Water treatment intelligent operation and maintenance method, system and equipment

Abstract

This application is applicable to the field of water treatment technology, and particularly relates to an intelligent operation and maintenance method, system and device for water treatment. The method includes: accurately understanding the operating conditions and treatment effects of the water treatment system by obtaining water quality information and system images; obtaining key parameters of the water treatment system based on the water quality information, and obtaining characteristic data of the system images according to the key parameters, effectively quantifying and analyzing the operating performance of the water treatment system; determining the first operation and maintenance correction parameters of the water treatment system based on the key parameters of the water treatment system; determining the second operation and maintenance correction parameters of the water treatment system according to the first operation and maintenance correction parameters of the water treatment system and the characteristic data of the system images, so as to achieve targeted optimization to improve the stability and efficiency of the water treatment system; obtaining the water treatment operation and maintenance results according to the second operation and maintenance correction parameters of the water treatment system, more sensitively responding to water quality changes, improving the operation and maintenance work efficiency and timeliness, and enhancing the optimization pertinence.

Description

Technical Field

[0001] This application belongs to the technical field of water treatment, and particularly relates to a water treatment intelligent operation and maintenance method, system, and equipment. Background Art

[0002] Water treatment operation and maintenance refers to the daily management and maintenance of water treatment facilities to ensure their normal operation and meet the expected water quality standards. This includes multiple aspects such as drinking water treatment, wastewater treatment (industrial wastewater and domestic sewage). The operation and maintenance work has a wide range of contents, and professional knowledge and technical support are required from the daily inspection and maintenance of equipment to the emergency treatment in case of emergencies.

[0003] Traditional water treatment intelligent operation and maintenance mainly relies on regular manual sampling and laboratory analysis. There is a time lag between sampling and obtaining results, resulting in decisions being based on outdated information. Secondly, due to the periodic sampling method, the data can only reflect the state at a specific moment and cannot capture the dynamic process of water quality changes. In addition, frequent manual sampling and laboratory analysis are costly and greatly affected by human factors, with low accuracy. Summary of the Invention

[0004] The embodiments of this application provide a water treatment intelligent operation and maintenance method, system, and equipment, which can solve the problems in the water treatment intelligent operation and maintenance process that relying on regular manual sampling and laboratory analysis cannot capture the dynamic process of water quality changes, resulting in high operation and maintenance costs and unable to be implemented in a timely and effective manner.

[0005] In a first aspect, the embodiments of this application provide a water treatment intelligent operation and maintenance method, including:

[0006] Obtain water quality information and system images; wherein, the water quality information is used to reflect the treatment effect of the current water treatment system, and the system images are used to reflect the status of the water treatment system and its components;

[0007] Based on the water quality information, obtain the key parameters of the water treatment system, and obtain the characteristic data of the system images according to the key parameters; wherein, the key parameters are used to reflect the performance indicators of the water treatment system that need to be monitored, and the characteristic data are used to reflect the actual status of the water treatment system corresponding to the key parameters;

[0008] According to the key parameters of the water treatment system, determine the first operation and maintenance correction parameters of the water treatment system;

[0009] According to the first operation and maintenance correction parameters of the water treatment system and the characteristic data of the system images, determine the second operation and maintenance correction parameters of the water treatment system;

[0010] According to the second operation and maintenance correction parameters of the water treatment system, obtain the water treatment operation and maintenance results.

[0011] In the embodiments of the present application, the above technical solutions have at least the following technical effects:

[0012] The intelligent operation and maintenance method for water treatment provided by the embodiments of the present application can accurately understand the operation status and treatment effect of the water treatment system by obtaining water quality information and system images. Based on the water quality information, key parameters of the water treatment system are obtained, and characteristic data of the system images are obtained according to the key parameters, effectively quantifying and analyzing the operation performance of the water treatment system. According to the key parameters of the water treatment system, the first operation and maintenance correction parameters of the water treatment system are determined, providing a scientific basis for operation optimization. According to the first operation and maintenance correction parameters of the water treatment system and the characteristic data of the system images, the second operation and maintenance correction parameters of the water treatment system are determined to achieve targeted optimization, improving the stability and efficiency of the water treatment system. According to the second operation and maintenance correction parameters of the water treatment system, the water treatment operation and maintenance results are obtained, more sensitively responding to water quality changes, improving the operation and maintenance work efficiency and timeliness, and enhancing the optimization pertinence.

[0013] In a second aspect, an intelligent operation and maintenance system for water treatment provided by the embodiments of the present application includes:

[0014] An acquisition unit for acquiring water quality information and system images; wherein, the water quality information is used to reflect the treatment effect of the current water treatment system, and the system images are used to reflect the status of the water treatment system and its components;

[0015] A parameter unit for obtaining key parameters of the water treatment system based on the water quality information and obtaining characteristic data of the system images according to the key parameters; wherein, the key parameters are used to reflect the performance indicators of the water treatment system to be monitored, and the characteristic data are used to reflect the actual status of the water treatment system corresponding to the key parameters;

[0016] A correction unit for determining the first operation and maintenance correction parameters of the water treatment system according to the key parameters of the water treatment system;

[0017] An adjustment unit for determining the second operation and maintenance correction parameters of the water treatment system according to the first operation and maintenance correction parameters of the water treatment system and the characteristic data of the system images;

[0018] A result unit for obtaining water treatment operation and maintenance results according to the second operation and maintenance correction parameters of the water treatment system.

[0019] In a third aspect, an intelligent operation and maintenance device for water treatment provided by the embodiments of the present application includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any one of the above aspects is implemented.

[0020] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a water treatment intelligent operation and maintenance device, the water treatment intelligent operation and maintenance device is caused to execute the method according to any one of the above aspects.

