Multi-source data fusion and remote diagnosis method and system for sewage treatment equipment

Abstract

The invention relates to the technical field of sewage treatment equipment operation and maintenance, and provides a multi-source data fusion and remote diagnosis method and system for sewage treatment equipment. The method comprises the following steps: when it is monitored that a risk component exists in target components of the sewage treatment equipment, obtaining abnormal verification data corresponding to the risk component; whether the risk component is abnormal or not is verified based on the abnormal verification data and a risk verification rule corresponding to the target component, and an abnormal verification result is obtained; under the condition that the exception verification result indicates that the risk component is abnormal, determining an exception handling mode for the risk equipment based on abnormal operation data and exception verification data in the component operation data corresponding to the risk component; and generating exception prompt information based on the exception handling mode. By adopting the technical scheme, a multi-dimensional verification mechanism of the abnormal state can be established, so that the misjudgment risk of a single data source is reduced, and the reliability of a fault diagnosis conclusion is ensured.

Description

Technical Field

[0001] This application relates to the technical field of sewage treatment equipment operation and maintenance, and particularly to a multi-source data fusion and remote diagnosis method and system for sewage treatment equipment. Background Art

[0002] The operation and maintenance management of sewage treatment equipment is a key link to ensure sewage treatment effect and environmental protection. With the development of the sewage treatment industry, the types of equipment are becoming increasingly rich, the operation scale is constantly expanding, and the stable operation of the equipment is directly related to the effluent quality and environmental compliance. However, as typical electromechanical intelligent equipment, sewage treatment equipment involves various components such as multiple types of sensors, controllers, and frequency converters during actual operation, and its operating state is greatly affected by environmental, water quality, and working condition changes. Therefore, how to achieve efficient monitoring and intelligent diagnosis of equipment has become an important research direction in the industry.

[0003] In related technologies, the operation management of sewage treatment equipment usually adopts the following means: manual inspection combined with on-site maintenance, where the inspection is completed by regularly observing the equipment operation status and reading instrument data; local control of the PLC / SCADA system to achieve automatic control and local data collection of some key equipment; a single-point alarm system based on local data collection to monitor and alarm indicators such as pump body temperature and current; and the data assistance function of management systems such as OA / ERP for archiving and statistical analysis of equipment operation records.

[0004] Although the technical means in related technologies can solve the equipment management problem to a certain extent, the data sources are fragmented and there is no centralized and unified management, which leads to monitoring based only on simple rules during the monitoring process, greatly affecting the accuracy of equipment monitoring. Therefore, improvement is needed. Summary of the Invention

[0005] To help improve the accuracy of equipment monitoring, this application provides a multi-source data fusion and remote diagnosis method and system for sewage treatment equipment.

[0006] In a first aspect, this application provides a multi-source data fusion and remote diagnosis method for sewage treatment equipment, adopting the following technical solution: A multi-source data fusion and remote diagnosis method for sewage treatment equipment, the method comprising: For each target component in the sewage treatment equipment, risk monitoring is performed on the target component based on the component operation data of the target component and the risk monitoring rules corresponding to the target component; When a risk component is detected, the abnormal verification data corresponding to the risk component is obtained; Verify whether there is an abnormality in the risk component based on the abnormal verification data and the risk verification rule corresponding to the target component, and obtain an abnormal verification result; When the abnormal verification result indicates that there is an abnormality in the risk component, determine the abnormal handling method for the risk device based on the abnormal operation data and the abnormal verification data that are abnormal in the component operation data corresponding to the risk component; Generate an abnormal prompt message based on the abnormal handling method.

[0007] By adopting the above technical solution, a multi-dimensional verification mechanism for abnormal states can be established, thereby reducing the misjudgment risk of a single data source and ensuring the reliability of the fault diagnosis conclusion.

[0008] Optionally, the obtaining the abnormal verification data corresponding to the risk component when the risk component is monitored includes: When the risk component is monitored, determine the risk type corresponding to the risk component based on the abnormal operation data; Determine the verification data acquisition method based on the risk type; When the verification data acquisition method includes its own data, determine the associated operation data associated with the abnormal operation data in the component operation data corresponding to the risk component as the abnormal verification data; When the verification data acquisition method includes associated component data, determine the abnormal verification data from the component operation data of the associated component corresponding to the risk component.

[0009] By adopting the above technical solution, dynamic adaptation of the verification data acquisition method can be realized, automatically select the optimal data source for different risk types, effectively reduce the processing volume of irrelevant data, and improve the accuracy of abnormal judgment through multi-dimensional data cross-verification.

[0010] Optionally, the determining the abnormal verification data from the component operation data of the associated component corresponding to the risk component includes: When there are two or more risk components, determine whether the associated component is a risk component; When the associated component is a risk component, determine the device operation data with abnormalities in the operation data corresponding to the associated component as the abnormal verification data; When the associated component is not the risk component, determine whether the type of the associated component is the same as that of the risk component; When the type of the associated component is the same as that of the risk component, the data of the same type as the abnormal operation data in the component operation data corresponding to the associated component is determined as the abnormal verification data; When the type of the associated component is different from that of the risk component, the abnormal verification data is determined from the component operation data corresponding to the associated component based on the risk type.

[0011] By adopting the above technical solution, a three - level screening mechanism of risk status judgment, type matching, and risk type adaptation can be established, which helps to solve the problem that traditional methods often adopt a unified data acquisition strategy when dealing with multiple risk components, resulting in redundant verification data or missing key associated information. Furthermore, it can effectively improve the pertinence and reliability of the abnormal verification data.

[0012] Optionally, the abnormal verification data includes numerical verification data represented by values, the risk verification rule includes a change matching rule, and the change matching rule is used to record the correspondence between the change situation of the numerical verification data determined when the risk component is abnormal and the change situation of the abnormal verification data. The verification of whether the risk component is abnormal based on the abnormal verification data and the risk verification rule corresponding to the target component to obtain an abnormal verification result includes: Monitor the change situation of the numerical verification data and the change situation of the abnormal verification data; Based on the change matching rule, determine whether the change situation of the numerical verification data matches the change situation of the abnormal verification data; When the change situation of the verification data matches the change situation of the verification data, generate an abnormal verification result indicating that the risk component is abnormal.

[0013] By adopting the above technical solution, the abnormal verification can be carried out by using the matching relationship between the change situation of the numerical verification data and the change situation of the abnormal verification data, and it can effectively identify whether there are potential faults in the risk component. Compared with the traditional single - threshold judgment method, this embodiment significantly improves the accuracy and reliability of the abnormal verification result by introducing the change matching rule and combining dynamic data characteristics for comprehensive analysis, thus providing a strong guarantee for the stable operation of the equipment.

