Twin modeling device and method for integrated water circulation system

Abstract

The invention provides an integrated water circulation system twinborn modeling device and method, and the method comprises the steps: carrying out the twinborn modeling of each water circulation assembly in a water circulation system, and obtaining a digital twinborn model of each water circulation assembly; further extracting a time delay dependency relationship between twinborn water quality state variables of each digital twinborn model in a water quality trend change process; according to the loop topological graph and the time delay dependency relationship of the water circulation system, determining a dynamic constraint boundary of each digital twinborn model for twinborn water quality state variables in the closed-loop water circulation process; performing simulation credibility verification on the water quality variation of each digital twin model through all the dynamic constraint boundaries to obtain simulation confidence of each digital twin model on the water quality variation; and dynamically updating the twinborn water quality state variables of each digital twinborn model according to the simulation confidence of all the water quality variations. By adopting the scheme of the invention, twin modeling of the water quality change nonlinear feedback effect in the integrated water circulation system can be realized.

Description

Technical Field

[0001] This application relates to the field of twin modeling technology. More specifically, this application relates to an integrated water cycle system twin modeling device and method. Background Art

[0002] Twin modeling is a technology that creates a virtual model corresponding to a physical system. By reflecting the dynamic changes of the physical system through the virtual model, it can accurately reproduce the behavior in the real world in a virtual environment, helping people better understand the operation 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 manufacturing, twin models can be used to monitor the operating status of equipment, detect faults in advance or optimize the production process. In this way, twin modeling not only improves the accuracy of operations but also realizes the intelligent management of equipment, effectively reducing resource waste, lowering risks, and improving the sustainability and responsiveness of equipment.

[0003] As an efficient water resource management mode, the integrated water cycle system has received increasing attention. However, when there are complex interactions and feedback mechanisms among different water cycle components in the integrated water cycle system, the water quality change shows a significant non-linear feedback effect. The non-linear feedback effect of water quality change refers to that when various factors in the system interact with each other, the change of a certain variable (such as pollutant concentration) will affect other parts through multiple channels, thus triggering the overall dynamic change of the system. This non-linear feedback effect usually shows time delay, threshold effect and unpredictability, and it is often difficult to accurately describe through traditional linear models. In response to this complex non-linear 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, it can reflect the operating status of the integrated water cycle system in real time, so as to simulate the non-linear feedback effect of water quality change in the integrated water cycle system and capture the dynamic process of water quality change. Therefore, how to achieve the twin modeling of the non-linear feedback effect of water quality change in the integrated water cycle system has become a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides an integrated water cycle system twin modeling device and method, which can achieve the twin modeling of the non-linear feedback effect of water quality change in the integrated water cycle system.

[0005] In a first aspect, this application provides a method for dynamically updating a twin model of an integrated water cycle system, which is used for the integrated water cycle system twin modeling device to perform dynamic update of the twin model. The method includes the following steps: Perform twin modeling on each water cycle component in the integrated water cycle system of the target industrial park to obtain the digital twin models of each water cycle component; Based on the water quality mapping data of each digital twin model, extract the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model; According to the loop topology diagram of the integrated water cycle system and the time-delay dependence relationship between the twin water quality state variables, determine the dynamic constraint boundary of each digital twin model for the twin water quality state variables during the closed-loop water cycle process; Perform simulation credibility verification on the water quality change amount of the circulating water during the closed-loop water cycle process of each digital twin model through all the dynamic constraint boundaries, and obtain the simulation confidence level of each digital twin model for the water quality change amount during the closed-loop water cycle process; Dynamically update the twin water quality state variables of each digital twin model according to the simulation confidence levels of all water quality change amounts.

[0006] In some embodiments, performing twin modeling on each water cycle component in the integrated water cycle system of the target industrial park to obtain the digital twin models of each water cycle component specifically includes: 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, perform digital mapping on each water cycle component according to the physical parameters of each water cycle component to obtain the digital twin models of each water cycle component.

[0007] In some embodiments, based on the water quality mapping data of each digital twin model, extracting the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model specifically includes: Obtain the historical water quality data of the water cycle components corresponding to each digital twin model; Perform trend analysis on the historical water quality data of the water cycle components corresponding to each digital twin model to obtain the water quality change trend of the water cycle components corresponding to each digital twin model; Determine the twin water quality stability index of each digital twin model according to the water quality mapping data of each digital twin model and the water quality change trend of the water cycle components corresponding to each digital twin model; Determine the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model through the twin water quality stability index of each digital twin model.

