A 2 Fault diagnosis method, system, equipment and storage medium for / O process system

Abstract

The present invention discloses an A 2 / O process system fault diagnosis method, system, equipment and storage medium, the method comprises: obtaining the A to be diagnosed according to a preset cycle 2 / O process system process operation data, perform abnormal data detection on the process operation data to remove abnormal data to obtain effective operation data; determine the abnormal factors causing the abnormality of each water outlet index according to multiple water outlet indexes in the effective operation data, and establish a fault tree indicating the abnormal causes of each water outlet index according to the abnormal factors; determine the A according to the established fault tree indicating the abnormal causes of each water outlet index 2 / O process system effluent index abnormal fault diagnosis results. This method can intuitively determine the corresponding relationship between the fault and the cause of the fault, providing an accurate basis for troubleshooting.

Description

Technical Field

[0001] The present invention relates to the field of sewage treatment, and in particular to an A 2 / O process system fault diagnosis method and system. Background Art

[0002] In the operation of sewage treatment plants, the most important assessment indicator is the effluent quality. However, due to the uneven level of operators and the lack of professional knowledge to guide the operation of water plants, some water plants are inefficient or abnormal operation, and even water quality indicators are abnormal. When the effluent quality indicators are abnormally exceeded, it is necessary to find the cause of the failure and deal with it as soon as possible to prevent the situation from getting worse. However, there is currently no method or system to quickly determine the cause of the failure.

[0003] In view of this, the present invention is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a 2 The fault diagnosis method, system, equipment and storage medium of the / O process system can establish an intuitive correspondence between abnormal water output indicators and factors causing the abnormalities, and then quickly confirm the cause of the fault and obtain a diagnosis result, thereby solving the above-mentioned technical problems existing in the prior art.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] The embodiment of the present invention provides a 2 / O process system fault diagnosis method, including:

[0007] Get the A to be diagnosed according to the preset cycle 2 / O process operation data of process system;

[0008] Perform abnormal data detection on the process operation data to remove the abnormal data and obtain effective operation data;

[0009] According to the multiple water output indicators in the effective operation data, determining the abnormal factors causing the abnormality of each water output indicator, and establishing a fault tree indicating the abnormal causes of each water output indicator according to the abnormal factors;

[0010] According to the established fault tree indicating the abnormal causes of each water output index, determine the A 2 / O process system effluent index abnormal fault diagnosis results.

[0011] The embodiment of the present invention also provides a method A for implementing the method of the present invention as claimed in claim 2 / O process system fault diagnosis system, including:

[0012] Data acquisition unit, abnormal data detection unit, water discharge index fault tree establishment unit and diagnosis result output unit; wherein,

[0013] The data acquisition unit is connected to the diagnosed A 2 / O process system communication connection, can obtain the A to be diagnosed according to the preset cycle 2 / O process operation data of process system;

[0014] The abnormal data detection unit is in communication connection with the data acquisition unit, and can perform abnormal data detection on the process operation data acquired by the data acquisition unit to remove the abnormal data and obtain effective operation data;

[0015] The water discharge index fault tree establishing unit is in communication connection with the abnormal data detection unit, and can determine the abnormal factors causing the abnormality of each water discharge index according to the multiple water discharge indexes in the effective operation data obtained by the abnormal data detection unit, and establish a fault tree indicating the abnormal causes of each water discharge index according to the abnormal factors;

[0016] The diagnostic result output unit is in communication with the water output index fault tree establishment unit, and can determine and output the A according to the fault tree indicating the abnormal causes of each water output index established by the water output index fault tree establishment unit. 2 / O process system effluent index abnormal fault diagnosis results.

[0017] The embodiment of the present invention further provides a processing device, comprising:

[0018] at least one memory for storing one or more programs;

[0019] At least one processor can execute one or more programs stored in the memory. When the one or more programs are executed by the processor, the processor can implement the method described in the present invention.

[0020] The embodiment of the present invention further provides a readable storage medium storing a computer program, which can implement the method described in the present invention when the computer program is executed by a processor.

[0021] Compared with the prior art, the present invention provides 2 The fault diagnosis method, system, device and storage medium of the / O process system have the following beneficial effects:

[0022] By obtaining A 2 / O process system’s process operation data, and after removing abnormal data to obtain valid data, a fault tree corresponding to the water outlet index is established. Then, based on the fault tree, the fault cause causing the abnormal water outlet index can be intuitively and quickly obtained, and the fault diagnosis result can be obtained, providing accurate data support for subsequent fault repair. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0024] Figure 1 A provided in the embodiment of the present invention 2 Flow chart of the fault diagnosis method of / O process system.

[0025] Figure 2 A provided in the embodiment of the present invention 2 Abnormal data detection flow chart of the fault diagnosis method of / O process system.

[0026] Figure 3 A provided in the embodiment of the present invention 2 Schematic diagram of the inlet SS fault tree of the fault diagnosis method of the / O process system.

[0027] Figure 4 A provided in the embodiment of the present invention 2 Schematic diagram of the inlet COD fault tree for the fault diagnosis method of the / O process system.

[0028] Figure 5 A provided in the embodiment of the present invention 2 Schematic diagram of the inlet ammonia nitrogen fault tree for the fault diagnosis method of the / O process system.

[0029] Figure 6 A provided in the embodiment of the present invention 2 Schematic diagram of the inlet TN fault tree of the fault diagnosis method of the / O process system.

[0030] Figure 7 A provided in the embodiment of the present invention 2 Flow chart of the carbon source addition judgment method of the inlet TN fault tree of the fault diagnosis method of the / O process system.

[0031] Figure 8 A provided in the embodiment of the present invention 2 Schematic diagram of the composition of the fault diagnosis system of the / O process system.

[0032] Fig. 9 A provided in the embodiment of the present invention 2 Schematic diagram of the abnormal data detection unit structure of the fault diagnosis system of the / O process system.

[0033] Fig.10 A provided in the embodiment of the present invention2 Schematic diagram of the unit composition of the effluent index fault tree establishment system of the fault diagnosis system of the / O process system. DETAILED DESCRIPTION

[0034] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in combination with the specific content of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, which does not constitute a limitation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of the present invention.

[0035] First, the terms that may be used in this article are explained as follows:

[0036] The term “and / or” means that either or both of them can be realized at the same time. For example, X and / or Y means both “X” or “Y” and “X and Y”.

[0037] The terms "include", "comprises", "contains", "has" or other descriptions with similar semantics should be interpreted as non-exclusive inclusion. For example, including certain technical feature elements (such as raw materials, components, ingredients, carriers, dosage forms, materials, dimensions, parts, components, mechanisms, devices, steps, procedures, methods, reaction conditions, processing conditions, parameters, algorithms, signals, data, products or products, etc.) should be interpreted as including not only certain technical feature elements explicitly listed, but also other technical feature elements known in the art that are not explicitly listed.

[0038] The term "consisting of..." means excluding any technical feature elements not explicitly listed. If this term is used in a claim, it will make the claim closed, so that it does not contain technical feature elements other than the technical feature elements explicitly listed, except for the conventional impurities related to them. If this term only appears in a clause of a claim, it only limits the elements explicitly listed in the clause, and the elements recorded in other clauses are not excluded from the overall claim.

[0039] The term "parts by mass" refers to the mass ratio relationship between multiple components. For example, if it is described that component X is x parts by mass and component Y is y parts by mass, then the mass ratio of component X to component Y is x:y. 1 part by mass can represent any mass, for example, 1 part by mass can be represented as 1 kg or 3.1415926 kg. The sum of the parts by mass of all components is not necessarily 100 parts, but can be greater than 100 parts, less than 100 parts or equal to 100 parts. Unless otherwise specified, the parts, proportions and percentages described herein are all measured by mass.

[0040] Unless otherwise specified or limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this article can be understood according to specific circumstances.