[0014] Optionally, the risk verification rule includes the verification range corresponding to the numerical verification data, and the verification range is determined when the risk component is abnormal. After determining whether the change situation of the numerical verification data matches the change situation of the abnormal verification data, it further includes: In the case where the change situation of the verification data does not match the change situation of the verification data, determine whether the numerical verification data falls within the corresponding verification range; In the case where the numerical verification data includes more than two and some of the numerical verification data do not fall within the corresponding verification range, determine a verification adjustment parameter based on the number of pending verification data that do not fall within the corresponding verification range; Based on the verification adjustment parameter, adjust the verification range corresponding to the pending verification data to obtain an adjusted range, and the adjusted range is greater than the verification range; Determine whether the pending verification data falls within the corresponding adjusted range; In the case where each of the pending verification data falls within the corresponding adjusted range, generate an abnormal verification result indicating that the risk component is abnormal.

[0015] By adopting the above technical solution, it is possible to perform multi-level verification on the change situation of numerical verification data, not only considering the matching of the change trend, but also introducing a dynamic adjustment mechanism for the verification range. Especially in the case where there are partial deviations in multiple groups of numerical verification data, by adjusting the verification range, the comprehensiveness and rationality of the abnormal verification result are ensured, thereby providing a more scientific basis for the status evaluation of risk components.

[0016] Optionally, the abnormal verification data includes the operation and maintenance data of the risk component. Before monitoring the change situation of the numerical verification data and the change situation of the abnormal verification data, it further includes: Analyze the operation and maintenance data to obtain risk prediction data corresponding to the risk component; Determine whether there is risk prediction data that matches the abnormal operation data; In the case where there is no risk prediction data that matches the abnormal operation data, perform the step of determining whether the numerical verification data falls within the verification range corresponding to the risk data.

[0017] By adopting the above technical solution, before performing abnormal verification on the risk component in combination with the abnormal verification data, abnormal verification can be performed on the risk component in combination with historical operation and maintenance data. In this way, not only the accuracy of fault diagnosis is improved, but also the false alarm rate is reduced, thereby enhancing the reliability and efficiency of the entire remote diagnosis system for sewage treatment equipment.

[0018] Optionally, the risk component includes a water pump, the numerical verification data includes the vibration data of the water pump, and the abnormal verification data includes the current data and outlet pressure data of the water pump. The determining whether the change situation of the numerical verification data matches the change situation of the abnormal verification data based on the change matching rule includes: When the vibration data continuously rises within a preset time duration, determine whether the fluctuation amplitude of the current data reaches a preset fluctuation amplitude threshold and whether the outlet pressure data decreases; When the fluctuation amplitude reaches the fluctuation amplitude threshold and the outlet pressure data decreases, determine that the change situation of the numerical verification data matches the change situation of the abnormal verification data.

[0019] By adopting the above technical solution, when the vibration data of the water pump continuously rises within a certain time, the fluctuation amplitude of the current data and the change trend of the outlet pressure data can be further combined for comprehensive analysis, so as to improve the accuracy of abnormal judgment of the water pump.

[0020] Optionally, determining the abnormal handling method for the risk device based on the abnormal operation data and the abnormal verification data existing in the component operation data corresponding to the risk component includes: Input the abnormal operation data and the abnormal verification data into a pre-trained expert model to obtain the abnormal handling method; Wherein, the expert model is trained and constructed based on using an expert rule library; After generating the abnormal prompt information based on the abnormal handling method, it further includes: Obtain the maintenance records of the sewage treatment equipment; Extract operation and maintenance data from the maintenance records, and update the expert rule library based on the operation and maintenance data.

[0021] By adopting the above technical solution, it can automatically determine the accurate handling method for the abnormal situation of the sewage treatment equipment, and optimize the handling strategy through continuous learning, so as to provide accurate reference for the work of operation and maintenance personnel.

[0022] Optionally, when the abnormal verification result indicates that the risk component is abnormal, it further includes: Perform abnormal prediction on the sewage treatment equipment based on the abnormal operation data to obtain an abnormal prediction result; Update the equipment status of the associated equipment based on the abnormal prediction result and the position of the sewage treatment equipment in the sewage treatment process flow; Predict the sewage treatment effect based on the equipment status of each preset reference equipment in the sewage treatment process flow to obtain a predicted treatment result.

[0023] By adopting the above technical solution, when an abnormality is detected, the impact of the abnormality on the sewage treatment effect can be accurately judged in combination with the sewage treatment process flow, which can further help to assist operation and maintenance personnel in making operation and maintenance decisions.

[0024] In a second aspect, the present application provides a multi-source data fusion and remote diagnosis system for sewage treatment equipment, adopting the following technical solutions: A multi-source data fusion and remote diagnosis system for sewage treatment equipment, the system includes a data acquisition component and a cloud server communicatively connected to the data acquisition component; The data acquisition component is used to acquire the component operation data of each component in the sewage treatment equipment and send it to the cloud server; A cloud operation and maintenance platform runs on the cloud server, and the cloud operation and maintenance platform is used to implement any one of the multi-source data fusion and remote diagnosis methods for sewage treatment equipment provided in the first aspect.

[0025] In summary, the present application includes at least one of the following beneficial technical effects: 1. Establish a multi-dimensional verification mechanism for abnormal states, reduce the misjudgment risk of a single data source, and ensure the reliability of the fault diagnosis conclusion.

[0026] 2. Determine a reasonable abnormal handling method based on abnormal operation data and verification data and generate a prompt message, which helps the operation and maintenance personnel to take measures quickly, reduce the impact of equipment failures, and ensure the stable operation and treatment efficiency of the sewage treatment system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a schematic flowchart of a multi-source data fusion and remote diagnosis method for sewage treatment equipment provided by an embodiment of the present application; Figure 2 is a schematic flowchart of a method for obtaining verification data provided by an embodiment of the present application; Figure 3 is a schematic flowchart of a method for determining abnormal verification data provided by an embodiment of the present application; Figure 4 is a schematic flowchart of a method for determining an abnormal verification result provided by an embodiment of the present application; Figure 5 is a schematic flowchart of a method for determining an abnormal handling method provided by an embodiment of the present application; Figure 6 is a schematic flowchart of a method for updating the sewage treatment effect provided by an embodiment of the present application; Figure 7 is a schematic structural diagram of a multi-source data fusion and remote diagnosis system for sewage treatment equipment provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In order to make the purpose, technical solutions and advantages of the present application clearer, the following will be combined with the attached Figures 1-7Examples are provided to further elaborate on this application. It should be understood that the specific examples described herein are for the purpose of explaining this application and not for limiting it.