[0008] In some embodiments, determining the dynamic constraint boundaries of each digital twin model for the twin water quality state variables during the closed-loop water circulation process according to the time-delay dependence relationship between the loop topology diagram of the integrated water circulation system and the twin water quality state variables specifically includes: Constructing the state transition matrix of the digital twin models corresponding to each water circulation component in the integrated water circulation system according to the loop topology diagram of the integrated water circulation system; Determining the constraint factors of the water quality change of each digital twin model during the closed-loop water circulation process through the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model; Determining the dynamic constraint boundaries of each digital twin model for the twin water quality state variables during the closed-loop water circulation process according to the state transition matrix of the digital twin models corresponding to each water circulation component and the constraint factors of the water quality change of each digital twin model during the closed-loop water circulation process.

[0009] In some embodiments, performing a simulation credibility check on the water quality change amount of the circulating water of each digital twin model during the closed-loop water circulation process through all the dynamic constraint boundaries, and obtaining the simulation confidence level of each digital twin model for the water quality change amount during the closed-loop water circulation process specifically includes: Determining the water quality change amount of the circulating water of each digital twin model during the closed-loop water circulation process; Performing a confidence evaluation on the water quality change amount of the circulating water of each digital twin model during the closed-loop water circulation process through all the dynamic constraint boundaries, and obtaining the error confidence interval of the simulation estimation of the water quality change amount of each digital twin model during the closed-loop water circulation process; Determining the simulation confidence level of each digital twin model for the water quality change amount during the closed-loop water circulation process through the error confidence interval of the simulation estimation of the water quality change amount of each digital twin model during the closed-loop water circulation process.

[0010] In some embodiments, dynamically updating the twin water quality state variables of each digital twin model according to the simulation confidence levels of all the water quality change amounts specifically includes: Determining the dynamic correction value of the twin water quality state variables of each digital twin model through the simulation confidence levels of all the water quality change amounts; Updating the twin water quality state variables of each digital twin model according to the dynamic correction value of the twin water quality state variables of each digital twin model.

[0011] In some embodiments, the types of the water circulation components specifically include pumps, valves, pipelines, heat exchangers, filters, water towers, water storage tanks, etc.

[0012] Second aspect, the present application provides an integrated water cycle system twin modeling device, and the integrated water cycle system twin modeling device includes a twin model dynamic update unit, which is characterized in that the twin model dynamic update unit includes: A twin modeling module, configured to perform twin modeling on each water cycle component in the integrated water cycle system of the target industrial park to obtain digital twin models of each water cycle component; A processing module, configured to extract the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model 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 cycle according to the loop topology diagram of the integrated water cycle system and the time-delay dependence relationship between the twin water quality state variables; The processing module is further configured to perform simulation credibility verification on the water quality change amount of the circulating water in each digital twin model during the closed-loop water cycle through all the dynamic constraint boundaries, and obtain the simulation confidence degree of each digital twin model on the water quality change amount during the closed-loop water cycle; An execution module, configured to dynamically update the twin water quality state variables of each digital twin model according to the simulation confidence degrees of all the water quality change amounts.

[0013] Third aspect, the present application provides a computer device, the computer device includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned twin model dynamic update method of the integrated water cycle system.

[0014] Fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned twin model dynamic update method of the integrated water cycle system is implemented.

[0015] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects: In the integrated water cycle system twin modeling device and method provided by the present application, digital twin models of each water cycle component in the integrated water cycle system of the target industrial park are obtained through twin modeling of each water cycle component; based on the water quality mapping data of each digital twin model, the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model is extracted; according to the loop topology diagram of the integrated water cycle system and the time-delay dependence relationship between the twin water quality state variables, the dynamic constraint boundary of each digital twin model on the twin water quality state variables during the closed-loop water cycle process is determined; the simulation credibility verification of the water quality change amount of the circulating water during the closed-loop water cycle process of each digital twin model is carried out through all the dynamic constraint boundaries, and the simulation confidence degree of each digital twin model on the water quality change amount during the closed-loop water cycle process is obtained; the twin water quality state variables of each digital twin model are dynamically updated according to the simulation confidence degrees of all the water quality change amounts.