[0041] When concentration, temperature, pressure, size or other parameters are expressed in the form of a numerical range, the numerical range should be understood to specifically disclose all ranges formed by the pairing of any upper limit, lower limit, and preferred value in the numerical range, regardless of whether the range is explicitly stated; for example, if a numerical range of "2 to 8" is stated, the numerical range should be interpreted as including ranges such as "2 to 7", "2 to 6", "5 to 7", "3 to 4 and 6 to 7", "3 to 5 and 7", "2 and 5 to 7", etc. Unless otherwise specified, the numerical ranges stated herein include both their end values ​​and all integers and fractions within the numerical range.

[0042] The orientation or position relationship indicated by terms such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc. are based on the orientation or position relationship shown in the drawings and are only for the convenience and simplification of description, and do not explicitly or implicitly indicate that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation of this document.

[0043] The following is the A provided by the present invention 2 The fault diagnosis method and system of the / O process system are described in detail. The contents not described in detail in the embodiments of the present invention belong to the prior art known to professional and technical personnel in the field. If the specific conditions are not specified in the embodiments of the present invention, the conventional conditions in the field or the conditions recommended by the manufacturer are followed. If the manufacturer of the reagents or instruments used in the embodiments of the present invention is not specified, they are all conventional products that can be purchased commercially.

[0044] like Figure 1 As shown, the embodiment of the present invention provides a 2 / O process system fault diagnosis method, including:

[0045] Get the A to be diagnosed according to the preset cycle 2 / O process operation data of process system;

[0046] Perform abnormal data detection on the process operation data to remove the abnormal data and obtain effective operation data;

[0047] According to the multiple water output indicators in the effective operation data, determining the abnormal factors causing the abnormality of each water output indicator, and establishing a fault tree indicating the abnormal causes of each water output indicator according to the abnormal factors;

[0048] According to the established fault tree indicating the abnormal causes of each water output index, determine the A 2 / O process system effluent index abnormal fault diagnosis results.

[0049] like Figure 2 As shown, in the above method, the A to be diagnosed is obtained 2 / O The process operation data of the process system includes: manually reported effluent data and online monitoring effluent data;

[0050] The process operation data is subjected to abnormal data detection and removal in the following manner, including:

[0051] Performing abnormal value detection on the manually reported water output data through a box plot to determine false alarm values ​​and / or false alarm values ​​as abnormal data, and removing the detected abnormal data from the manually reported water output data to obtain manually reported valid data;

[0052] The online monitoring water output data is detected according to a predetermined experience range of water output indicators, data not within the experience range of water output indicators is determined as abnormal data, and the detected abnormal data is removed from the manually reported water output data to obtain effective online monitoring data.

[0053] In the above method, the empirical range of the water discharge index is predetermined in the following manner, including:

[0054] According to the detected A 2 / O The historical data of the process system and the expert experience are used to divide the online monitoring effluent data and determine the empirical range of the effluent indicators;

[0055] The method further includes: testing the manually reported valid data and the online monitoring valid data by pairing t, determining the difference between the manually reported valid data and the online monitoring valid data according to the test result, and if it is determined that the difference does not exceed a preset value, confirming that the manually reported valid data meets the accuracy requirement;

[0056] It also includes: performing abnormal processing on the online monitoring valid data in the following manner, including:

[0057] After removing outliers, two-sided and one-sided t-tests were performed on the valid online monitoring data to determine whether the effluent water quality data exceeded the discharge standard.

[0058] In the above method, the multiple water output indicators in the effective operation data are:

[0059] Outlet SS, effluent COD, effluent ammonia nitrogen and effluent TN.

[0060] In the above method, the abnormal factors causing the abnormality of each water output index are determined in the following manner, and a fault tree indicating the abnormal causes of each water output index is established according to the abnormal factors, including:

[0061] If the effluent SS of the effluent index is confirmed to be greater than 10 mg / L, the effluent SS is confirmed to be abnormal. Based on the influencing factors of sludge load, dissolved oxygen in the aerobic zone and C / N ratio that can affect the SVI that causes the effluent SS abnormality, an effluent SS fault tree indicating the cause of the effluent SS abnormality is established;

[0062] If the effluent COD of the effluent indicator is confirmed to be greater than 50 mg / L, the effluent COD is confirmed to be abnormal. Based on the sludge load and effluent SS concentration, which are the influencing factors that can cause the effluent COD abnormality, a effluent COD fault tree indicating the cause of the effluent COD abnormality is established;

[0063] If the effluent ammonia nitrogen of the effluent indicator NH4-N is greater than 5mg / L, it is confirmed that the effluent ammonia nitrogen is abnormal. According to the influencing factors that can cause the abnormal effluent ammonia nitrogen, such as sludge load and dissolved oxygen in the aerobic zone, a fault tree of effluent ammonia nitrogen indicating the cause of the abnormal effluent ammonia nitrogen is established;

[0064] If the effluent TN of the effluent indicator is confirmed to be greater than 15 mg / L, the effluent TN is confirmed to be abnormal. Based on the influencing factors that can cause the effluent TN abnormality, such as effluent ammonia nitrogen, anoxic pool redox potential and C / N ratio, a effluent TN fault tree indicating the causes of the effluent TN abnormality is established.

[0065] like Figure 3 As shown, in the above method, the outflow SS fault tree is composed of a root node connected to four layers of leaf nodes from top to bottom, where:

[0066] The influencing factor corresponding to the root node is the increase in effluent SS, and the influencing factor corresponding to the first leaf node of the first layer connected to the root node is the inflow of the efficient sedimentation tank;

[0067] The SS of the first leaf node of the first layer is lower than the design value. The output end is connected to the first leaf node of the second layer. The influencing factor corresponding to the first leaf node of the second layer is that the effluent of the secondary sedimentation tank is too high;

[0068] The output end of the first leaf node of the second layer with a higher SVI value is connected to the first leaf node of the third layer. The influencing factor corresponding to the first leaf node of the third layer is poor sludge sedimentation performance and filamentous bacteria growth;

[0069] The normal output end of the SVI value of the first leaf node of the second layer is connected to the second leaf node of the third layer. The influencing factor corresponding to the second leaf node of the third layer is the low operating efficiency of the secondary sedimentation tank;

[0070] The first output end of the first leaf node of the third layer is connected to the first leaf node of the fourth layer, and the influencing factor corresponding to the first leaf node of the fourth layer is that the sludge load value is too high or too low;

[0071] The second output end of the first leaf node of the third layer is connected to the second leaf node of the fourth layer, and the influencing factor corresponding to the second leaf node of the fourth layer is that the C / N ratio is too low;

[0072] The output end of the HRT calculation of the second leaf node of the third layer is connected to the third leaf node of the fourth layer. The influencing factors corresponding to the third leaf node of the fourth layer are excessive water volume or excessive internal recirculation ratio;

[0073] The normal output end of the HRT calculation of the second leaf node of the third layer is connected to the fourth leaf node of the fourth layer. The influencing factors corresponding to the fourth leaf node of the fourth layer are that the DO at the outlet of the aerobic zone is too low or the sludge difficulty is too high;

[0074] The SS of the first leaf node of the first layer is higher than the design value. The output end is connected to the second leaf node of the first layer. The influencing factor corresponding to the second leaf node of the first layer is the problem of the operation of the high-efficiency sedimentation tank;

[0075] The output end of the HRT calculation of the second leaf node of the first layer is connected to the third leaf node of the second layer. The influencing factor corresponding to the third leaf node of the second layer is excessive water volume under heavy rain conditions;

[0076] The normal output end of the HRT calculation of the second leaf node of the first layer is connected to the fourth leaf node of the second layer, and the influencing factor corresponding to the fourth leaf node of the second layer is the PAM problem.

[0077] like Figure 4 As shown, the outlet COD fault tree consists of a root node connected to six layers of leaf nodes from top to bottom, where:

[0078] The influencing factor corresponding to the root node is the outflow COD exceeding the standard. The outflow SS exceeding standard output terminal of the root node is connected to the first leaf node of the first layer. The corresponding influencing factor of the first leaf node of the first layer is the outflow SS fault tree.

