An integrated water circulation system twin modeling device and method

Abstract

The present application provides a twin modeling device and method for an integrated water circulation system. By twinning each water circulation component in the water circulation system, a digital twin model of each water circulation component is obtained; then, the time delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process is extracted; the dynamic constraint boundary of each digital twin model on the twin water quality state variables during the closed-loop water circulation process is determined according to the loop topology diagram and the time delay dependency of the water circulation system; the simulation credibility of the water quality change of each digital twin model is verified through all dynamic constraint boundaries to obtain the simulation confidence of the water quality change of each digital twin model; the twin water quality state variables of each digital twin model are dynamically updated according to the simulation confidence of all water quality change quantities. The scheme of the present application can realize the twin modeling of the nonlinear feedback effect of water quality change in the integrated water circulation system.

Description

Technical Field

[0001] The present application relates to the technical field of twin modeling, and more specifically, to a device and method for twin modeling of an integrated water circulation system. Background Art

[0002] Twin modeling is a method of creating a virtual model corresponding to the physical system, reflecting the dynamic changes of the physical system through the virtual model, and accurately reproducing the behavior in the real world in the virtual environment, helping people better understand the operating mechanism of the physical system. Twin modeling is widely used to optimize system management and improve decision-making quality. For example, in water resource management, it can help predict the impact of water quality changes and environmental changes. In the manufacturing industry, twin models can be used to monitor the operating status of equipment, detect faults in advance, or optimize production processes. In this way, twin modeling not only improves the accuracy of operations, but also realizes the intelligent management of equipment, effectively reduces resource waste, reduces risks, and improves the sustainability and responsiveness of equipment.

[0003] Integrated water cycle systems are gaining increasing attention as an efficient water resource management model. However, when complex interactions and feedback mechanisms exist between different water cycle components within an integrated water cycle system, water quality changes exhibit significant nonlinear feedback effects. The nonlinear feedback effect of water quality changes refers to the interaction between various factors within the system, where changes in a certain variable (such as pollutant concentration) will affect other parts through multiple pathways, thereby triggering dynamic changes in the entire system. This nonlinear feedback effect is usually manifested as time lag, threshold effects, and unpredictability, and is often difficult to accurately describe using traditional linear models. To address this complex nonlinear feedback effect, twin modeling technology provides a new solution for understanding and predicting water quality changes. By constructing a digital twin model of the integrated water cycle system, the operating status of the integrated water cycle system is reflected in real time, thereby simulating the nonlinear feedback effect of water quality changes in the integrated water cycle system and capturing the dynamic process of water quality changes. Therefore, how to implement twin modeling of the nonlinear feedback effect of water quality changes in the integrated water cycle system has become a difficult problem facing the industry. Summary of the Invention

[0004] The present application provides a twin modeling device and method for an integrated water circulation system, which can realize twin modeling of the nonlinear feedback effect of water quality changes in the integrated water circulation system.

[0005] In a first aspect, the present application provides a method for dynamically updating a twin model of an integrated water circulation system, which is used for dynamically updating a twin model of an integrated water circulation system twin modeling device. The method comprises the following steps:

[0006] Conduct twin modeling of each water cycle component in the integrated water cycle system of the target industrial park to obtain a digital twin model of each water cycle component;

[0007] Based on the water quality mapping data of each digital twin model, the time-delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process is extracted;

[0008] Determining the dynamic constraint boundaries of each digital twin model on the twin water quality state variables during the closed-loop water circulation process based on the time-delay dependency relationship between the loop topology of the integrated water circulation system and the twin water quality state variables;

[0009] Through all dynamic constraint boundaries, the water quality change of each digital twin model in the closed-loop water circulation process is simulated and verified for credibility, and the simulation confidence of the water quality change of each digital twin model in the closed-loop water circulation process is obtained;

[0010] The twin water quality state variables of each digital twin model are dynamically updated according to the simulation confidence of all water quality changes.

[0011] In some embodiments, twin modeling is performed on each water cycle component in the integrated water cycle system of the target industrial park to obtain a digital twin model of each water cycle component, specifically including:

[0012] Initialize the digital twin platform;

[0013] Obtain the physical parameters of each water cycle component in the integrated water cycle system of the target industrial park;

[0014] Based on the digital twin platform, each water cycle component is digitally mapped according to its physical parameters to obtain a digital twin model of each water cycle component.

[0015] In some embodiments, based on the water quality mapping data of each digital twin model, extracting the time delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process specifically includes:

[0016] Obtain historical water quality data for each water cycle component corresponding to the digital twin model;

[0017] Performing trend analysis on historical water quality data of the water cycle component corresponding to each digital twin model to obtain a water quality change trend of the water cycle component corresponding to each digital twin model;

[0018] Determining a twin water quality stability index of each digital twin model based on the water quality mapping data of each digital twin model and the water quality change trend of the water cycle component corresponding to each digital twin model;

[0019] The twin water quality stability index of each digital twin model is used to determine the time-delay dependency between the twin water quality state variables of each digital twin model during the change of water quality trends.