[0021] It can be understood that the beneficial effects of the second to fourth aspects can be referred to the relevant descriptions in the above aspects, and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 is a flowchart of a water treatment intelligent operation and maintenance method provided by an embodiment of the present application;

[0024] Figure 2 is an execution diagram of a water treatment intelligent operation and maintenance method provided by an embodiment of the present application;

[0025] Figure 3 is an execution diagram of step S200 of a water treatment intelligent operation and maintenance method provided by an embodiment of the present application;

[0026] Figure 4 is an execution diagram of step S300 of a water treatment intelligent operation and maintenance method provided by an embodiment of the present application;

[0027] Figure 5 is an execution diagram of step S400 of a water treatment intelligent operation and maintenance method provided by an embodiment of the present application;

[0028] Figure 6 is a structural diagram of a water treatment intelligent operation and maintenance system provided by an embodiment of the present application;

[0029] Figure 7 is a structural diagram of a water treatment intelligent operation and maintenance device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0031] It should be understood that when used in the specification of this application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0032] It should also be understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0033] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrases "if determined" or "if the described condition or event is detected" can be interpreted as meaning "once determined", "in response to determining", "once the described condition or event is detected", or "in response to detecting the described condition or event" depending on the context.

[0034] In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are only used for differentiating descriptions and cannot be understood as indicating or implying relative importance.

[0035] The reference to "one embodiment" or "some embodiments" etc. described in the specification of this application means that a specific feature, structure or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0036] Traditional intelligent operation and maintenance of water treatment mainly relies on regular manual sampling and laboratory analysis. There is a time lag between sampling and obtaining results, resulting in decisions being based on outdated information. Secondly, due to the periodic sampling method, the data can only reflect the state at a specific moment and cannot capture the dynamic process of water quality change. In addition, frequent manual sampling and laboratory analysis are costly and are greatly affected by human factors, with low accuracy.

[0037] To solve the above problems, the embodiments of the present application provide a water treatment intelligent operation and maintenance method, system and device. In this method, by obtaining water quality information and system images, the operation status and treatment effect of the water treatment system can be accurately understood. Based on the water quality information, the key parameters of the water treatment system are obtained, and the characteristic data of the system images are obtained according to the key parameters, effectively quantifying and analyzing the operation performance of the water treatment system. According to the key parameters of the water treatment system, the first operation and maintenance correction parameters of the water treatment system are determined, providing a scientific basis for operation optimization. According to the first operation and maintenance correction parameters of the water treatment system and the characteristic data of the system images, the second operation and maintenance correction parameters of the water treatment system are determined to achieve targeted optimization, so as to improve the stability and efficiency of the water treatment system. According to the second operation and maintenance correction parameters of the water treatment system, the water treatment operation and maintenance results are obtained, more sensitively responding to water quality changes, improving the operation and maintenance work efficiency and timeliness, and enhancing the optimization pertinence.

[0038] The water treatment intelligent operation and maintenance method provided by the embodiments of the present application can be applied to water treatment intelligent operation and maintenance devices. At this time, the water treatment intelligent operation and maintenance device is the execution subject of the water treatment intelligent operation and maintenance method provided by the embodiments of the present application. The embodiments of the present application do not impose any restrictions on the specific type of the water treatment intelligent operation and maintenance device.

[0039] For example, the water treatment intelligent operation and maintenance device can be various types of intelligent monitoring devices. The water treatment intelligent operation and maintenance device can include, but is not limited to, desktop computers, smart large screens, smart TVs, handheld devices with wireless communication functions, computing devices, computers, laptop computers, etc.

[0040] To better understand the water treatment intelligent operation and maintenance method provided by the embodiments of the present application, the following provides an exemplary introduction to the specific implementation process of the water treatment intelligent operation and maintenance method provided by the embodiments of the present application.

[0041] Figure 1 The schematic flowchart of the water treatment intelligent operation and maintenance method provided by the embodiments of the present application is shown. Figure 2 The execution step flowchart of the water treatment intelligent operation and maintenance method provided by the embodiments of the present application is shown. The water treatment intelligent operation and maintenance method includes:

[0042] S100, obtaining water quality information and system images; wherein, the water quality information is used to reflect the treatment effect of the current water treatment system, and the system images are used to reflect the status of the water treatment system and its components.

[0043] It can be understood that water quality information can include indicators such as the pH value of the water body, dissolved oxygen content, chemical oxygen demand (COD), total suspended solids (TSS), ammonia nitrogen concentration, etc., which are used to reflect the effect of the water treatment process and the safety of water quality. The system image reflects the visual information of the water treatment system and its various components (such as filters, pumps, pipes, etc.), and can help detect the operating status of the equipment, whether there are faults or abnormalities. The water quality information is obtained through water quality monitoring instruments or sensors, which can include water quality sensors, on-line analyzers, etc., and can measure various indicators in the water in real time or regularly and upload them to the central data system. The system image can be obtained through cameras or monitoring devices installed at various key parts of the system, and the system image is used to provide visual data about the appearance of the equipment, operating status, and potential faults.

[0044] A water treatment system is a collection of a series of processes and equipment used to purify or improve water quality, and is designed to remove pollutants, impurities, harmful microorganisms, and other unwanted substances in the water.

[0045] S200, obtain the key parameters of the water treatment system based on the water quality information, and obtain the characteristic data of the system image according to the key parameters; wherein, the key parameters are used to reflect the performance indicators of the water treatment system that need to be monitored, and the characteristic data is used to reflect the actual state of the water treatment system corresponding to the key parameters.

[0046] It can be understood that the key parameters of the water treatment system can be determined through the obtained water quality information, and the relevant system image characteristic data can be extracted through these key parameters. The key parameters can be indicators closely related to the water treatment effect, such as influent water quality, treatment efficiency, effluent quality, etc. Through water quality information analysis, the change trends and states of these indicators can be determined, so as to identify potential problems that may exist in the water treatment system. The characteristic data refers to the useful information extracted from the system image, which helps to further analyze the operating status of the equipment. For example, the characteristic data can include changes in the appearance of the equipment, whether there are fault indicator lights on, the wear status of the operating components, etc. Based on the key parameters of the water treatment system, the corresponding system image can be found through the mapping relationship to judge whether the equipment is operating normally and whether maintenance or adjustment is required. Image processing techniques, such as edge detection, object recognition and other algorithms, can be combined to extract characteristic information from the image.

