiple reference effect nodes can be determined, historical data of the multiple nodes can be screened, pre-sewage detection is carried out, conformance comparison values corresponding to the multiple reference effect nodes are calculated, arrangement and comparison are carried out, trial control parameters are successively selected, and photocatalytic angle adjustment of sewage treatment is carried out. And adjustment planning and trial of photocatalytic irradiation are carried out, so that the most appropriate photocatalytic irradiation angle is quickly found, and the photocatalytic sewage treatment effect is ensured.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment technology, and in particular relates to a smart control method and system for wastewater treatment equipment based on big data. Background Technology

[0002] Wastewater treatment is the process of purifying domestic sewage, industrial wastewater, and other polluted water bodies through physical, chemical, and biological methods. This process removes harmful substances, suspended solids, organic pollutants, heavy metals, pathogens, and other contaminants, ensuring that the wastewater meets national or local discharge or reuse standards. This reduces environmental harm and protects water resources and ecosystems.

[0003] Photocatalytic wastewater treatment is one of the common wastewater treatment methods.

[0004] In existing technologies, the refraction of light in wastewater is affected by multiple factors, which mainly affect the propagation path and refraction angle of light. These factors can easily cause the propagation path of light in the medium to deviate, resulting in a reduction in the irradiation area of ​​the photocatalyst bed and affecting the photocatalytic wastewater treatment effect. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent control method and system for wastewater treatment equipment based on big data, aiming to solve the technical problems existing in the prior art mentioned in the background.

[0006] The embodiments of the present invention are implemented as follows:

[0007] A smart control method for wastewater treatment equipment based on big data, the method specifically includes the following steps:

[0008] Identify the current source of wastewater, match the corresponding historical data of the source, identify the effect of the historical data of the source, determine multiple reference effect nodes, and filter the historical data of multiple nodes;

[0009] Pre-testing of wastewater is performed to obtain pre-testing data, which is then compared with historical data from multiple nodes to calculate the matching comparison values ​​corresponding to multiple reference effect nodes.

[0010] Multiple matching comparison values ​​are sorted and compared, and trial control parameters are selected from the historical data of the nodes, and the photocatalytic angle of wastewater treatment is adjusted accordingly.

[0011] Post-treatment wastewater is tested to obtain post-treatment data and determine whether the treatment is up to standard. If the treatment is not up to standard, new trial control parameters are selected.

[0012] As a further limitation of the technical solution of this embodiment of the invention, the steps of determining the current source of sewage, matching the corresponding historical data of the source, identifying the effect of the historical data of the source, determining multiple reference effect nodes, and filtering the historical data of multiple nodes specifically include the following steps:

[0013] Identify the current source of the wastewater;

[0014] Match the current wastewater source with historical data from the preset historical database;

[0015] Based on preset performance standard data, the source historical data is compared, and multiple reference performance nodes are selected;

[0016] Based on the multiple reference effect nodes, filter multiple corresponding node historical data from the source historical data.

[0017] As a further limitation of the technical solution of this embodiment of the invention, the step of performing pre-testing of wastewater, obtaining pre-testing data, comparing it with historical data of multiple nodes, and calculating the matching comparison value corresponding to multiple reference effect nodes specifically includes the following steps:

[0018] Select the pre-detection location and determine multiple raw water characteristic parameters;

[0019] At the aforementioned pre-detection location, pre-sewage detection is performed according to multiple raw water characteristic parameters to obtain pre-detection data;

[0020] Based on the aforementioned raw water characteristic parameters, select multiple node characteristic data from the historical data of multiple nodes;

[0021] The preceding detection data is compared with the characteristic data of multiple nodes to calculate the matching comparison value corresponding to multiple reference effect nodes.

[0022] As a further limitation of the technical solution of this embodiment of the invention, the calculation formula for the plurality of matching comparison values ​​is as follows:

[0023]

[0024] Where k is the k-th reference effect node, i is the i-th raw water characteristic parameter, and each reference effect node has a total of m raw water characteristic parameters, p i P represents the value of the i-th raw water characteristic parameter in the pre-detection data. ki P represents the value of the i-th raw water characteristic parameter in the node characteristic data corresponding to the k-th reference effect node. imax P represents the maximum value of the i-th raw water characteristic parameter. imin w is the minimum value of the i-th raw water characteristic parameter. iThe preset weights are the i-th raw water characteristic parameters.

