Sewage treatment equipment fault determination method and device and computer readable storage medium

Abstract

The invention discloses a sewage treatment equipment fault determination method and device and a computer readable storage medium. The method for determining the fault of the sewage treatment equipment comprises the following steps: collecting sewage data of the sewage treatment equipment at a plurality of continuous moments in a preset period; feature extraction is carried out on the sewage data at the first moment through a feature extraction model according to the incidence relation between the sewage data at the first moment and sewage data at the second moment in the preset period, first feature information is generated, and the second moment is the moment before the first moment in the preset period; and performing classification processing on the first feature information through a classification model, and determining whether the sewage treatment equipment has a fault or not.

Description

Technical Field

[0001] The present application relates to the technical field of sewage treatment, and in particular to a method and device for determining a fault of sewage treatment equipment and a computer-readable storage medium. Background Art

[0002] In recent years, the progress of urban construction in my country has accelerated, and the living standards of residents have been greatly improved. However, at the same time, more and more industrial production and daily life wastewater have been generated in cities, leading to increasingly serious problems such as water pollution. In recent years, with the large-scale construction of sewage treatment plants in various regions, this problem has been alleviated to a certain extent. However, since sewage treatment involves a large number of physical, biological and chemical reactions, the process involves a variety of monitoring, detection and sensor equipment, and is in a harsh environment for a long time, sewage treatment plant failures occur from time to time. When a sewage treatment plant fails, it will affect its operating efficiency and cannot guarantee whether the discharged sewage is safe. At the same time, since sewage treatment plants are usually equipped with large equipment such as pumping stations and blowers, once a failure occurs, it may also threaten the personal safety of the staff. Therefore, it is particularly important to conduct comprehensive fault diagnosis of the process and key equipment of the sewage treatment process. At present, most of the control and management processes of sewage treatment plants in my country still require human participation, resulting in low efficiency of sewage treatment plants and high labor costs. Fault detection and diagnosis during the operation of sewage treatment plants mainly rely on experienced technical experts to calculate and analyze historical data collected in the database, find abnormal values ​​in historical operation data, make judgments based on experience, and finally give diagnostic results and specific response plans. With the increase in the number and scale of sewage treatment plants, relying solely on experts for manual diagnosis is not efficient and standard enough to meet the requirements of sewage treatment.

[0003] The publication number is CN112241128A, and the name is control method, system, equipment and medium for abnormality of sewage treatment equipment. The method includes the following steps: obtaining abnormal information of sewage treatment equipment, the abnormal information includes alarm information and / or maintenance information; obtaining prompt information corresponding to the abnormal information, the prompt information includes the abnormality handling method corresponding to the abnormal information; calculating the number of abnormal information currently existing, if the number of abnormal information is greater than 1, displaying all prompt information in an overlapping manner; judging whether the abnormal information has been processed; if the processing is completed, closing the corresponding prompt information; if the processing is not completed, when a closing request is detected, closing the prompt information in response to the closing request, and redisplaying the prompt information.

[0004] The publication number is CN118964833A, and the name is a method for monitoring faults of sewage treatment equipment based on cloud computing. The specific steps include collecting water signals and electrical signals of water treatment equipment through sensors and transmitting them to a cloud computing platform, using a distributed database of the cloud computing platform to store water signals and electrical signals and preprocess the data, processing and analyzing the water signals and electrical signals, using machine learning algorithms to build a fault detection model, using historical fault data and regular operation data of water treatment equipment for label training, evaluating the performance of the fault detection model through cross-validation, using the monitoring service of the cloud computing platform to monitor the operation status of the equipment in real time, predicting the operation data of the water treatment equipment according to the fault detection model, and triggering an alarm mechanism when an abnormal fault of the water treatment equipment is predicted.

[0005] With respect to the technical problems existing in the above-mentioned prior art of using manual labor to diagnose faults of equipment in sewage treatment plants, resulting in low diagnostic efficiency and high costs, no effective solution has been proposed so far. Summary of the invention

[0006] The embodiments of the present application provide a method, device and computer-readable storage medium for determining faults in sewage treatment equipment, so as to at least solve the technical problems existing in the prior art of manually diagnosing faults of equipment in sewage treatment plants, resulting in low diagnostic efficiency and high costs.

[0007] According to one aspect of an embodiment of the present application, a method for determining a fault in a sewage treatment equipment is provided, comprising: collecting sewage data of the sewage treatment equipment at multiple consecutive moments in a preset period; performing feature extraction on the sewage data at the first moment according to a correlation between the sewage data at the first moment and the sewage data at the second moment in the preset period through a feature extraction model to generate first feature information, wherein the second moment is a moment before the first moment in the preset period; and classifying the first feature information through a classification model to determine whether the sewage treatment equipment has a fault.