[0047] In a possible implementation, please refer to Figure 3 , S200, obtain the key parameters of the water treatment system based on the water quality information, and obtain the characteristic data of the system image according to the key parameters, including:

[0048] S210, perform a first identification based on the water quality information to determine the current water quality state; wherein, the current water quality state is the first water quality state or the second water quality state.

[0049] It is understandable that water quality information can be analyzed, and the water quality information can be identified by setting a threshold judgment method or a classification algorithm based on machine learning to determine the treatment status of the current water quality. For example, set a threshold to determine whether the water quality information meets the standard, or train a classification model (such as a support vector machine, decision tree, etc.) to classify the water quality data. The current water quality status can be the first water quality status (indicating normal water quality) or the second water quality status (indicating abnormal water quality), and the judgment needs to be based on the water quality data. The specific status settings of the first water quality status and the second water quality status can be determined in advance according to different requirements. The water quality information can include various water quality parameters, such as pH value, dissolved oxygen, chemical oxygen demand (COD), ammonia nitrogen content, turbidity, etc. The parameters in the water quality information can be used to reflect the quality of the water body. Especially in the water treatment process, the change of water quality can often indicate whether the system is operating normally. By analyzing these parameters, it can be determined whether the water quality is normal or there are problems.

[0050] Exemplarily, a threshold range can be set in advance for the water quality parameters, and the collected water quality parameters are compared with the set threshold. If the value of a certain water quality parameter exceeds the set threshold, it can be determined that the water quality is abnormal; otherwise, it can be considered that the water quality is normal. For example, the pH value is normal between 6.5 and 8.5. If it exceeds this range, it can be determined as abnormal.

[0051] S220. When the current water quality status is the second water quality status, a second identification is performed based on the water quality information to determine the abnormal water quality parameter information of the water quality information.

[0052] It is understandable that when the system identifies that the current water quality status is the second water quality status (i.e., abnormal water quality), it can further perform a more in-depth analysis based on the water quality information to identify the specific reasons for the abnormal water quality. The further identification process is called the second identification, and its goal is to determine the abnormal water quality parameter information, that is, to extract which specific water quality parameters exceed the normal range or have abnormal changes. Through the second identification process, it can be clarified which water quality indicators need to be monitored or adjusted key points, so as to effectively guide the subsequent treatment or correction measures.

[0053] Exemplarily, the second identification can be implemented based on the information of the water quality parameters using statistical analysis, principal component analysis (PCA), partial least squares method (PLS), etc., and based on the results of the second identification, the information of the abnormal water quality parameters can be extracted, that is, the abnormal water quality parameter information is obtained.

[0054] S230. Obtain the key parameters of the water treatment system according to the abnormal water quality parameter information of the water quality information, and obtain the characteristic data of the system image according to the key parameters.

[0055] It can be understood that problems that may exist in the water treatment system can be inferred by analyzing water quality anomaly parameters, and visual feedback on these problems can be provided through image data. That is, by establishing a mapping relationship between water quality anomalies and the water treatment system and environmental conditions, the extraction of image data can be guided based on these key parameters, providing data support for subsequent intelligent optimization and fault prediction.

[0056] The key parameters of the water treatment system refer to specific variables or indicators that can directly reflect the operating state of the water treatment system. Exemplarily, causal inference or association rule mining algorithms can be applied to achieve a directional mapping relationship from abnormal water quality parameter information to key parameters and then from key parameters to specific image data. For example, the dissolved oxygen level in the aeration tank is significantly lower than the normal range, which is mapped to key parameters such as the rotation speed, energy consumption, and aeration volume of the aerator, and the key parameters of the aerator point to the characteristic data of the relevant images inside the aeration tank.

[0057] In a possible implementation, S200, obtaining the key parameters of the water treatment system based on water quality information and obtaining the characteristic data of the system image according to the key parameters further includes:

[0058] S240, when the current water quality state is the first water quality state, obtaining an operation and maintenance result.

[0059] It can be understood that when the current water quality state is determined to be the first water quality state (i.e., normal water quality), the operation and maintenance result can be directly output to confirm that the current water treatment system does not need to be adjusted or repaired immediately. That is, the system is operating normally as expected, the water quality indicators meet the standards, and there are no signs of equipment failure or performance degradation. The water quality information can be identified by a rule engine, a threshold determination method, or a machine learning-based classification algorithm to determine the treatment state of the current water quality. If the water quality is stable and the system image shows that the equipment is normal, the operation and maintenance result is "operating normally". Whether the system is in a normal operating state can be judged by comparing the current state with historical data based on predefined rules or empirical data.

[0060] S300, determining the first operation and maintenance correction parameter of the water treatment system according to the key parameters of the water treatment system.

[0061] It can be understood that the required first operation and maintenance correction parameters can be determined based on the key parameters of the water treatment system. The key parameters may include water quality indicators (such as COD, ammonia nitrogen concentration, etc.), performance parameters of water treatment equipment (such as the cleanliness of filters, the operating efficiency of pumps, etc.). According to the changes in the key parameters, the parameters can be corrected to restore the water treatment system to its optimal operating state. Regression analysis, time series analysis, or optimization algorithms such as genetic algorithm (GA) or particle swarm optimization (PSO) can be used to predict the optimal adjustment strategy by analyzing historical data. The generated correction parameters will be used in subsequent adjustment steps to ensure the long-term stability of the water treatment system.

[0062] In a possible implementation, please refer to Figure 4 , S300, to determine the first operation and maintenance correction parameters of the water treatment system based on the key parameters of the water treatment system, including:

[0063] S310, to obtain the water treatment parameters of the water treatment system based on the key parameters of the water treatment system and the abnormal water quality parameter information of the water quality information; wherein, the water treatment parameters are parameters related to the abnormal water quality parameter information.