[0016] It can be seen that in the present application, the simulation credibility verification of the water quality change amount of the circulating water during the closed-loop water cycle process of each digital twin model can be carried out through all the dynamic constraint boundaries, and the simulation confidence degree of each digital twin model on the water quality change amount during the closed-loop water cycle process can be obtained; among them, first, the time-delay dependence relationship 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 each water cycle component in the integrated water cycle system. This process helps to capture the non-linear feedback effect of water quality change and can better simulate the non-linear characteristics such as time delay and threshold effect in the system; second, determining the dynamic constraint boundary of each digital twin model on the twin water quality state variables during the closed-loop water cycle process can accurately limit the action range of each water cycle component in the water quality change. This process helps to describe the non-linear feedback effect in the water quality change. By setting the dynamic constraint boundary, excessive or unreasonable water quality fluctuations in the system can be avoided, ensuring that the water quality change conforms to the actual operating conditions and the system feedback mechanism; then, through the simulation credibility verification of all the dynamic constraint boundaries, it can be ensured that the prediction of the water quality change amount of each digital twin model during the closed-loop water cycle process has a high degree of accuracy and reliability. Through this process, the simulation confidence degree of each digital twin model can be quantified, and its performance in the complex non-linear feedback effect can be further verified. This not only helps to confirm that the dynamic trend of the water quality change conforms to the actual operating state, but also can reveal the adaptability of the model when facing 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 degrees of all the water quality change amounts; in summary, the solution of the present application can realize the twin modeling of the non-linear feedback effect of water quality change in the integrated water cycle system. Description of the Drawings

[0017] Figure 1 is an exemplary flowchart of a method for dynamically updating a twin model of an integrated water circulation system according to some embodiments of the present application; Figure 2 is a schematic flowchart of a process for determining time-delay dependencies according to some embodiments of the present application; Figure 3 is a schematic flowchart of a process for determining simulation confidence according to some embodiments of the present application; Figure 4 is a schematic structural diagram of a twin model dynamic update unit according to some embodiments of the present application; Figure 5 is a schematic 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 implementation manners

[0018] To better understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0019] Refer to Figure 1 , which is an exemplary flowchart of a method for dynamically updating a twin model of an integrated water circulation system according to some embodiments of the present application. The method 100 for dynamically updating a twin model of an integrated water circulation system mainly includes the following steps: In step 101, twin modeling is performed on each water circulation component in the integrated water circulation system of the target industrial park to obtain digital twin models of each water circulation component.

[0020] In some embodiments, performing twin modeling on each water circulation component in the integrated water circulation system of the target industrial park to obtain digital twin models of each water circulation component can be implemented by the following steps: Initialize the digital twin platform; Obtain the physical parameters of each water circulation component in the integrated water circulation system of the target industrial park; Based on the digital twin platform, perform digital mapping on each water circulation component according to the physical parameters of each water circulation component to obtain digital twin models of each water circulation component.

[0021] It should be noted that the digital twin platform described in this application can adopt the Alibaba Cloud digital twin platform. Among them, the digital twin platform refers to an integrated solution platform, which aims to achieve efficient interaction and synchronization between the physical world and the digital model by creating a digital twin model of a physical entity, combined with 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 physical entities, constructing the digital model and system topology, configuring communication protocols and data collection interfaces, and initializing the simulation engine and analysis algorithms to achieve efficient mapping and dynamic synchronization between the physical system and the virtual model.

[0022] In addition, it should also be noted that the types of the water circulation components described in this application specifically include pumps, valves, pipes, heat exchangers, filters, water towers, water storage tanks, etc. The physical parameters of the water circulation components include parameters such as flow rate, pressure, temperature, power, rotational speed, efficiency, pipe diameter, heat exchange efficiency, water level, etc.

[0023] When specifically implemented, based on the digital twin platform, digital mapping of each water circulation component is performed according to the physical parameters of each water circulation component, and the digital twin models of each water circulation component can be implemented in the following manner, that is: First, the digital twin platform receives the physical parameters of each water circulation component and associates these physical parameters with the structural information of the water circulation component. Then, the digital twin platform uses known physical modeling methods (such as Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), etc.) to perform digital mapping on each water circulation component and generate the corresponding digital twin model.

[0024] In step 102, based on the water quality mapping data of each digital twin model, the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model is extracted.

[0025] In some embodiments, refer to Figure 2 As shown, this figure is a schematic flowchart of determining the time-delay dependence relationship in some embodiments of this application. In this embodiment, based on the water quality mapping data of each digital twin model, the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model can be implemented by the following steps: Obtain the historical water quality data of the water circulation components corresponding to each digital twin model; Perform trend analysis on the historical water quality data of the water circulation components corresponding to each digital twin model to obtain the water quality change trend of the water circulation components corresponding to each digital twin model; Determine the 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 components corresponding to each digital twin model; Determine the time-delay dependence relationship between the twin water quality state variables of each digital twin model during the water quality trend change through the twin water quality stability index of each digital twin model.