[0029] In related technologies, the operation and maintenance management of sewage treatment equipment mainly rely on manual inspections and decentralized data acquisition systems. The equipment operation data are scattered in multiple isolated systems such as PLC, SCADA, and independent sensors. A certain sewage treatment plant is equipped with more than ten types of equipment such as blowers, water pumps, and chemical dosing devices. The operation data of each type of equipment are stored in the local databases of different control cabinets. When the dissolved oxygen concentration in the aeration tank is abnormal, the operator needs to sequentially retrieve data from multiple platforms such as fan frequency, pipeline pressure, and current fluctuation, and manually compare the historical operation curves to determine the cause of the abnormality. This results in a fault diagnosis time of up to several hours and there is also a risk of misjudgment due to delayed data updates.

[0030] To solve the above problems, through analysis, it is found that the data island phenomenon leads to low abnormal diagnosis efficiency, and the data of a single sensor cannot accurately reflect the true state of the equipment. Through studying the equipment failure mechanism, it is found that the abnormalities of electromechanical equipment are usually accompanied by the coordinated changes of multiple parameters. For example, the wear of the water pump bearing will simultaneously cause an increase in the vibration amplitude and harmonic distortion of the current. Based on this, a multi-source data fusion and remote diagnosis method and system for sewage treatment equipment are proposed to solve the above problems.

[0031] An embodiment of this application discloses a multi-source data fusion and remote diagnosis method for sewage treatment equipment. Referring to Figure 1 , the multi-source data fusion and remote diagnosis method for sewage treatment equipment includes the following steps: Step 101, for each target component in the sewage treatment equipment, risk monitoring is performed on the target component based on the component operation data of the target component and the risk monitoring rules corresponding to the target component.

[0032] Among them, the target component refers to an electromechanical equipment unit with independent functions in the sewage treatment process. In actual implementation, the target component includes at least one of the core components such as the blower impeller, water pump bearing, and chemical dosing pump plunger.

[0033] The component operation data are used to reflect the real-time characteristics of the equipment operation state. Specifically, the component operation data can be collected through sensors set on the component, or can also be the working parameters of the component. The types and acquisition methods of the component operation data corresponding to different components may vary. In actual implementation, the component operation data can include at least one of parameters such as vibration acceleration, winding temperature, and working current.

[0034] The risk monitoring rules refer to the preset determination logic for the health state of the component. Specifically, algorithms such as threshold comparison, trend slope calculation, and waveform spectrum analysis can be used to automatically identify components that may have risks.

[0035] Step 102, when a risk component is detected, obtain the abnormal verification data corresponding to the risk component.

[0036] Among them, the abnormal verification data refers to the auxiliary monitoring data that has a physical or logical association with the target component and is used to cross-verify whether the risk component is abnormal. In actual implementation, the abnormal verification data can be the associated parameters of different sensors of the same device, or it can also be the component operation data of a device (such as upstream and downstream process equipment) that has an association relationship with the target device.

[0037] Step 103, based on the abnormal verification data and the risk verification rule corresponding to the target component, verify whether the risk component is abnormal to obtain an abnormal verification result.

[0038] Among them, the risk verification rule is used to verify whether a component with risks is abnormal, and the risk verification rule is set in advance. In one example, the setting method of the risk verification rule is the same as the setting method of the risk monitoring rule, and the difference between the two is that they are set for different types of data.

[0039] Step 104, when the abnormal verification result indicates that the risk component is abnormal, determine the abnormal handling method for the risk device based on the abnormal operation data and the abnormal verification data that are abnormal in the component operation data corresponding to the risk component.

[0040] Among them, the abnormal handling method refers to the disposal strategy for the verified abnormal state, and specifically can include parameter adjustment (such as frequency adjustment), device switching, manual maintenance, etc.

[0041] In one instance, it is also possible to find the most matching abnormal handling method from the abnormal handling database corresponding to the risk component based on the abnormal operation data and the abnormal verification data. Among them, the abnormal handling database is generated based on the historical maintenance records of the same type of components.

[0042] In another example, the corresponding relationship between the abnormal operation data and the abnormal verification data and the abnormal handling methods with different abnormal handling methods is set in advance. For example, different abnormal handling methods correspond to different data ranges, so that the corresponding abnormal handling method can be determined based on the matching relationship between the abnormal operation data and the abnormal verification data and the data range. Further, when the abnormal handling methods corresponding to the abnormal operation data and the abnormal verification data are different, two abnormal handling methods can be used simultaneously, which can help ensure the accuracy of the abnormal handling method.

[0043] In actual implementation, there may be differences in the exception handling methods corresponding to different risk components. At this time, the exception handling method to be used this time can be selected from all the exception handling methods corresponding to the risk components based on the abnormal operation data and the exception verification data.

[0044] Step 105, generate an exception prompt message based on the exception handling method.

[0045] Among them, the exception prompt message is used to remind the staff to handle the device exception. In an example, the exception prompt message corresponds to the exception handling method. For example: if the exception handling method includes parameter adjustment, the exception prompt message can include generating parameter adjustment suggestions to prompt the staff to adjust the device parameters; another example: if the exception handling method includes device switching, the exception handling prompt message can include generating device switching suggestions to prompt the staff to perform device switching.

[0046] Furthermore, the system can automatically execute the exception handling steps based on the exception handling method. For example: when the exception handling method includes adjusting the frequency, the system can generate a frequency adjustment instruction in combination with the actual situation or with a fixed adjustment amplitude to adjust the working frequency of the device; another example: when the exception handling method includes manual maintenance, the system can automatically generate and dispatch an operation and maintenance work order to improve the efficiency of manual maintenance.

[0047] To more clearly understand the technical solution provided in this embodiment, the following is illustrated with an example. For example: when the vibration value of the blower bearing exceeds the preset threshold, the risk monitoring rule automatically triggers an exception monitoring. The system synchronously retrieves the three-phase current harmonic data of the motor corresponding to the bearing as the exception verification data, and analyzes the current spectrum characteristics through Fourier transform. If the amplitude of the 2-fold rotating frequency component in the current spectrum increases synchronously, the risk verification rule determines that the mechanical looseness exception is established. At this time, based on the abnormal operation data and the exception verification data, a troubleshooting solution for the same type of fault is found from the historical maintenance records, a maintenance work order including the bearing tightening torque parameter and the lubricant model is generated, and it is automatically pushed to the mobile terminal of the operation and maintenance personnel.