[0064] It can be understood that the specific parameters for adjusting the water treatment system, that is, the water treatment parameters of the water treatment system, can be determined by combining the key parameters of the water treatment system and the abnormal water quality parameter information. The water treatment parameters are control parameters closely related to water quality anomalies, such as water flow rate, chemical dosage, aeration intensity, etc. The abnormal water quality parameter information can identify possible bottlenecks or insufficient links in the current treatment process, thus providing a basis for subsequent adjustments. The water treatment parameters can be adjusted through multi-objective optimization algorithms to improve water quality. For example, an abnormality in a specific water quality parameter may require adjusting the chemical dosage or increasing the aeration volume.

[0065] Exemplarily, methods such as correlation analysis, causal relationship modeling, or time series analysis can be applied to screen out the water treatment system operation parameters with a correlation higher than the preset range with the abnormal water quality parameter information among the key parameters of the water treatment system, and output a list of key parameters with the highest correlation with the abnormal water quality parameter information, that is, the water treatment parameters of the water treatment system are obtained.

[0066] S320, to determine the water quality parameter change information of the water quality information based on the abnormal water quality parameter information of the water quality information; wherein, the water quality parameter change information is used to reflect the specific water quality change situation of the water quality information.

[0067] It can be understood that the water quality parameter change information refers to the change trend of water quality parameters over time, such as the rising and falling trends of indicators such as chemical components and pollutant concentrations in water. The water quality parameter change information reflects the speed and magnitude of water quality changes and can reveal whether there are potential water treatment problems. For example, if the COD concentration rises sharply, it may indicate that there is a malfunction in the treatment system and it is unable to effectively decompose the organic matter in the water. The changes in water quality parameters can be determined through time series analysis, which can include methods such as moving window averaging, exponentially weighted moving average (EWMA), and autoregressive integrated moving average model (ARIMA) to identify abnormal trends and changes in the time series.

[0068] Exemplarily, the difference method can be used for abnormal water quality parameter information to calculate the change amount of COD concentration between adjacent time points or fixed interval time points in the time series. Based on the change amount result of the difference, the change rate of COD concentration can be further calculated to obtain the water quality parameter change information.

[0069] S330. Determine the first operation and maintenance correction parameter of the water treatment system according to the water quality parameter change information of the water quality information and the water treatment parameters of the water treatment system.

[0070] It can be understood that the first operation and maintenance correction parameter is used to adjust the parameters of the water treatment process, which can include chemical dosing amount, flow rate, aeration intensity, etc., in order to repair or compensate for abnormal water quality conditions. The water quality parameter change information and the water treatment parameters of the water treatment system are interrelated. For example, if the concentration of certain pollutants in the water suddenly rises, it may mean that there are problems in some links of the treatment system and relevant water treatment parameters need to be adjusted to restore the water quality. Optimization algorithms such as least squares regression and genetic algorithm (GA) can be used to find the optimal parameter adjustment value based on historical data and the current state to obtain the first operation and maintenance correction parameter of the water treatment system.

[0071] Optionally, S330. Determine the first operation and maintenance correction parameter of the water treatment system according to the water quality parameter change information of the water quality information and the water treatment parameters of the water treatment system, including:

[0072] S331. Align the time according to the water quality parameter change information of the water quality information and the water treatment parameters of the water treatment system, and determine the characteristic points of the water treatment parameters.

[0073] It can be understood that time alignment refers to synchronizing two time series (such as water quality changes and water treatment parameter changes) so that they match on the time axis for subsequent analysis and comparison. The characteristic points of water treatment parameters refer to the significant change points or trend points in the data, which can be the moments when the data changes significantly and has an important impact on subsequent adjustments. For example, the moment when the water quality parameter shows a significant change (such as a sharp rise in COD concentration) can be regarded as a characteristic point. Through time alignment, these characteristic points can be synchronized to analyze the relationship between them and the changes in water treatment parameters. Time alignment can be ensured by methods such as interpolation, Lagrange Interpolation, or Dynamic Time Warping (DTW).

[0074] S332, based on the characteristic points of water treatment parameters and the water quality parameter change information of water quality information, obtain the abnormal change information of water treatment parameters.

[0075] It can be understood that the abnormal change information of water treatment parameters refers to the information that the parameters of the water treatment system (such as chemical dosage, filtration rate, etc.) have abnormal changes at a certain moment or within a certain period of time. The abnormal changes can be caused by factors such as equipment failure, abnormal operation, or external environmental impact. For example, the change in water quality parameters may show a sharp fluctuation in pollutant concentration, and if the key parameters of the water treatment system are not adjusted properly at this time, it may lead to further deterioration of water quality. Therefore, by analyzing the correlation between the characteristic points of water treatment parameters and the water quality change information, it is possible to identify which water treatment parameters have abnormal fluctuations. This can be done by calculating deviation indices (such as the standard deviation, mean, and rate of change of the deviation). If the change in water treatment parameters exceeds a certain threshold or goes beyond the normal fluctuation range, it can be marked as an abnormal change. The abnormal change information of water treatment parameters can be determined by including rule-based checks, statistical models (such as standard deviation), or anomaly detection algorithms.

[0076] S333, based on the abnormal change information of water treatment parameters and the water quality parameter change information of water quality information, obtain the first operation and maintenance correction parameters of the water treatment system.

[0077] It can be understood that the first operation and maintenance correction parameters are used to adjust the operation parameters of the water treatment system to correct the water quality changes caused by abnormal water quality or equipment problems. The first operation and maintenance correction parameters of the water treatment system are calculated through optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) after identifying abnormal changes. The first operation and maintenance correction parameters can include adjusting the water flow rate, increasing the chemical dosage, adjusting the temperature of the reaction tank, etc., and the optimal parameter adjustment values can be calculated through a feedback control mechanism (such as a PID controller) or an optimization solver.

[0078] Exemplarily, S333, based on the abnormal change information of water treatment parameters and the water quality parameter change information of water quality information, obtain the first operation and maintenance correction parameter of the water treatment system, including:

[0079] S3331, based on the abnormal change information of water treatment parameters and the water quality parameter change information of water quality information, obtain the first deviation index of water treatment parameters and the second deviation index of water quality information.

