CN117929666B - Quality control method and device for industrial wastewater on-line monitoring data, electronic equipment and storage medium - Google Patents
Quality control method and device for industrial wastewater on-line monitoring data, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a quality control method of industrial wastewater on-line monitoring data, which comprises the following steps: sequentially acquiring real-time data acquired by the 1 st-N th online monitoring equipment according to the acquisition time difference; sequentially comparing the acquired real-time data acquired by the n-th online monitoring equipment with the n-th threshold index range corresponding to the real-time data; when the real-time data collected by the n-th online monitoring equipment is not matched with the n-th level threshold index range corresponding to the real-time data, inputting the real-time data collected by the n-th online monitoring equipment and the n-th level threshold index range corresponding to the real-time data into a pre-established and trained deep learning model, and thus obtaining a control instruction for adjusting parameters of the n-1-th level wastewater treatment equipment; and when the real-time data acquired by the N-level online monitoring equipment does not meet the emission standard, returning the industrial wastewater passing through the N-level online monitoring equipment to the wastewater treatment equipment which is subjected to parameter adjustment and closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment.
Description
Technical Field
The invention relates to the technical field of industrial wastewater treatment, in particular to a quality control method and device for industrial wastewater on-line monitoring data, electronic equipment and a storage medium.
Background
Industrial wastewater refers to waste liquid, wastewater, sewage and the like generated in the industrial production process. The waste water may contain a large amount of organic matters, heavy metal ions, toxic substances and the like, and the waste water is harmful to the environment and human health. Therefore, the industrial wastewater needs to be treated to be discharged or reused.
When the existing industrial wastewater supervision scheme is implemented, most of the industrial wastewater supervision schemes monitor and analyze wastewater after the wastewater treatment equipment through various sensors and detection equipment, and the obtained wastewater data are subjected to international standard comparison to judge whether the wastewater can be discharged or not, and if the wastewater is unqualified, the wastewater is repeatedly and circularly led into the wastewater treatment equipment through a guide pipe to be treated so as to be finally discharged.
However, the industrial wastewater monitoring system often comprises wastewater treatment equipment and a sensor which are matched together to complete, and when the wastewater treatment equipment is used for treating different industrial wastewater, the treatment parameters are required to be adjusted in real time so as to obtain better treatment effect. The existing industrial wastewater supervision scheme obviously cannot achieve the effect, thereby influencing the efficiency and quality of wastewater treatment.
Disclosure of Invention
The invention aims to provide a quality control method and device for industrial wastewater on-line monitoring data, electronic equipment and a storage medium, which can effectively solve the technical problems in the prior art.
On the one hand, the embodiment of the application discloses a quality control method for industrial wastewater on-line monitoring data, which comprises the following steps:
S1, sequentially acquiring real-time data acquired by 1 st-N-th online monitoring equipment according to acquisition time difference; the N-1 grade on-line monitoring equipment and the N grade on-line monitoring equipment form an N-1 grade wastewater treatment monitoring section, the N-1 grade wastewater treatment monitoring section is correspondingly provided with the N-1 grade wastewater treatment equipment, the 1 grade on-line monitoring equipment is connected with a wastewater treatment inlet, the N grade on-line monitoring equipment is connected with a wastewater treatment outlet, and industrial wastewater flowing in from the wastewater treatment inlet flows out from the wastewater treatment outlet after sequentially passing through the N-1 wastewater treatment equipment; the collection time difference between the N-level online monitoring equipment and the N-1 level online monitoring equipment is consistent with the time of industrial wastewater flowing from the N-1 level online monitoring equipment to the N level online monitoring equipment, wherein N is more than or equal to 2;
s2, sequentially comparing the acquired real-time data acquired by the n-th online monitoring equipment with the n-th threshold index range corresponding to the real-time data; the N-level threshold index range is determined according to real-time data acquired by N-1-level online monitoring equipment, wherein N is less than or equal to N;
S3, when the real-time data acquired by the n-th online monitoring equipment is not matched with the n-th level threshold index range corresponding to the real-time data, inputting the real-time data acquired by the n-th online monitoring equipment and the n-th level threshold index range corresponding to the real-time data into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting parameters of the n-1-th level wastewater treatment equipment, and adjusting the corresponding parameters of the n-1-th level wastewater treatment equipment according to the control instruction;
S4, when the real-time data collected by the N-level online monitoring equipment meets the industrial wastewater discharge standard, discharging the industrial wastewater passing through the N-level online monitoring equipment through the wastewater treatment outlet; when the real-time data collected by the N-th online monitoring equipment does not meet the industrial wastewater discharge standard, if the industrial wastewater flowing through the wastewater treatment equipment with the adjusted parameters at least comprises one wastewater treatment equipment, returning the industrial wastewater passing through the N-th online monitoring equipment to the wastewater treatment equipment with the adjusted parameters closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment, otherwise, returning the industrial wastewater of the N-th online monitoring equipment to the N-1-th wastewater treatment equipment through the return channel for wastewater circulation treatment.
Preferably, the n-level threshold index range is determined and obtained according to real-time data acquired by n-1-level online monitoring equipment and recorded current parameters of the n-1-level wastewater treatment equipment; the step S3 specifically includes:
s31, when real-time data acquired by the nth online monitoring equipment are not matched with the corresponding nth threshold index range, acquiring current actual parameters of the nth-1 level wastewater treatment equipment;
S32, if the current actual parameters of the n-1 level wastewater treatment equipment are inconsistent with the recorded current parameters of the n-1 level wastewater treatment equipment, adjusting the corresponding parameters of the n-1 level wastewater treatment equipment according to the recorded current parameters of the n-1 level wastewater treatment equipment;
S33, if the current actual parameters of the n-1-level wastewater treatment equipment are consistent with the recorded current parameters of the n-1-level wastewater treatment equipment, inputting real-time data acquired by the n-level online monitoring equipment, the corresponding n-level threshold index range and the current parameters of the n-1-level wastewater treatment equipment into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting the parameters of the n-1-level wastewater treatment equipment, adjusting the corresponding parameters of the n-1-level wastewater treatment equipment according to the control instruction, and recording the adjusted parameters of the n-1-level wastewater treatment equipment as the current parameters.