[0048] The implementation principle of a multi-source data fusion and remote diagnosis method for a sewage treatment device in an embodiment of the present application is as follows: For each target component in the sewage treatment device, risk monitoring of the target component is performed based on the component operation data of the target component and the risk monitoring rules corresponding to the target component; in the case of detecting a risk component, the abnormal verification data corresponding to the risk component is obtained; based on the abnormal verification data and the risk verification rules corresponding to the target component, whether the risk component has an abnormality is verified to obtain an abnormal verification result; in the case where the abnormal verification result indicates that the risk component has an abnormality, based on the abnormal operation data and the abnormal verification data existing in the component operation data corresponding to the risk component, the abnormal handling method for the risk device is determined; and an abnormal prompt message is generated based on the abnormal handling method. By adopting the above technical solution, a multi-dimensional verification mechanism for abnormal states can be established, thereby reducing the misjudgment risk of a single data source and ensuring the reliability of the fault diagnosis conclusion.

[0049] Furthermore, determining a reasonable abnormal handling method based on the abnormal operation data and the verification data and generating a prompt message helps the operation and maintenance personnel to take measures quickly, reduce the impact brought by equipment failures, and ensure the stable operation and treatment efficiency of the sewage treatment system.

[0050] In some embodiments, referring to Figure 2 , step 102, in the case of detecting a risk component, obtaining the abnormal verification data corresponding to the risk component includes the following steps: Step 201, in the case of detecting a risk component, determine the risk type corresponding to the risk component based on the abnormal operation data.

[0051] Among them, the risk type refers to the potential fault category of the equipment identified by analyzing the abnormal operation data, which is used to guide the selection direction of the verification data. In actual implementation, the risk type can be implemented based on a fault feature library match or a pattern recognition algorithm.

[0052] In an example, taking the risk component as a pump for illustration, if the abnormal operation data shows that the temperature of the pump body rises abnormally, it may be classified as an overheat risk type.

[0053] Step 202, determine the verification data acquisition method based on the risk type.

[0054] Among them, the verification data acquisition method refers to the strategy of dynamically selecting the data source of the abnormal verification data according to the risk type. Specifically, the data acquisition method may include: its own data and / or associated component data.

[0055] In this embodiment, the corresponding relationship between the risk type and the verification data acquisition method is preset. Specifically, it can be implemented through a predefined rule table or a decision tree model.

[0056] Step 203, when the data acquisition method for verification includes its own data, among the component operation data corresponding to the risk component, the associated operation data that has an association with the abnormal operation data is determined as the abnormal verification data.

[0057] Among them, the association relationship between the component operation data is set in advance. In one example, the association relationship is determined based on the logical association between the component operation data, and specifically can be determined through data correlation analysis.

[0058] In one instance, continue to take the risk component as the pump for illustration. If the data acquisition method for verification includes its own data, then at least one of the parameters such as vibration frequency and current fluctuation that are associated with temperature is selected from the operation data of the pump and determined as the abnormal verification data.

[0059] Step 204, when the data acquisition method for verification includes the data of associated components, the abnormal verification data is determined from the component operation data of the associated components corresponding to the risk component.

[0060] Among them, the association relationship between the components is set in advance. In one example, the association relationship is set based on the structural association between the components in the device, and specifically can be determined through the structural topology relationship of the device.

[0061] In one instance, continue to take the risk component as the pump for illustration. If the data acquisition method for verification includes the data of associated components, then the pipeline connected to the pump is determined as the associated component, and at least one of the component operation data such as pipeline pressure and flow rate in the component operation data corresponding to the pipeline is determined as the abnormal verification data.

[0062] In actual implementation, according to actual needs, the data acquisition method for verification can include both its own data and the data of associated components at the same time. For example: in the risk scenario of motor overload, by simultaneously acquiring its own current data and the gear wear data of the associated speed reducer, it is possible to quickly distinguish the abnormal phenomena caused by equipment overload and mechanical wear, and avoid misjudgment.

[0063] By adopting the above technical solution, dynamic adaptation of the data acquisition method for verification can be realized, automatically select the optimal data source for different risk types, effectively reduce the processing volume of irrelevant data, and at the same time improve the accuracy of abnormal judgment through multi-dimensional data cross-verification.

[0064] Specifically, when using its own data as verification data, the associated operation data associated with the abnormal operation data can be effectively utilized for cross-verification to improve the accuracy of abnormal judgment; when using the associated component data as verification data, valuable information can be mined from the operation data of the associated components, further enhancing the comprehensiveness and reliability of abnormal detection. Thus, refined verification of the abnormal conditions of risk components can be achieved, improving the efficiency and accuracy of fault diagnosis.

[0065] Based on the above technical solution, referring to Figure 3 , in step 204 above, determining the abnormal verification data from the component operation data of the associated components corresponding to the risk components includes the following steps: Step 301, when there are two or more risk components, determine whether the associated components are risk components.

[0066] Step 302, when the associated component is a risk component, determine the device operation data with abnormalities in the operation data corresponding to the associated component as the abnormal verification data.

[0067] Specifically, when the associated component is a risk component, it also has abnormal operation data. At this time, the abnormal operation data of the associated component is determined as the abnormal verification data, which can comprehensively analyze the abnormal data corresponding to the associated risk components during the verification process, thus helping to improve the accuracy of abnormal judgment.

[0068] Step 303, when the associated component is not a risk component, determine whether the types of the associated component and the risk component are the same.

[0069] Among them, the classification method of the component types is preset. For example: the component types include pumps. From this, when the associated component and the risk component are both pumps, it is determined that the types of the associated component and the risk component are the same.

[0070] Step 304, when the types of the associated component and the risk component are the same, determine the data with the same type as the abnormal operation data in the component operation data corresponding to the associated component as the abnormal verification data.

[0071] Specifically, since the risk component is obtained by monitoring the component operation data, and when the types of the associated component and the risk component are the same, it means that the associated component also has component operation data of the same type as the abnormal verification data. At this time, determining this component operation data as the verification data can help avoid the influence of the fluctuation of the component operation data on the abnormal judgment of the component, and thus the verification of the component abnormality can be achieved.

[0072] Step 305, in the case where the types of the associated component and the risk component are different, determine the abnormal verification data from the component operation data corresponding to the associated component based on the risk type.

[0073] Among them, the corresponding relationship between the risk type and the type of the component operation data is preset. For example: for a water pump, the risk type is current overload, and the associated component is the pipeline connected to the water pump. At this time, the abnormal verification data can be the pressure data of the pipeline.

[0074] In the above technical solution, by establishing a three-level screening mechanism of risk status judgment, type matching, and risk type adaptation, it can help solve the problem that traditional methods often adopt a unified data acquisition strategy when dealing with multiple risk components, resulting in redundant verification data or omission of key associated information. Furthermore, it can effectively improve the pertinence and reliability of the abnormal verification data.