[0020] In some embodiments, determining the dynamic constraint boundary of each digital twin model on the twin water quality state variable during the closed-loop water circulation process based on the time-delay dependency relationship between the loop topology of the integrated water circulation system and the twin water quality state variable specifically includes:

[0021] Constructing a state transition matrix of a digital twin model corresponding to each water circulation component in the integrated water circulation system according to the loop topology diagram of the integrated water circulation system;

[0022] The constraint factors of water quality changes of each digital twin model in the closed-loop water circulation process are determined through the time-delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process;

[0023] According to the state transfer matrix of the digital twin model corresponding to each water cycle component and the constraint factor of water quality change of each digital twin model during the closed-loop water cycle, the dynamic constraint boundary of each digital twin model on the twin water quality state variable during the closed-loop water cycle is determined.

[0024] In some embodiments, the water quality change of each digital twin model in the closed-circuit water circulation process is simulated and verified for credibility through all dynamic constraint boundaries. The simulation confidence of the water quality change of each digital twin model in the closed-circuit water circulation process is obtained, specifically including:

[0025] Determine the change in water quality of the circulating water during the closed-loop water circulation process for each digital twin model;

[0026] Conduct confidence assessments on the changes in circulating water quality during the closed-loop water circulation process for each digital twin model using all dynamic constraint boundaries, and obtain the error confidence intervals for the simulated estimates of water quality changes during the closed-loop water circulation process for each digital twin model.

[0027] The simulation confidence of each digital twin model for the water quality change in the closed-circuit water circulation process is determined by the error confidence interval of the simulation estimation of the water quality change in the closed-circuit water circulation process by each digital twin model.

[0028] In some embodiments, dynamically updating the twin water quality state variables of each digital twin model based on the simulation confidence of all water quality changes specifically includes:

[0029] Determine the dynamic correction value of each digital twin model's water quality state variable through the simulation confidence of all water quality changes;

[0030] The twin water quality state variables of each digital twin model are updated according to the dynamic correction value of the twin water quality state variables of each digital twin model.

[0031] In some embodiments, the types of water circulation components specifically include pumps, valves, pipes, heat exchangers, filters, water towers, water storage tanks, etc.

[0032] In a second aspect, the present application provides an integrated water circulation system twin modeling device, which includes a twin model dynamic update unit, characterized in that the twin model dynamic update unit includes:

[0033] The twin modeling module is used to perform twin modeling on each water cycle component in the integrated water cycle system of the target industrial park, obtaining a digital twin model of each water cycle component;

[0034] A processing module is used to extract the time-delayed dependency between the twin water quality state variables of each digital twin model during the water quality trend change process based on the water quality mapping data of each digital twin model;

[0035] The processing module is further configured to determine the dynamic constraint boundary of each digital twin model on the twin water quality state variables during the closed-loop water circulation process based on the time-delay dependency relationship between the loop topology of the integrated water circulation system and the twin water quality state variables;

[0036] The processing module is further configured to perform a simulation credibility check on the water quality change of each digital twin model during the closed-circuit water circulation process through all dynamic constraint boundaries, thereby obtaining a simulation confidence level of each digital twin model for the water quality change during the closed-circuit water circulation process;

[0037] The execution module is used to dynamically update the twin water quality state variables of each digital twin model according to the simulation confidence of all water quality changes.

[0038] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned method for dynamically updating the twin model of the integrated water circulation system.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for dynamically updating the twin model of the integrated water circulation system.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] In the integrated water circulation system twin modeling device and method provided in the present application, by twin modeling each water circulation component in the integrated water circulation system of the target industrial park, a digital twin model of each water circulation component is obtained; based on the water quality mapping data of each digital twin model, the time delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process is extracted; according to the loop topology diagram of the integrated water circulation system and the time delay dependency between the twin water quality state variables, the dynamic constraint boundary of the twin water quality state variable of each digital twin model during the closed-loop water circulation process is determined; the water quality change of each digital twin model during the closed-loop water circulation process is simulated and verified for credibility through all dynamic constraint boundaries, and the simulation confidence of the water quality change of each digital twin model during the closed-loop water circulation process is obtained; the twin water quality state variables of each digital twin model are dynamically updated according to the simulation confidence of all water quality changes.