Preferably, in the step S33, when the real-time data collected by the nth stage on-line monitoring device, the corresponding nth stage threshold index range and the current parameter of the nth-1 stage wastewater treatment device are input into a pre-established and trained deep learning model, the obtained new parameter of the nth-1 stage wastewater treatment device is the same as the recorded current parameter of the nth-1 stage wastewater treatment device, it is determined that the nth stage on-line monitoring device is abnormal, and a corresponding nth stage on-line monitoring device detection instruction is generated.
Preferably, the types of data indexes collected by the N on-line monitoring devices are the same; the treatment projects of the N-1 wastewater treatment devices on the industrial wastewater are the same or different;
The data indexes collected by each online monitoring device comprise PH value, biochemical oxygen demand, heavy metal content and nitrogen and phosphorus content; when the treatment projects of the N-1 wastewater treatment devices on industrial wastewater are the same, each wastewater treatment device comprises an acid-base adjusting unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit; when the treatment projects of the N-1 wastewater treatment devices on the industrial wastewater are different, the N-1 wastewater treatment devices form an acid-base adjusting unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit through combination.
On the other hand, the embodiment of the invention provides a quality control device for on-line monitoring data of industrial wastewater, which comprises:
The data acquisition unit is used for sequentially acquiring real-time data acquired by the 1 st-N-th online monitoring equipment according to the acquisition time difference; the N-1 grade on-line monitoring equipment and the N grade on-line monitoring equipment form an N-1 grade wastewater treatment monitoring section, the N-1 grade wastewater treatment monitoring section is correspondingly provided with the N-1 grade wastewater treatment equipment, the 1 grade on-line monitoring equipment is connected with a wastewater treatment inlet, the N grade on-line monitoring equipment is connected with a wastewater treatment outlet, and industrial wastewater flowing in from the wastewater treatment inlet flows out from the wastewater treatment outlet after sequentially passing through the N-1 wastewater treatment equipment; the collection time difference between the N-level online monitoring equipment and the N-1 level online monitoring equipment is consistent with the time of industrial wastewater flowing from the N-1 level online monitoring equipment to the N level online monitoring equipment, wherein N is more than or equal to 2;
The data comparison unit is used for sequentially comparing the acquired real-time data acquired by the n-th level online monitoring equipment with the n-th level threshold index range corresponding to the real-time data; the N-level threshold index range is determined according to real-time data acquired by N-1-level online monitoring equipment, wherein N is less than or equal to N;
The parameter adjusting unit is used for inputting the real-time data acquired by the n-th online monitoring equipment and the n-th level threshold index range corresponding to the real-time data acquired by the n-th online monitoring equipment into a pre-established and trained deep learning model when the real-time data acquired by the n-th level online monitoring equipment and the n-th level threshold index range corresponding to the real-time data are not matched, so as to obtain a control instruction for adjusting the parameters of the n-1-th level wastewater treatment equipment, and adjusting the corresponding parameters of the n-1-th level wastewater treatment equipment according to the control instruction;
The wastewater circulation treatment control unit is used for discharging the industrial wastewater passing through the Nth-stage online monitoring equipment through the wastewater treatment outlet when the real-time data acquired by the Nth-stage online monitoring equipment are judged to meet the industrial wastewater discharge standard; when the real-time data collected by the N-th online monitoring equipment does not meet the industrial wastewater discharge standard, if the industrial wastewater flowing through the wastewater treatment equipment with the adjusted parameters at least comprises one wastewater treatment equipment, returning the industrial wastewater passing through the N-th online monitoring equipment to the wastewater treatment equipment with the adjusted parameters closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment, otherwise, returning the industrial wastewater of the N-th online monitoring equipment to the N-1-th wastewater treatment equipment through the return channel for wastewater circulation treatment.
Preferably, in the data comparison unit, the n-th level threshold index range is determined and obtained according to real-time data collected by n-1-th level on-line monitoring equipment and recorded current parameters of the n-1-th level wastewater treatment equipment; the parameter adjusting unit is specifically configured to:
when the real-time data acquired by the nth level online monitoring equipment is not matched with the corresponding nth level threshold index range, acquiring the current actual parameters of the nth-1 level wastewater treatment equipment;
If the current actual parameters of the n-1-level wastewater treatment equipment are inconsistent with the recorded current parameters of the n-1-level wastewater treatment equipment, adjusting the corresponding parameters of the n-1-level wastewater treatment equipment according to the recorded current parameters of the n-1-level wastewater treatment equipment;
And if the current actual parameters of the n-1-level wastewater treatment equipment are consistent with the recorded current parameters of the n-1-level wastewater treatment equipment, inputting real-time data acquired by the n-level online monitoring equipment, the n-level threshold index range corresponding to the real-time data and the current parameters of the n-1-level wastewater treatment equipment into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting the parameters of the n-1-level wastewater treatment equipment, adjusting the corresponding parameters of the n-1-level wastewater treatment equipment according to the control instruction, and recording the adjusted parameters of the n-1-level wastewater treatment equipment as the current parameters.
Preferably, in the parameter adjustment unit, when the real-time data collected by the nth stage on-line monitoring device, the corresponding nth stage threshold index range and the current parameter of the nth-1 stage wastewater treatment device are input into a pre-established and trained deep learning model, the obtained new parameter of the nth-1 stage wastewater treatment device is the same as the recorded current parameter of the nth-1 stage wastewater treatment device, the nth stage on-line monitoring device is judged to have abnormality, and a corresponding nth stage on-line monitoring device detection instruction is generated.
Preferably, the types of data indexes collected by the N on-line monitoring devices are the same; the treatment projects of the N-1 wastewater treatment devices on the industrial wastewater are the same or different;
The data indexes collected by each online monitoring device comprise PH value, biochemical oxygen demand, heavy metal content and nitrogen and phosphorus content; when the treatment projects of the N-1 wastewater treatment devices on industrial wastewater are the same, each wastewater treatment device comprises an acid-base adjusting unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit; when the treatment projects of the N-1 wastewater treatment devices on the industrial wastewater are different, the N-1 wastewater treatment devices form an acid-base adjusting unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit through combination.