[0075] Specifically, since the operation data of the marked abnormal components is preferentially used as the verification benchmark during the determination of the abnormal verification data, it avoids the waste of resources for repeated acquisition of data of normal associated components; by ensuring the consistency of the verification data with the operation characteristics of the target component through device type matching, for example, using the flow rate data of the same type of water pump as the comparison benchmark; dynamically selecting verification parameters for different types of associated components, for example, establishing a verification relationship between the water pump with abnormal vibration data and the pressure change data of the associated pipeline, enhancing the effectiveness of data association between cross-type devices.

[0076] In some embodiments, the abnormal verification data includes numerical verification data represented by numerical values, and the risk verification rule includes a change matching rule.

[0077] Among them, the change matching rule is used to record the corresponding relationship between the change situation of the numerical verification data determined when the risk component is abnormal and the change situation of the abnormal verification data, and is used to capture the abnormal characteristics of the co-variation of multiple parameters. In actual implementation, the change matching relationship can be obtained through experiments or the experience of technicians. In an example, the change matching rule is implemented by using the association matrix of the change trends of different parameters in historical failure cases.

[0078] In actual implementation, the numerical verification data can include at least one of the data that can be quantified, such as vibration frequency, current value, pressure value, etc.

[0079] Correspondingly, referring to Figure 4 , in step 103 above, verify whether the risk component is abnormal based on the abnormal verification data and the risk verification rule corresponding to the target component, and obtain the abnormal verification result, including: Step 401, monitor the change situation of the numerical verification data and the change situation of the abnormal verification data.

[0080] Among them, the change situation can be represented by data such as the rise, fall, and fluctuation amplitude obtained by using time series analysis methods, which can reflect the change of data over time.

[0081] In one example, the target component is a water pump for illustration. When the vibration data of the water pump continuously rises within a preset time period, the water pump is determined as a risk component, and the current data and outlet pressure data of the water pump are determined as abnormal verification data, and the change situation of the current data and outlet pressure data of the water pump is monitored.

[0082] Among them, the vibration data refers to the physical parameters generated by mechanical vibration during the operation of the water pump, which is used to reflect the operation state of the mechanical structure of the water pump. In actual implementation, the vibration data can be collected by an acceleration sensor to collect the vibration frequency and amplitude data on the surface of the pump body.

[0083] The current data refers to the current value of the motor driving the water pump, which is used to characterize the change of the motor load. In actual implementation, the current data can be collected in real time through a frequency converter or a current transformer.

[0084] The outlet pressure data refers to the fluid pressure parameter at the output end of the water pump, which is used to monitor the water pump delivery efficiency. In actual implementation, a pressure transmitter can be specifically used for measurement.

[0085] Furthermore, the abnormal verification data includes the operation and maintenance data of the risk component. Before monitoring the change situation of the verification data and the change situation of the abnormal verification data, it also includes: parsing the operation and maintenance data to obtain the risk prediction data corresponding to the risk component; determining whether there is risk prediction data matching the abnormal operation data; in the case where there is no risk prediction data matching the abnormal operation data, performing the step of determining whether the numerical verification data falls within the verification range corresponding to the risk data; in the case where there is risk prediction data matching the abnormal operation data, directly generating an abnormal verification result indicating that the risk component is abnormal.

[0086] Among them, the operation and maintenance data refers to data such as historical maintenance records, component replacement cycle data, and fault handling records generated during the equipment maintenance process, which is used to reflect the correlation between the equipment maintenance state and potential risks.

[0087] The risk prediction data is used to establish the correlation between equipment anomalies and historical maintenance records, and also represents the possible anomalies of the equipment. In actual implementation, machine learning models or statistical analysis methods can be used to analyze the operation and maintenance data.

[0088] Optionally, determine whether there is risk prediction data that matches the abnormal operation data, including: determining the abnormal type corresponding to the abnormal operation data; determining whether there is an abnormality in the risk prediction data that matches the abnormal type; if so, determine that there is risk prediction data that matches the abnormal operation data; if not, determine that there is no risk prediction data that matches the abnormal operation data.

[0089] For example: During the operation of the sewage treatment equipment, when the vibration data of the water pump shows abnormal fluctuations, the system first retrieves the maintenance records of the water pump in the past three months, including the bearing replacement records and the number of seal repairs. By analyzing the maintenance records, it is determined that the water pump may be cavitating. When it is detected that the vibration data of the water pump continuously rises within a preset time period, since the rise in vibration data may be caused by cavitation, and the maintenance records indicate the risk of cavitation, an abnormality can be directly determined in combination with the maintenance records. Conversely, if the maintenance records do not indicate any abnormality that may cause the vibration data to rise, the numerical-based abnormality verification step is then continued.

[0090] In the above technical solution, by analyzing the operation and maintenance data to obtain risk prediction data, it is possible to effectively determine whether there is a matching prediction record for the abnormal operation data. If there is no matching risk prediction data, the abnormal situation is accurately located by further comparing the numerical verification data with the verification range. This method not only improves the accuracy of fault diagnosis but also reduces the false alarm rate, thereby enhancing the reliability and efficiency of the entire remote diagnosis system for sewage treatment equipment.

[0091] Step 402, determine whether the change situation of the numerical verification data matches the change situation of the abnormal verification data based on the change matching rule.

[0092] In an example, continuing with the example where the risk component includes a water pump, the numerical verification data includes the vibration data of the water pump, and the abnormal verification data includes the current data and the outlet pressure data of the water pump, correspondingly, determining whether the change situation of the numerical verification data matches the change situation of the abnormal verification data based on the change matching rule includes: when the vibration data continuously rises within a preset time period, determining whether the fluctuation amplitude of the current data reaches a preset fluctuation amplitude threshold and whether the outlet pressure data decreases; when the fluctuation amplitude reaches the fluctuation amplitude threshold and the outlet pressure data decreases, determining that the change situation of the numerical verification data matches the change situation of the abnormal verification data.

[0093] Among them, the preset time period refers to the continuous monitoring time window set according to the water pump type. In an example, the preset time period is determined within the time interval of 1 to 3 minutes.

[0094] The amplitude fluctuation threshold is determined according to the allowable range of current fluctuation set based on the rated power of the water pump, and is used to identify abnormal current fluctuations.

[0095] Step 403: When the change situation of the verification data matches the change situation of the verification data, generate an abnormal verification result indicating that there is an abnormality in the risk component.

[0096] Optionally, when the change situation of the verification data does not match the change situation of the verification data, an abnormal verification result indicating that there is no abnormality in the risk component can be directly generated, or abnormal verification can also be continued based on other methods.