[0025] As a further limitation of the technical solution of this embodiment of the invention, the step of arranging and comparing multiple matching comparison values, selecting trial control parameters from the node historical data, and adjusting the photocatalytic angle of wastewater treatment accordingly specifically includes the following steps:

[0026] According to the principle of descending from large to small, the multiple matching comparison values ​​are arranged, compared and recorded to obtain matching arrangement information;

[0027] Based on the matching arrangement information, the multiple reference effect nodes are arranged accordingly, and a reference trial node is selected.

[0028] Select the trial control parameters corresponding to the reference trial node from the historical data of the node;

[0029] According to the trial control parameters, the photocatalytic angle of the corresponding wastewater treatment is adjusted.

[0030] As a further limitation of the technical solution of this embodiment of the invention, the step of performing post-sewage testing, obtaining post-testing data, determining whether the treatment is qualified, and re-selecting trial control parameters when the treatment is unqualified specifically includes the following steps:

[0031] Select the post-detection location and determine multiple sample water characteristic parameters;

[0032] At the post-detection location, post-sewage detection is performed according to multiple sample water characteristic parameters to obtain post-detection data;

[0033] Based on preset performance standard data, the post-detection data is compared to determine whether the processing is qualified.

[0034] When a non-compliance is handled, the trial control parameters are reselected from the historical data of the node.

[0035] The intelligent control system for wastewater treatment equipment based on big data includes a node data filtering module, a consistency comparison calculation module, a trial control processing module, and a post-detection and judgment module, wherein:

[0036] The node data filtering module is used to determine the current source of sewage, match the corresponding historical data of the source, identify the effect of the historical data of the source, determine multiple reference effect nodes, and filter the historical data of multiple nodes.

[0037] The matching comparison calculation module is used to perform pre-sewage detection, obtain pre-detection data, compare it with historical data of multiple nodes, and calculate the matching comparison value corresponding to multiple reference effect nodes.

[0038] The trial control processing module is used to arrange and compare multiple matching comparison values, select trial control parameters from the node historical data, and adjust the photocatalytic angle of wastewater treatment accordingly.

[0039] The post-processing detection and judgment module is used to perform post-processing wastewater detection, acquire post-processing detection data, determine whether the treatment is qualified, and reselect trial control parameters if the treatment is unqualified.

[0040] As a further limitation of the technical solution of this embodiment of the invention, the node data filtering module specifically includes:

[0041] Wastewater source determination unit, used to determine the current source of wastewater;

[0042] The historical data matching unit is used to match the historical data of the current wastewater source from a preset historical record database.

[0043] The node comparison and selection unit is used to compare the source historical data based on preset effect standard data and select multiple reference effect nodes.

[0044] The node data filtering unit is used to filter multiple corresponding node historical data from the source historical data according to multiple reference effect nodes.

[0045] As a further limitation of the technical solution of this embodiment of the invention, the matching comparison calculation module specifically includes:

[0046] The pre-detection location selection unit is used to select the pre-detection location and determine multiple raw water characteristic parameters;

[0047] The pre-sewage detection unit is used to perform pre-sewage detection at the pre-detection location according to multiple raw water characteristic parameters and obtain pre-detection data.

[0048] The node data selection unit is used to filter multiple node characteristic data from multiple node historical data according to multiple raw water characteristic parameters;

[0049] The matching comparison calculation unit is used to compare the pre-detection data with the characteristic data of multiple nodes and calculate the matching comparison value corresponding to multiple reference effect nodes.

[0050] As a further limitation of the technical solution of this embodiment of the invention, the trial control processing module specifically includes:

[0051] The arrangement comparison unit is used to arrange, compare, and record multiple matching comparison values ​​according to the principle of descending order, so as to obtain matching arrangement information;

[0052] The node arrangement selection unit is used to arrange multiple reference effect nodes according to the matching arrangement information and select a reference trial node;

[0053] The control parameter selection unit is used to select the trial control parameters corresponding to the reference trial node from the node's historical data;