[0042] It can be seen that in this application, the water quality change of each digital twin model in the closed-loop water circulation process can be simulated and verified for credibility through all dynamic constraint boundaries, and the simulation confidence of each digital twin model in the water quality change in the closed-loop water circulation process can be obtained; among them, firstly, the time-delay dependency between the twin water quality state variables is extracted based on the water quality mapping data of each digital twin model, which can effectively reveal the interaction and feedback mechanism between the various water circulation components in the integrated water circulation system. This process helps to capture the nonlinear feedback effect of water quality changes and can better simulate the nonlinear characteristics such as time lag and threshold effect in the system; secondly, the dynamic constraint boundary of each digital twin model on the twin water quality state variables in the closed-loop water circulation process is determined, which can accurately limit the scope of action of each water circulation component in the water quality change. This process helps to describe the nonlinear feedback effect in water quality changes. The setting of dynamic constraint boundaries can avoid excessive or unreasonable water quality fluctuations in the system and ensure that water quality changes are in line with actual operating conditions and system feedback mechanisms; then, by performing simulation credibility verification on all dynamic constraint boundaries, it can ensure that each digital twin model has a high degree of accuracy and reliability in predicting water quality changes during a closed-loop water circulation process. Through this process, the simulation confidence of each digital twin model can be quantified, and its performance in complex nonlinear feedback effects can be further verified. This not only helps to confirm that the dynamic trend of water quality changes is in line with the actual operating status, but also reveals the model's adaptability in the face of different system conditions and water quality changes; finally, the twin water quality state variables of each digital twin model are dynamically updated according to the simulation confidence of all water quality changes; in summary, the solution of this application can realize twin modeling of nonlinear feedback effects of water quality changes in an integrated water circulation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is an exemplary flow chart of a method for dynamically updating a twin model of an integrated water circulation system according to some embodiments of the present application;

[0044] Figure 2 is a schematic diagram of a process for determining delay dependencies according to some embodiments of the present application;

[0045] Figure 3 is a schematic diagram of a process for determining simulation confidence according to some embodiments of the present application;

[0046] Figure 4 is a structural diagram of a twin model dynamic update unit according to some embodiments of the present application;

[0047] Figure 5 It is a structural diagram of a computer device for implementing a method for dynamically updating a twin model of an integrated water circulation system according to some embodiments of the present application. DETAILED DESCRIPTION

[0048] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0049] refer to Figure 1 , which is an exemplary flow chart of a method for dynamically updating a twin model of an integrated water cycle system according to some embodiments of the present application. The method 100 for dynamically updating a twin model of an integrated water cycle system mainly includes the following steps:

[0050] In step 101, twin modeling is performed on each water cycle component in the integrated water cycle system of the target industrial park to obtain a digital twin model of each water cycle component.

[0051] In some embodiments, twin modeling is performed on each water cycle component in the integrated water cycle system of the target industrial park. The digital twin model of each water cycle component can be obtained by the following steps:

[0052] Initialize the digital twin platform;

[0053] Obtain the physical parameters of each water cycle component in the integrated water cycle system of the target industrial park;

[0054] Based on the digital twin platform, each water cycle component is digitally mapped according to its physical parameters to obtain a digital twin model of each water cycle component.

[0055] It should be noted that the digital twin platform described in this application can adopt the Alibaba Cloud digital twin platform, where the digital twin platform refers to an integrated solution platform that aims to achieve efficient interaction and synchronization between the physical world and the digital model by creating a digital twin model of the physical entity and combining real-time data collection and analysis; initializing the digital twin platform usually includes deploying the basic software and hardware environment, accessing the real-time and historical data sources of the physical entity, building the digital model and system topology, configuring the communication protocol and data acquisition interface, and initializing the simulation engine and analysis algorithm to achieve efficient mapping and dynamic synchronization between the physical system and the virtual model.

[0056] In addition, it should be noted that the types of water circulation components described in this application specifically include pumps, valves, pipes, heat exchangers, filters, water towers, water storage tanks, etc., and the physical parameters of the water circulation components include flow rate, pressure, temperature, power, speed, efficiency, pipe diameter, heat exchange efficiency, water level and other parameters.

[0057] In specific implementation, based on the digital twin platform, each water cycle component is digitally mapped according to the physical parameters of each water cycle component, and the digital twin model of each water cycle component can be obtained in the following manner, namely: first, the digital twin platform receives the physical parameters of each water cycle component and associates these physical parameters with the structural information of the water cycle component. Then, the digital twin platform uses known physical modeling methods (such as computational fluid dynamics (CFD), finite element analysis (FEA), etc.) to digitally map each water cycle component and generate a corresponding digital twin model.

[0058] In step 102, based on the water quality mapping data of each digital twin model, the time delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process is extracted.

[0059] In some embodiments, reference Figure 2 As shown in FIG, this figure is a schematic diagram of the process of determining the time delay dependency in some embodiments of the present application. In this embodiment, based on the water quality mapping data of each digital twin model, the time delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process can be extracted by the following steps:

[0060] Obtain historical water quality data for each water cycle component corresponding to the digital twin model;

[0061] Performing trend analysis on historical water quality data of the water cycle component corresponding to each digital twin model to obtain a water quality change trend of the water cycle component corresponding to each digital twin model;

[0062] Determining a twin water quality stability index of each digital twin model based on the water quality mapping data of each digital twin model and the water quality change trend of the water cycle component corresponding to each digital twin model;

[0063] The twin water quality stability index of each digital twin model is used to determine the time-delay dependency between the twin water quality state variables of each digital twin model during the change of water quality trends.