[0026] It should be noted that the historical water quality data in this application represents an ordered data set composed of water quality parameters recorded every five minutes in the past week. Among them, the water quality parameters include pH value, turbidity, dissolved oxygen, and ammonia nitrogen content, and the 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. Among them, the twin water quality state variable represents the mapping representation of the water quality state variable in the digital twin model.

[0027] When specifically implemented, trend analysis is performed on the historical water quality data of the water cycle components corresponding to each digital twin model. The water quality change trend of the water cycle components corresponding to each digital twin model can be realized by the following method, that is: for each digital twin model, preprocess the historical water quality data of the water cycle components corresponding to the digital twin model (for example: outlier removal, missing value filling, and normalization processing, etc.), and then construct a long short-term memory network model. Train the long short-term memory network model with the preprocessed historical water quality data. After the long short-term memory network model is trained, output the prediction data of the water quality parameters for the next day. The prediction data is an ordered data composed of multiple predicted water quality state variables. Furthermore, use an existing linear fitting algorithm (such as the least squares support vector machine algorithm) to fit the prediction data, and use the fitted curve as the fitting curve of the water quality parameters. Thus, the ordered sequence composed of the slopes at each position on the fitting curve of the water quality parameters is used as the water quality change trend of the water cycle components corresponding to the digital twin model.

[0028] It should be noted that the water quality change trend in this application represents the trend characteristics of the change direction of water quality parameters in the time series.

[0029] In addition, it should be noted that the water quality mapping data in this application represents the water quality parameter data of the water cycle components mapped to the digital twin model recorded 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 water cycle components corresponding to the digital twin model.

[0030] When specifically implemented, the twin water quality stability index of each digital twin model can be determined according to the water quality mapping data of each digital twin model and the water quality change trend of the water cycle components corresponding to each digital twin model, which can be implemented in the following way, that is: for each digital twin model, use the existing linear fitting algorithm (such as the least squares support vector machine algorithm) to fit the water quality mapping data of the digital twin model, and use the obtained fitting curve as the fitting curve of the real-time water quality parameters. Take the ordered sequence composed of the slopes at each position on the fitting curve of the real-time water quality parameters as the real-time change sequence. Then, select the slopes of the overlapping part of the time with the real-time change sequence from the water quality change trend. Furthermore, calculate the mean square error between the selected slopes and all the slopes in the real-time change sequence, and use the obtained mean square error as the twin water quality stability index of the digital twin model.

[0031] It should be noted that the twin water quality stability index described in this application represents a parameter that measures the stability of 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.

[0032] When specifically implemented, the time-delay dependence relationship between the twin water quality state variables of each digital twin model during the water quality trend change can be determined through the twin water quality stability index of each digital twin model, which can be implemented in the following way, that is: first, use a clustering algorithm (such as the hierarchical clustering algorithm) to cluster the twin water quality stability indexes of all digital twin models to obtain multiple data clusters. Further calculate the distances between the cluster centers of each data cluster, and use the sum of the distances between all cluster centers as the time-delay dependence relationship between the twin water quality state variables of each digital twin model during the water quality trend change.

[0033] It should be noted that the time-delay dependence relationship described in this application represents the dynamic dependence characteristic in time formed between the twin water quality state variables of different digital twin models due to different response speeds during the water quality trend change; by calculating the distances between the cluster centers of different data clusters, the differences in the water quality response speeds of each digital twin model can be reflected. The larger the distance between the cluster centers, the more significant the time-delay difference in the reactions between different water quality state variables. Finally, the sum of the distances between all cluster centers is used to reflect the time-delay dependence relationship between the twin water quality state variables of each digital twin model during the water quality trend change.

[0034] In step 103, according to the loop topology diagram of the integrated water cycle system and the time-delay dependence relationship between the twin water quality state variables, determine the dynamic constraint boundary of each digital twin model for the twin water quality state variables during the closed-loop water cycle.

[0035] In some embodiments, determining the dynamic constraint boundaries of each digital twin model for the twin water quality state variables during the closed-loop water circulation process based on the time-delay dependence relationship between the loop topology diagram of the integrated water circulation system and the twin water quality state variables can be achieved through the following steps: Construct the state transition matrix of the digital twin models corresponding to each water circulation component in the integrated water circulation system according to the loop topology diagram of the integrated water circulation system; Determine the constraint factors for the water quality changes of each digital twin model during the closed-loop water circulation process through the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model; Determine the dynamic constraint boundaries of each digital twin model for the twin water quality state variables during the closed-loop water circulation process according to the state transition matrix of the digital twin models corresponding to each water circulation component and the constraint factors for the water quality changes of each digital twin model during the closed-loop water circulation process.