[0054] An angle adjustment control unit is used to adjust the photocatalytic angle of wastewater treatment according to the trial control parameters.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] This invention identifies multiple reference effect nodes and filters historical data from these nodes through effect recognition. It acquires pre-test data, compares it with historical data from these nodes, and calculates multiple matching comparison values. These values ​​are then compared and selected to determine trial control parameters and adjust the photocatalytic angle. If the treatment fails to meet the requirements, the trial control parameters are reselected. This method allows for the identification of multiple reference effect nodes, the filtering of historical data from these nodes, and the calculation of matching comparison values ​​corresponding to these reference effect nodes through pre-test wastewater detection. By comparing and selecting trial control parameters sequentially and adjusting the photocatalytic angle for wastewater treatment, it can plan and implement photocatalytic irradiation adjustments based on historical big data, thereby quickly finding the most suitable photocatalytic irradiation angle and ensuring the effectiveness of photocatalytic wastewater treatment. Attached Figure Description

[0057] Figure 1 A flowchart of the intelligent control method for sewage treatment equipment based on big data provided in an embodiment of the present invention is shown;

[0058] Figure 2 This diagram illustrates a flowchart of the method for filtering historical data from multiple nodes provided in an embodiment of the present invention.

[0059] Figure 3 A flowchart illustrating the calculation of multiple matching comparison values ​​in the method provided by an embodiment of the present invention is shown;

[0060] Figure 4 A flowchart of the photocatalytic angle adjustment process in the method provided by the embodiments of the present invention is shown;

[0061] Figure 5 The flowchart illustrating the reselection of trial control parameters in the method provided by an embodiment of the present invention is shown.

[0062] Figure 6 The following is an application architecture diagram of the intelligent control system for sewage treatment equipment based on big data provided in an embodiment of the present invention;

[0063] Figure 7This shows a structural block diagram of the node data filtering module in the system provided by an embodiment of the present invention;

[0064] Figure 8 This diagram illustrates the structural block diagram of the matching comparison calculation module in the system provided by an embodiment of the present invention;

[0065] Figure 9 A structural block diagram of the trial control processing module in the system provided by an embodiment of the present invention is shown. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0067] Understandably, in existing technologies, the refraction of light in wastewater is affected by multiple factors, which mainly affect the propagation path and refraction angle of light. These factors can easily cause the propagation path of light in the medium to deviate, resulting in a reduction in the irradiation area of ​​the photocatalyst bed and affecting the photocatalytic wastewater treatment effect.

[0068] To address the aforementioned issues, this invention discloses an intelligent control method and system for wastewater treatment equipment based on big data. This method determines the current wastewater source, matches corresponding historical data of that source, identifies the effects of the historical data, identifies multiple reference effect nodes, and filters historical data from these nodes. It then performs pre-treatment wastewater detection, acquires pre-treatment data, compares it with historical data from multiple nodes, and calculates the matching comparison values ​​corresponding to the multiple reference effect nodes. These matching comparison values ​​are then compared and selected from the historical data of the nodes for trial control parameters, and the photocatalytic angle of the wastewater treatment is adjusted accordingly. Finally, post-treatment wastewater detection is performed, acquiring post-treatment data and determining whether the treatment is qualified. If the treatment is unqualified, trial control parameters are reselected. This method can identify multiple reference effect nodes, filter historical data from multiple nodes, calculate matching comparison values ​​corresponding to multiple reference effect nodes through pre-treatment wastewater detection, compare and arrange these values, and sequentially select trial control parameters to adjust the photocatalytic angle of the wastewater treatment. Based on historical big data, it can plan and trial the adjustment of photocatalytic irradiation, thereby quickly finding the most suitable photocatalytic irradiation angle and ensuring the effectiveness of photocatalytic wastewater treatment.

[0069] Specifically, Figure 1 A flowchart of the intelligent control method for sewage treatment equipment based on big data provided in an embodiment of the present invention is shown.

[0070] In a preferred embodiment of the present invention, a smart control method for wastewater treatment equipment based on big data specifically includes the following steps:

[0071] Step S101: Determine the current source of sewage, match the corresponding historical data of the source, identify the effect of the historical data of the source, determine multiple reference effect nodes, and filter the historical data of multiple nodes.