[0064] It should be noted that the historical water quality data described in this application represents an ordered data set consisting of water quality parameters recorded every five minutes over the past week, wherein the water quality parameters include pH value, turbidity, dissolved oxygen and ammonia nitrogen content, and pH value, turbidity, dissolved oxygen and ammonia nitrogen content are all used as water quality state variables. The historical water quality data of each water cycle component can be obtained from the database of the integrated water cycle system twin modeling device, wherein the twin water quality state variable represents the mapping representation of the water quality state variable in the digital twin model.

[0065] In specific implementation, trend analysis is performed on the historical water quality data of the water cycle components corresponding to each digital twin model, and the water quality change trend of the water cycle components corresponding to each digital twin model is obtained in the following manner, namely: for each digital twin model, the historical water quality data of the water cycle components corresponding to the digital twin model are preprocessed (for example: outlier removal, missing value filling and normalization processing, etc.), and then a long-short-term memory network model is constructed, and the long-short-term memory network model is trained with the preprocessed historical water quality data. After the long-short-term memory network model training is completed, the predicted data of the water quality parameters for the next day is output, wherein the predicted data is ordered data composed of multiple predicted water quality state variables, and then, the existing linear fitting algorithm (such as the least squares support vector machine algorithm) is used to fit the predicted data, and the curve obtained by fitting is used as the fitting curve of the water quality parameter, so that the ordered sequence composed of the slopes at various positions on the fitting curve of the water quality parameter is used as the water quality change trend of the water cycle component corresponding to the digital twin model.

[0066] It should be noted that the water quality change trend described in this application represents the trend characteristics of the change direction of water quality parameters in the time series.

[0067] In addition, it should be noted that the water quality mapping data described in this application represents the water quality parameter data of the water cycle components mapped to the digital twin model in real time from the current moment to the past five minutes. The water quality mapping data can be obtained through the data transmission interface of the digital twin model corresponding to the water cycle components.

[0068] In specific implementation, the twin water quality stability index of each digital twin model is determined based on the water quality mapping data of each digital twin model and the water quality change trend of the water cycle component corresponding to each digital twin model. This can be achieved in the following way: for each digital twin model, the water quality mapping data of the digital twin model is fitted using an existing linear fitting algorithm (such as the least squares support vector machine algorithm), and the fitted curve is used as the fitting curve of the real-time water quality parameter, and the ordered sequence composed of the slopes at various positions on the fitting curve of the real-time water quality parameter is used as the real-time change sequence. Then, the slope of the part that overlaps with the time of the real-time change sequence is selected from the water quality change trend, and then the mean square error of the selected slope and all the slopes in the real-time change sequence is calculated, and the obtained mean square error is used as the twin water quality stability index of the digital twin model.

[0069] It should be noted that the twin water quality stability index described in this application represents a parameter that measures the stability of the twin water quality parameters within a time window, and is used to reflect the water quality stability of the water cycle components mapped by the digital twin model under the current operating state.

[0070] In specific implementation, the twin water quality stability index of each digital twin model is used to determine the time delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process. This can be achieved in the following way: first, a clustering algorithm (such as a hierarchical clustering algorithm) is used to cluster the twin water quality stability indexes of all digital twin models to obtain multiple data clusters, and the distance between the cluster centers of each data cluster is further calculated, and the sum of the distances between all cluster centers is used as the time delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process.

[0071] It should be noted that the time delay dependency relationship described in this application represents the temporal dynamic dependency characteristics between the twin water quality state variables of different digital twin models due to different response speeds during the change of water quality trends; by calculating the distances between the centers of different data clusters, the differences in water quality response speeds of each digital twin model can be reflected. The larger the cluster center distance, the more significant the time delay difference in the response between different water quality state variables. Finally, the time delay dependency relationship between the twin water quality state variables of each digital twin model during the change of water quality trends is reflected by the sum of all cluster center distances.

[0072] In step 103, the dynamic constraint boundary of each digital twin model on the twin water quality state variables in the closed-loop water circulation process is determined according to the time-delay dependency between the loop topology of the integrated water circulation system and the twin water quality state variables.

[0073] In some embodiments, determining the dynamic constraint boundaries of each digital twin model on the twin water quality state variables during the closed-loop water circulation process based on the time-delay dependency relationship between the loop topology of the integrated water circulation system and the twin water quality state variables can be achieved by the following steps:

[0074] Constructing a state transition matrix of a digital twin model corresponding to each water circulation component in the integrated water circulation system according to the loop topology diagram of the integrated water circulation system;

[0075] The constraint factors of water quality changes of each digital twin model in the closed-loop water circulation process are determined through the time-delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process;

[0076] According to the state transfer matrix of the digital twin model corresponding to each water cycle component and the constraint factor of water quality change of each digital twin model during the closed-loop water cycle, the dynamic constraint boundary of each digital twin model on the twin water quality state variable during the closed-loop water cycle is determined.

