Intelligent operation and maintenance method, system and equipment for water treatment

Abstract

The invention is suitable for the technical field of water treatment, and particularly relates to an intelligent operation and maintenance method, system and equipment for water treatment, and the method comprises the steps: obtaining water quality information and system images, and precisely knowing the operation condition and treatment effect of a water treatment system; key parameters of the water treatment system are obtained based on the water quality information, feature data of system images are obtained according to the key parameters, and the operation performance of the water treatment system is effectively quantified and analyzed; determining first operation and maintenance positive parameters of the water treatment system according to the key parameters 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, determining a second operation and maintenance parameter of the water treatment system to realize targeted optimization so as to improve the stability and efficiency of the water treatment system; according to the second operation and maintenance positive parameter of the water treatment system, a water treatment operation and maintenance result is obtained, water quality changes are responded more sensitively, operation and maintenance work efficiency and timeliness are improved, and optimization pertinence is improved.

Description

Technical Field

[0001] The present application belongs to the field of water treatment technology, and in particular, 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 to achieve the expected water quality standards. This includes drinking water treatment, wastewater treatment (industrial wastewater and domestic sewage) and other aspects. The operation and maintenance work covers a wide range of content, from daily inspection and maintenance of equipment to emergency response in unexpected situations, which requires professional knowledge and technical support.

[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, which leads to decisions based on outdated information. Secondly, due to the periodic sampling method, the data can only reflect the status at a specific moment and cannot capture the dynamic process of water quality changes. In addition, frequent manual sampling and laboratory analysis are costly, greatly affected by human factors, and have low accuracy. Summary of the invention

[0004] The embodiments of the present application provide a method, system and equipment for intelligent operation and maintenance of water treatment, which can solve the problem that the intelligent operation and maintenance of water treatment relies on regular manual sampling and laboratory analysis and cannot capture the dynamic process of water quality changes, resulting in high operation and maintenance costs and inability to implement it in a timely and effective manner.

[0005] In a first aspect, an embodiment of the present application provides a water treatment intelligent operation and maintenance method, including: 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; A water treatment operation and maintenance result is obtained according to the second operation and maintenance parameter of the water treatment system.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The intelligent operation and maintenance method for water treatment provided in the embodiment of the present application accurately understands the operating status and treatment effect of the water treatment system by acquiring water quality information and system images. The key parameters of the water treatment system are obtained based on the water quality information, and the characteristic data of the system image are obtained according to the key parameters, so as to effectively quantify and analyze the operating 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 to provide 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 image, the second operation and maintenance correction parameters of the water treatment system are determined to achieve targeted optimization 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, which respond to water quality changes more sensitively, improve the efficiency and timeliness of operation and maintenance work, and enhance the targeted optimization.

[0007] In a second aspect, the embodiment of the present application provides a water treatment intelligent operation and maintenance system, including: 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.

[0008] In a third aspect, an embodiment of the present application provides 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, wherein the processor implements a method as described in any one of the above aspects when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a water treatment intelligent operation and maintenance device, the water treatment intelligent operation and maintenance device executes the method of any one of the above aspects.

[0010] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the above aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 It is a flow chart of a water treatment intelligent operation and maintenance method provided by an embodiment of the present application; Figure 2 It is a schematic diagram of executing the intelligent operation and maintenance method for water treatment provided by one embodiment of the present application; Figure 3 Schematic diagram of the execution of step S200 of the water treatment intelligent operation and maintenance method provided in one embodiment of the present application; Figure 4 It is a schematic diagram of executing step S300 of the water treatment intelligent operation and maintenance method provided in one embodiment of the present application; Figure 5 It is a schematic diagram of executing step S400 of the water treatment intelligent operation and maintenance method provided in one embodiment of the present application; Figure 6 It is a structural diagram of a water treatment intelligent operation and maintenance system provided by an embodiment of the present application; Figure 7 It is a structural diagram of a water treatment intelligent operation and maintenance device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may 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 prevent unnecessary details from obstructing the description of the present application.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of 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 combinations thereof.

[0015] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] 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, which leads to decisions based on outdated information. Secondly, due to the periodic sampling method, the data can only reflect the status at a specific moment and cannot capture the dynamic process of water quality changes. In addition, frequent manual sampling and laboratory analysis are costly, greatly affected by human factors, and have low accuracy.