In yet another aspect, an embodiment of the present application discloses an electronic device, including a processor and a memory, where the memory is configured to store a computer program, the computer program includes program instructions, and the processor is configured to execute the quality control method for online monitoring data of industrial wastewater according to any one of the embodiments above.
In yet another aspect, embodiments of the present application disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the quality control method for the industrial wastewater online monitoring data according to any embodiment.
Compared with the prior art, the quality control method, the quality control device, the electronic equipment and the storage medium for the industrial wastewater on-line monitoring data provided by the embodiment of the invention have the following technical effects: sequentially flowing industrial wastewater through N on-line monitoring devices from a wastewater treatment inlet to a wastewater treatment outlet, forming a wastewater treatment monitoring section between two adjacent on-line monitoring devices, correspondingly arranging a wastewater treatment device on each wastewater treatment monitoring section, sequentially acquiring real-time data acquired by the N on-line monitoring devices according to the flowing speed of the industrial wastewater, comparing the acquired real-time data acquired by each on-line monitoring device with a corresponding threshold index range (acquired according to real-time data acquired by the previous on-line monitoring device), and inputting the real-time data acquired by the N-th on-line monitoring device and the N-th threshold index range corresponding to the N-th on-line monitoring device into a pre-established and trained deep learning model if the N-th on-line monitoring device is not matched, so as to acquire a control instruction for adjusting parameters of the N-1-th wastewater treatment device according to the control instruction; when the real-time data collected by the N-th online monitoring equipment meets the industrial wastewater discharge standard, discharging the industrial wastewater passing through the N-th online monitoring equipment through the wastewater treatment outlet; when the real-time data collected by the N-th online monitoring equipment does not meet the industrial wastewater discharge standard, if the industrial wastewater flowing through the wastewater treatment equipment with the adjusted parameters at least comprises one wastewater treatment equipment, returning the industrial wastewater passing through the N-th online monitoring equipment to the wastewater treatment equipment with the adjusted parameters closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment, otherwise, returning the industrial wastewater of the N-th online monitoring equipment to the N-1-th wastewater treatment equipment through the return channel for wastewater circulation treatment. Therefore, the quality control method, the quality control device, the electronic equipment and the storage medium for the industrial wastewater on-line monitoring data can effectively adjust the parameters of the wastewater treatment equipment according to the real-time data so as to improve the efficiency and the quality of wastewater treatment.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a quality control method for online monitoring data of industrial wastewater, which is provided by an embodiment of the invention.
Fig. 2 is a connection diagram of an industrial wastewater treatment detection system to which the quality control method for industrial wastewater on-line monitoring data provided by the preferred embodiment of the present invention is applicable.
Fig. 3 is a block diagram of a quality control device for on-line monitoring data of industrial wastewater according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an embodiment of the present invention provides a quality control (i.e., quality control) method for on-line monitoring data of industrial wastewater, the method comprising steps S1 to S4, wherein:
S1, sequentially acquiring real-time data acquired by 1 st-N-th online monitoring equipment according to acquisition time difference; the N-1 grade on-line monitoring equipment and the N grade on-line monitoring equipment form an N-1 grade wastewater treatment monitoring section, the N-1 grade wastewater treatment monitoring section is correspondingly provided with the N-1 grade wastewater treatment equipment, the 1 grade on-line monitoring equipment is connected with a wastewater treatment inlet, the N grade on-line monitoring equipment is connected with a wastewater treatment outlet, and industrial wastewater flowing in from the wastewater treatment inlet flows out from the wastewater treatment outlet after sequentially passing through the N-1 wastewater treatment equipment; the collection time difference between the N-level online monitoring equipment and the N-1 level online monitoring equipment is consistent with the time of industrial wastewater flowing from the N-1 level online monitoring equipment to the N level online monitoring equipment, wherein N is more than or equal to 2;
s2, sequentially comparing the acquired real-time data acquired by the n-th online monitoring equipment with the n-th threshold index range corresponding to the real-time data; the N-level threshold index range is determined according to real-time data acquired by N-1-level online monitoring equipment, wherein N is less than or equal to N;
S3, when the real-time data acquired by the n-th online monitoring equipment is not matched with the n-th level threshold index range corresponding to the real-time data, inputting the real-time data acquired by the n-th online monitoring equipment and the n-th level threshold index range corresponding to the real-time data into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting parameters of the n-1-th level wastewater treatment equipment, and adjusting the corresponding parameters of the n-1-th level wastewater treatment equipment according to the control instruction;
S4, when the real-time data collected by the N-level online monitoring equipment meets the industrial wastewater discharge standard, discharging the industrial wastewater passing through the N-level online monitoring equipment through the wastewater treatment outlet; when the real-time data collected by the N-th online monitoring equipment does not meet the industrial wastewater discharge standard, if the industrial wastewater flowing through the wastewater treatment equipment with the adjusted parameters at least comprises one wastewater treatment equipment, returning the industrial wastewater passing through the N-th online monitoring equipment to the wastewater treatment equipment with the adjusted parameters closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment, otherwise, returning the industrial wastewater of the N-th online monitoring equipment to the N-1-th wastewater treatment equipment through the return channel for wastewater circulation treatment.
The n-level threshold index range is determined and obtained according to real-time data acquired by n-1-level online monitoring equipment and recorded current parameters of the n-1-level wastewater treatment equipment; the step S3 specifically includes:
s31, when real-time data acquired by the nth online monitoring equipment are not matched with the corresponding nth threshold index range, acquiring current actual parameters of the nth-1 level wastewater treatment equipment;
S32, if the current actual parameters of the n-1 level wastewater treatment equipment are inconsistent with the recorded current parameters of the n-1 level wastewater treatment equipment, adjusting the corresponding parameters of the n-1 level wastewater treatment equipment according to the recorded current parameters of the n-1 level wastewater treatment equipment;
S33, if the current actual parameters of the n-1-level wastewater treatment equipment are consistent with the recorded current parameters of the n-1-level wastewater treatment equipment, inputting real-time data acquired by the n-level online monitoring equipment, the corresponding n-level threshold index range and the current parameters of the n-1-level wastewater treatment equipment into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting the parameters of the n-1-level wastewater treatment equipment, adjusting the corresponding parameters of the n-1-level wastewater treatment equipment according to the control instruction, and recording the adjusted parameters of the n-1-level wastewater treatment equipment as the current parameters.