[0072] In this embodiment of the invention, the current source of sewage is determined, and relevant matching is performed in a preset historical record database according to the current source of sewage to obtain the source historical data corresponding to the current source of sewage. Then, based on the preset effect standard data, the detection effect of different time periods in the source historical data is compared, multiple reference effect nodes are selected, and then multiple corresponding node historical data are filtered from the source historical data according to the multiple reference effect nodes.

[0073] It is understandable that the historical data contains detection and adjustment data recorded when the current sewage source discharged sewage at different historical time periods, during which sewage treatment was carried out.

[0074] It is understandable that by comparing the detection effect of the source historical data with the effect standard data, it is possible to determine the detection period in the source historical data where the detection effect of the detection data meets the effect standard data. By determining the initial time node of the detection period and tracing back from the initial time node according to the preset processing period, the corresponding reference effect node can be determined.

[0075] Specifically, Figure 2 A flowchart illustrating the method for filtering historical data from multiple nodes provided in an embodiment of the present invention is shown.

[0076] In another preferred embodiment of the present invention, the steps of determining the current source of wastewater, matching the corresponding historical data of the source, identifying the effect of the historical data of the source, determining multiple reference effect nodes, and filtering the historical data of multiple nodes specifically include the following steps:

[0077] Step S1011: Determine the current source of sewage.

[0078] Step S1012: Match the source history data corresponding to the current sewage source from the preset historical record database.

[0079] Step S1013: Based on preset effect standard data, compare the source historical data and select multiple reference effect nodes.

[0080] Step S1014: According to the multiple reference effect nodes, filter multiple corresponding node historical data from the source historical data.

[0081] Furthermore, the intelligent control method for wastewater treatment equipment based on big data also includes the following steps:

[0082] Step S102: Perform pre-testing of wastewater, obtain pre-testing data, compare it with historical data of multiple nodes, and calculate the matching comparison value corresponding to multiple reference effect nodes.

[0083] In this embodiment of the invention, a pre-detection location is selected from the influent area of ​​the wastewater treatment tank, and multiple raw water characteristic parameters are determined. Then, according to these parameters, corresponding pre-detection wastewater is performed at the pre-detection location to obtain pre-detection data. Furthermore, based on the raw water characteristic parameters, multiple node characteristic data are selected from historical data of multiple nodes. The pre-detection data is then compared with the multiple node characteristic data to calculate a matching comparison value. Specifically, the formula for calculating the multiple matching comparison values ​​is as follows:

[0084]

[0085] Where k is the k-th reference effect node, i is the i-th raw water characteristic parameter, and each reference effect node has a total of m raw water characteristic parameters, p i P represents the value of the i-th raw water characteristic parameter in the pre-detection data. ki P represents the value of the i-th raw water characteristic parameter in the node characteristic data corresponding to the k-th reference effect node. imax P represents the maximum value of the i-th raw water characteristic parameter. imin w is the minimum value of the i-th raw water characteristic parameter. i The preset weights are the i-th raw water characteristic parameters.

[0086] It is understandable that, in the calculation formulas for multiple matching comparison values, through Calculate the correlation between the i-th raw water characteristic parameter in the pre-detection data and the i-th raw water characteristic parameter in multiple node characteristic data, and then... The correlation between the sum of the preset weights is compared to calculate the matching comparison value C. k It can determine the correlation between the preceding detection data and the characteristic data of multiple nodes, and then determine the correlation between the current data and multiple reference effect nodes.

[0087] It is understood that in the embodiments of the present invention, multiple raw water characteristic parameters include: turbidity, temperature, pH value, flow rate, etc.

[0088] Specifically, Figure 3 A flowchart illustrating the calculation of multiple matching comparison values ​​in the method provided by an embodiment of the present invention is shown.

[0089] In another preferred embodiment of the present invention, the step of performing pre-testing of wastewater, obtaining pre-testing data, comparing it with historical data of multiple nodes, and calculating the matching comparison value corresponding to multiple reference effect nodes specifically includes the following steps:

[0090] Step S1021: Select the pre-detection location and determine multiple raw water characteristic parameters.

[0091] Step S1022: At the pre-detection location, pre-sewage detection is performed according to multiple raw water characteristic parameters to obtain pre-detection data.

[0092] Step S1023: According to the multiple raw water characteristic parameters, filter multiple node characteristic data from the multiple node historical data.