[0008] According to another aspect of an embodiment of the present application, a sewage treatment equipment fault determination device is also provided, including: a data acquisition module, used to collect sewage data of the sewage treatment equipment at multiple consecutive moments in a preset period; an information generation module, used to extract features of the sewage data at the first moment in the preset period according to the correlation relationship between the sewage data at the first moment and the sewage data at the second moment through a feature extraction model, and generate first feature information, wherein the second moment is a moment before the first moment in the preset period; and a fault determination module, used to classify the first feature information through a classification model to determine whether the sewage treatment equipment has a fault.

[0009] According to another aspect of an embodiment of the present application, a sewage treatment equipment fault determination device is also provided, including: a processor; and a memory, connected to the processor, for providing the processor with instructions for processing the following processing steps: collecting sewage data of the sewage treatment equipment at multiple consecutive moments in a preset cycle; performing feature extraction on the sewage data at the first moment according to the correlation between the sewage data at the first moment and the sewage data at the second moment in the preset cycle through a feature extraction model to generate first feature information, wherein the second moment is a moment before the first moment in the preset cycle; and classifying the first feature information through a classification model to determine whether the sewage treatment equipment has a fault.

[0010] According to another aspect of an embodiment of the present application, a computer system is also provided, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the above method.

[0011] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0012] According to another aspect of the embodiments of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0013] In the embodiment of the present application, the diagnostic system collects sewage data at different times corresponding to each sewage treatment system, and realizes comprehensive monitoring of the dynamic changes of the sewage treatment process. In addition, the technical solution uses a feature extraction model to analyze the correlation of sewage data at different times, so as to extract feature information based on the correlation with the sewage data at the previous moment, so as to focus on the influence between the sewage data at different times and trace the source of the fault. In addition, the technical solution combines the classification model to efficiently classify the extracted first feature information, improves the speed of fault diagnosis, and enhances the reliability of the diagnosis result. Therefore, the technical solution reduces the dependence on manual experience and subjective judgment through model processing, reduces the misjudgment rate caused by human factors, and also reduces the workload of technical personnel. In addition, the technical solution can timely warn of potential faults through automated and intelligent fault diagnosis, and take measures in advance to avoid major accidents. Thus, the technical problems of low diagnostic efficiency and high cost caused by manual fault diagnosis of equipment in sewage treatment plants in the prior art are solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present application; Figure 2 is a schematic diagram of a fault diagnosis system for sewage treatment equipment according to Example 1 of the present application; Figure 3 is a flow chart of a method for determining a fault of a sewage treatment equipment according to Example 1 of the present application; Figure 4 is a schematic diagram of a feature extraction model according to Example 1 of the present application; Figure 5 is a schematic diagram of a classification model according to Example 1 of the present application; Figure 6 is a sequential flow chart of the method for determining a fault of a sewage treatment equipment according to Example 1 of the present application; Figure 7 is a schematic diagram of a sewage treatment equipment fault determination device according to Embodiment 2 of the present application; and Figure 8 It is a schematic diagram of a sewage treatment equipment fault determination device according to Example 3 of the present application. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0016] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0017] Example 1 According to this embodiment, a method embodiment of a sewage treatment equipment fault determination method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0018] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computing device for implementing a method for determining a fault in a sewage treatment device. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0019] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computing device. As involved in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0020] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for determining the fault of sewage treatment equipment in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the method for determining the fault of sewage treatment equipment of the above-mentioned application is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0021] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computing device. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0022] The display may be, for example, a touch screen liquid crystal display (LCD) that may enable a user to interact with a user interface of the computing device.

[0023] It should be noted that, in some optional embodiments, the above Figure 1 The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing devices described above.

[0024] Figure 2 Schematic diagram of the fault diagnosis system for sewage treatment equipment according to this embodiment. Figure 2 As shown, the system includes: a sewage treatment plant 100 and a diagnostic system 200.

[0025] The diagnostic system 200 is used to diagnose whether each sewage treatment equipment in the sewage treatment plant 100 has a fault.

[0026] The sewage treatment equipment in the sewage treatment plant 100 includes equipment 101-10m. The sewage treatment equipment 101-10m may be, for example, a screen decontamination machine, a grit chamber, an anaerobic reactor, an aerobic reactor, a secondary sedimentation tank, a sludge dewatering machine, and other equipment.

[0027] Wastewater treatment is a complex process that usually includes multiple steps and stages, each of which is performed by specialized equipment to perform specific tasks. The following is the general working order of screen scrubbers, grit chambers, anaerobic reactors, aerobic reactors, secondary sedimentation tanks, and sludge dewatering machines when treating sewage: After entering the sewage treatment plant, the sewage first passes through the screen scrubber. The main purpose of the screen scrubber is to filter out large particulate matter in the sewage, such as plastic bags, branches, paper, etc., to prevent these substances from clogging subsequent treatment equipment. The sewage that has undergone preliminary filtration then flows into the grit chamber. Here, heavier inorganic particles, such as sand and mud, settle to the bottom of the tank due to gravity and are removed. Next, the sewage enters the anaerobic reactor. At this stage, the sewage breaks down complex organic matter into simple compounds, such as methane and carbon dioxide, through the action of anaerobic microorganisms in an oxygen-deficient environment. Then, the sewage enters the aerobic reactor. Sufficient oxygen is provided here, allowing aerobic microorganisms to further break down the remaining organic matter and convert it into harmless substances, such as carbon dioxide and water. The wastewater after biological treatment flows into the secondary sedimentation tank, where the biological flocs (i.e. activated sludge) generated during the treatment process are separated from the water by gravity sedimentation. Finally, the sludge separated from the secondary sedimentation tank will be sent to the sludge dewatering machine for dehydration treatment to reduce the water content of the sludge for subsequent disposal or utilization.