Further, in the step S33, when the real-time data collected by the nth stage on-line monitoring device, the corresponding nth stage threshold index range and the current parameter of the nth-1 stage wastewater treatment device are input into the pre-established and trained deep learning model, the obtained new parameter of the nth-1 stage wastewater treatment device is the same as the recorded current parameter of the nth-1 stage wastewater treatment device, it is determined that the nth stage on-line monitoring device is abnormal, and a corresponding nth stage on-line monitoring device detection instruction is generated.
It can be understood that in this embodiment, the types of data indexes collected by N online monitoring devices are the same; the treatment projects of the N-1 wastewater treatment equipment on the industrial wastewater are the same or different.
The data indexes collected by each online monitoring device comprise PH value, biochemical oxygen demand, heavy metal content and nitrogen and phosphorus content; when the treatment projects of the N-1 wastewater treatment devices on industrial wastewater are the same, each wastewater treatment device comprises an acid-base adjusting unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit; when the treatment projects of the N-1 wastewater treatment devices on the industrial wastewater are different, the N-1 wastewater treatment devices form an acid-base adjusting unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit through combination.
Next, a detailed description will be given of a quality control method of the industrial wastewater online monitoring data disclosed in this embodiment with reference to fig. 2. As shown in fig. 2, industrial wastewater sequentially flows through N on-line monitoring devices 101 (a 1 st-stage on-line monitoring device and a 2 nd-stage on-line monitoring device … … nth-stage on-line monitoring device are sequentially arranged between the wastewater treatment inlet 301 and the wastewater treatment outlet 302) from the wastewater treatment inlet 301 to the wastewater treatment outlet 302, a wastewater treatment monitoring section is formed between two adjacent on-line monitoring devices 101 (for example, a1 st-stage wastewater treatment monitoring section is formed between the 1 st-stage on-line monitoring device and the 2 nd-stage on-line monitoring device, and so on), a wastewater treatment device 201 is correspondingly arranged on each wastewater treatment monitoring section (specifically, the 1 st-stage wastewater treatment monitoring section is correspondingly provided with the 1 st-stage wastewater treatment device, and so on), so that the industrial wastewater firstly passes through the 1 st-stage on-line monitoring device after entering from the wastewater treatment inlet 301, then passes through the 1 st-stage on-line monitoring device to obtain initial wastewater index data before the industrial wastewater treatment, and then passes through the 2 nd-stage on-line monitoring device after the related treatment by the 1 st-stage on-line monitoring device to obtain wastewater index data after the industrial wastewater is treated by the 1 st-stage on-wastewater treatment device, and so on-stage wastewater discharge index data after the last stage (N-stage 1 is discharged after the wastewater treatment device passes through the last stage on-stage monitoring device) and the wastewater treatment device) is in accordance with the wastewater index data after the wastewater treatment data is obtained after the wastewater treatment device is discharged by the stage on the stage 1.
Meanwhile, the real-time data acquired by N on-line monitoring devices are sequentially acquired according to the flow speed of the industrial wastewater, and the acquired real-time data acquired by each on-line monitoring device is compared with a corresponding threshold index range (determined according to the real-time data acquired by the previous on-line monitoring device) to perform data index judgment. For example, a stage 1 wastewater treatment monitoring section will be described. When industrial wastewater passes through the 1 st-level on-line monitoring device, the 1 st-level on-line monitoring device collects real-time data (the data are regarded as data A here for convenience of description) and sends the real-time data to the background server (or the control end), a2 nd-level threshold index range corresponding to the real-time data (data A) collected by the 1 st-level on-line monitoring device can be obtained by searching a database of the background server, and it is noted that the 2 nd-level threshold index range refers to a theoretical threshold index reference range obtained after the industrial wastewater is processed by the 1 st-level wastewater treatment device on the basis of the real-time data collected by the 1 st-level on-line monitoring device, and so on. It can be appreciated that for each stage of wastewater treatment monitoring segment, the database may pre-store a range of threshold indicators corresponding to different real-time data. Thus, when industrial wastewater is processed by the 1 st-stage wastewater treatment equipment and then reaches the 2 nd-stage online monitoring equipment, the 2 nd-stage online monitoring equipment collects real-time data (the data are regarded as data B for convenience of description) and sends the real-time data to the background server, and the background server compares the real-time data B collected by the 2 nd-stage online monitoring equipment with the 2 nd-stage threshold index range, if the comparison is matched, the real-time data B collected by the 2 nd-stage online monitoring equipment is not abnormal. If the comparison is not matched, the condition that the real-time data B collected by the 2 nd-level online monitoring equipment is abnormal is indicated. Then, the reason why the real-time data B collected by the 2 nd-level online monitoring equipment is abnormal needs to be further found out. In this embodiment, the reason why the real-time data collected by each stage of the on-line monitoring device is abnormal is mainly determined from three aspects, including: the parameters of the wastewater treatment equipment are changed due to human factors or other reasons, the current parameters of the wastewater treatment equipment are not suitable for treating industrial wastewater, and the collected data are abnormal due to the abnormality of the line monitoring equipment.
In particular, the stage 1 wastewater treatment monitoring section will be described as an example. In one embodiment, when the real-time data collected by the level 2 on-line monitoring device is not matched with the corresponding level 2 threshold index range, the current actual parameter of the level 1 wastewater treatment device is firstly obtained, then the current actual parameter of the level 1 wastewater treatment device is compared with the current parameter of the level 1 wastewater treatment device recorded by the background server, if the current actual parameter of the level 1 wastewater treatment device is inconsistent with the current parameter of the level 1 wastewater treatment device recorded by the background server, the parameter of the level 1 wastewater treatment device is changed due to the existence of human or other reasons, and the parameter of the level 1 wastewater treatment device can be adjusted to correct the parameter based on the current parameter of the level 1 wastewater treatment device recorded by the background server, and if the current actual parameter of the level 1 wastewater treatment device is consistent with the current parameter of the level 1 wastewater treatment device, the reason can be eliminated.