[0093] Step S1024: Compare the pre-detection data with the characteristic data of multiple nodes, and calculate the matching comparison value corresponding to multiple reference effect nodes.

[0094] Furthermore, the intelligent control method for wastewater treatment equipment based on big data also includes the following steps:

[0095] Step S103: Arrange and compare multiple matching comparison values, select trial control parameters from the node historical data, and adjust the photocatalytic angle of wastewater treatment accordingly.

[0096] In this embodiment of the invention, multiple matching comparison values ​​are arranged, compared and recorded according to the principle of descending from large to small to obtain matching arrangement information. Then, according to the matching arrangement information, multiple reference effect nodes are arranged accordingly. Reference trial nodes are selected one by one according to the principle of descending from large to small. The trial control parameters corresponding to the reference trial nodes are selected from the node historical data. Then, according to the trial control parameters, the photocatalytic lighting equipment for wastewater treatment is adjusted accordingly.

[0097] Specifically, Figure 4 A flowchart of the photocatalytic angle adjustment process in the method provided in the embodiment of the present invention is shown.

[0098] In another preferred embodiment of the present invention, the step of arranging and comparing multiple matching comparison values, selecting trial control parameters from the node historical data, and adjusting the photocatalytic angle of wastewater treatment accordingly specifically includes the following steps:

[0099] Step S1031: Arrange, compare and record multiple matching comparison values ​​according to the principle of descending from large to small to obtain matching arrangement information.

[0100] Step S1032: Based on the matching arrangement information, arrange the multiple reference effect nodes accordingly and select a reference trial node.

[0101] Step S1033: Select the trial control parameters corresponding to the reference trial node from the node historical data.

[0102] Step S1034: Adjust the photocatalytic angle of the wastewater treatment according to the trial control parameters.

[0103] Furthermore, the intelligent control method for wastewater treatment equipment based on big data also includes the following steps:

[0104] Step S104: Perform post-treatment wastewater testing, obtain post-treatment test data, and determine whether the treatment is qualified. If the treatment is unqualified, reselect trial control parameters.

[0105] In this embodiment of the invention, a post-detection location is selected from the effluent area of ​​the wastewater treatment tank, and multiple sample water characteristic parameters are determined. Then, according to these parameters, corresponding post-wastewater detection is performed at the post-detection location to obtain post-detection data. Based on preset effect standard data, the post-detection data is compared to determine if the treatment is qualified. If the treatment is qualified, photocatalytic wastewater treatment continues according to the already adjusted photocatalytic angle. If the treatment is unqualified, a new reference trial node is selected from the largest to the smallest, and corresponding trial control parameters are selected from the node's historical data. The photocatalytic lighting equipment for wastewater treatment is adjusted accordingly, and post-wastewater detection is performed again until the treatment is deemed qualified.

[0106] It is understood that in the embodiments of the present invention, multiple water sample characteristic parameters include: turbidity, pH value, chemical oxygen demand, biochemical oxygen demand, etc.

[0107] Specifically, Figure 5 A flowchart illustrating the reselection of trial control parameters in the method provided by an embodiment of the present invention is shown.

[0108] In another preferred embodiment of the present invention, the step of performing post-treatment wastewater testing, obtaining post-treatment testing data, determining whether the treatment is qualified, and re-selecting trial control parameters when the treatment is unqualified specifically includes the following steps:

[0109] Step S1041: Select the post-detection location and determine multiple sample water characteristic parameters.

[0110] Step S1042: At the post-detection location, perform post-sewage detection according to multiple sample water characteristic parameters to obtain post-detection data.

[0111] Step S1043: Based on preset effect standard data, compare the post-detection data to determine whether the processing is qualified.

[0112] Step S1044: When processing non-compliance, reselect trial control parameters from the node's historical data.

[0113] Furthermore, Figure 6 The following is an application architecture diagram of the intelligent control system for sewage treatment equipment based on big data provided in an embodiment of the present invention.

[0114] Specifically, in another preferred embodiment provided by the present invention, the intelligent control system for sewage treatment equipment based on big data includes:

[0115] The node data filtering module 101 is used to determine the current source of sewage, match the corresponding historical data of the source, identify the effect of the historical data of the source, determine multiple reference effect nodes, and filter multiple node historical data.

