Water resource allocation engineering simulation real-time rendering method based on digital twinning

Abstract

The present application relates to the technical field of real-time rendering, in particular to a water resource allocation engineering simulation real-time rendering method based on digital twinning, comprising the following steps: collecting current load capacity data, network state data and user equipment performance data of all rendering nodes through real-time network monitoring based on a digital twinning platform, and performing data summarization; performing real-time load analysis according to the summarized data, determining all node resource allocation requirements, and generating load prediction analysis results. In the present application, the load capacity, network state and user equipment performance of all rendering nodes are monitored and analyzed in real time, allowing for immediate adjustment of resource allocation, thereby more accurately matching resources and requirements, avoiding resource waste and improving rendering efficiency; by collecting user viewpoint movement and interaction behavior data, rendering objects are adjusted one by one, ensuring personalized and high-quality rendering output, and bringing higher practicality to water resource allocation engineering simulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of real-time rendering, in particular to a water resource allocation engineering simulation real-time rendering method based on digital twinning. BACKGROUND

[0002] Real-time rendering is a computer graphics technique that focuses on quickly generating images for immediate display, allowing users to interact with scenes without noticeable delays, which is particularly important in video games, virtual reality, augmented reality, and simulation and visualization software. The core of real-time rendering is to balance rendering speed and image quality to ensure a smooth and visually appealing experience. In addition, real-time rendering involves a variety of complex calculations such as ray tracing, occlusion processing, mapping, dynamic lighting and shadow calculation, aiming to reproduce the visual effects of the physical world as realistically as possible.

[0003] The water resource allocation engineering simulation real-time rendering method refers to using real-time rendering technology to simulate and visualize various engineering scenarios of water resource allocation, which can help engineers and decision-makers intuitively understand water resource flow, distribution and possible environmental impact. By simulating different water resource management and distribution schemes, real-time adjustment and optimization are supported, playing an important role in planning and emergency management, and having wide practicality in urban planning, irrigation system design, flood risk assessment and other aspects.

[0004] The prior art often faces problems of insufficient or excessive resource allocation when dealing with real-time rendering in large-scale and complex data environments, resulting in unsatisfactory system running efficiency and response speed. Static resource allocation methods lack sufficient flexibility and are difficult to adapt to rapid changes in user behavior and network environment, especially in large-scale user interaction scenarios, the performance of the system often cannot meet the needs of high-quality rendering. In addition, existing technologies often fail to fully utilize user behavior data to optimize the rendering process, which leads to the fact that the rendering result may not match the actual needs of the user, affecting user experience and the intuitiveness of operation. For example, in the simulation rendering of water resource management, the specific water flow dynamics or distribution changes that users are interested in cannot be reflected in real time, which may lead to inaccurate decision support, affecting the efficiency and safety of the entire project. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art, and the water resource allocation engineering simulation real-time rendering method based on digital twinning is proposed.

[0006] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: the water resource allocation engineering simulation real-time rendering method based on digital twinning includes the following steps:

[0007] S1: Based on the digital twin platform, collect all rendering node current load capacity data, network state data and user equipment performance data through real-time network monitoring, and perform data aggregation. According to the aggregated data, perform real-time load analysis, determine all node resource allocation requirements, and generate load prediction analysis results;

[0008] S2: Based on the load prediction analysis results, adjust resource quota and optimize network bandwidth allocation, adjust the resource configuration of all rendering nodes on the digital twin platform combined with the adjustment results, update the resource allocation strategy in real time, and generate a dynamic resource configuration table;

[0009] S3: According to the user's viewpoint movement and interaction behavior data, collect and analyze the detail level requirements of the visual focus area, adjust each object in the scene one by one, adjust the detail level and rendering precision of each object, match user requirements, and generate an adaptive detail level adjustment table;

[0010] S4: According to the dynamic resource configuration table and adaptive detail level adjustment table, allocate rendering tasks for all nodes, synchronize monitoring data consistency and rendering task execution efficiency, optimize water resource configuration engineering simulation real-time rendering quality and system response speed, and output the final rendering result.