[0028] Each of the sewage treatment equipment 101-10m is equipped with a sensor for providing sewage data. For example, a screen decontamination machine is provided with a liquid level sensor for monitoring the inlet liquid level to ensure the normal operation of the equipment.

[0029] The grit chamber is equipped with suspended matter sensors to monitor the concentration of suspended matter in the water and evaluate the grit settling effect.

[0030] The anaerobic reactor is equipped with a pH sensor to monitor the pH value in the reactor and ensure the best living conditions for anaerobic microorganisms.

[0031] The aerobic reactor is equipped with a dissolved oxygen sensor to monitor the dissolved oxygen concentration and ensure that aerobic microorganisms have enough oxygen to degrade organic matter.

[0032] The secondary sedimentation tank is equipped with a suspended matter sensor to monitor the concentration of suspended matter in the water and evaluate the sedimentation effect.

[0033] The sludge dewatering machine is equipped with a pressure sensor to monitor the pressure during the dewatering process and ensure the safe operation of the equipment.

[0034] Under the above operating environment, according to the first aspect of this embodiment, a method for determining a fault of a sewage treatment equipment is provided. The method comprises: Figure 2 The diagnostic system 200 shown in FIG. Figure 3 A schematic diagram showing the process of the method is shown in FIG. Figure 3 As shown, the method includes: S302: Collecting sewage data of the sewage treatment equipment at multiple consecutive moments in a preset period; S304: extracting features of the sewage data at the first moment according to the correlation between the sewage data at the first moment and the sewage data at the second moment in the preset period by using a feature extraction model to generate first feature information, wherein the second moment is a moment before the first moment in the preset period; and S306: Classify the first feature information through a classification model to determine whether the sewage treatment equipment fails.

[0035] Specifically, the diagnostic system 200 determines a preset period, for example, the preset period has a total of n consecutive moments. Thus, the diagnostic system 200 collects sewage data A1~A1 at n moments in the preset period through sensors installed on each sewage treatment equipment 101~10m. n Where i=1~n, the sewage data A at the i-th moment i : A1=[a 1,1 ,a 2,1 ,...,a m,1 ] T ; A2=[a 1,2 ,a 2,2 ,...,a m,2 ] T ; ... A n =[a 1,n ,a 2,n ,...,a m,n ] T .

[0036] where a j,i Represents the sewage data of the jth sewage treatment equipment at the i-th moment, j=1~m.

[0037] Furthermore, the diagnostic system 200 is pre-set with a feature extraction model. The feature extraction model may be an attention model, which is used to extract features from sewage data. And the feature extraction model is pre-trained by the diagnostic system 200 according to the sample sewage data through the gradient descent method. Thus, the diagnostic system 200 determines the sewage data at the first moment in the preset period, and the sewage data at the second moment corresponding to each first moment. The second moment is the moment before the first moment in the preset period. The first moment is moment 1 to moment m, and the second moment corresponding to the i-th first moment is moment 1 to moment i. Then the sewage data at the i-th first moment is A i , the sewage data at the second moment corresponding to the i-th first moment (i.e., moment 1 to moment i) are A1~A i .

[0038] For example, the sewage data at the first first moment is A1, and the sewage data at the second moment (i.e., moment 1) corresponding to the first first moment is A1; The sewage data at the second first moment is A2, and the sewage data at the second moment (i.e., moment 1 to moment 2) corresponding to the second first moment is A1 to A2; … The sewage data at the first moment of n is A n , the sewage data at the second moment corresponding to the nth first moment (i.e., moment 1 to moment n) is A1~A n .

[0039] In the sewage treatment process, the sewage data at the next moment will be affected by the sewage data at the previous moment. Therefore, the diagnostic system 200 uses the feature extraction model to extract the sewage data at the previous moment (i.e., the sewage data A at the second moment). g , g=1~i) and the sewage data at the next moment (i.e., the sewage data A at the first moment i ), for the sewage data A at the first moment i Perform feature extraction to generate first feature information.

[0040] Furthermore, the diagnosis system 200 is pre-set with a classification model, so that the diagnosis system 200 classifies the first feature information through the classification model to determine whether the sewage treatment equipment 101~10m has a fault.