[0080] It can be understood that the deviation index is an indicator to measure the difference between the actual parameter and the expected or standard value, and is usually used to judge whether the parameter is normal. The first deviation index of water treatment parameters is used to measure the difference between the change of water treatment parameters and the predetermined target. For example, if the water flow rate or chemical dosage exceeds the set range, the deviation index will show a higher value. The second deviation index of water quality information is used to measure the difference between the water quality change (such as COD, pH value, etc.) and the normal water quality value. A larger or smaller deviation index usually means abnormal water quality. The deviation index can be calculated using standard deviation, mean difference or other statistical methods. The deviation index helps to quantify the deviation degree of water treatment parameters and the deviation degree of water quality information, providing clear reference data for subsequent correction.

[0081] Exemplarily, let the set target of a water treatment parameter be Pt, and the actually measured value be Pa, then the deviation index P can be calculated in the following way: P = |Pa - Pt| / Pt, where P represents the deviation index of this water treatment parameter, Pt is the target value, and Pa is the actual value. The larger the deviation index, the more the actual value of this water treatment parameter deviates from the target value. Similarly, the second deviation index of water quality information can be calculated in the same way.

[0082] S3332, based on the first deviation index of water treatment parameters and the second deviation index of water quality information, construct a dynamic deviation function of the first deviation index and the second deviation index.

[0083] It can be understood that the dynamic deviation function is a mathematical model used to describe the dynamic change of the deviation between water quality parameters and water treatment system parameters over time. Through the dynamic deviation function, the future deviation trend can be predicted, and the operation and maintenance strategy can be adjusted accordingly. The dynamic deviation function can be modeled based on real-time data through methods such as regression analysis and time series prediction. For example, methods such as dynamic time warping (DTW) and autoregressive model (AR) can help extract the dynamic change trends of water quality parameters and water treatment parameters.

[0084] Exemplarily, S3332, based on the first deviation index of water treatment parameters and the second deviation index of water quality information, construct a dynamic deviation function of the first deviation index and the second deviation index, including:

[0085] S33321. Determine the time lag parameter and the response parameter of the first deviation index and the second deviation index based on the first deviation index of the water treatment parameter and the second deviation index of the water quality information. The time lag parameter is used to reflect the time lag effect of the first deviation index on the second deviation index, and the response parameter is used to reflect the response of the second deviation index to the first deviation index.

[0086] It can be understood that the time lag parameter describes the time delay of the water treatment reaction in the system compared to the water quality change. The change in water quality usually shows a certain lag reaction in the water treatment system. The relationship between the water quality change and the water treatment response can be modeled. By analyzing the lag of the water quality change on the water treatment system, statistical methods such as the cross - correlation function or the autoregressive model can be used to determine the time lag. The cross - correlation function can determine the lag time between the water quality change and the water treatment reaction. The least - squares regression can be used to fit the time lag and the relationship between the water quality change and the water treatment response using the first deviation index and the second deviation index. Based on the regression analysis or the cross - correlation function analysis, a lag time τ (unit: hours, minutes, etc.) is obtained, that is, G(t - τ)=F(t), where G(t - τ) represents the change in the first deviation index and F(t) represents the change in the second deviation index. This lag time τ represents the time difference between the occurrence of the water quality deviation and the start of the response of the water treatment system.

[0087] The response parameter is used to describe the reaction intensity of the water treatment parameter to the water quality change. It defines the influence degree of the second deviation index on the first deviation index and can be obtained through fitting and optimizing the data, that is, αG(t - τ) β =F(t). The response parameters α and β reflect the response degree and influence direction of the water quality change (the second deviation index) on the water treatment parameter (the first deviation index). It can be a non - linear coefficient, indicating the influence degree of the change in the water quality deviation on the water treatment deviation. The response parameter can be fitted through a non - linear regression model, especially suitable for the complex relationships in the water treatment process. For example, methods such as polynomial regression, exponential regression, or support vector machines can be used for fitting.

[0088] S33322. Construct a deviation function model of the first deviation index and the second deviation index based on the time lag parameter and the response parameter of the first deviation index and the second deviation index.

[0089] It can be understood that when the time lag parameter τ and the response parameter α are determined, a comprehensive dynamic model can be established to describe the influence of the water quality change on the water treatment system. For example, assume that the relationship between the water quality deviation F(t) and the water treatment parameter deviation G(t) during the water treatment process is: F(t)=αG(t−τ) β+G(t), where β is the non - linear coefficient that controls the response intensity of water quality deviation to water treatment deviation. It means that after a time lag τ of the water quality change G(t), a non - linear response to the impact on the water treatment system is carried out, and the response intensity is controlled by parameters α and β.

[0090] S33323, based on the first deviation index, the second deviation index and the deviation function model, obtain the dynamic deviation function of the first deviation index and the second deviation index.

[0091] It can be understood that after establishing the deviation function model of the first deviation index and the second deviation index, the dynamic deviation function can be calculated through this model. The dynamic deviation function is based on real - time data and outputs the dynamic relationship between the first deviation index and the second deviation index according to the deviation function model. The dynamic deviation function is used to optimize the water treatment process, such as adjusting the chemical dosage or treatment flow rate to cope with water quality changes.

[0092] S3333, determine the first operation and maintenance correction parameter of the water treatment system according to the dynamic deviation function of the first deviation index and the second deviation index.

[0093] It can be understood that the first operation and maintenance correction parameter of the water treatment system can be determined by including dynamic optimization algorithms (such as simulated annealing, particle swarm optimization, etc.), and various parameters of the water treatment system can be adjusted to achieve the desired water quality goal. According to the first deviation index and the second deviation index, set the target water quality of the water treatment system. The goal can include specific pollutant concentrations, pH values, turbidity, etc. Use dynamic optimization algorithms (such as simulated annealing, particle swarm optimization, etc.) to adjust the operating parameters of the water treatment system to minimize the deviation between the first deviation index and the second deviation index.