[0077] It should be noted that the loop topology diagram described in this application represents a topological diagram consisting of the physical connections and information flow relationships between the various water circulation components in the integrated water circulation system. In the loop topology diagram, each node represents a water circulation component, and each edge represents the transmission path of water flow, energy flow or other material flow, connecting different components.

[0078] In specific implementation, the state transfer matrix of the digital twin model corresponding to each water circulation component in the integrated water circulation system is constructed according to the loop topology diagram of the integrated water circulation system. This can be achieved in the following manner, namely: first, extract each water circulation component and its physical connection and information flow relationship from the loop topology diagram, and obtain the working status of each water circulation component. For example, the working status of the water circulation component for the water pump includes "enabled" and "disabled", and the working status of the water circulation component for the valve includes "open" and "closed". The working status of each water circulation component is mapped to the virtual working status of the digital twin model corresponding to each water circulation component. Secondly, based on the connection path and flow direction information between the water circulation components in the loop topology diagram, the state transfer conditions of the digital twin model corresponding to each water circulation component are determined. Specifically, for example, if the loop topology diagram shows that the water pump and the valve are connected sequentially through pipes, the potential impact of the water pump state change on the valve state can be defined accordingly. Furthermore, combined with the path logic in the loop topology diagram, the physical characteristics and control logic of each water circulation component are analyzed. For example, the start and stop of the water pump may depend on the flow sensor data. If the sensor feedback flow exceeds a certain threshold, the water pump is triggered to change from the "disabled" state to the "enabled" state, and the enabled state may reversely act on the pressure of the downstream pipeline, thereby affecting the opening state of the valve. In addition, the information flow is analyzed according to the control signal transmission between the water circulation components in the loop topology diagram. For example, after the sensor detects the change of water quality parameters, the integrated water circulation system sends a signal to adjust the state of certain water circulation components, such as adjusting valves or starting filters. Through these physical and information flow relationships, the state transfer conditions of the digital twin model corresponding to the water circulation component are gradually constructed. Finally, the state transfer matrix of the digital twin model corresponding to each water circulation component is constructed through the virtual working state and state transfer rules of the digital twin model corresponding to each water circulation component.

[0079] It should be noted that the state transfer matrix described in this application represents the conditional matrix of the transition relationship between different states of the digital twin model mapped by the water cycle component, and each element in the state transfer matrix represents the transition condition from one working state to another.

[0080] In specific implementation, the constraint factor of water quality change of each digital twin model in the closed-loop water circulation process is determined by the time-delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process. This can be achieved in the following way, namely: obtain the twin water quality stability index of each digital twin model, multiply the twin water quality stability index of each digital twin model by the time-delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process, and use the multiplied values ​​as the constraint factors of water quality change of each digital twin model in the closed-loop water circulation process.

[0081] It should be noted that the constraint factor represents a control parameter for adjusting the change rate of the water quality state variable of each digital twin model during the closed-loop water circulation process.

[0082] In specific implementation, the dynamic constraint boundary of each digital twin model on the twin water quality state variable in the closed-loop water circulation process is determined according to the state transition matrix of the digital twin model corresponding to each water cycle component and the constraint factor of the water quality change of each digital twin model in the closed-loop water circulation process. This can be achieved in the following way: for each digital twin model, the state transition matrix of the digital twin model is decomposed into eigenvalues ​​to obtain multiple eigenvalues, and then the constraint factor of the water quality change of the digital twin model in the closed-loop water circulation process is used as the weight of the water quality state variable value. Furthermore, the weighted water quality state variable value is divided by each eigenvalue, and then the maximum value is selected from all the values ​​obtained by division, and the maximum value is used as the dynamic constraint upper boundary value. Then, the minimum value is selected from all the values ​​obtained by division, and the minimum value is used as the dynamic constraint lower boundary value. Then, the interval composed of the dynamic constraint lower boundary value and the dynamic constraint upper boundary value is used as the dynamic constraint boundary of the twin water quality state variable of the digital twin model in the closed-loop water circulation process.

[0083] It should be noted that the dynamic constraint boundary described in this application represents the modeling constraint interval that is dynamically adjusted over time when the digital twin model captures the nonlinear feedback effect of water quality changes.

[0084] In step 104, the simulation credibility of the water quality change of each digital twin model during the closed-circuit water circulation process is verified through all dynamic constraint boundaries to obtain the simulation confidence of the water quality change of each digital twin model during the closed-circuit water circulation process.