[0041] As described in the background technology, in recent years, the progress of urban construction in my country has accelerated, and the living standards of residents have been greatly improved. However, at the same time, more and more industrial production and daily life wastewater have been generated in cities, resulting in increasingly serious problems such as water pollution. In recent years, with the large-scale construction of sewage treatment plants in various regions, this problem has been alleviated to a certain extent. However, since sewage treatment involves a large number of physical, biological and chemical reactions, the process involves a variety of monitoring, detection and sensor equipment, and is in a harsh environment for a long time, sewage treatment plant failures occur from time to time. When a sewage treatment plant fails, it will affect its operating efficiency and cannot guarantee whether the discharged sewage is safe. At the same time, since sewage treatment plants are usually equipped with large equipment such as pumping stations and blowers, once a failure occurs, it may also threaten the personal safety of the staff. Therefore, it is particularly important to conduct comprehensive fault diagnosis of the process and key equipment of the sewage treatment process. At this stage, most of the control and management processes of sewage treatment plants in my country still require human participation, resulting in low efficiency of sewage treatment plants and high labor costs. Fault detection and diagnosis during the operation of sewage treatment plants mainly rely on experienced technical experts to calculate and analyze historical data collected in the database, find abnormal values ​​in historical operation data, make judgments based on experience, and finally give diagnostic results and specific response plans. With the increase in the number and scale of sewage treatment plants, relying solely on experts for manual diagnosis is not efficient and standard enough to meet the requirements of sewage treatment.

[0042] In view of the technical problems described above, through the technical solutions of the embodiments of the present application, the diagnostic system collects sewage data at different times corresponding to each sewage treatment system, and realizes comprehensive monitoring of the dynamic changes of the sewage treatment process. In addition, the technical solution adopts a feature extraction model to analyze the correlation of sewage data at different times, so as to extract feature information according to the correlation with the sewage data at the previous moment, so as to focus on the influence between the sewage data at different times and trace the source of the fault. In addition, the technical solution combines the classification model to efficiently classify the extracted first feature information, improves the speed of fault diagnosis, and enhances the reliability of the diagnosis result. Therefore, the technical solution reduces the dependence on manual experience and subjective judgment through model processing, reduces the misjudgment rate caused by human factors, and also reduces the workload of technical personnel. In addition, the technical solution can timely warn potential faults through automated and intelligent fault diagnosis, and take measures in advance to avoid major accidents. Thus, the technical problems of low diagnostic efficiency and high cost caused by manual fault diagnosis of equipment in sewage treatment plants in the prior art are solved.

[0043] Optionally, the operation of extracting features from the sewage data at the first moment in a preset period according to the correlation between the sewage data at the first moment and the sewage data at the second moment in a preset period to generate the first feature information includes: linearly transforming the sewage data at multiple consecutive moments in the preset period through the feature extraction model to generate corresponding query vectors, key vectors and value vectors; calculating the similarity between the query vector at the first moment and the key vector at the second moment through the feature extraction model; normalizing the similarity through the softmax function through the feature extraction model to generate a corresponding normalized value; and using the normalized value as a weight to perform a weighted sum operation with the corresponding value vector at the second moment through the feature extraction model to generate the first feature information.

[0044] Specifically, the diagnostic system 200 uses a preset weight matrix to extract the sewage data A at n moments through a feature extraction model. i Perform linear transformation to generate the corresponding query vector Q i , key vector K i Sum value vector V i .

[0045] Furthermore, the diagnostic system 200 determines the sewage data A at the first moment through the feature extraction model. i The corresponding query vector Q i , key vector K i Sum value vector V i , and determine the sewage data A at the second moment g The corresponding query vector Q g , key vector K g Sum value vector V g . refer to Figure 4 As shown, the diagnostic system 200 then calculates the query vector Q at the first moment through the feature extraction model i and the key vector K at the second moment g (Right now, Figure 4 K1~K i ) g,i (Right now, Figure 4 The s 1,i ~s i,i ). The method for calculating the similarity can be the inner product method. The similarity calculation formula is as follows: s g,i =F(Q i ,K g )=Q i K g .

[0046] For example, for the query vector Q1 at the first moment and the key vector K at the second moment g The similarity betweeng,1 , the similarity s between the query vector Q1 and the key vector K1 can be calculated 1,1 : s 1,1 =F(Q1,K1)=Q1K1.

[0047] For the query vector Q2 at the first moment and the key vector K at the second moment g The similarity between g,2 , the similarity s between the query vector Q2 and the key vector K1 can be calculated 1,2 : s 1,2 =F(Q2,K1)=Q2K1.

[0048] Calculate the similarity s between the query vector Q2 and the key vector K2 2,2 : s 2,2 =F(Q2,K2)=Q2K2.

[0049] For the query vector Q3 at the first moment and the key vector K at the second moment g The similarity between g,3 , the similarity s between the query vector Q3 and the key vector K1 can be calculated 1,3 : s 1,3 =F(Q3,K1)=Q3K1.

[0050] Calculate the similarity s between the query vector Q3 and the key vector K2 2,3 : s 2,3 =F(Q3,K2)=Q3K2.

[0051] Calculate the similarity s between the query vector Q3 and the key vector K3 3,3 : s 3,3 =F(Q3,K3)=Q3K3.