[0020] To solve the above problems, the embodiments of the present application provide a method, system and equipment for intelligent operation and maintenance of water treatment. In this method, by obtaining water quality information and system images, the operating status and treatment effect of the water treatment system can be accurately understood. The key parameters of the water treatment system are obtained based on the water quality information, and the characteristic data of the system image are obtained according to the key parameters, so as to effectively quantify and analyze the operating 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 to provide 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 image, the second operation and maintenance correction parameters of the water treatment system are determined to achieve targeted optimization 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, which responds to water quality changes more sensitively, improves the efficiency and timeliness of operation and maintenance work, and enhances the targeted optimization.

[0021] The intelligent operation and maintenance method for water treatment provided in the embodiment of the present application can be applied to intelligent operation and maintenance equipment for water treatment. In this case, the intelligent operation and maintenance equipment for water treatment is the executor of the intelligent operation and maintenance method for water treatment provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of intelligent operation and maintenance equipment for water treatment.

[0022] For example, the water treatment intelligent operation and maintenance equipment can be various types of intelligent monitoring equipment. The water treatment intelligent operation and maintenance equipment 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.

[0023] In order to better understand the water treatment intelligent operation and maintenance method provided in the embodiment of the present application, the specific implementation process of the water treatment intelligent operation and maintenance method provided in the embodiment of the present application is exemplarily introduced below.

[0024] Figure 1 A schematic flow chart of the water treatment intelligent operation and maintenance method provided in an embodiment of the present application is shown. Figure 2 The flowchart of the execution steps of the water treatment intelligent operation and maintenance method provided in the embodiment of the present application is shown, and the water treatment intelligent operation and maintenance method includes: 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 image is used to reflect the status of the water treatment system and its components.

[0025] It can be understood that water quality information can include indicators such as pH value, dissolved oxygen content, chemical oxygen demand (COD), total suspended solids (TSS), and ammonia nitrogen concentration of the water body, 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.), which can help detect the operating status of the equipment and whether there are any faults or abnormalities. Water quality information is obtained through water quality monitoring instruments or sensors, which can include water quality sensors, online analyzers, etc., which 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 equipment installed in various key parts of the system. The system image is used to provide visual data on the appearance, operating status, and potential faults of the equipment.

[0026] A water treatment system is a collection of processes and equipment used to purify or improve the quality of water. It is designed to remove pollutants, impurities, harmful microorganisms and other unwanted substances from water.

[0027] S200, acquiring key parameters of the water treatment system based on water quality information, and acquiring 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.

[0028] It can be understood that the key parameters of the water treatment system can be determined by the acquired water quality information, and the relevant system image feature data can be extracted through these key parameters. Key parameters can be indicators closely related to the water treatment effect, such as inlet water quality, treatment efficiency, outlet water quality, etc. The changing trend and state of these indicators can be determined by water quality information analysis, so as to identify potential problems that may exist in the water treatment system. Feature data refers to useful information extracted from the system image, which helps to further analyze the operating status of the equipment. For example, feature data can include changes in the appearance of the equipment, whether there is a fault indicator light on, the wear status of the operating parts, etc. Based on the key parameters of the water treatment system, the corresponding system image can be found through the mapping relationship to determine whether the equipment is operating normally and whether maintenance or adjustment is required. Image processing technology, such as edge detection, object recognition and other algorithms, can be combined to extract feature information from the image.

[0029] In one possible implementation, see Figure 3 , S200, obtain key parameters of the water treatment system based on the water quality information, and obtain characteristic data of the system image according to the key parameters, including: S210, 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.

[0030] It can be understood that water quality information can be analyzed, and water quality information can be identified by setting a threshold judgment method or a classification algorithm based on machine learning to determine the current water quality treatment status. For example, a threshold is set to determine whether the water quality information meets the standard, or a classification model (such as a support vector machine, a decision tree, etc.) is trained to classify the water quality data. The current water quality state can be a first water quality state (indicating normal water quality) or a second water quality state (indicating abnormal water quality), and it needs to be judged based on water quality data. The specific state settings of the first water quality state and the second water quality state can be predetermined according to different needs. Water quality information can include a variety of 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 is a problem.

[0031] For example, a threshold range can be set for the water quality parameter in advance, and the collected water quality parameter can be compared with the set threshold. If the value of a water quality parameter exceeds the set threshold, it can be determined that the water quality is abnormal, otherwise, the water quality can be considered normal. For example, a pH value between 6.5 and 8.5 is normal, and if it exceeds this range, it can be determined to be abnormal.