[0036] It should be noted that the loop topology diagram in this application represents the topology diagram formed by the physical connection and information flow relationship between each water circulation component 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 flows, connecting different components.

[0037] When specifically implemented, the state transition matrix of the digital twin models corresponding to each water circulation component in the integrated water circulation system can be constructed according to the loop topology diagram of the integrated water circulation system in the following way: First, extract each water circulation component and its physical connection and information flow relationship from the loop topology diagram, and obtain the working state of each water circulation component. For example, the working states of water circulation components such as pumps include "enabled" and "disabled", and the working states of water circulation components such as valves include "open" and "closed". Map the working state of each water circulation component to the virtual working state 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, determine the state transition conditions of the digital twin models corresponding to each water circulation component. Specifically, for example, if the loop topology diagram shows that a pump and a valve are sequentially connected by a pipeline, the potential impact of the pump state change on the valve state can be defined accordingly. Further, combined with the path logic in the loop topology diagram, analyze the physical characteristics and control logic of each water circulation component. For example, the start and stop of a pump may depend on the flow sensor data. If the sensor feedback flow exceeds a certain threshold, the pump is triggered to change from the "disabled" state to the "enabled" state, and this enabled state may act on the pressure of the downstream pipeline in reverse, thus affecting the opening state of the valve. In addition, analyze the information flow according to the control signal transmission between the water circulation components in the loop topology diagram. For example, after the sensor monitors the change of water quality parameters, the integrated water circulation system sends a signal to adjust the state of some water circulation components, such as adjusting the valve or starting the filter. Through these physical and information flow relationships, gradually construct the state transition conditions of the digital twin models corresponding to the water circulation components. Finally, construct the state transition matrix of the digital twin models corresponding to each water circulation component through the virtual working state and state transition rules of the digital twin models corresponding to each water circulation component.

[0038] It should be noted that the state transition matrix described in this application represents the conditional matrix of the transfer relationship between the digital twin models mapped by the water circulation components in different states. Each element in the state transition matrix represents the transfer condition from one working state to another working state.

[0039] When specifically implemented, the constraint factor of the water quality change of each digital twin model in the closed-loop water circulation process can be determined through the time-delay dependence relationship between the twin water quality state variables of each digital twin model during the water quality trend change in the following way: 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 dependence relationship between the twin water quality state variables of each digital twin model during the water quality trend change, and use the multiplied values as the constraint factors of the water quality change of each digital twin model in the closed-loop water circulation process.

[0040] It should be noted that the constraint factor represents a control parameter for adjusting the change rate of water quality state variables in each digital twin model during the closed-loop water circulation process.

[0041] When specifically implemented, the dynamic constraint boundary of each digital twin model for the twin water quality state variables during the closed-loop water circulation process can be determined according to the state transition matrix of the digital twin model corresponding to each water circulation component and the constraint factor of the water quality change in each digital twin model during the closed-loop water circulation process, which can be achieved by the following method: that is, for each digital twin model, perform eigenvalue decomposition on the state transition matrix of the digital twin model to obtain multiple eigenvalues, and then use the constraint factor of the water quality change in the digital twin model during the closed-loop water circulation process as the weight of the water quality state variable value. Further, divide the weighted water quality state variable values by each eigenvalue, then select the maximum value from all the divided values and use the obtained maximum value as the dynamic constraint upper boundary value, and select the minimum value from all the divided values and use the obtained minimum value as the dynamic constraint lower boundary value. Furthermore, use the interval composed of the dynamic constraint lower boundary value and the dynamic constraint upper boundary value as the dynamic constraint boundary of the digital twin model for the twin water quality state variables during the closed-loop water circulation process.

[0042] It should be noted that the dynamic constraint boundary described in this application represents a modeling constraint interval that changes dynamically over time when the digital twin model captures the non-linear feedback effect of water quality changes.

[0043] In step 104, perform a simulation credibility check on the water quality change amount of the circulating water in each digital twin model during the closed-loop water circulation process through all the dynamic constraint boundaries, and obtain the simulation confidence level of each digital twin model for the water quality change amount during the closed-loop water circulation process.