And then, inputting the real-time data acquired by the 2 nd-level online monitoring equipment, the 2 nd-level threshold index range corresponding to the real-time data and the current parameters of the 1 st-level wastewater treatment equipment into a pre-established and trained deep learning model, so as to obtain a control instruction for adjusting the parameters of the 1 st-level wastewater treatment equipment, adjusting the corresponding parameters of the 1 st-level wastewater treatment equipment according to the control instruction, and recording the adjusted parameters of the 1 st-level wastewater treatment equipment as the current parameters. It can be appreciated that in the deep learning model, training learning can be performed on the deep learning model by collecting a large amount of historical data in advance, so that new parameters which currently need to be adjusted on the 1 st-stage wastewater treatment equipment can be obtained from the historical data. It is to be understood that the deep learning model adopted in the present embodiment may be a deep reinforcement learning model familiar to those skilled in the art, and is not particularly limited herein. When the new parameters obtained through the deep learning model are inconsistent with the parameters recorded by the background server, the current parameters of the 1 st-stage wastewater treatment equipment are unsuitable for treating the industrial wastewater, and the parameters of the 1 st-stage wastewater treatment equipment can be correspondingly adjusted according to the new parameters output by the deep learning model.
In addition, when a new parameter for adjusting the level 1 wastewater treatment apparatus cannot be acquired from the history data by using the deep learning model, the description is not in the level 1 wastewater treatment apparatus, and this indicates that the current parameter of the level 1 wastewater treatment apparatus does not need to be adjusted, in which case the system uniformly sets the parameter output by the deep learning model to the original parameter (i.e., the parameter recorded by the background server). Namely, when the new parameters of the 1 st-stage wastewater treatment equipment obtained through the deep learning model are the same as the recorded current parameters of the 1 st-stage wastewater treatment equipment, judging that the 2 nd-stage online monitoring equipment is abnormal, and generating a corresponding 2 nd-stage online monitoring equipment detection instruction.
It will be appreciated that the process of the process monitoring of the other stage wastewater treatment monitoring section is substantially identical to the process of the process monitoring of the stage 1 wastewater treatment monitoring section, and will not be described in detail herein.
Further, the industrial wastewater is treated by the last-stage (N-1-stage) wastewater treatment equipment and the wastewater index data obtained by the last-stage (N-stage) online monitoring equipment can be directly discharged through the wastewater treatment outlet 302 (corresponding switches can be arranged on the wastewater treatment outlet 302) if the wastewater index data meets the discharge standard. If the emission standard is not met, the industrial wastewater is proved to be substandard after passing through the previous multi-stage wastewater treatment monitoring section, and the industrial wastewater is required to be treated and monitored again.
As shown in fig. 2, in the present embodiment, between the nth stage on-line monitoring apparatus 101 and the wastewater treatment outlet 302, a return passage through which industrial wastewater can be returned to each wastewater treatment apparatus 201 is provided by piping, and it is understood that a valve (e.g., a three-way valve 401) may be provided on the return passage (branch) of each wastewater treatment apparatus 201 to control the opening and closing of the return passage (branch). Because the industrial wastewater treatment detection system provided by the embodiment is divided into the multi-stage wastewater treatment monitoring section, when the industrial wastewater is treated by the last-stage wastewater treatment equipment and the wastewater index data obtained by the last-stage on-line monitoring equipment does not meet the discharge standard, the working wastewater is returned to the wastewater treatment equipment of the wastewater treatment monitoring section with abnormal wastewater data indexes (particularly, the wastewater treatment equipment is subjected to parameter adjustment) through the corresponding reflux channels for wastewater circulation treatment. For example, when the wastewater data index of the grade 2 wastewater treatment monitoring section is abnormal and the grade 2 wastewater treatment equipment is subjected to parameter adjustment, if the industrial wastewater does not meet the discharge standard, the industrial wastewater returns to the grade 2 wastewater treatment equipment through the return channel for cyclic treatment. When there are a plurality of wastewater treatment monitoring sections with corresponding wastewater data indexes abnormal (specifically, wastewater treatment equipment is subjected to parameter adjustment), for example, when the wastewater data indexes of the 2 nd-stage wastewater treatment monitoring section and the 4 th-stage wastewater treatment monitoring section are abnormal and subjected to parameter adjustment, industrial wastewater which does not meet the discharge standard is returned to the wastewater treatment equipment (namely, the 2 nd-stage wastewater treatment equipment) which is subjected to parameter adjustment and is closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment. It can be understood that when the wastewater data index of the wastewater treatment monitoring section is not abnormal, industrial wastewater which does not meet the discharge standard is returned to the 1 st-stage wastewater treatment equipment through the return channel for wastewater circulation treatment.
Referring to fig. 3, this embodiment discloses a quality control device for industrial wastewater on-line monitoring data, including:
A data acquisition unit 31, configured to sequentially acquire real-time data acquired by the 1 st to nth online monitoring devices according to the acquisition time difference; the N-1 grade on-line monitoring equipment and the N grade on-line monitoring equipment form an N-1 grade wastewater treatment monitoring section, the N-1 grade wastewater treatment monitoring section is correspondingly provided with the N-1 grade wastewater treatment equipment, the 1 grade on-line monitoring equipment is connected with a wastewater treatment inlet, the N grade on-line monitoring equipment is connected with a wastewater treatment outlet, and industrial wastewater flowing in from the wastewater treatment inlet flows out from the wastewater treatment outlet after sequentially passing through the N-1 wastewater treatment equipment; the collection time difference between the N-level online monitoring equipment and the N-1 level online monitoring equipment is consistent with the time of industrial wastewater flowing from the N-1 level online monitoring equipment to the N level online monitoring equipment, wherein N is more than or equal to 2;
The data comparison unit 32 is configured to sequentially compare the real-time data acquired by the n-th online monitoring device with the n-th level threshold indicator range corresponding to the real-time data; the N-level threshold index range is determined according to real-time data acquired by N-1-level online monitoring equipment, wherein N is less than or equal to N;
A parameter adjustment unit 33, configured to input, when the real-time data collected by the nth level online monitoring device and the nth level threshold indicator range corresponding to the real-time data collected by the nth level online monitoring device are not matched, the real-time data collected by the nth level online monitoring device and the nth level threshold indicator range corresponding to the real-time data are input into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting parameters of the nth-1 level wastewater treatment device, and adjust corresponding parameters of the nth-1 level wastewater treatment device according to the control instruction;
A wastewater circulation treatment control unit 34, configured to discharge the industrial wastewater passing through the nth online monitoring device through the wastewater treatment outlet when it is determined that the real-time data collected by the nth online monitoring device meets the industrial wastewater discharge standard; when the real-time data collected by the N-th online monitoring equipment does not meet the industrial wastewater discharge standard, if the industrial wastewater flowing through the wastewater treatment equipment with the adjusted parameters at least comprises one wastewater treatment equipment, returning the industrial wastewater passing through the N-th online monitoring equipment to the wastewater treatment equipment with the adjusted parameters closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment, otherwise, returning the industrial wastewater of the N-th online monitoring equipment to the N-1-th wastewater treatment equipment through the return channel for wastewater circulation treatment.