[0032] S220: When the current water quality state is the second water quality state, a second identification is performed based on the water quality information to determine abnormal water quality parameter information of the water quality information.

[0033] It can be understood that when the system identifies that the current water quality state is the second water quality state (i.e., abnormal water quality), it can further conduct a more in-depth analysis based on the water quality information to identify the specific cause of 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 undergone abnormal changes. Through the second identification process, it can be clear which water quality indicators need to be monitored or adjusted, thereby effectively guiding subsequent processing or correction measures.

[0034] Exemplarily, the second identification can be implemented based on the information of water quality parameters using statistical analysis, principal component analysis (PCA), partial least squares (PLS), etc., and based on the result of the second identification, the information of abnormal water quality parameters can be extracted, that is, the abnormal water quality parameter information is obtained.

[0035] S230, acquiring key parameters of the water treatment system according to the abnormal water quality parameter information of the water quality information, and acquiring characteristic data of the system image according to the key parameters.

[0036] It can be understood that the possible problems of the water treatment system can be inferred by analyzing the abnormal water quality parameters, and visual feedback of these problems can be provided through image data. That is, by establishing a mapping relationship between abnormal water quality 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.

[0037] The key parameters of the water treatment system refer to specific variables or indicators that can directly reflect the operating status of the water treatment system. For example, causal reasoning 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 through 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 the aerator's speed, energy consumption, aeration volume and other key parameters. The key parameters of the aerator point to the feature data of the relevant image inside the aeration tank.

[0038] In a possible implementation, S200, acquiring key parameters of the water treatment system based on water quality information, and acquiring characteristic data of the system image according to the key parameters, further includes: S240, when the current water quality state is the first water quality state, obtain the operation and maintenance result.

[0039] It can be understood that when the current water quality state is judged to be the first water quality state (i.e., the water quality is normal), the operation and maintenance results 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 the equipment has no signs of failure or performance degradation. The water quality information can be identified through the rule engine, the threshold judgment method or the classification algorithm based on machine learning to determine the current water quality treatment status. If the water quality is stable and the system image shows that the equipment is normal, the operation and maintenance result is "normal operation". Based on predefined rules or empirical data, it can be judged whether the system is in a normal operating state by comparing the current state with historical data.

[0040] S300, determining first operation and maintenance parameters of the water treatment system according to key parameters of the water treatment system.

[0041] 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 filter cleanliness, pump operating efficiency, etc.). According to the changes in key parameters, the parameters can be corrected to restore the water treatment system to the optimal operating state. Regression analysis, time series analysis or optimization algorithms, such as genetic algorithms (GA) or particle swarm optimization (PSO), can be used to predict the best 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.

[0042] In one possible implementation, see Figure 4 S300, determining the first operation and maintenance parameters of the water treatment system according to the key parameters of the water treatment system, including: S310, obtaining water treatment parameters of the water treatment system according to key parameters of the water treatment system and 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.

[0043] It can be understood that the key parameters of the water treatment system and the abnormal water quality parameter information can be combined to determine the specific parameters of the water treatment system that need to be adjusted, that is, the water treatment parameters of the water treatment system. Water treatment parameters are control parameters that are closely related to abnormal water quality, such as water flow, dosage of reagents, aeration intensity, etc. Abnormal water quality parameter information can identify bottlenecks or insufficient links that may occur in the current treatment process, thereby providing a basis for subsequent adjustments. Water treatment parameters can be adjusted to improve water quality through a multi-objective optimization algorithm. For example, an abnormality in a specific water quality parameter may require adjusting the dosage of reagents or increasing the aeration volume.

[0044] Exemplarily, methods such as correlation analysis, causal relationship modeling or time series analysis can be applied to screen out water treatment system operating parameters whose correlation with abnormal water quality parameter information is higher than a preset range among the key parameters of the water treatment system, and output a list of key parameters that are most correlated with the abnormal water quality parameter information, thereby obtaining the water treatment parameters of the water treatment system.

[0045] S320, determining water quality parameter change information of the water quality information according to 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.