[0079] The normal output end of the effluent SS of the root node is connected to the second leaf node of the first layer, and the corresponding influencing factor of the second leaf node of the first layer is the excessive dissolved COD;

[0080] The output end of the second leaf node of the first layer is connected to the first leaf node of the third layer, and the influencing factor corresponding to the first leaf node of the third layer is the aeration of activated sludge after effluent cleaning;

[0081] The sCOD of the first leaf node of the third layer is greatly reduced, and the output end is connected to the first leaf node of the third layer. The influencing factor corresponding to the first leaf node of the third layer is the problem of the operation of the biochemical system;

[0082] The sCOD of the first leaf node of the third layer has not changed significantly. The output end is connected to the second leaf node of the third layer. The influencing factor corresponding to the second leaf node of the third layer is that the influent difficult-to-degrade components are too high (i.e., the influent difficult-to-degrade sCOD is too high);

[0083] The normal output end of the secondary sedimentation tank influent sCOD of the first leaf node of the third layer is connected to the first leaf node of the fourth layer. The influencing factor corresponding to the first leaf node of the fourth layer is the sCOD released by the anaerobic digestion of the secondary sedimentation tank;

[0084] The output end of the secondary sedimentation tank inlet sCOD too high of the first leaf node of the third layer is connected to the second leaf node of the fourth layer. The influencing factor corresponding to the second leaf node of the fourth layer is the operation problem of the aeration tank.

[0085] The output end of the first leaf node of the fourth layer is connected to the first leaf node of the fifth layer, and the influencing factor corresponding to the first leaf node of the fifth layer is to increase the DO value at the outlet of the aeration tank;

[0086] The output end of the DO or aeration volume of the second leaf node of the fourth layer is low, and the output end is connected to the second leaf node of the fifth layer. The influencing factor corresponding to the second leaf node of the fifth layer is insufficient aeration volume;

[0087] The DO or aeration volume normal output terminal of the second leaf node of the fourth layer is connected to the third leaf node of the fifth layer, and the influencing factor corresponding to the third leaf node of the fifth layer is the sludge bacterial community;

[0088] The first output end of the third leaf node of the fifth layer is connected to the first leaf node of the sixth layer, and the influencing factor corresponding to the first leaf node of the sixth layer is that the influent COD load is too high;

[0089] The second output end of the third leaf node of the fifth layer is connected to the first leaf node of the sixth layer, and the influencing factor corresponding to the second leaf node of the sixth layer is sludge aging;

[0090] like Figure 5 As shown, the effluent ammonia nitrogen fault tree consists of a root node connected to four layers of leaf nodes from top to bottom, where:

[0091] The influencing factor corresponding to the root node is the excessive ammonia nitrogen in the effluent, and the influencing factor corresponding to the first leaf node of the first layer connected to the root node is the sludge concentration MLSS;

[0092] The normal output end of the sludge concentration of the first leaf node of the first layer is connected to the first leaf node of the second layer, and the influencing factor corresponding to the first leaf node of the second layer is temperature;

[0093] The output end of the first leaf node of the first layer with too low sludge concentration is connected to the second leaf node of the second layer. The influencing factors corresponding to the second leaf node of the second layer are extending SRT and increasing sludge concentration;

[0094] The temperature too low output end of the first leaf node of the second layer is connected to the first leaf node of the third layer, and the influencing factor corresponding to the first leaf node of the third layer is to increase the sludge concentration;

[0095] The temperature too low output end of the first leaf node of the second layer is connected to the second leaf node of the third layer. The influencing factors corresponding to the second leaf node of the third layer are increasing the DO and gas volume of the aeration tank;

[0096] The effective output end of the second leaf node of the third layer is connected to the first leaf node of the fourth layer, and the influencing factor corresponding to the first leaf node of the fourth layer is to increase the frequency of fan control;

[0097] The invalid output end of the second leaf node of the third layer is connected to the second leaf node of the fourth layer. The influencing factors corresponding to the second leaf node of the fourth layer are bacterial degradation or shock by influent toxicity, and adding new mud;

[0098] like Figure 6 As shown, the outflow TN fault tree consists of a root node connected to four layers of leaf nodes from top to bottom, where:

[0099] The influencing factor corresponding to the root node is the effluent TN exceeding the standard, and the influencing factor corresponding to the first leaf node of the first layer connected to the root node is the effluent ammonia nitrogen;

[0100] The normal output end of the effluent ammonia nitrogen of the first leaf node of the first layer is connected to the first leaf node of the second layer, and the influencing factor corresponding to the first leaf node of the second layer is the influent C / N ratio;

[0101] The output end of the first leaf node of the first layer where the outflow ammonia nitrogen does not meet the standard is connected to the second leaf node of the second layer, and the influencing factor corresponding to the second leaf node of the second layer is the outflow ammonia nitrogen failure;

[0102] The inlet C / N too low output end of the first leaf node of the second layer is connected to the first leaf node of the third layer, and the influencing factor corresponding to the first leaf node of the third layer is the addition of carbon source;

[0103] The output end of the first leaf node of the second layer with an influent C / N ratio greater than 4 is connected to the second leaf node of the third layer, and the influencing factor corresponding to the second leaf node of the third layer is the redox potential of the anoxic pool;

[0104] The effect of the first leaf node of the third layer is not obvious. The output end is connected to the first leaf node of the fourth layer. The influencing factor corresponding to the first leaf node of the fourth layer is that there is a problem with the quality of the carbon source.

[0105] The effective output end of the first leaf node of the third layer is connected to the second leaf node of the fourth layer, and the influencing factor corresponding to the second leaf node of the fourth layer is insufficient carbon source;

[0106] The redox potential too high output end of the second leaf node of the third layer is connected to the third leaf node of the fourth layer. The influencing factor corresponding to the third leaf node of the fourth layer is the high DO of the return liquid of the aerobic pool, which controls the fan;

[0107] The redox potential reasonable output end of the second leaf node of the third layer is connected to the fourth leaf node of the fourth layer, and the influencing factor corresponding to the fourth leaf node of the fourth layer is to make a carbon source addition judgment.

[0108] In the above effluent TN fault tree, the process of determining carbon source addition is as follows: Figure 7 shown.

[0109] like Figure 8 As shown, the embodiment of the present invention also provides a method A for implementing the above method 2 / O process system fault diagnosis system, including:

[0110] Data acquisition unit, abnormal data detection unit, water discharge index fault tree establishment unit and diagnosis result output unit; wherein,

[0111] The data acquisition unit is connected to the diagnosed A 2 / O process system communication connection, can obtain the A to be diagnosed according to the preset cycle 2 / O process operation data of process system;

[0112] The abnormal data detection unit is in communication connection with the data acquisition unit, and can perform abnormal data detection on the process operation data acquired by the data acquisition unit to remove the abnormal data and obtain effective operation data;

[0113] The water discharge index fault tree establishing unit is in communication connection with the abnormal data detection unit, and can determine the abnormal factors causing the abnormality of each water discharge index according to the multiple water discharge indexes in the effective operation data obtained by the abnormal data detection unit, and establish a fault tree indicating the abnormal causes of each water discharge index according to the abnormal factors;

[0114] The diagnostic result output unit is in communication with the water output index fault tree establishment unit, and can determine and output the A according to the fault tree indicating the abnormal causes of each water output index established by the water output index fault tree establishment unit. 2 / O process system effluent index abnormal fault diagnosis results.