[0052] Similarly, for the query vector Q at the first moment n and the key vector K at the second moment g The similarity between g,n , the query vector Q can be calculated n The similarity s between the key vector K1 1,n : s 1,n =F(Q n ,K1)=Q n K1.

[0053] Calculate the query vector Q n The similarity s between the key vector K22,n : s 2,n =F(Q n ,K2)=Q n K2.

[0054] And so on, calculate the query vector Q n With the key vector K n The similarity between n,n .

[0055] s n,n =F(Q n ,K n )=Q n K n .

[0056] Furthermore, the diagnostic system 200 uses the feature extraction model to convert the similarity s into g,i Perform normalization processing to generate the corresponding normalized value p g,i : .

[0057] where d k Denotes the query vector Q i feature dimension.

[0058] Furthermore, the diagnostic system 200 uses a feature extraction model to convert the normalized value p g,i (Right now, Figure 4 p 1,i ~p i,i ) as the weight and the corresponding second moment value vector V g (Right now, Figure 4 V1~V i ) performs weighted summation to generate the first feature information [WA]=[W1,W2,...,W n ]. The first feature information W i The calculation formula is: .

[0059] Therefore, the technical solution calculates the correlation between sewage data at different times, and extracts key features based on previous sewage data, thereby reducing human errors and shortening the time for fault diagnosis.

[0060] Optionally, the sewage data at multiple consecutive moments in a preset period are linearly transformed through a feature extraction model to generate corresponding query vectors, key vectors and value vectors, including: the sewage data at multiple consecutive moments in a preset period are linearly transformed through a feature extraction model according to a preset weight matrix to generate query vectors, key vectors and value vectors.

[0061] Specifically, the diagnostic system 200 is pre-set with a weight matrix B for performing linear transformation Q , B K , B V . The weight matrix B Q Used to generate query vector, weight matrix B K Used to generate key vector, weight matrix B V Used to generate a vector of values.

[0062] Thus, the diagnostic system 200 extracts the feature according to the sewage data A i and the weight matrix B Q , generate query vector Q i : Q i =A i B Q .

[0063] The diagnostic system 200 uses a feature extraction model based on the sewage data A i and the weight matrix B K , generate the key vector K i : K i =A i B K .

[0064] The diagnostic system 200 uses a feature extraction model based on the sewage data A i and the weight matrix B V , generating a value vector V i : V i =A i B V .

[0065] Thus, the technical solution generates a query vector, a key vector, and a value vector based on the weight matrix, thereby dynamically adjusting the focus of attention according to the relative importance of sewage data at different moments, thereby accurately locating abnormal signals in complex data streams.

[0066] Optionally, the operation of classifying the first feature information through a classification model to determine whether the sewage treatment equipment is faulty includes: processing the first feature information through a fully connected layer of the classification model to generate second feature information; and classifying the second feature information through multiple binary classifiers to determine whether the sewage treatment equipment is faulty.

[0067] Specifically, refer to Figure 5As shown, the diagnosis system 200 is pre-set with a classification model, which includes a fully connected layer and multiple binary classifiers. The classification model is pre-trained by the diagnosis system 200 using the feature information output by the feature extraction model as a sample through a gradient descent method.

[0068] The diagnostic system 200 inputs the first feature information [WA] output by the feature extraction model into the classification model. The classification model inputs the first feature information [WA] into the hidden layer through the input layer of the fully connected layer. After the hidden layer processes the first feature information [WA], the output layer outputs the second feature information [PA] corresponding to the first feature information [WA] = [pa1, pa2, ..., pa L ] T .

[0069] Furthermore, the diagnostic system 200 inputs the second characteristic information into a binary classifier, and classifies the second characteristic information through the binary classifier to determine whether each of the sewage treatment equipment 101 - 10m has a fault.

[0070] Therefore, the technical solution determines whether there is a fault in the sewage treatment equipment through a classification model, which not only improves the speed of fault diagnosis but also enhances the reliability of the diagnosis result.

[0071] Optionally, the second feature information is classified and processed by multiple binary classifiers to determine whether the sewage treatment equipment has a fault, including: determining the probability of a fault in the sewage treatment equipment based on the second feature information by a binary classifier corresponding to the sewage treatment equipment; and determining whether the corresponding sewage treatment equipment has a fault based on the probability.

[0072] Specifically, refer to Figure 5 As shown, the diagnostic system 200 outputs the second feature information [PA]=[pa1, pa2, ..., pa L ] T , and the diagnostic system 200 is pre-set with a binary classifier corresponding to the second feature information [PA], wherein the number of the binary classifiers is the same as the number of sewage treatment equipment, and each binary classifier is used to classify different sewage treatment equipment to determine the probability of failure of the sewage treatment equipment.