[0046] It can be understood that water quality parameter change information refers to the trend of water quality parameters changing over time, such as the rising and falling trends of indicators such as the chemical composition and pollutant concentration in the water. 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 be that the treatment system has failed and cannot effectively decompose the organic matter in the water. Changes in water quality parameters can be determined through time series analysis. It can include sliding window averaging, exponentially weighted average (EWMA) and autoregressive integrated moving average model (ARIMA) to identify abnormal trends and changes in time series.

[0047] For example, the abnormal water quality parameter information can be subjected to differential method to calculate the change in COD concentration between adjacent time points or fixed interval time points in a time series. Based on the differential change result, the COD concentration change rate can be further calculated to obtain water quality parameter change information.

[0048] S330: Determine 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.

[0049] It can be understood that the first operation and maintenance correction parameters are parameters used to adjust the water treatment process, which may include the dosage of reagents, flow rate, aeration intensity, etc., in order to repair or compensate for abnormal water quality conditions. The water quality parameter change information is interrelated with the water treatment parameters of the water treatment system. For example, if the concentration of certain pollutants in the water quality suddenly rises, it may mean that there is a problem in some link of the treatment system, and it is necessary to restore the water quality by adjusting the relevant water treatment parameters. Optimization algorithms, such as least squares regression and genetic algorithms (GA), can be used to find the best parameter adjustment values ​​based on historical data and current status to obtain the first operation and maintenance correction parameters of the water treatment system.

[0050] Optionally, S330, 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: S331, performing time alignment based on water quality parameter change information of the water quality information and water treatment parameters of the water treatment system to determine characteristic points of the water treatment parameters.

[0051] 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 significant change points or trend points in the data. They can be moments when the data changes significantly and have an important impact on subsequent adjustments. For example, moments when water quality parameters change significantly (such as a sharp increase in COD concentration) can be regarded as characteristic points. Through time alignment, these characteristic points can be synchronized and their relationship with changes in water treatment parameters can be analyzed. Time alignment can ensure the alignment of two time series through methods such as interpolation, Lagrange Interpolation, or Dynamic Time Warping (DTW).

[0052] S332, obtaining abnormal change information 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.

[0053] It can be understood that the abnormal change information of water treatment parameters refers to the abnormal change information of the parameters of the water treatment system (such as the dosage of the reagent, the filtration speed, etc.) at a certain moment or within a period of time. The abnormal change may be caused by factors such as equipment failure, abnormal operation or external environmental influence. For example, the change of water quality parameters may show a sharp fluctuation in the concentration of pollutants. At this time, if the key parameters of the water treatment system are not properly adjusted, it may cause the water quality to deteriorate further. Therefore, by analyzing the association between the characteristic points of the 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 the deviation index (for example, the standard deviation, mean and change rate of the deviation). If the change of the water treatment parameter exceeds a certain threshold or exceeds the normal fluctuation range, it can be marked as an abnormal change. The abnormal change information of the water treatment parameter can be determined by including rule-based checks, statistical models (such as standard deviation) or anomaly detection algorithms.

[0054] S333, obtaining a first operation and maintenance correction parameter of the water treatment system based on the abnormal change information of the water treatment parameter and the water quality parameter change information of the water quality information.

[0055] It can be understood that the first operation and maintenance correction parameter is used to adjust the operating 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 parameter of the water treatment system is calculated by an optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.) after identifying the abnormal changes. The first operation and maintenance correction parameter may include adjusting the water flow, increasing the dosage of the reagent, adjusting the temperature of the reaction tank, etc., and the optimal parameter adjustment value can be calculated by a feedback control mechanism (such as a PID controller) or an optimization solver.

[0056] Exemplarily, S333, based on the abnormal change information of the water treatment parameters and the water quality parameter change information of the water quality information, obtaining the first operation and maintenance correction parameter of the water treatment system includes: S3331, based on the abnormal change information of the water treatment parameters and the water quality parameter change information of the water quality information, obtain a first deviation index of the water treatment parameters and a second deviation index of the water quality information.

[0057] It can be understood that the deviation index is an indicator that measures the difference between the actual parameter and the expected or standard value, and is usually used to judge whether the parameter is normal or not. 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 the dosage of the reagent exceeds the set range, the deviation index will be expressed as a higher value. The second deviation index of water quality information is used to measure the difference between water quality changes (such as COD, pH value, etc.) and normal water quality values. A larger or smaller deviation index usually means that the water quality is abnormal. Calculating the deviation index can be achieved using standard deviation, mean difference or other statistical methods. The deviation index helps to quantify the degree of deviation of water treatment parameters and the degree of deviation of water quality information, and provide clear reference data for subsequent corrections.