[0011] As a further scheme of the present application, the load prediction analysis result acquisition step is:

[0012] S111: Collect load data, network state data, and user equipment performance data of each rendering node of the digital twin platform through real-time network monitoring ;

[0013] S112: Aggregate the load capacity data, network state data and user equipment performance data, apply the formula to,

[0014] ;

[0015] wherein, is the weight coefficient, respectively adjusting the influence of load capacity data, network state data and user equipment performance data in the aggregated data, generating the aggregated data set ;

[0016] S113: Perform real-time load analysis on the aggregated data set, calculate the weighted deviation average,

[0017] ; ​​​​​

[0018] wherein, is the load imbalance score, is the th data point value in the data set , is the average value of the data set , is the weight, reflecting the importance of the difference data point, is the curtain index, used to adjust the sensitivity of the deviation, is the total number of data points, generating real-time analysis results;

[0019] S114: Based on the real-time analysis results, calculate the water resource allocation engineering simulation resource allocation demand and future load prediction of all nodes,

[0020] ;

[0021] wherein, is the load imbalance score of the resource category , is the relative importance weight of the resource category , is the total number of resource types, is the adjustment parameter, used to control the influence strength of the load imbalance score on the prediction results, is a constant, generating load prediction analysis results .

[0022] As a further scheme of the present application, the resource quota adjustment step is:

[0023] S211: Extract the key performance indicators in the load prediction analysis results F , generating a predicted performance indicator data set ;

[0024] S212: According to the predicted performance indicator data set , calculate the difference between the current water resource allocation engineering simulation real-time rendering quota and the predicted demand,

[0025] ;

[0026] wherein, denotes the predicted resource quota, denotes the current resource quota, obtaining a demand difference data set ;

[0027] S213: According to the demand difference data set , adjust the resource quota, and the adjustment formula is,

[0028] ;

[0029] wherein, , and are adjustment coefficients, for enhancing adjustment intensity, for ensuring basic adjustment, for smoothing adjustment, obtaining new resource quota .

[0030] As a further scheme of the present application, the step of obtaining the dynamic resource configuration table is:

[0031] S221: applying the new resource quota to the digital twin platform, updating the resource configuration of each rendering node, obtaining an updated node configuration ;

[0032] S222: based on the updated node configuration , calculating the contribution ratio of each node to the total resource demand, adjusting the resource allocation strategy,

[0033] ;

[0034] wherein, is the updated node configuration, is the total number of nodes, is the total resource demand of the node, is an adjustment factor that affects the sensitivity of configuration difference, obtaining a resource allocation strategy ;

[0035] S223: applying the updated resource allocation strategy and node configuration , combined with the formula,

[0036] ;

[0037] wherein, is the total amount of resource configuration, and are adjustment parameters for adjusting the weights of configuration and strategy, and output the dynamic resource configuration table.

[0038] As a further scheme of the present application, the step of analyzing the detail level requirement of the visual focus area is:

[0039] S311: collecting viewpoint tracking and interaction data from user equipment, obtaining a user behavior data set ;

[0040] S312: Based on the user behavior dataset Statistical analysis was conducted to determine the user's visual focus area and analyze detailed level requirements, using formulas.

[0041] ;

[0042] in, This represents the average value. Indicates the first User viewpoint data in this interaction It is the threshold for the duration of the viewpoint. It is the weight of the viewpoint data. It is the attenuation constant, which controls the effect of the difference between viewpoint data and time threshold. This indicates the number of interactions, yielding detailed requirements analysis results for the visual focus area.

[0043] S313: Based on the analysis results of the detail level requirements for the visual focus areas, determine the required rendering detail level for each visual focus area, and obtain the detail level requirement data. .