[0044] In some embodiments, refer to Figure 3 As shown, this figure is a schematic flow chart for determining the simulation confidence level in some embodiments of this application. In this embodiment, performing a simulation credibility check on the water quality change amount of the circulating water in each digital twin model during the closed-loop water circulation process through all the dynamic constraint boundaries, and obtaining the simulation confidence level of each digital twin model for the water quality change amount during the closed-loop water circulation process can be achieved by the following steps: First, in step 1041, determine the water quality change amount of the circulating water in each digital twin model during the closed-loop water circulation process; Secondly, in step 1042, perform a confidence evaluation on the water quality change amount of the circulating water in each digital twin model during the closed-loop water circulation process through all the dynamic constraint boundaries, and obtain the error confidence interval of the simulation estimation of the water quality change amount in each digital twin model during the closed-loop water circulation process; Then, in step 1043, the simulation confidence of each digital twin model in the closed-loop water circulation process for the water quality change amount is determined through the error confidence interval estimated by each digital twin model for the water quality change amount during the closed-loop water circulation process.

[0045] It should be noted that the water quality change amount described in this application represents a parameter for the degree of water quality fluctuation of the circulating water within a given time period during the closed-loop water circulation process. As a preferred embodiment, the water quality change amount of the circulating water in the closed-loop water circulation process for each digital twin model can be determined in the following manner, that is: for each digital twin model, first, standardize the water quality mapping data of the digital twin model, then perform differential analysis on the standardized data, and then sum all the values obtained after the differential analysis, and use the sum value as the water quality change amount of the circulating water in the closed-loop water circulation process for the digital twin model.

[0046] It should be noted that the water quality change amount described in this application represents a parameter for the degree of water quality fluctuation of the circulating water within a given time period during the closed-loop water circulation process.

[0047] Specifically, when implemented, the confidence evaluation of the water quality change amount of the circulating water in the closed-loop water circulation process for each digital twin model is performed through all the dynamic constraint boundaries, and the error confidence interval estimated by each digital twin model for the water quality change amount during the closed-loop water circulation process can be realized in the following manner, that is: for each digital twin model, if the water quality change amount value of the circulating water in the closed-loop water circulation process is within the dynamic constraint boundary corresponding to the digital twin model, then calculate the difference between the water quality change amount value and the central value of each dynamic constraint boundary (i.e., the median of the dynamic constraint upper boundary value and the dynamic constraint lower boundary value), and use the set composed of all the differences as the central error sample set, and use statistical analysis methods (such as the normal distribution confidence interval calculation method based on the standard deviation) to calculate the confidence interval of the central error sample set, and use the obtained confidence interval as the error confidence interval estimated by the digital twin model for the water quality change amount during the closed-loop water circulation process; if the water quality change amount value of the circulating water in the closed-loop water circulation process is not within the dynamic constraint boundary corresponding to the digital twin model, then calculate the difference between the water quality change amount value and the dynamic constraint upper boundary value of each dynamic constraint boundary and the difference between the dynamic constraint lower boundary value, and use the set composed of all the differences as the boundary error sample set. Further, use statistical analysis methods (such as the normal distribution confidence interval calculation method based on the standard deviation) to calculate the confidence interval of the boundary error sample set, and use the obtained confidence interval as the error confidence interval estimated by the digital twin model for the water quality change amount during the closed-loop water circulation process.

[0048] 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 non-linear feedback effect of water quality change during the closed-loop water circulation process. Among them, the error confidence interval is composed of an upper confidence boundary and a lower confidence boundary.

[0049] When specifically implemented, the simulation confidence degree of each digital twin model for the water quality change amount during the closed-loop water circulation process can be determined through the error confidence interval of the simulation estimation of the water quality change amount by each digital twin model during the closed-loop water circulation process, and the following method can be adopted, that is: for each digital twin model, add the upper confidence boundary of the error confidence interval of the simulation estimation of the water quality change amount by the digital twin model during the closed-loop water circulation process to the water quality change amount of the circulating water by the digital twin model during the closed-loop water circulation process, and use the obtained added value as the upper simulation constraint boundary of the water quality change amount. Then, subtract the lower confidence boundary of the error confidence interval of the simulation estimation of the water quality change amount by the digital twin model during the closed-loop water circulation process from the water quality change amount of the circulating water by the digital twin model during the closed-loop water circulation process, and use the obtained subtracted value as the lower simulation constraint boundary of the water quality change amount. Then, take the mean value between the upper simulation constraint boundary and the lower simulation constraint boundary of the water quality change amount as the simulation confidence degree of each digital twin model for the water quality change amount during the closed-loop water circulation process.

[0050] It should be noted that the simulation confidence degree described in this application represents the credible degree of the impact of the digital twin model simulating the non-linear feedback effect of water quality change on the water quality change amount during the closed-loop water circulation process.

[0051] In step 105, the twin water quality state variables of each digital twin model are dynamically updated according to the simulation confidence degrees of all water quality change amounts.