[0058] For example, if the set target of a water treatment parameter is Pt and the actual measured value is Pa, then the deviation index P can be calculated as follows: P=|Pa−Pt| / Pt, where P represents the deviation index of the 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 the water treatment parameter deviates from the target value. The second deviation index of similar water quality information can also be calculated in this way.

[0059] S3332: Construct 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.

[0060] It can be understood that the dynamic deviation function is a mathematical model used to describe the dynamic changes in the deviation between water quality parameters and water treatment system parameters over time. Through the dynamic deviation function, future deviation trends 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 regression analysis, time series prediction and other methods. 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.

[0061] Exemplarily, S3332, 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, including: S33321, based on the first deviation index of the water treatment parameter and the second deviation index of the water quality information, determine the time hysteresis parameter and response parameter of the first deviation index and the second deviation index; wherein the time hysteresis 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.

[0062] It can be understood that the time hysteresis parameter describes the time delay of the water treatment response in the system compared to the water quality change. Changes in water quality usually have a certain lag reaction in the water treatment system. The relationship between water quality changes and water treatment responses can be modeled. By analyzing the lag of the impact of water quality changes on the water treatment system, statistical methods such as cross-correlation functions or autoregressive models are used to determine the time lag. The cross-correlation function (Cross-Correlation) can determine the lag time between water quality changes and water treatment responses. The relationship between time lag and water quality changes on water treatment responses can be fitted using the first deviation index and the second deviation index using least squares regression. Based on regression analysis or cross-correlation function analysis, a lag time τ (unit: hours, minutes, etc.) is obtained, that is, G (t-τ) = F (t), G (t-τ) represents the change of the first deviation index, and F (t) represents the change of the second deviation index. The lag time τ represents the time difference between the occurrence of water quality deviation and the start of the water treatment system response.

[0063] The response parameter is used to describe the intensity of the response of water treatment parameters to water quality changes. It defines the influence of the second deviation index on the first deviation index and can be obtained by 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 water quality change (second deviation index) on water treatment parameters (first deviation index). It can be a nonlinear coefficient, indicating the influence degree of water quality deviation change on water treatment deviation. The response parameters can be fitted by nonlinear regression model, which is especially suitable for complex relationships in water treatment process. For example, polynomial regression, exponential regression or support vector machine can be used for fitting.

[0064] S33322: Construct 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.

[0065] It can be understood that once the time hysteresis parameter τ and the response parameter α are determined, a comprehensive dynamic model can be established to describe the impact of water quality changes on the water treatment system. For example, suppose that the relationship between the water quality deviation F(t) and the water treatment parameter deviation G(t) in the water treatment process is: F(t)=αG(t−τ) β+G(t), β is a nonlinear coefficient that controls the response intensity of water quality deviation to water treatment deviation. It means that after a time lag τ, the water quality change G(t) will have a nonlinear response to the impact on the water treatment system, and the response intensity is controlled by parameters α and β.

[0066] S33323, obtaining a dynamic deviation function of the first deviation index and the second deviation index according to the first deviation index, the second deviation index and the deviation function model.

[0067] It can be understood that after the deviation function model of the first deviation index and the second deviation index is established, the dynamic deviation function can be calculated through the 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 dosage of the reagent or the treatment flow to cope with changes in water quality.

[0068] S3333: Determine a first operation and maintenance parameter of the water treatment system according to a dynamic deviation function of the first deviation index and the second deviation index.

[0069] It is understood that the first operation and maintenance correction parameter of the water treatment system can be determined by including a dynamic optimization algorithm (such as simulated annealing, particle swarm optimization, etc.), and the various parameters of the water treatment system can be adjusted to achieve the desired water quality target. According to the first deviation index and the second deviation index, the target water quality of the water treatment system is set. The target can include specific pollutant concentrations, pH values, turbidity, etc. A dynamic optimization algorithm (such as simulated annealing, particle swarm optimization, etc.) is used to adjust the operating parameters of the water treatment system to minimize the deviation between the first deviation index and the second deviation index.