[0116] In this embodiment of the invention, the node data filtering module 101 determines the current sewage source, performs relevant matching in a preset historical record database according to the current sewage source, obtains the source historical data corresponding to the current sewage source, and then compares the detection effect of different time periods in the source historical data based on preset effect standard data, selects multiple reference effect nodes, and then filters multiple corresponding node historical data from the source historical data according to the multiple reference effect nodes.

[0117] Specifically, Figure 7 The diagram shows the structural block diagram of the node data filtering module 101 in the system provided by the embodiment of the present invention.

[0118] In another preferred embodiment provided by the present invention, the node data filtering module 101 specifically includes:

[0119] Wastewater source determination unit 1011 is used to determine the current wastewater source.

[0120] The historical data matching unit 1012 is used to match the source historical data corresponding to the current sewage source from a preset historical record database.

[0121] The node comparison and selection unit 1013 is used to compare the source historical data based on preset effect standard data and select multiple reference effect nodes.

[0122] The node data filtering unit 1014 is used to filter multiple corresponding node historical data from the source historical data according to multiple reference effect nodes.

[0123] Furthermore, the intelligent control system for wastewater treatment equipment based on big data also includes:

[0124] The consistency comparison calculation module 102 is used to perform pre-sewage detection, obtain pre-detection data, compare it with historical data of multiple nodes, and calculate the consistency comparison value corresponding to multiple reference effect nodes.

[0125] In this embodiment of the invention, the matching comparison calculation module 102 selects a pre-detection location from the influent area of ​​the wastewater treatment tank and determines multiple raw water characteristic parameters. Then, according to these parameters, it performs corresponding pre-detection wastewater testing at the pre-detection location to obtain pre-detection data. Furthermore, based on the raw water characteristic parameters, it filters multiple node characteristic data from historical data of multiple nodes, and then compares the pre-detection data with the multiple node characteristic data to calculate the matching comparison value. Specifically, the formula for calculating the multiple matching comparison values ​​is as follows:

[0126]

[0127] Where k is the k-th reference effect node, i is the i-th raw water characteristic parameter, and each reference effect node has a total of m raw water characteristic parameters, p i P represents the value of the i-th raw water characteristic parameter in the pre-detection data. ki P represents the value of the i-th raw water characteristic parameter in the node characteristic data corresponding to the k-th reference effect node. imax P represents the maximum value of the i-th raw water characteristic parameter. imin w is the minimum value of the i-th raw water characteristic parameter. i The preset weights are the i-th raw water characteristic parameters.

[0128] Specifically, Figure 8 The diagram shows the structural block diagram of the matching comparison calculation module 102 in the system provided by the embodiment of the present invention.

[0129] In another preferred embodiment provided by the present invention, the matching comparison calculation module 102 specifically includes:

[0130] The pre-position selection unit 1021 is used to select the pre-detection position and determine multiple raw water characteristic parameters.

[0131] The pre-sewage detection unit 1022 is used to perform pre-sewage detection at the pre-detection location according to multiple raw water characteristic parameters and obtain pre-detection data.

[0132] The node data selection unit 1023 is used to filter multiple node characteristic data from multiple node historical data according to multiple raw water characteristic parameters.

[0133] The matching comparison calculation unit 1024 is used to compare the pre-detection data with the multiple node characteristic data and calculate the matching comparison value corresponding to multiple reference effect nodes.

[0134] Furthermore, the intelligent control system for wastewater treatment equipment based on big data also includes:

[0135] The trial control processing module 103 is used to arrange and compare multiple matching comparison values, select trial control parameters from the node historical data, and adjust the photocatalytic angle of wastewater treatment accordingly.

[0136] In this embodiment of the invention, the trial control processing module 103 arranges, compares, and records multiple matching comparison values ​​according to the principle of descending from large to small, obtains matching arrangement information, and then arranges multiple reference effect nodes according to the matching arrangement information. Following the principle of descending from large to small, the reference trial nodes are selected one by one, and the trial control parameters corresponding to the reference trial nodes are selected from the node historical data. Then, according to the trial control parameters, the photocatalytic lighting equipment for wastewater treatment is adjusted accordingly to achieve the corresponding photocatalytic angle.

[0137] Specifically, Figure 9 The diagram shows the structural block diagram of the trial control processing module 103 in the system provided by the embodiment of the present invention.