[0115] like Fig. 9 As shown, in the above system, the abnormal data detection unit includes:

[0116] Manually reported water output data acquisition module, manually reported water output data processing module, online monitoring water output data acquisition module, online monitoring water output data processing module and matching module; wherein,

[0117] The manual reporting water data acquisition module can obtain the diagnosed A 2 / O Manual reporting of effluent data from process systems;

[0118] The manually reported water output data processing module is in communication connection with the manually reported water output data acquisition module, and can perform abnormal value detection on the manually reported water output data acquired by the manually reported water output data acquisition module through a box plot to determine false alarm values ​​and / or false alarm values ​​as abnormal data, and remove the detected abnormal data from the manually reported water output data to obtain manually reported valid data;

[0119] The online monitoring water output data acquisition module is connected to the diagnosed A 2 / O process system communication connection, can obtain the diagnosis A 2 / O process system online monitoring of effluent data;

[0120] The online monitoring water output data processing module is in communication connection with the online monitoring water output data acquisition module, and can detect the online monitoring water output data according to the predetermined experience range of the water output index, determine the data that is not within the experience range of the water output index as abnormal data, and remove the detected abnormal data from the manually reported water output data to obtain the online monitoring valid data;

[0121] like Fig.10 As shown, the water discharge index fault tree establishing unit includes:

[0122] Outlet SS fault tree establishment submodule, outlet COD fault tree establishment submodule, outlet ammonia nitrogen fault tree establishment submodule and outlet TN fault tree establishment submodule; among which,

[0123] The effluent SS fault tree establishment submodule can establish an effluent SS fault tree indicating the cause of the effluent SS abnormality based on the influencing factors of the SVI that can affect the effluent SS abnormality, such as sludge load, dissolved oxygen in the aerobic zone, and C / N ratio; when the effluent SS of the effluent indicator is greater than 10 mg / L and the effluent SS is confirmed to be abnormal, the effluent SS fault tree indicating the cause of the effluent SS abnormality can be established;

[0124] The effluent COD fault tree establishment submodule can establish an effluent COD fault tree indicating the cause of the effluent COD abnormality based on the sludge load and effluent SS concentration, which are influencing factors that can cause the effluent COD abnormality, when the effluent COD of the effluent indicator is greater than 50 mg / L and the effluent COD is confirmed to be abnormal;

[0125] The effluent ammonia nitrogen fault tree establishment submodule can establish an effluent ammonia nitrogen fault tree indicating the cause of the effluent ammonia nitrogen abnormality based on the influencing factors of sludge load and dissolved oxygen in the aerobic zone that can cause the effluent ammonia nitrogen abnormality when the effluent NH4-N of the effluent ammonia nitrogen indicator is greater than 5 mg / L and the effluent ammonia nitrogen is confirmed to be abnormal;

[0126] The outlet TN fault tree establishment submodule can establish an outlet TN fault tree indicating the cause of the outlet TN abnormality based on the influencing factors that can cause the outlet TN abnormality, such as the outlet TN of the outlet water indicator is greater than 15 mg / L and the outlet TN is confirmed to be abnormal.

[0127] The embodiment of the present invention further provides a processing device, including:

[0128] at least one memory for storing one or more programs;

[0129] At least one processor can execute one or more programs stored in the memory, and when the one or more programs are executed by the processor, the processor can implement the above method.

[0130] The embodiment of the present invention further provides a readable storage medium storing a computer program, which can implement the above method when the computer program is executed by a processor.

[0131] In summary, it can be seen that embodiment A of the present invention 2 / O process system fault diagnosis method and system, by obtaining A 2 / O process system’s process operation data, and after removing abnormal data to obtain valid data, a fault tree corresponding to the water outlet index is established. Then, based on the fault tree, the fault cause causing the abnormal water outlet index can be intuitively and quickly obtained, and the fault diagnosis result can be obtained, providing accurate data support for subsequent fault repair.

[0132] In order to more clearly demonstrate the technical solution and technical effects provided by the present invention, the following specific embodiments are used to describe the A provided in the embodiments of the present invention. 2 The fault diagnosis method and system of the / O process system are described in detail.

[0133] Example 1

[0134] This embodiment provides a 2 / O process system fault diagnosis method, the fault described in this method refers to the phenomenon that the effluent water quality exceeds the standard and the energy consumption and drug consumption of operation suddenly increase significantly from the perspective of operation. These influencing factors will have a great impact on the operation stability, safety and operation cost, and will also lead to the problem of failure to meet environmental protection requirements.

[0135] First, the urban sewage treatment plant A 2 / O process system failure is specifically defined as follows:

[0136] The faults referred to by this method specifically include: sudden and significant increase or decrease in the online instrument data of effluent water quality, sudden and significant increase or decrease in operating energy consumption, sudden and significant decrease or increase in operating carbon source drug consumption, and significant increase or decrease in operating phosphorus removal drug dosage (Table 1).

[0137] Table 1 is A 2 / O Process System Fault Definition

[0138]

[0139]

[0140] like Figure 1 As shown, the method comprises the following steps:

[0141] Get the A to be diagnosed according to the preset cycle 2 / O process operation data of the process system, performing abnormal data detection on the process operation data to remove abnormal data and obtain effective operation data;

[0142] According to the multiple water output indicators in the effective operation data, determining the abnormal factors causing the abnormality of each water output indicator, and establishing a fault tree indicating the abnormal causes of each water output indicator according to the abnormal factors;

[0143] According to the established fault tree indicating the abnormal causes of each water output index, determine the A 2 / O process system effluent index abnormal fault diagnosis results.

[0144] The specific implementation of each of the above steps is further described in detail below.

[0145] (I) First, the processing method of abnormal data detection is explained, starting from the A to be diagnosed 2 / O The process operation data of the process system includes manually reported effluent data and online monitoring effluent data;

[0146] Since faults are identified based on online instruments, the accuracy of instrument data is critical. Therefore, abnormal values ​​need to be identified to avoid panic and misoperation caused by instrument problems.

[0147] In addition to online instrument data, sewage treatment plants conduct daily manual testing (daily average), and the indicators involved generally include COD, BOD5, SS, pH, NH4 +-N, TN, TP: Of course, daily manual testing also has problems such as human error or false reporting, low precision of experimental instruments, and abnormal data caused by monitoring equipment failure. Therefore, if certain methods can be adopted to detect abnormal data, it will be very helpful for management departments to timely discover abnormal conditions in sewage treatment plants, facilitate further data statistical analysis, and provide a basis for subsequent fault diagnosis. The realization of abnormal data detection in urban sewage treatment plants will provide powerful technical measures for management departments to supervise sewage treatment plants.

[0148] The definition of abnormal data in the present invention is: abnormal data caused by problems such as manual report filling or intentional false reporting, human error detection or low accuracy of detection instruments, online monitoring equipment failure, and water discharge data that does not meet the standards in the urban sewage treatment system. In practical applications, it is very dangerous to ignore the existence of abnormal values ​​in the data. Including singular values ​​in the analysis process without elimination will have an adverse effect on the results.

[0149] The detection process of abnormal data of the present invention is as follows Figure 2 As shown in the figure, based on four different types of abnormal data, the detection process can be divided into the following parts.

[0150] (11) Abnormalities in manual reporting of incorrect or false reports:

[0151] Based on the box plot principle, outlier detection is performed on the manually reported data to observe whether there are false positives or false positives. When abnormal data is confirmed, it is eliminated.

[0152] (12) Abnormalities caused by human factors or low precision of laboratory testing instruments:

[0153] The paired t-test is used to compare and analyze the monthly data from online monitoring equipment and manually reported data to see whether there are significant differences between the manually reported data and the online monitoring data, so as to find out whether the manually reported data are abnormal due to human factors or low accuracy of experimental equipment.

[0154] (13) Outlier detection based on logical relationships between data:

[0155] Outlier detection is performed based on the logical relationship between the data, for example, COD is greater than BOD5, TN is greater than the sum of ammonia nitrogen, nitrate nitrogen and nitrite nitrogen, TP is greater than orthophosphate phosphorus; and the corresponding BOD5 / COD value of the sewage plant effluent is less than 50%.

[0156] (14) Abnormal water output not meeting the standard:

[0157] When the online monitoring data is within the empirical range, the real-time online monitoring data is judged to be excessive based on the discharge standard of the effluent water quality. If it is excessive, the subsequent fault diagnosis module is entered to find out the cause of the fault in time and take corresponding solutions. According to the hypothesis test principle, the online monitoring data after removing the outliers is subjected to bilateral and unilateral t tests to analyze whether the effluent water quality data on that day exceeds the discharge standard.