[0094] Exemplarily, use particle swarm optimization (PSO) to search for suitable correction parameters. The algorithm searches for the optimal solution by simulating the movement of particles. The initial particle positions (i.e., the initial values of each operation and maintenance correction parameter) can be initialized, the fitness of each particle (i.e., the deviation function value) can be calculated, and the positions of the particles can be updated according to the fitness (such as a preset maximum number of iterations or a fitness threshold) until a predetermined termination condition is reached to obtain the first operation and maintenance correction parameter.

[0095] S400, determine the second operation and maintenance correction parameter of the water treatment system according to the first operation and maintenance correction parameter of the water treatment system and the characteristic data of the system image.

[0096] It can be understood that the second operation and maintenance correction parameter of the water treatment system is calculated based on the first operation and maintenance correction parameter and the characteristic data of the system image. The characteristic data of the system image can include key information reflecting the water treatment state in the image (such as water quality changes, equipment status, etc.). By analyzing this information, the possible impact of the real-time equipment state on water quality changes can be calculated, and the system operation can be further adjusted and optimized specifically.

[0097] In a possible implementation, please refer to Figure 5 , S400, to determine the second operation and maintenance correction parameter of the water treatment system according to the first operation and maintenance correction parameter of the water treatment system and the characteristic data of the system image, including:

[0098] S410, to determine the operation and maintenance parameter correction sample of the water treatment system according to the first operation and maintenance correction parameter of the water treatment system and the characteristic data of the system image.

[0099] It can be understood that the characteristic data of the system image mainly refers to the key information extracted through image processing methods, which is closely related to the equipment operation state, environmental conditions, possible faults or abnormal situations in the water treatment process. The characteristic data of the system image can include:

[0100] Equipment status image: such as the operation status images of equipment such as pumping stations, reaction tanks, filters, etc., which reflect whether the equipment is operating normally (such as the rotation speed of pumps, the clogging situation of filters).

[0101] Visual characteristics of water quality changes: For example, by analyzing the clarity and color changes of the water body in the image, the changes in water quality can be indirectly reflected.

[0102] Image information of environmental changes: such as temperature sensor images, image data of humidity changes, etc.

[0103] The operation and maintenance parameter correction sample is a data set used to correct the first operation and maintenance correction parameter, which contains the characteristic data of the system image and the first operation and maintenance correction parameter. The operation and maintenance parameter correction sample can be constructed in the form of a data structure and stored in a database or sample pool.

[0104] S420, input the operation and maintenance parameter correction sample of the water treatment system into the second operation and maintenance parameter correction model to obtain the second operation and maintenance correction parameter of the water treatment system; among them, the second operation and maintenance parameter correction model is a machine learning model obtained through training.

[0105] It can be understood that the second operation and maintenance parameter correction model is a machine learning model obtained through training, which is used to reveal the internal influence of device real-time information on parameter changes. Image data can be collected from the water treatment system, including device status images, water quality change images, and environmental change images. Each image should contain some key information, such as water quality changes, device status, etc. The image data can be obtained through sensors, cameras, or other monitoring devices. Image processing algorithms can be used to extract features from the system images, such as the operating status of the device (e.g., whether the device is operating normally, the clogging condition of the filter, etc.), water quality changes (such as clarity, color changes, etc.), and environmental information (such as humidity, temperature, etc.). Common feature extraction methods include edge detection, texture analysis, color space conversion, etc. And by combining the first operation and maintenance correction parameter as an input sample, each image and its corresponding water treatment parameter are labeled, and the adjustment parameter of the first operation and maintenance correction parameter, that is, the second operation and maintenance correction parameter, is used as the output sample and labeled, so as to obtain the input sample and the target value (output sample) corrected by model prediction.

[0106] Exemplarily, assume that a model for correcting the operation and maintenance parameters of a water treatment system is constructed. The second operation and maintenance parameter correction model takes the first correction parameter (such as some adjustment parameters of the current water treatment system) and image features (such as device status images, water quality change images, etc.) as inputs, and predicts the second correction parameter (such as the optimized system correction parameter). The following Table 1 is an example table showing the sample data and the target output:

[0107] Table 1

[0108]

[0109] S500, according to the second operation and maintenance correction parameter of the water treatment system, obtain the water treatment operation and maintenance result.

[0110] It can be understood that the second operation and maintenance correction parameter is the optimal operation parameter calculated by a machine learning model based on the analysis results of the device status (feature data extracted from the system image) and water quality data. The second operation and maintenance correction parameter can guide the operation optimization of the water treatment system to restore its normal treatment effect. The acquisition of the water treatment operation and maintenance result is a feedback loop process, which can be to perform actual operations according to the corrected parameters, and then monitor the water quality change through sensors to determine whether the operation and maintenance effect reaches the expected level. If the second operation and maintenance correction parameter correctly adjusts the operation of the water treatment system, the water quality will gradually return to the qualified standard. On the contrary, if the water treatment operation and maintenance result does not meet the expectation, the parameters can be further adjusted and the status of the water treatment system can be re-evaluated.

[0111] In a possible implementation manner, S500, according to the second operation and maintenance correction parameter of the water treatment system, obtain the water treatment operation and maintenance result, including:

[0112] S510, send the second operation and maintenance correction parameter of the water treatment system to the water treatment system, and obtain the real-time water quality information and real-time system image.

[0113] It can be understood that the second operation and maintenance correction parameter can be sent to the water treatment system to trigger the actual operation of the water treatment system. The second correction parameter will affect the control strategy in the water treatment process, such as adjusting the chemical dosage, flow rate, reaction tank temperature, etc., to restore the water quality and optimize the system operation. By sending the second operation and maintenance correction parameter to the control unit of the water treatment system, the control unit adjusts the parameters of automation equipment (such as flow meters, chemical dosing devices, temperature control equipment, etc.) and collects real-time water quality information and system images to further monitor the effect after adjustment.

[0114] S520, obtain the water treatment operation and maintenance result according to the real-time water quality information and real-time system image.