[0070] Exemplarily, a particle swarm optimization (PSO) is used to search for suitable correction parameters, and the algorithm searches for the optimal solution by simulating the movement of particles. The first operation correction parameters can be obtained by initializing the particle position (i.e., the initial value of each operation correction parameter), calculating the fitness of each particle (i.e., the deviation function value), and updating the particle position according to the fitness (such as a preset maximum number of iterations or a fitness threshold) until a predetermined termination condition is reached.

[0071] S400, 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 characteristic data of the system image.

[0072] 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 may include key information reflecting the water treatment status in the image (such as water quality changes, equipment status, etc.). By analyzing this information, the possible impact of the real-time status of the equipment on the water quality change is calculated. The system operation can be further adjusted and optimized.

[0073] In one possible implementation, see Figure 5 S400, determining a 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: S410, determining an operation and maintenance parameter correction sample of the water treatment system according to a first operation and maintenance correction parameter of the water treatment system and characteristic data of the system image.

[0074] It can be understood that the characteristic data of the system image mainly refers to the key information extracted by the image processing method, which is closely related to the equipment operation status, environmental conditions, possible failures or abnormal conditions in the water treatment process. The characteristic data of the system image can include: Equipment status images: Such as operating status images of equipment such as pump stations, reaction tanks, and filters, which reflect whether the equipment is operating normally (e.g. pump speed, filter blockage).

[0075] Visual characteristics of water quality changes: For example, image analysis of water clarity, color changes, etc. can indirectly reflect changes in water quality.

[0076] Image information of environmental changes: such as temperature sensor images, image data of humidity changes, etc.

[0077] The operation and maintenance parameter correction sample is a data set used to correct the first operation and maintenance correction parameter, including 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 by means of a data structure and stored in a database or a sample pool.

[0078] S420, inputting the operation and maintenance parameter correction sample of the water treatment system 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.

[0079] 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 inherent influence of real-time equipment information on parameter changes. Image data can be collected from the water treatment system, including equipment status images, water quality change images, and environmental change images. Each image should contain some key information, such as water quality changes, equipment status, etc. Image data can be obtained through sensors, cameras, or other monitoring devices. Image processing algorithms can be used to extract features in system images, such as equipment operating status (for example, whether the equipment is operating normally, filter clogging, 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 annotated, and the adjustment parameter of the first operation and maintenance correction parameter, that is, the second operation and maintenance correction parameter, is used as an output sample and annotated, that is, the input sample and the target value (output sample) corrected by model prediction are obtained.

[0080] For example, it is assumed that a model is constructed for correcting the operation and maintenance parameters of a water treatment system. The second operation and maintenance parameter correction model predicts the second correction parameter (such as the optimized system correction parameter) by taking the first correction parameter (such as some adjustment parameters of the current water treatment system) and image features (such as equipment status images, water quality change images, etc.) as input. Table 1 below is an example table showing sample data and target output: Table 1 S500: Obtain a water treatment operation and maintenance result according to a second operation and maintenance parameter of the water treatment system.

[0081] It can be understood that the second operation and maintenance correction parameters are the optimal operating parameters calculated by the machine learning model based on the analysis results of the equipment status (feature data extracted from the system image) and water quality data. The second operation and maintenance correction parameters can guide the operation optimization of the water treatment system to restore its normal treatment effect. The acquisition of water treatment operation and maintenance results is a feedback loop process. It can be actual operation based on the corrected parameters, and then monitor the water quality changes through sensors to determine whether the operation and maintenance effect reaches the expected level. If the second operation and maintenance correction parameters correctly adjust the operation of the water treatment system, the water quality will gradually return to the qualified standard. Conversely, if the water treatment operation and maintenance results do not meet expectations, the parameters can be further adjusted and the status of the water treatment system can be re-evaluated.

[0082] In a possible implementation, S500, obtaining a water treatment operation and maintenance result according to the second operation and maintenance parameter of the water treatment system includes: S510, 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 image.

[0083] It is understandable 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 dosage of the reagent, the flow rate, the temperature of the reaction tank, etc., to restore the water quality and optimize the system operation. The second operation and maintenance correction parameter can be sent to the control unit of the water treatment system, and the control unit can adjust the parameters of the automation equipment (such as flow meter, reagent doser, temperature control equipment, etc.), collect real-time water quality information and system images, and further monitor the effect of the adjustment.

[0084] S520, obtaining water treatment operation and maintenance results based on the real-time water quality information and the real-time system image.