[0158] In the above abnormal data detection, the abnormal data detection for manually reported water output data is based on statistical abnormal data detection, as follows:

[0159] (111) Manual report outlier detection based on box plot and data logic

[0160] Using the box plot in statistics, we can detect outliers in manually reported data. It provides an intuitive method to identify outliers in data, and the identification results are relatively objective. According to the relevant regulations on the operation and supervision of urban sewage treatment plants, manually reported data are submitted to the higher-level management department in the form of monthly reports. Table 2 shows the monthly report data of some effluent indicators from a certain urban sewage treatment plant. The outlier detection results of this group of data using the box plot analysis method are as follows: Figure 2 It can be seen that there are no abnormal values ​​for SS, BOD5, ammonia nitrogen, and TN, while two abnormal values ​​were detected for COD on the 5th and 19th day of the month. According to the logical relationship between the data, the BOD5 data on the first day is abnormal.

[0161] Table 2 Some effluent index data of a certain city sewage treatment plant in October 2021 (Note: Unit: mg / L)

[0162]

[0163]

[0164] (112) Outlier Detection of Online Monitoring Data

[0165] According to existing regulations, online water quality monitoring equipment must be installed at the inlet and outlet of the sewage treatment plant and connected to the monitoring terminal of the superior environmental protection department to achieve supervision and management of the sewage treatment plant and performance assessment. The validity of online monitoring data is an important prerequisite for calculating the emission of major pollutants and determining whether the pollutants meet the emission standards, which is crucial for the assessment of sewage treatment plants. The monitoring data collected by the online monitoring equipment is reported every 2 hours during the period from 00:00 to 24:00 every day, so 12 data can be collected on each effluent indicator (generally including pH, COD, ammonia nitrogen, TN, TP and SS) on the same day. In order to ensure the demand for real-time fault diagnosis of subsequent online monitoring data, it is necessary to combine the effluent water quality index requirements (here set according to Level 1A), and combine the historical data of urban sewage treatment plants and expert experience to define the experience range of online monitoring data (effluent) (SS: 3-30 mg / L; COD: 10-100 mg / L; ammonia nitrogen: 0-15 mg / L; TN: 4-30 mg / L). When the data is not within this range, it is considered that the data is abnormal due to the failure of the online monitoring equipment. When there is no abnormal data caused by equipment failure, that is, when the online monitoring data is within the experience range, the real-time online monitoring data is judged to be excessive based on the effluent water quality discharge standard. The following is the result of sending the effluent index data collected by a certain urban sewage treatment plant on a certain day to the monitoring center, as shown in Table 3.

[0166] Table 3 is the online monitoring data of the daily discharge index of a certain city sewage treatment plant. Unit: mg / L

[0167] Serial number Monitoring time SS Ammonia nitrogen TN TP COD 1 00:00 7 0.43 11.32 0.270 24.6 2 02:00 6 0.50 10.28 0.260 23.4 3 04:00 6 0.45 11.43 0.320 23.0 4 06:00 6 0.47 12.51 0.330 23.4 5 08:00 6 0.75 12.40 0.320 29.5 6 10:00 7 0.93 12.50 0.340 30.9 7 12:00 7 1.04 13.27 0.230 33.0 8 14:00 6 0.51 9.96 0.270 23.8 9 16:00 6 0.63 8.09 0.250 28.8 10 18:00 7 0.66 9.35 0.270 30.6 11 20:00 0 0.47 10.44 0.240 26.1 12 22:00 4 0.37 12.65 0.230 25.6

[0168] As can be seen from Table 3 above, at 20:00 on the same day, the SS showed an abnormal value of 0.0, which was not within the empirical range. The indicators of ammonia nitrogen, TN, and COD were normal on the same day, with no abnormal values. According to the sewage plant, the abnormal value appeared because the suspended matter probe signal transmission failed, so the suspended matter result was 0.0. Considering that the abnormal SS was caused by equipment failure, the abnormal value should be eliminated. At this time, the sewage plant staff took samples in time for laboratory analysis and determination. It was found that the SS value for this period was 5.0 mg / L, which replaced the abnormal value. After data correction, the data for this period did not exceed the 10 mg / L specified by the national level A, and the effluent was qualified without the need for subsequent fault diagnosis and treatment.

[0169] (113) Data significance difference analysis based on paired t test

[0170] In the supervision and management of sewage treatment plants, the authenticity and reliability of manual reports and online monitoring data are routine assessment items. Online monitoring data comes from online monitoring equipment, and its data is calibrated by regular zero drift to keep the measurement accurate; manual reports are reported in the form of monthly reports, and the measured data comes from laboratory analysis and detection. The validity of the data will inevitably be affected by the accuracy of the experimental equipment and the human detection operation; for a well-functioning sewage treatment plant, the manual reports and online monitoring data are generally basically consistent or there is a relatively regular deviation. Therefore, this embodiment uses online monitoring data as a benchmark, and uses paired t-test means to determine whether there is a significant difference between the manual reporting data and the online monitoring data, so as to verify whether there is an abnormality between the manual reporting data and the online detection data. At present, since the regulatory authorities require that the water quality report reported is a monthly report, with days as the specific unit, and the online monitoring data is hourly monitoring data, it is necessary to take the average of all 24 hours of data on the day as the effective daily average of the online monitoring data. In addition, the data for the paired t-test are all valid data after outlier detection and correction of outliers.

[0171] (II) Secondly, the process of establishing a fault tree based on the effluent data of the urban sewage treatment plant is explained:

[0172] At present, most of the sewage treatment plants in my country adopt activated sludge process, among which A 2 The / O process is relatively mature and widely used. However, since the urban sewage treatment system is a complex biochemical process, it is greatly affected by the influent water quality, water volume fluctuations and the mutual influence of various process sections. It is often difficult to maintain long-term stable operation. There are many problems, such as substandard effluent and floating sludge in the secondary sedimentation tank. 2 The common faults of I / O process are classified and used as the basis for subsequent research. 2 Common faults in the / O process are mainly divided into: effluent water quality exceeding the standard, abnormal sludge properties, etc., which may be caused by abnormal fluctuations in influent water quality or water volume, toxic substances in influent, unreasonable operating parameters, equipment operation failure, unreasonable equipment selection, substandard material quality, etc.

[0173] (21) Abnormal fluctuations in influent water quality and quantity

[0174] In general, the influent flow, pollutant composition and organic matter concentration of urban sewage treatment plants will show regular fluctuations. However, under special circumstances, such as industrial wastewater discharge, rainstorm erosion, etc., it is easy to cause a sudden increase in sludge load and C / N imbalance, resulting in a decrease in the system's HRT, DO, insufficient aeration or sludge expansion and sludge activity inhibition, which in turn causes the occurrence of effluent COD, ammonia nitrogen, TN, SS, TP and other excessive phenomena.

[0175] (22) Toxic Substance Inhibition

[0176] When there is industrial wastewater in the influent, there may be some toxic substances in the influent, such as high concentrations of chloride ions, heavy metal ions, surfactants, volatile organic compounds or pesticides, which may inhibit the activity of microorganisms, resulting in reduced efficiency and causing the effluent water quality to exceed the standard.