Wherein, in the data comparison unit 32, the n-th level threshold index range is determined according to real-time data collected by the n-1-th level on-line monitoring device and the recorded current parameters of the n-1-th level wastewater treatment device; the parameter adjustment unit 33 is specifically configured to:
when the real-time data acquired by the nth level online monitoring equipment is not matched with the corresponding nth level threshold index range, acquiring the current actual parameters of the nth-1 level wastewater treatment equipment;
If the current actual parameters of the n-1-level wastewater treatment equipment are inconsistent with the recorded current parameters of the n-1-level wastewater treatment equipment, adjusting the corresponding parameters of the n-1-level wastewater treatment equipment according to the recorded current parameters of the n-1-level wastewater treatment equipment;
And if the current actual parameters of the n-1-level wastewater treatment equipment are consistent with the recorded current parameters of the n-1-level wastewater treatment equipment, inputting real-time data acquired by the n-level online monitoring equipment, the n-level threshold index range corresponding to the real-time data and the current parameters of the n-1-level wastewater treatment equipment into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting the parameters of the n-1-level wastewater treatment equipment, adjusting the corresponding parameters of the n-1-level wastewater treatment equipment according to the control instruction, and recording the adjusted parameters of the n-1-level wastewater treatment equipment as the current parameters.
Further, in the parameter adjustment unit 33, when the real-time data collected by the nth stage on-line monitoring device, the corresponding nth stage threshold index range and the current parameter of the nth-1 stage wastewater treatment device are input into the pre-established and trained deep learning model, the obtained new parameter of the nth-1 stage wastewater treatment device is the same as the recorded current parameter of the nth-1 stage wastewater treatment device, it is determined that the nth stage on-line monitoring device is abnormal, and a corresponding nth stage on-line monitoring device detection instruction is generated.
It can be understood that in this embodiment, the types of data indexes collected by N online monitoring devices are the same; the treatment projects of the N-1 wastewater treatment devices on the industrial wastewater are the same or different;
The data indexes collected by each online monitoring device comprise PH value, biochemical oxygen demand, heavy metal content and nitrogen and phosphorus content; when the treatment projects of the N-1 wastewater treatment devices on industrial wastewater are the same, each wastewater treatment device comprises an acid-base adjusting unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit; when the treatment projects of the N-1 wastewater treatment devices on the industrial wastewater are different, the N-1 wastewater treatment devices form an acid-base adjusting unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit through combination.
The specific implementation manner of the quality control device for the industrial wastewater online monitoring data in this embodiment may refer to the description of the quality control method for the industrial wastewater online monitoring data in the foregoing embodiment, which is not repeated herein.
As shown in fig. 4, an embodiment of the present invention provides an electronic device 300, including a memory 310 and a processor 320, where the memory 310 is configured to store one or more computer instructions, and the processor 320 is configured to invoke and execute the one or more computer instructions, so as to implement a quality control method of industrial wastewater online monitoring data as described in any of the above.
That is, the electronic device 300 includes: the industrial wastewater online monitoring system comprises a processor 320 and a memory 310, wherein the memory 310 stores computer program instructions, and the processor 320 is caused to execute any of the quality control methods of the industrial wastewater online monitoring data when the computer program instructions are executed by the processor.
Further, as shown in fig. 4, the electronic device 300 further includes a network interface 330, an input device 340, a hard disk 350, and a display device 360.
The interfaces and devices described above may be interconnected by a bus architecture. The bus architecture may be a bus and bridge that may include any number of interconnects. One or more Central Processing Units (CPUs), represented in particular by processor 320, and various circuits of one or more memories, represented by memory 310, are connected together. The bus architecture may also connect various other circuits together, such as peripheral devices, voltage regulators, and power management circuits. It is understood that a bus architecture is used to enable connected communications between these components. The bus architecture includes, in addition to a data bus, a power bus, a control bus, and a status signal bus, all of which are well known in the art and therefore will not be described in detail herein.
The network interface 330 may be connected to a network (e.g., the internet, a local area network, etc.), and may obtain relevant data from the network and store the relevant data in the hard disk 350.
The input device 340 may receive various instructions from an operator and transmit the instructions to the processor 320 for execution. The input device 340 may include a keyboard or pointing device (e.g., a mouse, a trackball, a touch pad, or a touch screen, among others).
The display device 360 may display results obtained by the processor 320 executing instructions.
The memory 310 is used for storing programs and data necessary for the operation of the operating system, and data such as intermediate results in the calculation process of the processor 320.
It will be appreciated that memory 310 in embodiments of the invention may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM), erasable Programmable Read Only Memory (EPROM), electrically Erasable Programmable Read Only Memory (EEPROM), or flash memory, among others. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. The memory 310 of the apparatus and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some implementations, the memory 310 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system 311 and applications 312.
The operating system 311 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application programs 312 include various application programs such as a Browser (Browser) and the like for implementing various application services. A program implementing the method of the embodiment of the present invention may be included in the application program 312.