[0052] In some embodiments, the dynamic update of the twin water quality state variables of each digital twin model according to the simulation confidence degrees of all water quality change amounts can be implemented by the following steps: Determine the dynamic correction value of the twin water quality state variable of each digital twin model through the simulation confidence degrees of all water quality change amounts; Update the twin water quality state variables of each digital twin model according to the dynamic correction value of the twin water quality state variable of each digital twin model.

[0053] In specific implementation, the dynamic correction value of the twin water quality state variable of each digital twin model can be determined by the simulation confidence of all water quality change amounts and can be implemented in the following manner, that is: for each digital twin model, obtain the water quality state variable at the current moment through the data transmission interface of the corresponding water cycle component of the digital twin model, and then obtain the predicted water quality state variable at the current moment from the predicted data of the water quality parameters. Furthermore, subtract the predicted water quality state variable from the water quality state variable, then multiply the obtained subtracted value by the simulation confidence of the digital twin model for the water quality change amount in the closed-loop water cycle process, and use the obtained multiplied value as the dynamic correction value of the twin water quality state variable of the digital twin model.

[0054] It should be noted that the dynamic correction value described in this application represents the parameter for real-time correction of the twin water quality state variable when the digital twin model simulates the non-linear feedback effect of water quality change in the closed-loop water cycle process.

[0055] In specific implementation, the update of the twin water quality state variable of each digital twin model according to the dynamic correction value of the twin water quality state variable of each digital twin model can be implemented in the following manner, that is: for each digital twin model, add the dynamic correction value of the twin water quality state variable of the digital twin model to the twin water quality state variable of the digital twin model, and then replace the original twin water quality state variable with the obtained added variable, so as to complete the update process of the twin water quality state variable of each digital twin model.

[0056] In addition, on the other hand of this application, in some embodiments, this application provides an integrated water cycle system twin modeling device, which includes a twin model dynamic update unit. Refer to Figure 4 , this figure is a schematic structural diagram of the twin model dynamic update unit shown according to some embodiments of this 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: The 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 digital twin models of each water cycle component; The processing module 402. In this application, the processing module 402 is used to extract the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process based on the water quality mapping data of each digital twin model; 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 for the twin water quality state variable in the closed-loop water cycle process according to the loop topology diagram of the integrated water cycle system and the time-delay dependence relationship between the twin water quality state variables; In addition, the processing module 402 described in this application is further configured to perform simulation credibility verification on the water quality change amount of the circulating water in each digital twin model during the closed-loop water circulation process through all dynamic constraint boundaries, and obtain the simulation confidence degree of each digital twin model for the water quality change amount during the closed-loop water circulation process; The execution module 403. In this application, the execution module 403 is mainly configured to dynamically update the twin water quality state variables of each digital twin model according to the simulation confidence degrees of all water quality change amounts.

[0057] In addition, this 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.

[0058] In some embodiments, refer to Figure 5 , this figure is a schematic structural diagram of a computer device for implementing the method for dynamically updating the twin model of the integrated water circulation system according to some embodiments of this application. The method for dynamically updating the twin model of the integrated water circulation system in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0059] The processor 501 can be a general-purpose central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more for controlling the execution of the method for dynamically updating the twin model of the integrated water circulation system in this application.

[0060] The communication bus 502 can be used to transmit information between the above components.

[0061] The memory 503 can be a read only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), or other types of dynamic storage devices that can store information and instructions. It can also be an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 can exist independently and be connected to the processor 501 through the communication bus 502. The memory 503 can also be integrated with the processor 501.

[0062] Among them, the memory 503 is used to store the program code for executing the solution of this application, and is controlled by the processor 501 to execute. The processor 501 is used to execute the program code stored in the memory 503. The program code can include one or more software modules. The methods described in the above method embodiments can be implemented by one or more software modules in the program code in the processor 501 and the memory 503.

[0063] 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 networks (WLAN), etc.

[0064] In a specific implementation, as an embodiment, the computer device can include multiple processors, and each of these processors can be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, the processor can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0065] The computer device described above may be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device may be a desktop computer, a laptop 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 the present application do not limit the type of the computer device.

[0066] In addition, the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the dynamic update method of the twin model of the integrated water circulation system described above is implemented.

[0067] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A dynamic update method for the twin model of an integrated water cycle system, which is used for the twin model dynamic update of an integrated water cycle system twin modeling device, characterized in that, The method includes the following steps: Perform twin modeling on each water cycle component in the integrated water cycle system of the target industrial park to obtain the digital twin models of each water cycle component; Based on the water quality mapping data of each digital twin model, extract the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model; Determine the dynamic constraint boundary of each digital twin model for the twin water quality state variables during the closed-loop water cycle according to the loop topology diagram of the integrated water cycle system and the time-delay dependence relationship between the twin water quality state variables; Perform simulation credibility verification on the water quality change amount of the circulating water during the closed-loop water cycle of each digital twin model through all the dynamic constraint boundaries, and obtain the simulation confidence level of each digital twin model for the water quality change amount during the closed-loop water cycle; Dynamically update the twin water quality state variables of each digital twin model according to the simulation confidence levels of all the water quality change amounts.