[0177] (23) Abnormal operation of the process section

[0178] A 2 The functions of each process section in the / O process are different. Improper control of the conditions in each process section will cause abnormal discharge of the corresponding effluent. At the same time, the anaerobic section, the anoxic section and the aerobic section will affect each other. If there is a negative interaction, it will have a greater impact on the effluent quality. For example, the DO value at the end of the aerobic section is likely to cause the effluent ammonia nitrogen to fail to meet the standard when the aeration volume is low. However, excessive DO concentration will also increase the redox potential (ORP) of the anoxic section as the nitrification liquid flows back into the anoxic section, destroying the anoxic environment and causing the effluent TN to exceed the standard. At the same time, excessive DO will also destroy the dissolved oxygen conditions in the anaerobic section and affect biological phosphorus removal. In addition, due to fluctuations in the quality and volume of the influent, the operator needs to make manual or automatic parameter adjustments to meet the requirements of the effluent standard. However, due to incorrect parameter settings, it is also possible to cause low operating efficiency of each section, thereby affecting the effluent quality. Due to some reasons that have not been discovered in time, the efficiency of the process section has been reduced, which will also cause the effluent to exceed the standard.

[0179] (24) Abnormal sludge properties

[0180] Abnormal sludge properties refer to the low sludge activity and poor sedimentation performance caused by the growth of filamentous bacteria, toxic substances or inhibitors in the influent, improper sludge discharge, etc. during operation, which leads to excessive COD, ammonia nitrogen, TN, SS, TP, etc. in the effluent.

[0181] (25) Material quality does not meet standards

[0182] During operation, there may be a situation where the quality of the carbon source and phosphorus removal agent does not meet the standard, resulting in the addition of the agent but being ineffective, causing the system to fail to meet the standard.

[0183] The factors affecting the effluent water quality indicators are as follows:

[0184] The method of the present invention can establish a fault tree causal relationship between abnormal effluent water quality (fault phenomenon) and the cause of the fault, and then find out the cause of the fault that causes the effluent index to exceed the standard, by introducing the intermediate process node, that is, the process operation parameter of the process section. The process operation parameter can reflect the operating conditions of the sewage treatment plant, and its value has a direct impact on the quality of the effluent water. To this end, it is first necessary to analyze the process operation parameters that affect the effluent water quality indicators, find out the influencing factors corresponding to each effluent indicator, and finally establish a fault tree with fault diagnosis function. This embodiment selects five indicators of effluent COD, SS, ammonia nitrogen, TN, and TP from urban sewage treatment plants as the research subjects.

[0185] On the basis of analyzing the factors affecting the effluent water quality index, a fault tree for each effluent index is established. On the other hand, the values ​​of some influencing factors change very little during the actual operation and can basically be treated as constants. For practical problems, if they are fully considered, it will cause inaccurate model diagnosis. Therefore, it is necessary to compromise between the objectivity of the actual problem and the operability of the model, and reflect A as much as possible. 2 Therefore, this embodiment does not consider the following conditions when constructing the causal relationship fault tree:

[0186] (41) For actual sewage treatment plants, since the change in influent flow has a daily periodicity and HRT is generally greater than 24 hours, it can be considered that the change in HRT is small to a certain extent. The total hydraulic retention time of the sewage treatment plant studied is 19.4 hours. The hydraulic retention time in the aerobic zone is calculated to be 12.6 hours, which meets the requirement of more than 8 hours, is conducive to the biological nitrification process, and can also ensure the full absorption of phosphorus; the hydraulic retention time of sewage in the anaerobic zone is 1.15 hours, which can also ensure the effective release of phosphorus. Therefore, for urban sewage treatment plants with stable operation, HRT is relatively stable and can basically not be used as a routine analysis factor.

[0187] (42) The pH range of urban sewage treatment plants is generally between 6.5 and 7.5, with little variation, which can ensure the stability of the process and can therefore be considered a constant;

[0188] A fault tree of four effluent indicators and process influencing parameters was established in the following way.

[0189] (51) Outlet SS fault tree

[0190] The main influencing factor of effluent suspended solids is SVI. If SVI is too high, sludge will easily swell, causing the effluent suspended solids to exceed the standard. There are three influencing factors of SVI, namely sludge load (F / M), dissolved oxygen in aerobic zone and C / N ratio.

[0191] The ratio of sludge load depends on the ratio of influent flow, COD and MLSS. A high F / M indicates that the energy level of activated sludge is high, the sludge structure is loose, and good sludge flocs cannot be formed, resulting in an increase in SVI. If F / M is too low, sludge aging may occur, and the SVI is generally less than 50.

[0192] As the DO concentration in the aerobic zone of the sludge decreases, filamentous bacteria multiply in large numbers, causing the SVI to increase and the sludge settling performance to deteriorate.

[0193] The C / N ratio is insufficient, the bacterial flocs are easily dispersed, the filamentous bacteria have a competitive advantage and increase abnormally, causing the sludge to begin to swell.

[0194] When the effluent SS exceeds the standard, we can find out the cause of the water quality exceeding the standard according to the status of the process parameters. For example, if the dissolved oxygen in the aerobic zone is too low, resulting in an abnormal increase in SVI, we can increase the dissolved oxygen concentration in the aerobic zone by adjusting the fan frequency to increase the aeration volume or increase the air valve opening to stabilize the effluent water quality. Figure 3 shown.

[0195] (52) Outlet COD fault tree

[0196] The main factors affecting effluent COD are sludge load and effluent SS concentration. Excessive sludge load will exceed the processing capacity of microorganisms, causing effluent COD to exceed the standard. The SVI value is mainly affected by three factors in the effluent SS fault tree. When the SVI value is too high, sludge expansion is likely to occur, causing effluent SS to exceed the standard, which will indirectly cause the possibility of effluent COD exceeding the standard. Figure 4 shown.

[0197] (53) Outlet ammonia nitrogen fault tree

[0198] The main factors affecting the effluent ammonia nitrogen are sludge load and dissolved oxygen in the aerobic zone. Due to changes in the influent water quality, if the sludge load is high, the metabolism of heterotrophic microorganisms in the aerobic zone is vigorous, consuming a large amount of dissolved oxygen, which will also cause the dissolved oxygen in the aerobic zone to be low. In addition, fan failure or low air volume without timely adjustment will also cause the DO in the aerobic zone to be continuously low, causing abnormal effluent ammonia nitrogen. Figure 5 shown.

[0199] (54) Outlet TN Fault Tree

[0200] The removal of TN in effluent mainly involves nitrification and denitrification processes. Any obstruction in any process will affect the overall removal effect. For these two processes, the main influencing factors are the poor nitrification effect, resulting in substandard effluent ammonia nitrogen, the anoxic tank redox potential (ORP) and the C / N ratio. Low DO in the aerobic zone and excessive sludge load will affect the nitrification process; excessive dissolved oxygen content in the anoxic zone will affect the denitrification process, which is often measured by the ORP of the anoxic tank; in addition, the denitrification process requires sufficient carbon source to maintain biological denitrification, so C / N is also an important influencing factor. The fault tree of TN in effluent is as follows: Figure 6 In the established effluent TN fault tree, the method of determining carbon source addition is as follows: Figure 7 shown.

[0201] The method and system of the embodiment of the present invention can improve the operation supervision capability of the sewage treatment plant, because the influent water quality (C / N ratio, substrate concentration, etc.), influent flow rate, F / M, DO, ORP, SVI of activated sludge are comprehensively considered. 30 The influence of effluent index, aeration volume, internal and external recirculation volume, sludge concentration, temperature, dosage (phosphorus removal agent, carbon source agent) on effluent quality (SS, COD, NH3-N, TN) was analyzed. The effluent quality index fault tree was established through domain expert knowledge for fault diagnosis. The Bayesian network method was used to achieve the A-level fault diagnosis of urban sewage treatment plants. 2 / O Daily fault diagnosis at the management and control level of process systems.

[0202] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0203] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or in any form that the information constitutes prior art known to those skilled in the art.