[0073] For example, the second feature information pa1 and pa2 are feature information of the sewage treatment equipment 101, and the binary classifier 1 is used to determine the probability of failure of the sewage treatment equipment 101. Thus, the diagnostic system 200 inputs the second feature information pa1 and pa2 into the binary classifier 1 through the classification model, and thereby determines the probability pb1 of failure of the sewage treatment equipment 101 through the binary classifier 1 according to the second feature information pa1 and pa2.

[0074] The second characteristic information pa3 and pa4 are characteristic information of the sewage treatment equipment 103, and the binary classifier 2 is used to determine the probability of failure of the sewage treatment equipment 102. Thus, the diagnostic system 200 inputs the second characteristic information pa3 and pa4 into the binary classifier 2 through the classification model, and thereby determines the probability pb2 of failure of the sewage treatment equipment 102 according to the second characteristic information pa3 and pa4 through the binary classifier 2.

[0075] Similarly, the second feature information pa L-1 and pa L is the characteristic information of the sewage treatment equipment 10m, and the binary classifier m is used to determine the probability of failure of the sewage treatment equipment 10m. Thus, the diagnostic system 200 classifies the second characteristic information pa L-1 and pa L Input to the binary classifier m, so that the binary classifier m can be used to classify the second feature information pa L-1 and pa L Determine the probability pb of failure of sewage treatment equipment 10m m .

[0076] Furthermore, the diagnostic system 200 determines the probability pb j (j=1~m) is greater than a preset threshold (for example, 0.5). If it is greater than the preset threshold, it is determined that the corresponding sewage treatment equipment has failed.

[0077] Therefore, this technical solution sets a binary classification model for each sewage treatment device, so as to determine whether each sewage treatment device has a fault and accurately locate the sewage treatment device with a fault.

[0078] In summary, reference Figure 6 As shown, the sequential steps for determining whether each sewage treatment equipment has a fault are as follows: S601: the diagnostic system 200 collects sewage data of the sewage treatment equipment at multiple consecutive moments in a preset period; S602: The diagnostic system 200 uses a feature extraction model to linearly transform the sewage data at multiple consecutive moments in a preset period according to a preset weight matrix to generate a query vector, a key vector, and a value vector; S603: the diagnosis system 200 calculates the similarity between the query vector at the first moment and the key vector at the second moment through the feature extraction model; S604: The diagnostic system 200 normalizes the similarity through the softmax function using the feature extraction model to generate a corresponding normalized value; S605: The diagnostic system 200 uses the normalized value as a weight to perform a weighted sum operation with the corresponding value vector at the second moment through the feature extraction model to generate first feature information; S606: The diagnosis system 200 processes the first feature information through the fully connected layer of the classification model to generate second feature information; S607: The diagnostic system 200 determines the probability of failure of the sewage treatment equipment according to the second feature information by using a binary classifier corresponding to the sewage treatment equipment; S608: The diagnostic system 200 determines whether a corresponding sewage treatment equipment fails based on the probability.

[0079] In addition, reference Figure 1 As shown, according to the second aspect of this embodiment, a computer system is provided, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the above method.

[0080] In addition, reference Figure 1 As shown, according to the third aspect of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0081] In addition, reference Figure 1 As shown, according to a fourth aspect of this embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0082] Thus, according to this embodiment, the diagnostic system collects sewage data at different times corresponding to each sewage treatment system, and realizes comprehensive monitoring of the dynamic changes of the sewage treatment process. And the technical solution adopts a feature extraction model to analyze the correlation of sewage data at different times, so as to extract feature information according to the correlation with the sewage data at the previous moment, so as to pay attention to the influence between the sewage data at different times and trace the source of the fault. And the technical solution combines the classification model to efficiently classify the extracted first feature information, improves the speed of fault diagnosis, and enhances the reliability of the diagnosis result. Thus, the technical solution reduces the dependence on manual experience and subjective judgment through model processing, reduces the misjudgment rate caused by human factors, and also reduces the workload of technical personnel. And the technical solution can timely warn potential faults through automated and intelligent fault diagnosis, and take measures in advance to avoid major accidents. And thus solves the technical problems of low diagnostic efficiency and high cost caused by manual fault diagnosis of equipment in sewage treatment plants in the prior art.

[0083] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0084] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0085] Example 2 Figure 7 FIG. 7 shows a sewage treatment equipment fault determination device 700 according to this embodiment, which corresponds to the method according to the first aspect of embodiment 1. Figure 7 As shown, the device 700 includes: a data acquisition module 710, which is used to collect sewage data of the sewage treatment equipment at multiple consecutive moments in a preset period; an information generation module 720, which is used to extract features of the sewage data at the first moment in the preset period according to the correlation between the sewage data at the first moment and the sewage data at the second moment through a feature extraction model to generate first feature information, wherein the second moment is a moment before the first moment in the preset period; and a fault determination module 730, which is used to classify the first feature information through a classification model to determine whether the sewage treatment equipment has a fault.