[0044] As a further aspect of the present invention, the step of obtaining the adaptive level of detail adjustment table is as follows:

[0045] S321: Based on the detailed level of requirement data Each object's detail level was adjusted one by one to ensure that each item matched the user's needs, resulting in the adjusted object detail data. ;

[0046] S322: Based on the adjusted object details data The rendering precision of each object is calculated to ensure that every level of detail matches the user's needs, and formulas are applied.

[0047] ;

[0048] in, Representation Object Rendering accuracy data, It is an object Adjusted detailed data, Represents data points at the level of detail. This represents the total number of data points. It is detailed level requirement data. The weights in the rendering are set based on the analysis results of the detail requirements of the visual focus area, resulting in the final rendering accuracy data. ;

[0049] S323: Combining the final rendering precision data , combine scene requirements, adjust the rendering accuracy of each object in the water resource allocation engineering simulation real-time rendering process,

[0050] ;

[0051] wherein, is a scene factor, reflecting scene requirements, including light intensity, visual complexity or the frequency of user interaction, to obtain an adaptive detail level adjustment table T.

[0052] As a further scheme of the present application, the rendering task allocation step is:

[0053] S411: analyze the dynamic resource allocation table, using the formula

[0054] ;

[0055] the total capacity of the resource of the computing node , wherein, is the resource item of node in the configuration table, is the number of resource types, to obtain a node resource overview ;

[0056] S412: according to the adaptive detail level adjustment table , calculate the resource requirement of each task in the water resource allocation engineering simulation real-time rendering process,

[0057] ;

[0058] wherein, is the detail requirement of the th object of the task , is the expected visual impact coefficient of the target task , is the number of objects, to obtain a task resource requirement analysis result ;

[0059] S413: combine the node resource overview and the task resource requirement analysis result , use a resource allocation algorithm to allocate tasks to all nodes,

[0060] ;

[0061] wherein, is the priority coefficient of the task , is the total number of tasks, is the number of resource types, is a regulating factor for balancing resource allocation, obtaining a task allocation table .

[0062] As a further scheme of the present application, the step of obtaining the final rendering result is:

[0063] S421: Real-time data consistency monitoring according to the task allocation table, using the formula,

[0064] ;

[0065] wherein, represents the data consistency score of node , is the monitoring data on node , is the expected data, is the importance weight of data item , represents the number of data items, obtaining consistency monitoring records ;

[0066] S422: Collect and analyze digital twin platform execution logs, using the formula, ;

[0067] wherein, represents the task execution efficiency on node , is the execution time of task on node , is the efficiency weight of task , is a small constant of stability, avoiding division by zero error, is the number of tasks, obtaining task execution efficiency analysis results ;

[0068] S423: Adjust resource configuration according to the consistency monitoring records and task execution efficiency analysis results , using the formula,

[0069] ;

[0070] wherein, represents the score of final rendering effect and response speed, and are regulating coefficients, balancing rendering efficiency and data consistency, represents the number of nodes, outputting the final rendering result.

[0071] Compared with the prior art, the application has the advantages and positive effects that:

[0072] In the application, by real-time monitoring and analyzing the load capacity, network state and user equipment performance of all rendering nodes, instant adjustment of resource allocation is allowed, so that the resource and demand are more accurately matched, resource waste is avoided and rendering efficiency is improved, by collecting the viewpoint movement and interaction behavior data of users, rendering objects are adjusted one by one, the individualization and high quality of rendering output are ensured, the high resource configuration flexibility and rendering quality control bring higher practicability and application breadth to water resource configuration engineering simulation, especially in real-time decision support in urban planning and emergency management. BRIEF DESCRIPTION OF DRAWINGS

[0073] Figure 1 It is a workflow schematic diagram of the application;

[0074] Figure 2 It is a flowchart of obtaining the load prediction analysis result of the application;

[0075] Figure 3 It is a flowchart of adjusting the resource quota of the application;

[0076] Figure 4 It is a flowchart of obtaining the dynamic resource configuration table of the application;

[0077] Figure 5 It is a flowchart of analyzing the detail level requirement of the visual focus area of the application;

[0078] Figure 6 It is a flowchart of obtaining the adaptive detail level adjustment table of the application;

[0079] Figure 7 It is a flowchart of allocating the rendering task of the application;

[0080] Figure 8 It is a flowchart of obtaining the final rendering result of the application. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical scheme and advantages of the application more clear, the application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application, and are not used to limit the application.