Claims

1. A 2 A fault diagnosis method for a / O process system, characterized in that: include: Get the A to be diagnosed according to the preset cycle 2 / O process operation data of process system; Perform abnormal data detection on the process operation data to remove the abnormal data and obtain effective operation data; According to the multiple effluent indicators in the effective operation data, the abnormal factors causing the abnormality of each effluent indicator are determined, and a fault tree indicating the abnormal causes of each effluent indicator is established according to the abnormal factors; the multiple effluent indicators in the effective operation data are: effluent SS, effluent COD, effluent ammonia nitrogen and effluent TN; According to the established fault tree indicating the abnormal causes of each water output index, determine the A 2 / O Fault diagnosis results of abnormal effluent indicators of process system; Determine the abnormal factors that cause the abnormality of each water output index in the following manner, and establish a fault tree indicating the abnormal causes of each water output index based on the abnormal factors, including: If the effluent SS of the effluent index is confirmed to be greater than 10 mg / L, the effluent SS is confirmed to be abnormal. Based on the influencing factors of sludge load, dissolved oxygen in the aerobic zone and C / N ratio that can affect the SVI that causes the effluent SS abnormality, an effluent SS fault tree indicating the cause of the effluent SS abnormality is established; The outflow SS fault tree is composed of a root node connected to four layers of leaf nodes from top to bottom, where: The influencing factor corresponding to the root node is the increase in effluent SS, and the influencing factor corresponding to the first leaf node of the first layer connected to the root node is the inflow of the efficient sedimentation tank; The SS of the first leaf node of the first layer is lower than the design value. The output end is connected to the first leaf node of the second layer. The influencing factor corresponding to the first leaf node of the second layer is that the effluent of the secondary sedimentation tank is too high; The output end of the first leaf node of the second layer with a higher SVI value is connected to the first leaf node of the third layer. The influencing factor corresponding to the first leaf node of the third layer is poor sludge sedimentation performance and filamentous bacteria growth; The normal output end of the SVI value of the first leaf node of the second layer is connected to the second leaf node of the third layer. The influencing factor corresponding to the second leaf node of the third layer is the low operating efficiency of the secondary sedimentation tank; The first output end of the first leaf node of the third layer is connected to the first leaf node of the fourth layer, and the influencing factor corresponding to the first leaf node of the fourth layer is that the sludge load value is too high or too low; The second output end of the first leaf node of the third layer is connected to the second leaf node of the fourth layer, and the influencing factor corresponding to the second leaf node of the fourth layer is that the C / N ratio is too low; The output end of the HRT calculation of the second leaf node of the third layer is connected to the third leaf node of the fourth layer. The influencing factors corresponding to the third leaf node of the fourth layer are excessive water volume or excessive internal recirculation ratio; The normal output end of the HRT calculation of the second leaf node of the third layer is connected to the fourth leaf node of the fourth layer. The influencing factors corresponding to the fourth leaf node of the fourth layer are that the DO at the outlet of the aerobic zone is too low or the sludge difficulty is too high; The SS of the first leaf node of the first layer is higher than the design value. The output end is connected to the second leaf node of the first layer. The influencing factor corresponding to the second leaf node of the first layer is the problem of the operation of the high-efficiency sedimentation tank; The output end of the HRT calculation of the second leaf node of the first layer is connected to the third leaf node of the second layer. The influencing factor corresponding to the third leaf node of the second layer is excessive water volume under heavy rain conditions; The normal output end of the HRT calculation of the second leaf node of the first layer is connected to the fourth leaf node of the second layer. The influencing factor corresponding to the fourth leaf node of the second layer is the PAM problem; If the effluent COD of the effluent indicator is confirmed to be greater than 50 mg / L, the effluent COD is confirmed to be abnormal. Based on the sludge load and effluent SS concentration, which are the influencing factors that can cause the effluent COD abnormality, a effluent COD fault tree indicating the cause of the effluent COD abnormality is established; The outlet COD fault tree consists of a root node connected to six layers of leaf nodes from top to bottom, where: The influencing factor corresponding to the root node is the outflow COD exceeding the standard. The outflow SS exceeding standard output terminal of the root node is connected to the first leaf node of the first layer. The corresponding influencing factor of the first leaf node of the first layer is the outflow SS fault tree. The normal output end of the effluent SS of the root node is connected to the second leaf node of the first layer, and the corresponding influencing factor of the second leaf node of the first layer is the excessive dissolved COD; The output end of the second leaf node of the first layer is connected to the first leaf node of the third layer, and the influencing factor corresponding to the first leaf node of the third layer is the aeration of activated sludge after effluent cleaning; The sCOD of the first leaf node of the third layer is greatly reduced, and the output end is connected to the first leaf node of the third layer. The influencing factor corresponding to the first leaf node of the third layer is the problem of the operation of the biochemical system; The sCOD of the first leaf node of the third layer has not changed significantly. The output end is connected to the second leaf node of the third layer. The influencing factor corresponding to the second leaf node of the third layer is that the influent difficult-to-degrade components are too high. The normal output end of the secondary sedimentation tank influent sCOD of the first leaf node of the third layer is connected to the first leaf node of the fourth layer. The influencing factor corresponding to the first leaf node of the fourth layer is the sCOD released by the anaerobic digestion of the secondary sedimentation tank; The output end of the secondary sedimentation tank inlet sCOD too high of the first leaf node of the third layer is connected to the second leaf node of the fourth layer. The influencing factor corresponding to the second leaf node of the fourth layer is the operation problem of the aeration tank. The output end of the first leaf node of the fourth layer is connected to the first leaf node of the fifth layer, and the influencing factor corresponding to the first leaf node of the fifth layer is to increase the DO value at the outlet of the aeration tank; The output end of the DO or aeration volume of the second leaf node of the fourth layer is low, and the output end is connected to the second leaf node of the fifth layer. The influencing factor corresponding to the second leaf node of the fifth layer is insufficient aeration volume; The DO or aeration volume normal output terminal of the second leaf node of the fourth layer is connected to the third leaf node of the fifth layer, and the influencing factor corresponding to the third leaf node of the fifth layer is the sludge bacterial community; The first output end of the third leaf node of the fifth layer is connected to the first leaf node of the sixth layer, and the influencing factor corresponding to the first leaf node of the sixth layer is that the influent COD load is too high; The second output end of the third leaf node of the fifth layer is connected to the first leaf node of the sixth layer, and the influencing factor corresponding to the second leaf node of the sixth layer is sludge aging; If the effluent ammonia nitrogen of the effluent indicator NH4-N is greater than 5mg / L, it is confirmed that the effluent ammonia nitrogen is abnormal. According to the influencing factors that can cause the abnormal effluent ammonia nitrogen, such as sludge load and dissolved oxygen in the aerobic zone, a fault tree of effluent ammonia nitrogen indicating the cause of the abnormal effluent ammonia nitrogen is established; The effluent ammonia nitrogen fault tree is composed of a root node connected to four layers of leaf nodes from top to bottom, where: The influencing factor corresponding to the root node is the excessive ammonia nitrogen in the effluent, and the influencing factor corresponding to the first leaf node of the first layer connected to the root node is the sludge concentration MLSS; The normal output end of the sludge concentration of the first leaf node of the first layer is connected to the first leaf node of the second layer, and the influencing factor corresponding to the first leaf node of the second layer is temperature; The output end of the first leaf node of the first layer with too low sludge concentration is connected to the second leaf node of the second layer. The influencing factors corresponding to the second leaf node of the second layer are extending SRT and increasing sludge concentration; The temperature too low output end of the first leaf node of the second layer is connected to the first leaf node of the third layer, and the influencing factor corresponding to the first leaf node of the third layer is to increase the sludge concentration; The temperature too low output end of the first leaf node of the second layer is connected to the second leaf node of the third layer. The influencing factors corresponding to the second leaf node of the third layer are increasing the DO and gas volume of the aeration tank; The effective output end of the second leaf node of the third layer is connected to the first leaf node of the fourth layer, and the influencing factor corresponding to the first leaf node of the fourth layer is to increase the frequency of fan control; The invalid output end of the second leaf node of the third layer is connected to the second leaf node of the fourth layer. The influencing factors corresponding to the second leaf node of the fourth layer are bacterial degradation or shock by influent toxicity, and adding new mud; If the effluent TN of the effluent indicator is confirmed to be greater than 15 mg / L, the effluent TN is confirmed to be abnormal. Based on the factors that can cause the effluent TN abnormality, such as effluent ammonia nitrogen, anoxic pool redox potential and C / N ratio, a effluent TN fault tree indicating the cause of the effluent TN abnormality is established; The outflow TN fault tree is composed of a root node connected to four layers of leaf nodes from top to bottom, where: The influencing factor corresponding to the root node is the effluent TN exceeding the standard, and the influencing factor corresponding to the first leaf node of the first layer connected to the root node is the effluent ammonia nitrogen; The normal output end of the effluent ammonia nitrogen of the first leaf node of the first layer is connected to the first leaf node of the second layer, and the influencing factor corresponding to the first leaf node of the second layer is the influent C / N ratio; The output end of the first leaf node of the first layer where the outflow ammonia nitrogen does not meet the standard is connected to the second leaf node of the second layer, and the influencing factor corresponding to the second leaf node of the second layer is the outflow ammonia nitrogen failure; The inlet C / N too low output end of the first leaf node of the second layer is connected to the first leaf node of the third layer, and the influencing factor corresponding to the first leaf node of the third layer is the addition of carbon source; The output end of the first leaf node of the second layer with an influent C / N ratio greater than 4 is connected to the second leaf node of the third layer, and the influencing factor corresponding to the second leaf node of the third layer is the redox potential of the anoxic pool; The effect of the first leaf node of the third layer is not obvious. The output end is connected to the first leaf node of the fourth layer. The influencing factor corresponding to the first leaf node of the fourth layer is that there is a problem with the quality of the carbon source. The effective output end of the first leaf node of the third layer is connected to the second leaf node of the fourth layer, and the influencing factor corresponding to the second leaf node of the fourth layer is insufficient carbon source; The redox potential too high output end of the second leaf node of the third layer is connected to the third leaf node of the fourth layer. The influencing factor corresponding to the third leaf node of the fourth layer is the high DO of the return liquid of the aerobic pool, which controls the fan; The redox potential reasonable output end of the second leaf node of the third layer is connected to the fourth leaf node of the fourth layer, and the influencing factor corresponding to the fourth leaf node of the fourth layer is to make a carbon source addition judgment.