[0138] In another preferred embodiment provided by the present invention, the trial control processing module 103 specifically includes:

[0139] The arrangement comparison unit 1031 is used to arrange, compare and record multiple matching comparison values ​​according to the principle of descending order, so as to obtain matching arrangement information.

[0140] The node arrangement selection unit 1032 is used to arrange multiple reference effect nodes according to the matching arrangement information and select reference trial nodes.

[0141] The control parameter selection unit 1033 is used to select the trial control parameters corresponding to the reference trial node from the node historical data.

[0142] Angle adjustment control unit 1034 is used to adjust the photocatalytic angle of wastewater treatment according to the trial control parameters.

[0143] Furthermore, the intelligent control system for wastewater treatment equipment based on big data also includes:

[0144] The post-processing detection and judgment module 104 is used to perform post-processing wastewater detection, acquire post-processing detection data, and determine whether the treatment is qualified. If the treatment is unqualified, the module will reselect trial control parameters.

[0145] In this embodiment of the invention, the post-detection judgment module 104 selects a post-detection location from the effluent area of ​​the wastewater treatment tank and determines multiple sample water characteristic parameters. Then, according to the multiple sample water characteristic parameters, it performs corresponding post-detection wastewater detection at the post-detection location to obtain post-detection data. Based on preset effect standard data, it compares the post-detection data to determine whether the treatment is qualified. If the treatment is qualified, it continues to perform photocatalytic wastewater treatment according to the already completed photocatalytic angle adjustment. If the treatment is unqualified, it reselects a reference trial node according to the principle of descending from large to small, selects the corresponding trial control parameters from the node's historical data, adjusts the photocatalytic lighting equipment for wastewater treatment according to the corresponding photocatalytic angle, and then performs post-detection wastewater detection again until the treatment is qualified.

[0146] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0147] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0148] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A smart control method for wastewater treatment equipment based on big data, characterized in that, The method specifically includes the following steps: Identify the current source of wastewater, match the corresponding historical data of the source, identify the effect of the historical data of the source, determine multiple reference effect nodes, and filter the historical data of multiple nodes; Pre-testing of wastewater is performed to obtain pre-testing data, which is then compared with historical data from multiple nodes to calculate the matching comparison values ​​corresponding to multiple reference effect nodes. Multiple matching comparison values ​​are sorted and compared, and trial control parameters are selected from the historical data of the nodes, and the photocatalytic angle of wastewater treatment is adjusted accordingly. Post-treatment wastewater is tested to obtain post-treatment data and determine whether the treatment is up to standard. If the treatment is not up to standard, new trial control parameters are selected.

2. The intelligent control method for sewage treatment equipment based on big data according to claim 1, characterized in that, The process of determining the current source of wastewater, matching it with corresponding historical data, identifying the effects of the historical data, determining multiple reference effect nodes, and filtering historical data from multiple nodes specifically includes the following steps: Identify the current source of the wastewater; Match the current wastewater source with historical data from the preset historical database; Based on preset performance standard data, the source historical data is compared, and multiple reference performance nodes are selected; Based on the multiple reference effect nodes, filter multiple corresponding node historical data from the source historical data.

3. The intelligent control method for sewage treatment equipment based on big data according to claim 1, characterized in that, The process of conducting pre-testing of wastewater, acquiring pre-testing data, comparing it with historical data from multiple nodes, and calculating the matching comparison values ​​corresponding to multiple reference effect nodes specifically includes the following steps: Select the pre-detection location and determine multiple raw water characteristic parameters; At the aforementioned pre-detection location, pre-sewage detection is performed according to multiple raw water characteristic parameters to obtain pre-detection data; Based on the aforementioned raw water characteristic parameters, select multiple node characteristic data from the historical data of multiple nodes; The preceding detection data is compared with the characteristic data of multiple nodes to calculate the matching comparison value corresponding to multiple reference effect nodes.

4. The intelligent control method for sewage treatment equipment based on big data according to claim 3, characterized in that, The formula for calculating multiple matching comparison values ​​is as follows: Where k is the k-th reference effect node, i is the i-th raw water characteristic parameter, and each reference effect node has a total of m raw water characteristic parameters, p i P represents the value of the i-th raw water characteristic parameter in the pre-detection data. ki P represents the value of the i-th raw water characteristic parameter in the node characteristic data corresponding to the k-th reference effect node. imax P represents the maximum value of the i-th raw water characteristic parameter. imin w is the minimum value of the i-th raw water characteristic parameter. i The preset weights are the i-th raw water characteristic parameters.