[0085] In some embodiments, reference Figure 3 As shown in the figure, this figure is a schematic diagram of the process of determining simulation confidence in some embodiments of the present application. In this embodiment, the water quality change of each digital twin model in the closed-circuit water circulation process is simulated and verified for credibility. The simulation confidence of the water quality change of each digital twin model in the closed-circuit water circulation process can be obtained by the following steps:

[0086] First, in step 1041 , the water quality change of each digital twin model during the closed-circuit water circulation process is determined;

[0087] Next, in step 1042, a confidence assessment is performed on the water quality change of each digital twin model during the closed-circuit water circulation process using all dynamic constraint boundaries to obtain an error confidence interval for the simulated estimate of the water quality change of each digital twin model during the closed-circuit water circulation process.

[0088] Then, in step 1043, the simulation confidence of each digital twin model for the water quality change in the closed-circuit water circulation process is determined by the error confidence interval of the simulation estimate of the water quality change in each digital twin model in the closed-circuit water circulation process.

[0089] It should be noted that the water quality change described in this application represents a parameter of the degree of water quality fluctuation of circulating water within a given time period during a closed-circuit water circulation process. As a preferred embodiment, determining the water quality change of circulating water in each digital twin model during a closed-circuit water circulation process can be achieved in the following manner, namely: for each digital twin model, first, the water quality mapping data of the digital twin model is standardized, and then the standardized data is differentially analyzed, and then all values ​​obtained after the differential analysis are summed, and the summed value is used as the water quality change of the circulating water in the closed-circuit water circulation process of the digital twin model.

[0090] It should be noted that the water quality change amount described in this application represents a parameter indicating the degree of water quality fluctuation of circulating water within a given time period during a closed-circuit water circulation process.

[0091] In specific implementation, confidence assessment is performed on the water quality change of each digital twin model in the closed-circuit water circulation process through all dynamic constraint boundaries, and the error confidence interval of the simulated estimate of the water quality change of each digital twin model in the closed-circuit water circulation process can be obtained in the following way, namely: for each digital twin model, if the water quality change value of the circulating water of the digital twin model in the closed-circuit water circulation process is within the dynamic constraint boundary corresponding to the digital twin model, then the difference between the water quality change value and the center value of each dynamic constraint boundary (that is, the median of the upper boundary value of the dynamic constraint and the lower boundary value of the dynamic constraint) is calculated, and the set composed of all differences is used as the central error sample set, and the central error sample set is calculated using statistical analysis methods (such as the normal distribution confidence interval calculation method based on standard deviation) The confidence interval of the error sample set is obtained, and the obtained confidence interval is used as the error confidence interval of the digital twin model's simulation estimation of the water quality change during the closed-loop water circulation process; if the water quality change value of the circulating water in the digital twin model during the closed-loop water circulation process is not within the dynamic constraint boundary corresponding to the digital twin model, then the difference between the water quality change value and the dynamic constraint upper boundary value of each dynamic constraint boundary and the difference between the dynamic constraint lower boundary values ​​are calculated, and the set composed of all differences is used as the boundary error sample set. Furthermore, the boundary error sample set is subjected to a statistical analysis method (such as a normal distribution confidence interval calculation method based on standard deviation) to calculate the confidence interval of the boundary error sample set, and the obtained confidence interval is used as the error confidence interval of the digital twin model's simulation estimation of the water quality change during the closed-loop water circulation process.

[0092] It should be noted that the error confidence interval described in this application represents the credible fluctuation range of the simulation error generated when the digital twin model simulates the nonlinear feedback effect of water quality changes in a closed-loop water circulation process, wherein the error confidence interval consists of an upper confidence bound and a lower confidence bound.

[0093] In specific implementation, the simulation confidence of each digital twin model in the closed-circuit water circulation process is determined by the error confidence interval of the simulated estimate of the water quality change by each digital twin model in the closed-circuit water circulation process. This can be achieved in the following way, namely: for each digital twin model, the water quality change of the circulating water of the digital twin model in the closed-circuit water circulation process is added to the confidence upper bound of the error confidence interval of the simulated estimate of the water quality change by the digital twin model in the closed-circuit water circulation process, and the added value is used as the simulation constraint upper bound of the water quality change; then, the water quality change of the circulating water of the digital twin model in the closed-circuit water circulation process is subtracted from the confidence lower bound of the error confidence interval of the simulated estimate of the water quality change by the digital twin model in the closed-circuit water circulation process, and the subtraction value is used as the simulation constraint lower bound of the water quality change; then, the average between the simulation constraint upper bound of the water quality change and the simulation constraint lower bound of the water quality change is used as the simulation confidence of the water quality change of each digital twin model in the closed-circuit water circulation process.

[0094] It should be noted that the simulation confidence level described in this application indicates the degree of credibility of the digital twin model in simulating the impact of the nonlinear feedback effect of water quality changes on the amount of water quality changes during a closed-loop water circulation process.

[0095] In step 105, the twin water quality state variables of each digital twin model are dynamically updated according to the simulation confidence of all water quality changes.