2. A according to claim 1 2 A fault diagnosis method for a / O process system, characterized in that: A to be diagnosed 2 / O The process operation data of the process system includes: manually reported effluent data and online monitoring effluent data; The process operation data is subjected to abnormal data detection and removal in the following manner, including: Performing abnormal value detection on the manually reported water output data through a box plot to determine false alarm values ​​and / or false alarm values ​​as abnormal data, and removing the detected abnormal data from the manually reported water output data to obtain manually reported valid data; The online monitoring water output data is detected according to a predetermined empirical range of water output indicators, data not within the empirical range of water output indicators is determined as abnormal data, and the detected abnormal data is removed from the online monitoring water output data to obtain online monitoring valid data.

3. A according to claim 2 2 A fault diagnosis method for a / O process system, characterized in that: The empirical ranges for the effluent specifications are predetermined in the following manner, including: According to the detected A 2 / O The historical data of the process system and the expert experience are used to divide the online monitoring effluent data and determine the empirical range of the effluent indicators; The method further includes: testing the manually reported valid data and the online monitoring valid data by pairing t, determining the difference between the manually reported valid data and the online monitoring valid data according to the test result, and if it is determined that the difference does not exceed a preset value, confirming that the manually reported valid data meets the accuracy requirement; It also includes: performing abnormal processing on the online monitoring valid data in the following manner, including: After removing outliers, two-sided and one-sided t tests were performed on the effective online monitoring data to determine whether the effluent water quality data exceeded the discharge standard.

4. A method for implementing the method according to any one of claims 1 to 3 2 / O process system fault diagnosis system, characterized in that, include: Data acquisition unit, abnormal data detection unit, water discharge index fault tree establishment unit and diagnosis result output unit; wherein, The data acquisition unit is connected to the diagnosed A 2 / O process system communication connection, can obtain the A to be diagnosed according to the preset cycle 2 / O process operation data of process system; The abnormal data detection unit is in communication connection with the data acquisition unit, and can perform abnormal data detection on the process operation data acquired by the data acquisition unit to remove the abnormal data and obtain effective operation data; The water discharge index fault tree establishing unit is in communication connection with the abnormal data detection unit, and can determine the abnormal factors causing the abnormality of each water discharge index according to the multiple water discharge indexes in the effective operation data obtained by the abnormal data detection unit, and establish a fault tree indicating the abnormal causes of each water discharge index according to the abnormal factors; The diagnostic result output unit is in communication with the water output index fault tree establishment unit, and can determine and output the A according to the fault tree indicating the abnormal causes of each water output index established by the water output index fault tree establishment unit. 2 / O process system effluent index abnormal fault diagnosis results.

5. A according to claim 4 2 / O process system fault diagnosis system, characterized in that, The abnormal data detection unit comprises: Manually reported water output data acquisition module, manually reported water output data processing module, online monitoring water output data acquisition module, online monitoring water output data processing module and matching module; wherein, The manual reporting water data acquisition module can obtain the diagnosed A 2 / O Manual reporting of effluent data from process systems; The manually reported water output data processing module is in communication connection with the manually reported water output data acquisition module, and can perform abnormal value detection on the manually reported water output data acquired by the manually reported water output data acquisition module through a box plot to determine false alarm values ​​and / or false alarm values ​​as abnormal data, and remove the detected abnormal data from the manually reported water output data to obtain manually reported valid data; The online monitoring water output data acquisition module is connected to the diagnosed A 2 / O process system communication connection, can obtain the diagnosis A 2 / O process system online monitoring of effluent data; The online monitoring water output data processing module is in communication connection with the online monitoring water output data acquisition module, and can detect the online monitoring water output data according to the predetermined experience range of the water output index, determine the data that is not within the experience range of the water output index as abnormal data, and remove the detected abnormal data from the manually reported water output data to obtain the online monitoring valid data; The water discharge index fault tree establishing unit comprises: Outlet SS fault tree establishment submodule, outlet COD fault tree establishment submodule, outlet ammonia nitrogen fault tree establishment submodule and outlet TN fault tree establishment submodule; among which, The effluent SS fault tree establishment submodule can establish an effluent SS fault tree indicating the cause of the effluent SS abnormality based on the influencing factors of the SVI that can affect the effluent SS abnormality, such as sludge load, dissolved oxygen in the aerobic zone, and C / N ratio; when the effluent SS of the effluent indicator is greater than 10 mg / L and the effluent SS is confirmed to be abnormal, the effluent SS fault tree indicating the cause of the effluent SS abnormality can be established; The effluent COD fault tree establishment submodule can establish an effluent COD fault tree indicating the cause of the effluent COD abnormality based on the sludge load and effluent SS concentration, which are influencing factors that can cause the effluent COD abnormality, when the effluent COD of the effluent indicator is greater than 50 mg / L and the effluent COD is confirmed to be abnormal; The effluent ammonia nitrogen fault tree establishment submodule can establish an effluent ammonia nitrogen fault tree indicating the cause of the effluent ammonia nitrogen abnormality based on the influencing factors of sludge load and dissolved oxygen in the aerobic zone that can cause the effluent ammonia nitrogen abnormality when the effluent NH4-N of the effluent ammonia nitrogen indicator is greater than 5 mg / L and the effluent ammonia nitrogen is confirmed to be abnormal; The outlet TN fault tree establishment submodule can establish an outlet TN fault tree indicating the cause of the outlet TN abnormality based on the influencing factors that can cause the outlet TN abnormality, such as the outlet TN of the outlet water indicator is greater than 15 mg / L and the outlet TN is confirmed to be abnormal.

6. A processing device, characterized in that: include: at least one memory for storing one or more programs; At least one processor is capable of executing one or more programs stored in the memory, and when the one or more programs are executed by the processor, the processor is able to implement the method described in any one of claims 1 to 3.

7. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 can be implemented.

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