[0097] Specifically, continuing to take the water pump as an example of the risk component, when cavitation occurs in the water pump, the mechanical vibration will continuously increase, and at this time, the vibration sensor detects an upward trend in the amplitude. At the same time, cavitation causes abnormal motor load, and the current sensor captures a fluctuation amplitude exceeding the normal range. In addition, cavitation affects the suction capacity and fluid delivery efficiency of the pump, resulting in a decrease in the outlet pressure. Through continuous data tracking within a set time window (such as 2 minutes), when these three conditions are simultaneously met, the system will rule out single-data abnormalities caused by environmental interference or short-term operating condition changes and accurately determine that cavitation exists in the water pump.

[0098] The above technical solution can accurately identify potential cavitation phenomena during the operation of the water pump. When the vibration data of the water pump continuously rises within a specific time, combined with the changes in the current fluctuation amplitude and the outlet pressure for comprehensive judgment, it effectively improves the accuracy of fault diagnosis. This multi-dimensional data linkage analysis method not only reduces the misjudgment rate but also can early warn of equipment abnormalities, thereby avoiding equipment damage caused by cavitation, extending the service life of the water pump, and ensuring the stable operation of the sewage treatment system.

[0099] In this embodiment, the matching relationship between the change situation of the numerical verification data and the change situation of the abnormal verification data can be used for abnormal verification, which can effectively identify whether there are potential faults in the risk components. Compared with the traditional single-threshold judgment method, this embodiment significantly improves the accuracy and reliability of the abnormal verification result by introducing a change matching rule and combining dynamic data characteristics for comprehensive analysis, thus providing a strong guarantee for the stable operation of the equipment.

[0100] Furthermore, the risk verification rule includes the verification range corresponding to the numerical verification data, and the verification range is determined when the risk component is abnormal.

[0101] Among them, the verification range refers to the effective interval of pre-set numerical verification data, which is used to preliminarily determine whether the data conforms to the abnormal pattern. In actual implementation, the risk verification rule can be determined by adding a safety margin to the statistical value of historical abnormal data. For example, the normal fluctuation range of the vibration amplitude of the water pump is set to 2.5 - 3.8 mm / s.

[0102] Correspondingly, in step 402, after determining whether the change situation of the numerical verification data matches the change situation of the abnormal verification data based on the change matching rule, the following steps are further included: Step 404, in the case where the change situation of the verification data does not match the change situation of the verification data, determine whether the numerical verification data falls within the corresponding verification range.

[0103] Step 405, in the case where there are more than two numerical verification data and some of the numerical verification data do not fall within the corresponding verification range, determine the verification adjustment parameter based on the number of pending verification data that do not fall within the corresponding verification range.

[0104] Among them, the verification adjustment parameter is related to the number of pending verification data and is used to determine the adjustment range of the verification range. Specifically, the number of pending verification data can indirectly reflect the probability of the risk component being abnormal. That is, the fewer the number of pending verification data, the more numerical verification data fall within the verification range, and the greater the probability of the corresponding risk component being abnormal, and vice versa.

[0105] In one example, the verification adjustment parameter is determined based on the number of pending verification data and the total number of verification data. For example, the ratio of the number of pending verification data to the total amount of verification data is determined as the verification adjustment parameter. In actual implementation, the number of pending verification data can also be directly determined as the verification adjustment parameter.

[0106] Optionally, in the case where all numerical verification data do not fall within the corresponding verification range, generate an abnormal verification result indicating that the risk component is not abnormal; in the case where all the data verification data fall within the corresponding verification range, generate an abnormal verification result indicating that the risk component is abnormal.

[0107] Step 406, adjust the verification range corresponding to the pending verification data based on the verification adjustment parameter to obtain the adjusted range.

[0108] Among them, the adjusted range refers to the data determination interval expanded by the verification adjustment parameter, and the adjusted range is larger than the verification range.

[0109] Optionally, adjust the verification range corresponding to the to-be-verified data based on the verification adjustment parameter to obtain an adjusted range, including: determining an adjustment amplitude based on the verification adjustment parameter; adjusting the verification range based on the adjustment amplitude to obtain an adjusted range. Wherein, the adjustment amplitude is negatively correlated with the quantity of the to-be-verified data.

[0110] In one example, adjusting the verification range based on the adjustment amplitude includes: increasing the upper limit of the verification range by the adjustment amplitude and / or decreasing the lower limit of the verification range by the adjustment amplitude.

[0111] In one example, the verification adjustment parameter is the ratio of the quantity of the to-be-verified data to the total quantity of the verification data. Correspondingly, determining the adjustment amplitude based on the verification adjustment parameter includes: determining the product of the difference between 1 and the verification adjustment parameter and the length of the verification range as the verification adjustment amplitude.

[0112] In another example, the verification adjustment parameter is the quantity of the to-be-verified data. Correspondingly, determining the adjustment amplitude based on the verification adjustment parameter includes: determining the ratio of the maximum adjustment amplitude to the quantity of the verification adjustment parameter as the verification adjustment amplitude.

[0113] Step 407, determine whether the to-be-verified data falls within the corresponding adjusted range.

[0114] Step 408, when each to-be-verified data falls within the corresponding adjusted range, generate an abnormal verification result indicating that there is an abnormality in the risk component.

[0115] Optionally, when there is verification data that does not fall within the corresponding adjusted range, generate an abnormal prompt message indicating that there is no abnormality in the risk component.

[0116] By adopting the above technical solution, it is possible to perform multi-level verification on the change situation of numerical verification data, not only considering the matching of the change trend, but also introducing a dynamic adjustment mechanism for the verification range. This design effectively addresses the misjudgment problem that may be caused by data fluctuations during actual operation, improving the accuracy and reliability of abnormal judgment. Especially when there are partial deviations in multiple groups of numerical verification data, by adjusting the verification range, the comprehensiveness and rationality of the abnormal verification result are ensured, thereby providing a more scientific basis for the status assessment of risk components.

[0117] In some embodiments, referring to Figure 5 , in the above step 104, determining the abnormal handling method for the risk device based on the abnormal operation data and abnormal verification data existing in the component operation data corresponding to the risk component includes: Step 501, input the abnormal operation data and abnormal verification data into a pre-trained expert model to obtain an abnormal handling method.

[0118] Among them, the expert model is constructed based on the expert rule base. In one example, the expert model is implemented using an algorithm model based on a decision tree or a rule engine, and automatically generates an exception handling method through rule matching and logical reasoning.

[0119] The expert rule base refers to a database that stores device exception diagnosis rules, which is used to store diagnosis rules formed from historical operation and maintenance experience, and can be specifically constructed in the form of a relational database or a knowledge graph.

[0120] Correspondingly, after step 105 of generating exception prompt information based on the exception handling method, the following steps are also included: Step 502, obtain the maintenance records of the sewage treatment equipment.