[0082] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly specified.

[0083] Embodiment one

[0084] Please refer to Figure 1 The present application provides a technical solution: a water resource allocation engineering simulation real-time rendering method based on digital twinning, comprising the following steps:

[0085] S1: Based on the digital twinning platform, collect all rendering node current load capacity data, network state data and user equipment performance data through real-time network monitoring, and perform data summarization, perform real-time load analysis according to the summarized data, determine all node resource allocation requirements, and generate load prediction analysis results;

[0086] S2: Based on the load prediction analysis results, adjust the resource quota and optimize the network bandwidth allocation, adjust the resource configuration of all rendering nodes on the digital twinning platform combined with the adjustment results, update the resource allocation strategy in real time, and generate a dynamic resource configuration table;

[0087] S3: According to the user's viewpoint movement and interaction behavior data, collect and analyze the detail level requirements of the visual focus area, adjust each object in the scene one by one, adjust the detail level and rendering precision of each object, match user requirements, and generate an adaptive detail level adjustment table;

[0088] S4: According to the dynamic resource configuration table and the adaptive detail level adjustment table, allocate rendering tasks for all nodes, synchronously monitor data consistency and rendering task execution efficiency, optimize water resource allocation engineering simulation real-time rendering quality and system response speed, and output the final rendering result.

[0089] The load prediction analysis results include platform performance utilization rate, network delay index and device response level, the dynamic resource configuration table includes node allocation amount, network bandwidth configuration and performance optimization measures, the adaptive detail level adjustment table includes detail adjustment range, precision adjustment parameter and user demand adaptation degree, and the final rendering result includes rendering quality evaluation result, system response speed and data consistency.

[0090] Please refer to Figure 2 The acquisition step of the load prediction analysis result is:

[0091] S111: Collect load data for each rendering node of the digital twin platform through real-time network monitoring. Network status data and user equipment performance data ;

[0092] S112: Load capacity data Network status data and user equipment performance data To summarize, use a formula.

[0093] ;

[0094] in, These are weighting coefficients that adjust the influence of load capacity data, network status data, and user equipment performance data in the aggregated data, generating the aggregated dataset. ;

[0095] S113: Summary dataset Perform real-time load analysis and calculate the weighted average deviation.

[0096] ;

[0097] in, It is the load imbalance score. It is the first Data points in the dataset The value in It is a dataset The average value, These are weights, reflecting the importance of the disparate data points. The spectral density is used to adjust the sensitivity to deviation. This represents the total number of data points, generating real-time analysis results.

[0098] S114: Based on real-time analysis results, calculate the water resource allocation requirements and future load forecasts for all nodes in the engineering simulation of water resource allocation. ;

[0099] in, It is a resource category The load imbalance score, It is a resource category The relative importance weights, It is the total number of resource types. These are adjustment parameters used to control the load imbalance fraction. The strength of the impact on the prediction results It is a constant, generating load forecasting analysis results. .

[0100] Assume:

[0101] (load capacity);

[0102] (network status);

[0103] (user device performance);

[0104] Set ;

[0105] Calculate numerator:

[0106]

[0107] Calculate denominator:

[0108]

[0109] Calculate

[0110]

[0111] The value 56 represents the average load capacity after considering various types of data, which can be used to compare the load situations of different nodes and help decision-makers optimize resource allocation.