[0096] In some embodiments, dynamically updating the twin water quality state variables of each digital twin model based on the simulation confidence of all water quality changes can be achieved by using the following steps:

[0097] Determine the dynamic correction value of each digital twin model's water quality state variable through the simulation confidence of all water quality changes;

[0098] The twin water quality state variables of each digital twin model are updated according to the dynamic correction value of the twin water quality state variables of each digital twin model.

[0099] In specific implementation, the dynamic correction value of the twin water quality state variable of each digital twin model is determined by the simulation confidence of all water quality changes. This can be achieved in the following way: for each digital twin model, the water quality state variable at the current moment is obtained through the data transmission interface of the water cycle component corresponding to the digital twin model, and then the predicted water quality state variable at the current moment is obtained from the predicted data of the water quality parameters. Then, the predicted water quality state variable is subtracted from the water quality state variable, and the subtracted value is multiplied by the simulation confidence of the digital twin model for the water quality change in the closed-loop water circulation process, and the multiplied value is used as the dynamic correction value of the twin water quality state variable of the digital twin model.

[0100] It should be noted that the dynamic correction value described in this application represents the parameter of the twin water quality state variable that is corrected in real time when the digital twin model simulates the nonlinear feedback effect of water quality changes in a closed-loop water circulation process.

[0101] In specific implementation, the twin water quality state variables of each digital twin model are updated according to the dynamic correction value of the twin water quality state variables of each digital twin model. This can be achieved in the following way: for each digital twin model, the dynamic correction value of the twin water quality state variable of the digital twin model is added to the twin water quality state variable of the digital twin model, and then the variable obtained by the addition replaces the original twin water quality state variable, thereby completing the update process of the twin water quality state variables of each digital twin model.

[0102] In addition, in another aspect of the present application, in some embodiments, the present application provides an integrated water cycle system twin modeling device, which includes a twin model dynamic update unit, referring to Figure 4 , which is a schematic structural diagram of a twin model dynamic update unit according to some embodiments of the present application. The twin model dynamic update unit 400 includes: a twin modeling module 401, a processing module 402 and an execution module 403, which are described as follows:

[0103] Twin modeling module 401. In this application, the twin modeling module 401 is mainly used to perform twin modeling on each water cycle component in the integrated water cycle system of the target industrial park to obtain a digital twin model of each water cycle component;

[0104] Processing module 402, in this application, is used to extract the time-delayed dependency between the twin water quality state variables of each digital twin model during the water quality trend change process based on the water quality mapping data of each digital twin model;

[0105] It should be noted that the processing module 402 in this application is also used to determine the dynamic constraint boundary of each digital twin model on the twin water quality state variable during the closed-loop water circulation process based on the time-delay dependency relationship between the loop topology of the integrated water circulation system and the twin water quality state variable;

[0106] In addition, the processing module 402 in the present application is also used to perform a simulation credibility check on the water quality change of each digital twin model during the closed-circuit water circulation process through all dynamic constraint boundaries, and obtain the simulation confidence of the water quality change of each digital twin model during the closed-circuit water circulation process;

[0107] Execution module 403. In this application, execution module 403 is mainly used to dynamically update the twin water quality state variables of each digital twin model according to the simulation confidence of all water quality changes.

[0108] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned method for dynamically updating the twin model of the integrated water circulation system.

[0109] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a method for dynamically updating a twin model of an integrated water circulation system according to some embodiments of the present application. The method for dynamically updating a twin model of an integrated water circulation system in the above embodiment can be implemented by Figure 5 The computer device 500 shown in FIG. 5 is implemented as shown in FIG. 5 . The computer device 500 includes at least one processor 501 , a communication bus 502 , a memory 503 , and at least one communication interface 504 .

[0110] The processor 501 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more executions of the twin model dynamic update method for controlling the integrated water circulation system in this application.

[0111] The communication bus 502 may be used to transmit information between the aforementioned components.

[0112] The memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD ROM) or other optical disc storage, an optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory 503 may be independent and connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0113] The memory 503 is used to store program code for executing the solution of the present application, and is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The method described in the above method embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0114] The communication interface 504 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0115] In a specific implementation, as an example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0116] The aforementioned computer device can be a general-purpose computer device or a dedicated computer device. In a specific implementation, the computer device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of this application do not limit the type of computer device.

[0117] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for dynamically updating the twin model of the integrated water circulation system.

[0118] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0119] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for dynamically updating a twin model of an integrated water circulation system, which is used for dynamically updating a twin model of an integrated water circulation system twin modeling device, and is characterized in that: The method comprises the following steps: Conduct twin modeling of each water cycle component in the integrated water cycle system of the target industrial park to obtain a digital twin model of each water cycle component; Based on the water quality mapping data of each digital twin model, the time-delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process is extracted; Determining the dynamic constraint boundaries of each digital twin model on the twin water quality state variables during the closed-loop water circulation process based on the time-delay dependency relationship between the loop topology of the integrated water circulation system and the twin water quality state variables; Through all dynamic constraint boundaries, the water quality change of each digital twin model in the closed-loop water circulation process is simulated and verified for credibility, and the simulation confidence of the water quality change of each digital twin model in the closed-loop water circulation process is obtained; The twin water quality state variables of each digital twin model are dynamically updated according to the simulation confidence of all water quality changes.