[0086] Optionally, the information generation module 720 includes: a first generation submodule, which is used to linearly transform the sewage data of multiple consecutive moments in a preset period through a feature extraction model to generate corresponding query vectors, key vectors and value vectors; a first calculation submodule, which is used to calculate the similarity between the query vector at the first moment and the key vector at the second moment through the feature extraction model; a second generation submodule, which is used to normalize the similarity through a softmax function through the feature extraction model to generate a corresponding normalized value; and a third generation submodule, which is used to use the normalized value as a weight to perform a weighted sum operation with the corresponding value vector at the second moment through the feature extraction model to generate the first feature information.

[0087] Optionally, the first generation submodule includes: a first generation unit, which is used to linearly transform sewage data at multiple consecutive moments in a preset period according to a preset weight matrix through a feature extraction model to generate a query vector, a key vector and a value vector.

[0088] Optionally, the fault determination module 730 includes: a fourth generation submodule, used to process the first feature information through the fully connected layer of the classification model to generate second feature information; and a classification submodule, used to classify the second feature information through multiple binary classifiers to determine whether the sewage treatment equipment has a fault.

[0089] Optionally, the classification submodule includes: a first determination unit, used to determine the probability of a failure of the sewage treatment equipment according to the second feature information through a binary classifier corresponding to the sewage treatment equipment; and a second determination unit, used to determine whether the corresponding sewage treatment equipment fails according to the probability.

[0090] Thus, according to this embodiment, the diagnostic system collects sewage data at different times corresponding to each sewage treatment system, and realizes comprehensive monitoring of the dynamic changes of the sewage treatment process. And the technical solution adopts a feature extraction model to analyze the correlation of sewage data at different times, so as to extract feature information according to the correlation with the sewage data at the previous moment, so as to pay attention to the influence between the sewage data at different times and trace the source of the fault. And the technical solution combines the classification model to efficiently classify the extracted first feature information, improves the speed of fault diagnosis, and enhances the reliability of the diagnosis result. Thus, the technical solution reduces the dependence on manual experience and subjective judgment through model processing, reduces the misjudgment rate caused by human factors, and also reduces the workload of technical personnel. And the technical solution can timely warn potential faults through automated and intelligent fault diagnosis, and take measures in advance to avoid major accidents. And thus solves the technical problems of low diagnostic efficiency and high cost caused by manual fault diagnosis of equipment in sewage treatment plants in the prior art.

[0091] Example 3 Figure 8 FIG. 8 shows a sewage treatment equipment fault determination device 800 according to this embodiment, which corresponds to the method according to the first aspect of embodiment 1. Figure 8 As shown, the device 800 includes: a processor 810; and a memory 820, which is connected to the processor 810 and is used to provide the processor 810 with instructions for processing the following processing steps: collecting sewage data of the sewage treatment equipment at multiple consecutive moments in a preset cycle; extracting features of the sewage data at the first moment according to the correlation between the sewage data at the first moment and the sewage data at the second moment in the preset cycle through a feature extraction model to generate first feature information, wherein the second moment is a moment before the first moment in the preset cycle; and classifying the first feature information through a classification model to determine whether the sewage treatment equipment has a fault.

[0092] Optionally, the operation of extracting features from the sewage data at the first moment in a preset period according to the correlation between the sewage data at the first moment and the sewage data at the second moment in a preset period to generate the first feature information includes: linearly transforming the sewage data at multiple consecutive moments in the preset period through the feature extraction model to generate corresponding query vectors, key vectors and value vectors; calculating the similarity between the query vector at the first moment and the key vector at the second moment through the feature extraction model; normalizing the similarity through the softmax function through the feature extraction model to generate a corresponding normalized value; and using the normalized value as a weight to perform a weighted sum operation with the corresponding value vector at the second moment through the feature extraction model to generate the first feature information.

[0093] Optionally, the sewage data at multiple consecutive moments in a preset period are linearly transformed through a feature extraction model to generate corresponding query vectors, key vectors and value vectors, including: the sewage data at multiple consecutive moments in a preset period are linearly transformed through a feature extraction model according to a preset weight matrix to generate query vectors, key vectors and value vectors.

[0094] Optionally, the operation of classifying the first feature information through a classification model to determine whether the sewage treatment equipment is faulty includes: processing the first feature information through a fully connected layer of the classification model to generate second feature information; and classifying the second feature information through multiple binary classifiers to determine whether the sewage treatment equipment is faulty.

[0095] Optionally, the second feature information is classified and processed by multiple binary classifiers to determine whether the sewage treatment equipment has a fault, including: determining the probability of a fault in the sewage treatment equipment based on the second feature information by a binary classifier corresponding to the sewage treatment equipment; and determining whether the corresponding sewage treatment equipment has a fault based on the probability.