5. The intelligent control method for sewage treatment equipment based on big data according to claim 1, characterized in that, The process of arranging and comparing multiple matching comparison values, selecting trial control parameters from the node's historical data, and adjusting the corresponding photocatalytic angle for wastewater treatment specifically includes the following steps: According to the principle of descending from large to small, the multiple matching comparison values ​​are arranged, compared and recorded to obtain matching arrangement information; Based on the matching arrangement information, the multiple reference effect nodes are arranged accordingly, and a reference trial node is selected. Select the trial control parameters corresponding to the reference trial node from the historical data of the node; According to the trial control parameters, the photocatalytic angle of the corresponding wastewater treatment is adjusted.

6. The intelligent control method for sewage treatment equipment based on big data according to claim 1, characterized in that, The process of conducting post-treatment wastewater testing, acquiring post-treatment data, determining whether the treatment is up to standard, and re-selecting trial control parameters when the treatment fails to meet standards specifically includes the following steps: Select the post-detection location and determine multiple sample water characteristic parameters; At the post-detection location, post-sewage detection is performed according to multiple sample water characteristic parameters to obtain post-detection data; Based on preset performance standard data, the post-detection data is compared to determine whether the processing is qualified. When a non-compliance is handled, the trial control parameters are reselected from the historical data of the node.

7. A smart control system for wastewater treatment equipment based on big data, characterized in that: The system includes a node data filtering module, a matching comparison calculation module, a trial control processing module, and a post-detection and judgment module, wherein: The node data filtering module is used to determine the current source of sewage, match the corresponding historical data of the source, identify the effect of the historical data of the source, determine multiple reference effect nodes, and filter the historical data of multiple nodes. The consistency comparison calculation module is used to perform pre-sewage detection, obtain pre-detection data, compare it with historical data of multiple nodes, and calculate the consistency comparison value corresponding to multiple reference effect nodes. The trial control processing module is used to arrange and compare multiple matching comparison values, select trial control parameters from the node historical data, and adjust the photocatalytic angle of wastewater treatment accordingly. The post-processing detection and judgment module is used to perform post-processing wastewater detection, acquire post-processing detection data, determine whether the treatment is qualified, and reselect trial control parameters if the treatment is unqualified.

8. The intelligent control system for wastewater treatment equipment based on big data according to claim 7, characterized in that, The node data filtering module specifically includes: Wastewater source determination unit, used to determine the current source of wastewater; The historical data matching unit is used to match the historical data of the current wastewater source from a preset historical record database. The node comparison and selection unit is used to compare the source historical data based on preset effect standard data and select multiple reference effect nodes. The node data filtering unit is used to filter multiple corresponding node historical data from the source historical data according to multiple reference effect nodes.

9. The intelligent control system for wastewater treatment equipment based on big data according to claim 7, characterized in that, The matching comparison calculation module specifically includes: The pre-detection location selection unit is used to select the pre-detection location and determine multiple raw water characteristic parameters; The pre-sewage detection unit is used to perform pre-sewage detection at the pre-detection location according to multiple raw water characteristic parameters and obtain pre-detection data. The node data selection unit is used to filter multiple node characteristic data from multiple node historical data according to multiple raw water characteristic parameters; The matching comparison calculation unit is used to compare the pre-detection data with the characteristic data of multiple nodes and calculate the matching comparison value corresponding to multiple reference effect nodes.

10. The intelligent control system for wastewater treatment equipment based on big data according to claim 7, characterized in that, The trial control processing module specifically includes: The arrangement comparison unit is used to arrange, compare, and record multiple matching comparison values ​​according to the principle of descending order, so as to obtain matching arrangement information; The node arrangement selection unit is used to arrange multiple reference effect nodes according to the matching arrangement information and select a reference trial node; The control parameter selection unit is used to select the trial control parameters corresponding to the reference trial node from the node's historical data; An angle adjustment control unit is used to adjust the photocatalytic angle of wastewater treatment according to the trial control parameters.

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