[0115] It can be understood that the effect of the current water treatment process can be re-identified based on two main input sources, namely real-time water quality information and real-time system image. The water quality status can be determined by re-identifying the real-time water quality information. If the water quality indicators still do not meet the standards, the water treatment parameters can be recalculated and adjusted. Whether the equipment is in normal operation can be judged through image analysis. If the system image shows that the equipment has faults or needs further maintenance, finally, based on the real-time water quality information and real-time system image, the effect of the operation and maintenance process can be calculated to provide the final operation and maintenance result report, realizing targeted operation and maintenance of the water treatment process and improving the timeliness and accuracy of operation and maintenance.

[0116] Exemplarily, after identifying the real-time water quality information and real-time system image, a specific operation and maintenance report can be obtained through natural language processing (NLP) and templatized generation methods. The operation and maintenance report can include the water quality status (the first water quality status or the second water quality status) before and after the operation and maintenance process, the adjustment measures taken and their reasons, etc. The operation and maintenance report is also the water treatment operation and maintenance result.

[0117] Corresponding to the water treatment intelligent operation and maintenance method in the above embodiment, the embodiment of the present application also provides a water treatment intelligent operation and maintenance system, and each unit of the system can implement each step of the water treatment intelligent operation and maintenance method. Figure 6 The structural block diagram of the water treatment intelligent operation and maintenance system provided by the embodiment of the present application is shown. For the sake of convenience of description, only the part related to the embodiment of the present application is shown.

[0118] Refer to Figure 6 , the water treatment intelligent operation and maintenance system includes:

[0119] An acquisition unit for acquiring water quality information and system images; wherein, the water quality information is used to reflect the treatment effect of the current water treatment system, and the system image is used to reflect the status of the water treatment system and its components;

[0120] A parameter unit for obtaining key parameters of the water treatment system based on the water quality information, and obtaining characteristic data of the system image according to the key parameters; wherein, the key parameters are used to reflect the performance indicators of the water treatment system that need to be monitored, and the characteristic data is used to reflect the actual status of the water treatment system corresponding to the key parameters;

[0121] A correction unit for determining the first operation and maintenance correction parameters of the water treatment system based on the key parameters of the water treatment system;

[0122] An adjustment unit for determining the second operation and maintenance correction parameters of the water treatment system according to the first operation and maintenance correction parameters of the water treatment system and the characteristic data of the system image;

[0123] A result unit for obtaining the water treatment operation and maintenance result according to the second operation and maintenance correction parameters of the water treatment system.

[0124] It should be noted that the information interaction, execution process, etc. between the above systems / units, due to being based on the same concept as the method embodiments of the present application, for their specific functions and the technical effects brought, please refer to the method embodiment part for details, and will not be elaborated here.

[0125] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into one processing unit, or each unit module exists physically alone, or two or more unit modules are integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments and will not be elaborated here.

[0126] The embodiment of the present application also provides a water treatment intelligent operation and maintenance device, Figure 7 which is a structural schematic diagram of the water treatment intelligent operation and maintenance device provided by an embodiment of the present application. As Figure 7As shown, the intelligent operation and maintenance device 6 for water treatment in this embodiment includes: at least one processor 60 ( Figure 7 only one is shown in the figure), at least one memory 61 ( Figure 7 only one is shown in the figure), and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the intelligent operation and maintenance device 6 for water treatment implements the steps in any of the above-described embodiments of the intelligent operation and maintenance methods for water treatment, or enables the intelligent operation and maintenance device 6 for water treatment to implement the functions of each unit in the above-described system embodiments.

[0127] Exemplarily, the computer program 62 can be divided into one or more units. The one or more units are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 62 in the intelligent operation and maintenance device 6 for water treatment.

[0128] The intelligent operation and maintenance device for water treatment can be various types of intelligent monitoring devices. The intelligent operation and maintenance device for water treatment can include, but is not limited to, the processor 60 and the memory 61. Those skilled in the art can understand that Figure 7 this is only an example of the intelligent operation and maintenance device 6 for water treatment, and does not constitute a limitation on the intelligent operation and maintenance device 6 for water treatment. It can include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it can also include input and output devices, network access devices, buses, etc.

[0129] The processor 60 can be a central processing unit (CPU). The processor 60 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0130] The memory 61 may be an internal storage unit of the water treatment intelligent operation and maintenance device 6 in some embodiments, such as the hard disk or memory of the water treatment intelligent operation and maintenance device 6. The memory 61 may also be an external storage device of the water treatment intelligent operation and maintenance device 6 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the water treatment intelligent operation and maintenance device 6. Further, the memory 61 may also include both the internal storage unit and the external storage device of the water treatment intelligent operation and maintenance device 6. The memory 61 is used to store an operating system, application programs, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store the data that has been output or will be output.

[0131] An embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0132] An embodiment of the present application provides a computer program product, and when the computer program product runs on the water treatment intelligent operation and maintenance device, the water treatment intelligent operation and maintenance device implements the steps in any of the above method embodiments.

[0133] 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, to implement all or part of the processes in the above method embodiments of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps in the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to the water treatment intelligent operation and maintenance device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc.