[0096] Thus, according to this embodiment, the diagnostic system collects sewage data at different times corresponding to each sewage treatment system, and realizes comprehensive monitoring of the dynamic changes of the sewage treatment process. And the technical solution adopts a feature extraction model to analyze the correlation of sewage data at different times, so as to extract feature information according to the correlation with the sewage data at the previous moment, so as to pay attention to the influence between the sewage data at different times and trace the source of the fault. And the technical solution combines the classification model to efficiently classify the extracted first feature information, improves the speed of fault diagnosis, and enhances the reliability of the diagnosis result. Thus, the technical solution reduces the dependence on manual experience and subjective judgment through model processing, reduces the misjudgment rate caused by human factors, and also reduces the workload of technical personnel. And the technical solution can timely warn potential faults through automated and intelligent fault diagnosis, and take measures in advance to avoid major accidents. And thus solves the technical problems of low diagnostic efficiency and high cost caused by manual fault diagnosis of equipment in sewage treatment plants in the prior art.

[0097] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0098] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0100] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0101] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0102] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0103] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining a fault in sewage treatment equipment, characterized in that: include: Collecting sewage data of sewage treatment equipment at multiple consecutive moments in a preset period; Extracting features of the sewage data at the first moment in the preset period according to the correlation between the sewage data at the first moment and the sewage data at the second moment by using a feature extraction model to generate first feature information, wherein the second moment is a moment before the first moment in the preset period; and The first feature information is classified by a classification model to determine whether the sewage treatment equipment fails. The operation of extracting features of the sewage data at the first moment and generating first feature information by using a feature extraction model according to the correlation between the sewage data at the first moment and the sewage data at the second moment in the preset period includes: The sewage data at a plurality of consecutive moments in the preset period are linearly transformed by the feature extraction model to generate corresponding query vectors, key vectors and value vectors; Calculating the similarity between the query vector at the first moment and the key vector at the second moment by using the feature extraction model; Normalizing the similarity by using a softmax function through the feature extraction model to generate a corresponding normalized value; and The first feature information is generated by using the feature extraction model to perform a weighted sum operation on the normalized value as a weight and the corresponding value vector at the second moment.

2. The method according to claim 1, characterized in that The operation of linearly transforming the sewage data at multiple consecutive moments in the preset period through the feature extraction model to generate corresponding query vectors, key vectors and value vectors includes: The feature extraction model is used to linearly transform the sewage data at multiple consecutive moments in the preset period according to a preset weight matrix to generate the query vector, the key vector and the value vector.

3. The method according to claim 1, characterized in that The operation of classifying the first feature information by using a classification model to determine whether the sewage treatment equipment has a fault includes: Processing the first feature information through the fully connected layer of the classification model to generate second feature information; and The second feature information is classified and processed by multiple binary classifiers to determine whether the sewage treatment equipment fails.

4. The method according to claim 3, characterized in that The operation of classifying the second feature information by using a plurality of binary classifiers to determine whether the sewage treatment equipment has a fault includes: Determining the probability of failure of the sewage treatment equipment according to the second feature information by using a binary classifier corresponding to the sewage treatment equipment; and According to the probability, it is determined whether the corresponding sewage treatment equipment fails.

5. A sewage treatment equipment fault determination device, characterized in that: include: A data collection module, used to collect sewage data of the sewage treatment equipment at multiple consecutive moments in a preset period; An information generation module, configured to extract features of the sewage data at the first moment in the preset period according to the correlation between the sewage data at the first moment and the sewage data at the second moment in the preset period through a feature extraction model to generate first feature information, wherein the second moment is a moment before the first moment in the preset period; as well as A fault determination module is used to classify the first feature information through a classification model to determine whether the sewage treatment equipment has a fault, wherein the information generation module includes: A first generating submodule, configured to perform linear transformation on the sewage data at a plurality of consecutive moments in the preset period through the feature extraction model to generate corresponding query vectors, key vectors and value vectors; A first calculation submodule, configured to calculate the similarity between the query vector at the first moment and the key vector at the second moment by using the feature extraction model; A second generating submodule, configured to normalize the similarity by using a softmax function through the feature extraction model to generate a corresponding normalized value; and The third generating submodule is used to use the feature extraction model to perform a weighted sum operation on the normalized value as a weight and the corresponding value vector at the second moment to generate the first feature information.

6. A sewage treatment equipment fault determination device, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Collecting sewage data of sewage treatment equipment at multiple consecutive moments in a preset period; Extracting features of the sewage data at the first moment in the preset period according to the correlation between the sewage data at the first moment and the sewage data at the second moment by using a feature extraction model to generate first feature information, wherein the second moment is a moment before the first moment in the preset period; and The first feature information is classified by a classification model to determine whether the sewage treatment equipment fails. The operation of extracting features of the sewage data at the first moment and generating first feature information by using a feature extraction model according to the correlation between the sewage data at the first moment and the sewage data at the second moment in the preset period includes: The sewage data at a plurality of consecutive moments in the preset period are linearly transformed by the feature extraction model to generate corresponding query vectors, key vectors and value vectors; Calculating the similarity between the query vector at the first moment and the key vector at the second moment by using the feature extraction model; Normalizing the similarity by using a softmax function through the feature extraction model to generate a corresponding normalized value; and The first feature information is generated by using the feature extraction model to perform a weighted sum operation on the normalized value as a weight and the corresponding value vector at the second moment.

7. A computer system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 1.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

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