The processor 320 sequentially obtains real-time data collected by the 1 st to nth online monitoring devices according to the collection time difference when calling and executing the application program and the data stored in the memory 310, specifically, the program or the instruction stored in the application program 312; sequentially comparing the acquired real-time data acquired by the n-th online monitoring equipment with the n-th threshold index range corresponding to the real-time data; the N-level threshold index range is determined according to real-time data acquired by N-1-level online monitoring equipment, wherein N is less than or equal to N; when the real-time data collected by the n-th online monitoring equipment is not matched with the n-th level threshold index range corresponding to the real-time data, inputting the real-time data collected by the n-th online monitoring equipment and the n-th level threshold index range corresponding to the real-time data into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting the parameters of the n-1-th level wastewater treatment equipment, and adjusting the corresponding parameters of the n-1-th level wastewater treatment equipment according to the control instruction; when the real-time data collected by the N-th online monitoring equipment meets the industrial wastewater discharge standard, discharging the industrial wastewater passing through the N-th online monitoring equipment through the wastewater treatment outlet; when the real-time data collected by the N-th online monitoring equipment does not meet the industrial wastewater discharge standard, if the industrial wastewater flowing through the wastewater treatment equipment with the adjusted parameters at least comprises one wastewater treatment equipment, returning the industrial wastewater passing through the N-th online monitoring equipment to the wastewater treatment equipment with the adjusted parameters closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment, otherwise, returning the industrial wastewater of the N-th online monitoring equipment to the N-1-th wastewater treatment equipment through the return channel for wastewater circulation treatment.
The quality control method for the industrial wastewater on-line monitoring data disclosed in the above embodiment of the present invention may be applied to the processor 320 or implemented by the processor 320. Processor 320 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 320. The processor 320 may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, or discrete hardware components, which may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 310 and the processor 320 reads the information in the memory 310 and in combination with its hardware performs the steps of the method described above.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Specifically, the processor 320 is further configured to read the computer program, and execute any of the above quality control methods for online monitoring data of industrial wastewater.
The present application also provides a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the above-described method, such as the method performed by the above-described electronic device, which is not described herein in detail.
Alternatively, a storage medium such as a computer-readable storage medium to which the present application relates may be nonvolatile or may be volatile.
Alternatively, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like. The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of action described, as some steps may be performed in other order or simultaneously according to the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed methods and apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may be physically included separately, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform part of the steps of the transceiving method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RandomAccess Memory RAM), a magnetic disk, or an optical disk, etc., which can store program codes.
The foregoing disclosure is only illustrative of the preferred embodiments of the present invention and is not to be construed as limiting the scope of the invention, as it is understood by those skilled in the art that all or part of the above-described embodiments may be practiced without resorting to the equivalent thereof, which is intended to fall within the scope of the invention as defined by the appended claims.
Claims (8)
1. The quality control method for the industrial wastewater on-line monitoring data is characterized by comprising the following steps:
S1, sequentially acquiring real-time data acquired by 1 st-N-th online monitoring equipment according to acquisition time difference; the N-1 grade on-line monitoring equipment and the N grade on-line monitoring equipment form an N-1 grade wastewater treatment monitoring section, the N-1 grade wastewater treatment monitoring section is correspondingly provided with the N-1 grade wastewater treatment equipment, the 1 grade on-line monitoring equipment is connected with a wastewater treatment inlet, the N grade on-line monitoring equipment is connected with a wastewater treatment outlet, and industrial wastewater flowing in from the wastewater treatment inlet flows out from the wastewater treatment outlet after sequentially passing through the N-1 wastewater treatment equipment; the collection time difference between the N-level online monitoring equipment and the N-1 level online monitoring equipment is consistent with the time of industrial wastewater flowing from the N-1 level online monitoring equipment to the N level online monitoring equipment, wherein N is more than or equal to 2;
s2, sequentially comparing the acquired real-time data acquired by the n-th online monitoring equipment with the n-th threshold index range corresponding to the real-time data; the N-level threshold index range is determined according to real-time data acquired by N-1-level online monitoring equipment, wherein N is less than or equal to N;
S3, when the real-time data acquired by the n-th online monitoring equipment is not matched with the n-th level threshold index range corresponding to the real-time data, inputting the real-time data acquired by the n-th online monitoring equipment and the n-th level threshold index range corresponding to the real-time data into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting parameters of the n-1-th level wastewater treatment equipment, and adjusting the corresponding parameters of the n-1-th level wastewater treatment equipment according to the control instruction;
S4, when the real-time data collected by the N-level online monitoring equipment meets the industrial wastewater discharge standard, discharging the industrial wastewater passing through the N-level online monitoring equipment through the wastewater treatment outlet; when the real-time data collected by the N-th online monitoring equipment does not meet the industrial wastewater discharge standard, if the industrial wastewater flowing through the wastewater treatment equipment with the adjusted parameters at least comprises one wastewater treatment equipment, returning the industrial wastewater passing through the N-th online monitoring equipment to the wastewater treatment equipment with the adjusted parameters closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment, otherwise, returning the industrial wastewater of the N-th online monitoring equipment to the N-1-th wastewater treatment equipment through the return channel for wastewater circulation treatment;
The n-level threshold index range is determined and obtained according to real-time data acquired by n-1-level online monitoring equipment and recorded current parameters of the n-1-level wastewater treatment equipment; the step S3 specifically includes:
s31, when real-time data acquired by the nth online monitoring equipment are not matched with the corresponding nth threshold index range, acquiring current actual parameters of the nth-1 level wastewater treatment equipment;
S32, if the current actual parameters of the n-1 level wastewater treatment equipment are inconsistent with the recorded current parameters of the n-1 level wastewater treatment equipment, adjusting the corresponding parameters of the n-1 level wastewater treatment equipment according to the recorded current parameters of the n-1 level wastewater treatment equipment;
S33, if the current actual parameters of the n-1-level wastewater treatment equipment are consistent with the recorded current parameters of the n-1-level wastewater treatment equipment, inputting real-time data acquired by the n-level online monitoring equipment, the corresponding n-level threshold index range and the current parameters of the n-1-level wastewater treatment equipment into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting the parameters of the n-1-level wastewater treatment equipment, adjusting the corresponding parameters of the n-1-level wastewater treatment equipment according to the control instruction, and recording the adjusted parameters of the n-1-level wastewater treatment equipment as the current parameters.