[0134] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0135] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0136] In the embodiments provided in this application, it should be understood that the disclosed water treatment intelligent operation and maintenance system / water treatment intelligent operation and maintenance equipment and method can be implemented in other ways. For example, the water treatment intelligent operation and maintenance system / water treatment intelligent operation and maintenance equipment embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0137] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0138] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. A water treatment intelligent operation and maintenance method, characterized in that: include: Acquire water quality information and system images; wherein the water quality information is used to reflect the treatment effect of the current water treatment system, and the system image is used to reflect the status of the water treatment system and its components; Acquire key parameters of the water treatment system based on the water quality information, and acquire characteristic data of the system image according to the key parameters; wherein the key parameters are used to reflect the performance indicators of the water treatment system that need to be monitored, and the characteristic data are used to reflect the actual state of the water treatment system corresponding to the key parameters; Determining a first operation and maintenance parameter of the water treatment system according to the key parameter of the water treatment system; determining a second operation and maintenance parameter of the water treatment system according to the first operation and maintenance parameter of the water treatment system and the characteristic data of the system image; Obtaining a water treatment operation and maintenance result according to the second operation and maintenance parameter of the water treatment system; Wherein, determining the first operation and maintenance parameter of the water treatment system according to the key parameter of the water treatment system includes: Based on the abnormal change information of the water treatment parameter and the water quality parameter change information of the water quality information, a first deviation index of the water treatment parameter and a second deviation index of the water quality information are obtained; wherein the water treatment parameter is a parameter related to the abnormal water quality parameter information of the water quality information obtained based on the key parameter, and the water quality parameter change information is used to reflect the specific water quality change of the water quality information; based on the abnormal water quality parameter information of the water quality information, the water quality parameter change information of the water quality information is determined; Constructing a dynamic deviation function of the first deviation index and the second deviation index according to the first deviation index of the water treatment parameter and the second deviation index of the water quality information; A first operation and maintenance parameter of the water treatment system is determined according to a dynamic deviation function of the first deviation index and the second deviation index.

2. The water treatment intelligent operation and maintenance method according to claim 1, characterized in that: The step of acquiring key parameters of the water treatment system based on the water quality information, and acquiring feature data of the system image according to the key parameters, includes: Performing a first identification based on the water quality information to determine a current water quality state; wherein the current water quality state is a first water quality state or a second water quality state; When the current water quality state is a second water quality state, performing a second identification based on the water quality information to determine abnormal water quality parameter information of the water quality information; The key parameters of the water treatment system are obtained according to the abnormal water quality parameter information of the water quality information, and the characteristic data of the system image are obtained according to the key parameters.

3. The water treatment intelligent operation and maintenance method according to claim 2, characterized in that: The method of acquiring key parameters of the water treatment system based on the water quality information, and acquiring feature data of the system image according to the key parameters, further includes: When the current water quality state is the first water quality state, an operation and maintenance result is obtained.

4. The water treatment intelligent operation and maintenance method according to claim 2, characterized in that: Determining the first operation and maintenance parameter of the water treatment system according to the key parameter of the water treatment system includes: Obtaining water treatment parameters of the water treatment system according to the key parameters of the water treatment system and the abnormal water quality parameter information of the water quality information; A first operation and maintenance parameter of the water treatment system is determined according to the water quality parameter change information of the water quality information and the water treatment parameter of the water treatment system.

5. The water treatment intelligent operation and maintenance method according to claim 4, characterized in that: Determining a first operation and maintenance parameter of the water treatment system according to the water quality parameter change information of the water quality information and the water treatment parameter of the water treatment system includes: Performing time alignment according to the water quality parameter change information of the water quality information and the water treatment parameters of the water treatment system to determine the characteristic points of the water treatment parameters; Based on the characteristic points of the water treatment parameters and the water quality parameter change information of the water quality information, obtaining abnormal change information of the water treatment parameters; Based on the abnormal change information of the water treatment parameter and the water quality parameter change information of the water quality information, a first operation and maintenance correction parameter of the water treatment system is obtained.

6. The water treatment intelligent operation and maintenance method according to claim 1, characterized in that: The step of constructing a dynamic deviation function of the first deviation index and the second deviation index based on the first deviation index of the water treatment parameter and the second deviation index of the water quality information includes: Determine the time hysteresis parameter and the response parameter of the first deviation index and the second deviation index according to the first deviation index of the water treatment parameter and the second deviation index of the water quality information; wherein the time hysteresis parameter is used to reflect the time hysteresis effect of the first deviation index on the second deviation index, and the response parameter is used to reflect the response of the second deviation index to the first deviation index; constructing a deviation function model of the first deviation index and the second deviation index based on the time hysteresis parameter and the response parameter of the first deviation index and the second deviation index; A dynamic deviation function of the first deviation index and the second deviation index is obtained according to the first deviation index, the second deviation index and a deviation function model.

7. The water treatment intelligent operation and maintenance method according to claim 1, characterized in that: Determining a second operation and maintenance parameter of the water treatment system according to the first operation and maintenance parameter of the water treatment system and the characteristic data of the system image includes: Determining a correction sample of operation and maintenance parameters of the water treatment system according to the first operation and maintenance correction parameter of the water treatment system and the characteristic data of the system image; The operation and maintenance parameter correction sample of the water treatment system is input into a second operation and maintenance parameter correction model to obtain second operation and maintenance correction parameters of the water treatment system; wherein the second operation and maintenance parameter correction model is a machine learning model obtained through training.

8. The water treatment intelligent operation and maintenance method according to claim 1, characterized in that: The step of obtaining a water treatment operation and maintenance result according to the second operation and maintenance parameter of the water treatment system includes: Sending the second operation and maintenance parameter of the water treatment system to the water treatment system, and obtaining real-time water quality information and real-time system images; A water treatment operation and maintenance result is obtained according to the real-time water quality information and the real-time system image.

9. A water treatment intelligent operation and maintenance system, characterized in that: For implementing the method described in any one of claims 1 to 8, the water treatment intelligent operation and maintenance system comprises: An acquisition unit, used to acquire water quality information and a system image; wherein the water quality information is used to reflect the treatment effect of the current water treatment system, and the system image is used to reflect the status of the water treatment system and its components; A parameter unit, used to obtain key parameters of the water treatment system based on the water quality information, and to obtain characteristic data of the system image according to the key parameters; wherein the key parameters are used to reflect the performance indicators of the water treatment system to be monitored, and the characteristic data are used to reflect the actual state of the water treatment system corresponding to the key parameters; A correction unit, configured to determine a first operation and maintenance correction parameter of the water treatment system according to the key parameter of the water treatment system; an adjustment unit, configured to determine a second operation and maintenance parameter of the water treatment system according to the first operation and maintenance parameter of the water treatment system and the characteristic data of the system image; A result unit is used to obtain a water treatment operation and maintenance result according to the second operation and maintenance parameter of the water treatment system.

10. A water treatment intelligent operation and maintenance device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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