[0085] It can be understood that the effect of the current water treatment process can be re-identified based on two main input sources: real-time water quality information and real-time system images. The water quality status can be determined by re-identifying the real-time water quality information. If the water quality indicators are still not up to standard, the water treatment parameters can be recalculated and adjusted. Image analysis can be used to determine whether the equipment is in normal operation. If the system image shows that the equipment is faulty or needs further maintenance, the effect of the operation and maintenance process can be calculated based on the real-time water quality information and real-time system images, and the final operation and maintenance result report can be provided to achieve targeted operation and maintenance of the water treatment process and improve the timeliness and accuracy of operation and maintenance.

[0086] For example, after real-time water quality information and real-time system images are identified, a specific operation and maintenance report can be obtained through natural language processing (NLP) and template generation methods. The operation and maintenance report can include the water quality status before and after the operation and maintenance process (the first water quality status or the second water quality status), the adjustment measures that have been taken and their reasons, etc. The operation and maintenance report is also the water treatment operation and maintenance result.

[0087] Corresponding to the water treatment intelligent operation and maintenance method of 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 A structural block diagram of the water treatment intelligent operation and maintenance system provided in an embodiment of the present application is shown. For the sake of convenience of explanation, only the parts related to the embodiment of the present application are shown.

[0088] Reference Figure 6 , the water treatment intelligent operation and maintenance system includes: 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.

[0089] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0090] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0091] The present application also provides a water treatment intelligent operation and maintenance device, Figure 7 This is a schematic diagram of the structure of the water treatment intelligent operation and maintenance equipment provided in one embodiment of the present application. Figure 7 As shown, the water treatment intelligent operation and maintenance equipment 6 of this embodiment includes: at least one processor 60 ( Figure 7 Only one is shown), at least one memory 61 ( Figure 7Only one is shown) 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 water treatment intelligent operation and maintenance device 6 implements the steps in any of the above-mentioned water treatment intelligent operation and maintenance method embodiments, or the water treatment intelligent operation and maintenance device 6 implements the functions of each unit in the above-mentioned system embodiments.

[0092] Exemplarily, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the water treatment intelligent operation and maintenance device 6.

[0093] The water treatment intelligent operation and maintenance equipment can be various types of intelligent monitoring equipment. The water treatment intelligent operation and maintenance equipment can include but is not limited to a processor 60 and a memory 61. Those skilled in the art can understand that Figure 7 It is only an example of the water treatment intelligent operation and maintenance equipment 6 and does not constitute a limitation on the water treatment intelligent operation and maintenance equipment 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access equipment, buses, etc.

[0094] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0095] In some embodiments, the memory 61 may be an internal storage unit of the water treatment intelligent operation and maintenance device 6, such as a hard disk or memory of the water treatment intelligent operation and maintenance device 6. In other embodiments, the memory 61 may also be an external storage device of the water treatment intelligent operation and maintenance device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the water treatment intelligent operation and maintenance device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the water treatment intelligent operation and maintenance device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0096] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0097] 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 implements the steps in any of the above-mentioned method embodiments.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the water treatment intelligent operation and maintenance equipment, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0099] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0100] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] In the embodiments provided in the present 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 embodiment described above is only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present 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; A water treatment operation and maintenance result is obtained according to the second operation and maintenance parameter of the water treatment system.

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 characteristic 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 characteristic 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: According to the key parameters of the water treatment system and the abnormal water quality parameter information of the water quality information, a water treatment parameter of the water treatment system is obtained; wherein the water treatment parameter is a parameter related to the abnormal water quality parameter information; Determine water quality parameter change information of the water quality information according to 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 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 5, characterized in that: The step of obtaining the first operation and maintenance correction parameter of the water treatment system based on the abnormal change information of the water treatment parameter and the water quality parameter change information of the water quality information comprises: Based on the abnormal change information of the water treatment parameter and the water quality parameter change information of the water quality information, obtaining a first deviation index of the water treatment parameter and a second deviation index of the water quality information; 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.

7. The water treatment intelligent operation and maintenance method according to claim 6, 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.

8. 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; Inputting the operation and maintenance parameter correction sample of the water treatment system into a second operation and maintenance parameter correction model to obtain a second operation and maintenance correction parameter of the water treatment system; wherein the second operation and maintenance parameter correction model is a machine learning model obtained through training; And / or, 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: include: 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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