2. The method according to claim 1, wherein Perform twin modeling on each water cycle component in the integrated water cycle system of the target industrial park. The digital twin models of each water cycle component include: Initialize the digital twin platform; Obtain the physical parameters of each water cycle component in the integrated water cycle system of the target industrial park; Based on the digital twin platform, each water cycle component is digitally mapped according to its physical parameters to obtain a digital twin model of each water cycle component.

3. The method according to claim 1, wherein Based on the water quality mapping data of each digital twin model, the time delay dependency relationship between the twin water quality state variables of each digital twin model during the water quality trend change process is extracted, including: Obtain historical water quality data for each water cycle component corresponding to the digital twin model; Performing trend analysis on historical water quality data of the water cycle component corresponding to each digital twin model to obtain a water quality change trend of the water cycle component corresponding to each digital twin model; Determining a twin water quality stability index of each digital twin model based on the water quality mapping data of each digital twin model and the water quality change trend of the water cycle component corresponding to each digital twin model; The twin water quality stability index of each digital twin model is used to determine the time-delay dependency between the twin water quality state variables of each digital twin model during the change of water quality trends.

4. The method according to claim 1, wherein Determining the dynamic constraint boundaries of each digital twin model on the twin water quality state variables during the closed-loop water circulation process based on the time-delay dependency relationship between the loop topology of the integrated water circulation system and the twin water quality state variables specifically includes: Constructing a state transition matrix of a digital twin model corresponding to each water circulation component in the integrated water circulation system according to the loop topology diagram of the integrated water circulation system; The constraint factors of water quality changes of each digital twin model in the closed-loop water circulation process are determined through the time-delay dependency between the twin water quality state variables of each digital twin model during the water quality trend change process; According to the state transfer matrix of the digital twin model corresponding to each water cycle component and the constraint factor of water quality change of each digital twin model during the closed-loop water cycle, the dynamic constraint boundary of each digital twin model on the twin water quality state variable during the closed-loop water cycle is determined.

5. The method according to claim 1, wherein Through all dynamic constraint boundaries, the water quality change of each digital twin model in the closed-circuit water circulation process is simulated and verified for credibility. The simulation confidence of the water quality change of each digital twin model in the closed-circuit water circulation process is obtained, which specifically includes: Determine the change in water quality of the circulating water during the closed-loop water circulation process for each digital twin model; Conduct confidence assessments on the changes in circulating water quality during the closed-loop water circulation process for each digital twin model using all dynamic constraint boundaries, and obtain the error confidence intervals for the simulated estimates of water quality changes during the closed-loop water circulation process for each digital twin model. The simulation confidence of each digital twin model for the water quality change in the closed-circuit water circulation process is determined by the error confidence interval of the simulation estimation of the water quality change in the closed-circuit water circulation process by each digital twin model.

6. The method according to claim 1, wherein Dynamically update the twin water quality state variables of each digital twin model based on the simulation confidence of all water quality changes, specifically including: Determine the dynamic correction value of each digital twin model's water quality state variable through the simulation confidence of all water quality changes; The twin water quality state variables of each digital twin model are updated according to the dynamic correction value of the twin water quality state variables of each digital twin model.

7. The method according to claim 1, wherein The types of water circulation components specifically include pumps, valves, pipes, heat exchangers, filters, water towers, and water storage tanks.

8. An integrated water circulation system twin modeling device, comprising a twin model dynamic update unit, characterized in that: The twin model dynamic update unit includes: The twin modeling module is used to perform twin modeling on each water cycle component in the integrated water cycle system of the target industrial park, obtaining a digital twin model of each water cycle component; A processing module is used to extract the time-delayed dependency between the twin water quality state variables of each digital twin model during the water quality trend change process based on the water quality mapping data of each digital twin model; The processing module is further configured to determine the dynamic constraint boundary of each digital twin model on the twin water quality state variables during the closed-loop water circulation process based on the time-delay dependency relationship between the loop topology of the integrated water circulation system and the twin water quality state variables; The processing module is further configured to perform a simulation credibility check on the water quality change of each digital twin model during the closed-circuit water circulation process through all dynamic constraint boundaries, thereby obtaining a simulation confidence level of each digital twin model for the water quality change during the closed-circuit water circulation process; The execution module is used to dynamically update the twin water quality state variables of each digital twin model according to the simulation confidence of all water quality changes.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method for dynamically updating the twin model of the integrated water circulation system according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for dynamically updating a twin model of an integrated water circulation system according to any one of claims 1 to 7 is implemented.

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