2. The method according to claim 1, wherein in step S33, when the real-time data collected by the nth level on-line monitoring device, the corresponding nth level threshold index range, and the current parameter of the nth-1 level wastewater treatment device are input into a pre-established and trained deep learning model, and the obtained new parameter of the nth-1 level wastewater treatment device is the same as the recorded current parameter of the nth-1 level wastewater treatment device, it is determined that the nth level on-line monitoring device is abnormal, and a corresponding nth level on-line monitoring device detection command is generated.
3. The quality control method for on-line monitoring data of industrial wastewater according to claim 1, wherein the data index types collected by the N on-line monitoring devices are the same; the treatment projects of the N-1 wastewater treatment devices on the industrial wastewater are the same;
The data indexes collected by each online monitoring device comprise PH value, biochemical oxygen demand, heavy metal content and nitrogen and phosphorus content; each wastewater treatment device comprises an acid-base regulating unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit.
4. The utility model provides a quality control device of industrial waste water on-line monitoring data which characterized in that includes:
The data acquisition unit is used for sequentially acquiring real-time data acquired by the 1 st-N-th online monitoring equipment according to the acquisition time difference; the N-1 grade on-line monitoring equipment and the N grade on-line monitoring equipment form an N-1 grade wastewater treatment monitoring section, the N-1 grade wastewater treatment monitoring section is correspondingly provided with the N-1 grade wastewater treatment equipment, the 1 grade on-line monitoring equipment is connected with a wastewater treatment inlet, the N grade on-line monitoring equipment is connected with a wastewater treatment outlet, and industrial wastewater flowing in from the wastewater treatment inlet flows out from the wastewater treatment outlet after sequentially passing through the N-1 wastewater treatment equipment; the collection time difference between the N-level online monitoring equipment and the N-1 level online monitoring equipment is consistent with the time of industrial wastewater flowing from the N-1 level online monitoring equipment to the N level online monitoring equipment, wherein N is more than or equal to 2;
The data comparison unit is used for sequentially comparing the acquired real-time data acquired by the n-th level online monitoring equipment with the n-th level threshold index range corresponding to the real-time data; the N-level threshold index range is determined according to real-time data acquired by N-1-level online monitoring equipment, wherein N is less than or equal to N;
The parameter adjusting unit is used for inputting the real-time data acquired by the n-th online monitoring equipment and the n-th level threshold index range corresponding to the real-time data acquired by the n-th online monitoring equipment into a pre-established and trained deep learning model when the real-time data acquired by the n-th level online monitoring equipment and the n-th level threshold index range corresponding to the real-time data are not matched, so as to obtain a control instruction for adjusting the parameters of the n-1-th level wastewater treatment equipment, and adjusting the corresponding parameters of the n-1-th level wastewater treatment equipment according to the control instruction;
the wastewater circulation treatment control unit is used for discharging the industrial wastewater passing through the Nth-stage online monitoring equipment through the wastewater treatment outlet when the real-time data acquired by the Nth-stage online monitoring equipment are judged to meet the industrial wastewater discharge standard; when the real-time data collected by the N-th online monitoring equipment does not meet the industrial wastewater discharge standard, if the industrial wastewater flowing through the wastewater treatment equipment with the adjusted parameters at least comprises one wastewater treatment equipment, returning the industrial wastewater passing through the N-th online monitoring equipment to the wastewater treatment equipment with the adjusted parameters closest to the wastewater treatment inlet through a return channel for wastewater circulation treatment, otherwise, returning the industrial wastewater of the N-th online monitoring equipment to the N-1-th wastewater treatment equipment through the return channel for wastewater circulation treatment;
in the data comparison unit, the nth level threshold index range is determined and obtained according to real-time data acquired by the nth-1 level on-line monitoring equipment and recorded current parameters of the nth-1 level wastewater treatment equipment; the parameter adjusting unit is specifically configured to:
when the real-time data acquired by the nth level online monitoring equipment is not matched with the corresponding nth level threshold index range, acquiring the current actual parameters of the nth-1 level wastewater treatment equipment;
If the current actual parameters of the n-1-level wastewater treatment equipment are inconsistent with the recorded current parameters of the n-1-level wastewater treatment equipment, adjusting the corresponding parameters of the n-1-level wastewater treatment equipment according to the recorded current parameters of the n-1-level wastewater treatment equipment;
And if the current actual parameters of the n-1-level wastewater treatment equipment are consistent with the recorded current parameters of the n-1-level wastewater treatment equipment, inputting real-time data acquired by the n-level online monitoring equipment, the n-level threshold index range corresponding to the real-time data and the current parameters of the n-1-level wastewater treatment equipment into a pre-established and trained deep learning model, thereby obtaining a control instruction for adjusting the parameters of the n-1-level wastewater treatment equipment, adjusting the corresponding parameters of the n-1-level wastewater treatment equipment according to the control instruction, and recording the adjusted parameters of the n-1-level wastewater treatment equipment as the current parameters.
5. The apparatus according to claim 4, wherein in the parameter adjustment unit, when the real-time data collected by the nth level on-line monitoring device, the corresponding nth level threshold index range, and the current parameter of the nth-1 level wastewater treatment device are inputted into a pre-established and trained deep learning model, the new parameter of the nth-1 level wastewater treatment device is identical to the recorded current parameter of the nth-1 level wastewater treatment device, it is determined that the nth level on-line monitoring device is abnormal, and a corresponding nth level on-line monitoring device detection instruction is generated.
6. The quality control device for on-line monitoring data of industrial wastewater according to claim 4, wherein the data index types collected by the N on-line monitoring devices are the same; the treatment projects of the N-1 wastewater treatment devices on the industrial wastewater are the same;
The data indexes collected by each online monitoring device comprise PH value, biochemical oxygen demand, heavy metal content and nitrogen and phosphorus content; each wastewater treatment device comprises an acid-base regulating unit, a bioreactor unit, a heavy metal removing unit and a nitrogen-phosphorus removing unit.
7. An electronic device comprising a processor, a memory, wherein the memory is configured to store a computer program, the computer program comprising program instructions, the processor configured to invoke the program instructions to perform the quality control method of industrial wastewater online monitoring data according to any of claims 1-3.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform a quality control method of industrial wastewater on-line monitoring data according to any one of claims 1-3.
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