2. The method according to claim 1, characterized in that, Performing twin modeling on each water cycle component in the integrated water cycle system of the target industrial park to obtain the digital twin models of each water cycle component specifically includes: 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, perform digital mapping on each water cycle component according to the physical parameters of each water cycle component to obtain the digital twin models of each water cycle component.

3. The method according to claim 1, characterized in that Based on the water quality mapping data of each digital twin model, extracting the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model specifically includes: Obtain the historical water quality data of the water cycle components corresponding to each digital twin model; Perform trend analysis on the historical water quality data of the water cycle components corresponding to each digital twin model to obtain the water quality change trend of the water cycle components corresponding to each digital twin model; Determine the twin water quality stability index of each digital twin model according to the water quality mapping data of each digital twin model and the water quality change trend of the water cycle components corresponding to each digital twin model; Determine the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model through the twin water quality stability index of each digital twin model.

4. The method according to claim 1, wherein Determining the dynamic constraint boundary of each digital twin model for the twin water quality state variables during the closed-loop water cycle according to the loop topology diagram of the integrated water cycle system and the time-delay dependence relationship between the twin water quality state variables specifically includes: Construct the state transition matrix of the digital twin models corresponding to each water cycle component in the integrated water cycle system according to the loop topology diagram of the integrated water cycle system; Determine the constraint factor of the water quality change of each digital twin model during the closed-loop water cycle through the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model; Determine the dynamic constraint boundaries of each digital twin model for the twin water quality state variables during the closed-loop water circulation process based on the state transition matrix of the digital twin model corresponding to each water circulation component and the constraint factors of the water quality change of each digital twin model during the closed-loop water circulation process.

5. The method according to claim 1, characterized in that, Perform simulation credibility verification on the water quality change amount of the circulating water of each digital twin model during the closed-loop water circulation process through all the dynamic constraint boundaries, and obtain the simulation confidence degree of each digital twin model for the water quality change amount during the closed-loop water circulation process, specifically including: Determine the water quality change amount of the circulating water of each digital twin model during the closed-loop water circulation process; Perform confidence evaluation on the water quality change amount of the circulating water of each digital twin model during the closed-loop water circulation process through all the dynamic constraint boundaries, and obtain the error confidence interval of the simulation estimation of the water quality change amount of each digital twin model during the closed-loop water circulation process; Determine the simulation confidence degree of each digital twin model for the water quality change amount during the closed-loop water circulation process through the error confidence interval of the simulation estimation of the water quality change amount of each digital twin model during the closed-loop water circulation process.

6. The method according to claim 1, wherein Dynamically update the twin water quality state variables of each digital twin model according to the simulation confidence degrees of all the water quality change amounts, specifically including: Determine the dynamic correction value of the twin water quality state variable of each digital twin model through the simulation confidence degrees of all the water quality change amounts; Update the twin water quality state variables of each digital twin model according to the dynamic correction value of the twin water quality state variable of each digital twin model.

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

8. An integrated water cycle system twin modeling device, the integrated water cycle system twin modeling device includes a twin model dynamic update unit, characterized in that, The twin model dynamic update unit includes: A twin modeling module for performing twin modeling on each water circulation component in the integrated water circulation system of the target industrial park to obtain digital twin models of each water circulation component; A processing module for extracting the time-delay dependence relationship between the twin water quality state variables during the water quality trend change process of each digital twin model based on the water quality mapping data of each digital twin model; The processing module is further configured to determine the dynamic constraint boundaries of each digital twin model for the twin water quality state variables during the closed-loop water circulation process according to the loop topology diagram of the integrated water circulation system and the time-delay dependence relationship between the twin water quality state variables; The processing module is further configured to perform simulation credibility verification on the water quality change amount of the circulating water of each digital twin model during the closed-loop water circulation process through all the dynamic constraint boundaries, and obtain the simulation confidence degree of each digital twin model for the water quality change amount during the closed-loop water circulation process; An execution module for dynamically updating the twin water quality state variables of each digital twin model according to the simulation confidence degrees of all the water quality change amounts.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the method for dynamically updating the twin model of the integrated water circulation system according to any one of claims 1 to 7.

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

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