[0121] Among them, the maintenance record refers to the fault cause and treatment measures recorded during the actual maintenance process of the equipment. Specifically, the fault codes, maintenance steps, and replaced spare part information recorded in the work order system can be used to extract effective operation and maintenance experience data.

[0122] In one example, the maintenance record is uploaded to the equipment operation and maintenance system by the maintenance personnel during or after the equipment maintenance.

[0123] Step 503, extract operation and maintenance data from the maintenance records, and update the expert rule base based on the operation and maintenance data.

[0124] Specifically, when the verification result indicates that there is an abnormality in the risk component, the abnormal operation data and the verified abnormal verification data are input into the pre-trained expert model. In this way, the expert model can perform rule matching and logical reasoning according to the built-in expert rule base, and automatically generate corresponding exception handling methods, such as suggesting shutdown for maintenance or adjusting operation parameters. After the equipment is repaired, the system extracts the actual fault cause, treatment measures, and treatment effect data from the maintenance records, converts these data into new expert rules through a rule extraction algorithm, and dynamically updates the expert rule base. In this way, by continuously collecting maintenance data and updating the rule base, the diagnostic accuracy of the expert model can be gradually improved.

[0125] In one example, keywords related to the fault description in the maintenance record are extracted through natural language processing (NLP) technology to obtain operation and maintenance data.

[0126] By adopting the above technical solutions, it is possible to automatically determine the precise treatment method for abnormal situations of sewage treatment equipment and optimize the treatment strategy through continuous learning. Specifically, this solution uses a pre-trained expert model to analyze abnormal operation data and abnormal verification data, thereby generating targeted abnormal treatment methods, improving the accuracy and efficiency of fault handling. At the same time, after generating the abnormal prompt information, maintenance records are further obtained and operation and maintenance data therein are extracted to update the expert rule base, realizing the self-learning and self-optimization capabilities of the system, making subsequent abnormal handling more intelligent and efficient. This not only improves the automation level of equipment operation and maintenance, but also effectively reduces the misjudgment risk caused by insufficient human experience, providing a reliable guarantee for the stable operation of sewage treatment equipment.

[0127] In some embodiments, referring to Figure 6 , in step 104 above, when the abnormal verification result indicates that there is an abnormality in the risk component, the following steps are further included: Step 601, perform abnormal prediction on the sewage treatment equipment based on the abnormal operation data to obtain an abnormal prediction result.

[0128] Among them, the abnormal prediction result is used to indicate the possible abnormalities of the equipment. Specifically, since the equipment is composed of components, the state of the components will affect the state of the equipment. Therefore, when it is determined that there is an abnormality in the risk component, it is necessary to perform abnormal prediction on the sewage treatment equipment based on the abnormal operation data.

[0129] In one example, performing abnormal prediction on the sewage treatment equipment based on the abnormal operation data includes: determining the abnormal type corresponding to the risk component based on the abnormal operation data; performing abnormal prediction on the sewage treatment equipment based on the abnormal type corresponding to the risk component.

[0130] Among them, the corresponding relationship between the abnormal types of the components and the abnormal types of the sewage treatment equipment is preset.

[0131] In actual implementation, the abnormal types of the sewage treatment equipment can also be directly corresponding to the types of the component operation data of each component. In this way, the abnormal types of the sewage treatment equipment can be directly deduced based on the types of the abnormal operation data.

[0132] Furthermore, in order to improve the accuracy of the abnormal prediction result, the sewage treatment equipment can be subjected to abnormal prediction by combining the abnormal verification data.

[0133] Step 602, update the equipment status of the associated equipment based on the abnormal prediction result and the position of the sewage treatment equipment in the sewage treatment process flow.

[0134] Among them, the associated device refers to a device whose working effect is affected by the sewage treatment device. The association relationship between devices is reflected by the sewage treatment process flow. For example, the device located downstream of the sewage treatment device in the sewage treatment process is determined as an associated device. Specifically, it can be realized by parsing the process topology relationship map, such as the influence chain of the aeration tank water pump failure on the dissolved oxygen concentration.

[0135] Specifically, considering that sewage treatment is a process in which a series of devices work together according to a certain process flow, the abnormality of one device may affect the working effect of subsequent devices, and thus may affect the final sewage treatment effect. Therefore, it is necessary to consider in combination with the process flow during the influence judgment process.

[0136] In one example, the device status of the associated device is updated based on the anomaly prediction result and the position of the sewage treatment device in the sewage treatment process flow, including: updating the device status of the device directly related to the sewage treatment device in the associated device based on the anomaly prediction result, and determining the device with the updated device status as the updated device; updating the device status of the device directly related to the updated device in the associated device based on the device status of the updated device, and determining the updated device as the updated device until all associated devices are determined as updated devices.

[0137] In actual implementation, the status of each associated device can also be deduced directly based on the process flow diagram.

[0138] Step 603, predict the sewage treatment effect based on the device status of each reference device in the sewage treatment process flow to obtain a predicted treatment result.

[0139] Among them, the reference device refers to a device that directly affects the sewage treatment effect. Generally speaking, the reference device is close to the end of the sewage treatment process flow. For example, the reference device is the last three devices in the sewage treatment process flow. Since the device status of the reference device may be updated during the status update process, and the device status of the reference device directly affects the sewage treatment effect, it is necessary to re-predict the sewage treatment effect based on the device status of the reference device after the device status is updated.

[0140] The method of predicting the sewage treatment effect based on the device status of the reference device is preset. In one example, the sewage treatment effect can be predicted based on a trained effect prediction model, and the effect prediction model is trained based on the device status of the historically collected reference device and the corresponding sewage treatment effect.

[0141] Furthermore, after predicting the sewage treatment effect, it is possible to prompt the device operation and maintenance personnel in combination with the actual situation to assist the operation and maintenance personnel in making operation and maintenance decisions.

[0142] In the above technical solution, abnormal prediction can be performed on the sewage treatment equipment based on abnormal operation data, the equipment status of the equipment in the sewage treatment process can be updated based on the abnormal prediction result, and finally the sewage treatment effect can be predicted based on the equipment status of the reference equipment. In this way, when an abnormality is detected, the impact of the abnormality on the sewage treatment effect can be accurately judged, which can help the operation and maintenance personnel make operation and maintenance decisions.

[0143] This application also provides a multi-source data fusion and remote diagnosis system for sewage treatment equipment. Refer to Figure 7 , the multi-source data fusion and remote diagnosis system for sewage treatment equipment includes a data acquisition component 710 and a cloud server 720 communicatively connected to the data acquisition component 710.

[0144] The data acquisition component 710 is used to acquire the component operation data of each component in the sewage treatment equipment and send it to the cloud server 720. In one example, the data acquisition component 710 includes at least one of a current sensor, a voltage sensor, a temperature sensor, and a vibration sensor. Correspondingly, the data acquisition component 710 can be installed on the component to be monitored.

[0145] A cloud operation and maintenance platform runs on the cloud server 720, and the cloud operation and maintenance platform is used to implement the multi-source data fusion and remote diagnosis method for sewage treatment equipment provided in the above method embodiment.

[0146] The above are all the preferred embodiments of this application. The protection scope of this application is not limited by this. Any feature disclosed in this specification (including the abstract and drawings), unless specifically described, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically described, each feature is only an example of a series of equivalent or similar features.

Claims

1. A multi-source data fusion and remote diagnosis method for sewage treatment equipment, characterized in that: The method comprises: For each target component in the sewage treatment equipment, risk monitoring is performed on the target component based on the component operation data of the target component and the risk monitoring rules corresponding to the target component; When a risk component is detected, abnormality verification data corresponding to the risk component is obtained; Verify whether the risk component has an abnormality based on the abnormality verification data and the risk verification rule corresponding to the target component, and obtain an abnormality verification result; When the abnormality check result indicates that the risk component is abnormal, determining the abnormality handling method for the risk device based on abnormal operation data with abnormality in the component operation data corresponding to the risk component and the abnormality check data; Generate exception prompt information based on the exception handling method.

2. The method according to claim 1, characterized in that: When a risk component is detected, obtaining abnormality verification data corresponding to the risk component includes: In the case where the risk component is monitored, determining the risk type corresponding to the risk component based on the abnormal operation data; Determining a verification data acquisition method based on the risk type; In the case where the verification data acquisition method includes self-data, determining the associated operation data that has an associated relationship with the abnormal operation data in the component operation data corresponding to the risk component as the abnormal verification data; In the case where the verification data acquisition method includes associated component data, the abnormal verification data is determined from component operation data of an associated component corresponding to the risk component.

3. The method according to claim 2, characterized in that The determining the abnormality verification data from the component operation data of the associated component corresponding to the risk component includes: In the case where the risk components include more than two, determining whether the associated component is a risk component; In the case where the associated component is a risk component, determining the device operation data having abnormalities in the operation data corresponding to the associated component as the abnormal verification data; In a case where the associated component is not the risk component, determining whether the associated component and the risk component are of the same type; In a case where the associated component and the risk component are of the same type, determining data of the same type as the abnormal operation data in the component operation data corresponding to the associated component as the abnormal verification data; In a case where the associated component and the risk component are of different types, abnormality verification data is determined from component operation data corresponding to the associated component based on the risk type.

4. The method according to claim 1, characterized in that: The abnormality verification data includes numerical verification data represented by numerical values, the risk verification rule includes a change matching rule, the change matching rule is used to record the corresponding relationship between the change of the numerical verification data determined when the risk component is abnormal and the change of the abnormality verification data, and the abnormality verification result is obtained by verifying whether the risk component is abnormal based on the abnormality verification data and the risk verification rule corresponding to the target component, including: Monitoring the changes of the numerical verification data and the abnormal verification data; Determine whether the change of the numerical verification data matches the change of the abnormal verification data based on the change matching rule; In a case where the change in the verification data matches the change in the verification data, an abnormality verification result indicating that the risk component is abnormal is generated.

5. The method according to claim 4, characterized in that The risk verification rule includes a verification range corresponding to the numerical verification data, and the verification range is determined when the risk component is abnormal. After determining whether the change of the numerical verification data matches the change of the abnormal verification data, it also includes: In the event that the change in the verification data does not match the change in the verification data, determining whether the numerical verification data falls within a corresponding verification range; In the case where the numerical verification data includes more than two and part of the numerical verification data does not fall within the corresponding verification range, determining the verification adjustment parameter based on the number of pending verification data that does not fall within the corresponding verification range; Adjusting the verification range corresponding to the pending verification data based on the verification adjustment parameter to obtain an adjusted range, wherein the adjusted range is larger than the verification range; Determining whether the pending verification data falls within the corresponding adjusted range; When each of the pending verification data falls within the corresponding adjusted range, an abnormal verification result indicating that the risk component is abnormal is generated.

6. The method according to claim 4, characterized in that The abnormality verification data includes the operation and maintenance data of the risk component, and before monitoring the change of the numerical verification data and the change of the abnormality verification data, it also includes: Analyzing the operation and maintenance data to obtain risk prediction data corresponding to the risk component; Determining whether there is the risk prediction data matching the abnormal operation data; In the absence of the risk prediction data matching the abnormal operation data, the step of determining whether the numerical verification data falls within a verification range corresponding to the risk data is performed.

7. The method according to claim 4, characterized in that The risk component includes a water pump, the numerical verification data includes vibration data of the water pump, the abnormal verification data includes current data and outlet pressure data of the water pump, and determining whether a change in the numerical verification data matches a change in the abnormal verification data based on the change matching rule includes: In the case where the vibration data continuously rises within a preset time period, determining whether the fluctuation amplitude of the current data reaches a preset fluctuation amplitude threshold, and whether the outlet pressure data decreases; When the fluctuation amplitude reaches the fluctuation amplitude threshold and the outlet pressure data decreases, it is determined that the change of the numerical verification data matches the change of the abnormal verification data.

8. The method according to claim 1, characterized in that The determining of the abnormal handling method for the risky device based on the abnormal operation data and the abnormal verification data that are abnormal in the component operation data corresponding to the risky component includes: Inputting the abnormal operation data and the abnormal verification data into a pre-trained expert model to obtain the abnormal processing method; Wherein, the expert model is constructed based on training using an expert rule base; After generating the exception prompt information based on the exception handling method, the method further includes: Obtaining maintenance records of the sewage treatment equipment; Operation and maintenance data are extracted from the maintenance record, and an expert rule base is updated based on the operation and maintenance data.

9. The method according to claim 1, characterized in that: When the abnormality check result indicates that the risk component is abnormal, the method further includes: Performing abnormality prediction on the sewage treatment equipment based on the abnormal operation data to obtain an abnormality prediction result; Updating the device status of the associated device based on the abnormal prediction result and the position of the sewage treatment device in the sewage treatment process; The sewage treatment effect is predicted based on the equipment status of each preset reference equipment in the sewage treatment process to obtain a predicted treatment result.

10. A multi-source data fusion and remote diagnosis system for sewage treatment equipment, characterized in that: The system includes a data acquisition component and a cloud server in communication with the data acquisition component; The data acquisition component is used to acquire component operation data of each component in the sewage treatment equipment and send it to the cloud server; A cloud operation and maintenance platform runs on the cloud server, and the cloud operation and maintenance platform is used to implement the multi-source data fusion and remote diagnosis method of the sewage treatment equipment according to any one of claims 1 to 9.

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