[0112] Assume:

[0113] Data points: ;

[0114] Average: ;

[0115] Weight: ;

[0116] Power index: ;

[0117] Calculate :

[0118]

[0119] Calculate weighted sum:

[0120]

[0121] Calculate :

[0122]

[0123] The value 10.67 represents a measure of node load imbalance, the larger the value, the more serious the imbalance, which helps to identify the nodes that need to be adjusted in priority.

[0124] If , , , then

[0125] Take 2.71, calculate :

[0126]

[0127] Calculate :

[0128]

[0129] The value 0.345 represents the predicted resource demand possibility, the closer to 1, the more likely to need additional resources, which helps resource allocation and planning to ensure efficient operation of the system.

[0130] Please refer to Figure 3 , the adjustment steps of resource quota are as follows:

[0131] S211: Extract key performance indicators in load prediction analysis result F , generate predicted performance indicator data set ;

[0132] S212: According to the predicted performance indicator data set , calculate the difference between the current water resource configuration engineering simulation real-time rendering quota and the predicted demand

[0133]

[0134] Among them, represents the predicted resource quota, represents the current resource quota, and the demand difference data set is obtained;

[0135] S213: According to the demand difference data set , adjust the resource quota, and the adjustment formula is

[0136]

[0137] Among them, , and are adjustment coefficients, used to enhance the adjustment intensity, used to ensure basic adjustment, For smooth adjustment, get new resource quota .

[0138] Assume for a certain node :

[0139] ;

[0140] ;

[0141] ;

[0142] ;

[0143] ;

[0144] Calculate the difference:

[0145]

[0146] Calculate the new quota:

[0147]

[0148] Newly calculated quota Indicates that the adjusted resource quota is slightly increased, reflecting the subtle adjustment based on the predicted needs on the basis of the current configuration.

[0149] See Figure 4 , the acquisition step of the dynamic resource configuration table is:

[0150] S221: Apply the new resource quota to the digital twin platform, update the resource configuration of each rendering node, and get the updated node configuration ;

[0151] S222: Based on the updated node configuration , calculate the contribution ratio of each node to the total resource demand, adjust the resource allocation strategy,

[0152] wherein is the updated node configuration, is the total number of nodes, is the total resource demand of the node, is the adjustment factor, which affects the sensitivity of the configuration difference, and the resource allocation strategy ;

[0153] S223: Apply the updated resource allocation strategy and node configuration , combined with the formula,

[0154]

[0155] where, is the total amount of resource configuration, and is the adjustment parameter, used to adjust the weight of configuration and strategy, and output the dynamic resource configuration table.

[0156] Assume that the update configuration of three nodes is , then,

[0157] Take 2.71, calculate the exponential weight:

[0158]

[0159]

[0160]

[0161] Normalization:

[0162]

[0163] Similarly, the 0.866 and of other nodes can be obtained.

[0164] represents the first node accounts for of the total resource quota, which shows how the resources are allocated among nodes according to the configuration ratio.

[0165] Assume that the update configuration of three nodes and the resource allocation strategy are known:

[0166] ;

[0167] ;

[0168] Set , then,

[0169] Calculate the dynamic configuration contribution of each node:

[0170]

[0171]

[0172]

[0173] Cumulative total dynamic resource configuration:

[0174]

[0175] The final calculation It represents the weighted sum of the update configurations and resource allocation strategies of all nodes, and represents the total resource allocation of all nodes after considering their configuration and strategy weights. It helps to dynamically adjust and optimize the entire rendering network and ensure the reasonable allocation of resources among all nodes.

[0176] Please see Figure 5 The steps for analyzing the level of detail requirements in the visual focus area are as follows:

[0177] S311: Collect viewpoint tracking and interaction data from user devices to obtain user behavior datasets. ;

[0178] S312: Based on user behavior dataset Statistical analysis was conducted to determine the user's visual focus area and analyze detailed level requirements, using formulas.

[0179]

[0180] in, This represents the average value. Indicates the first User viewpoint data in this interaction It is the threshold for the duration of the viewpoint. It is the weight of the viewpoint data. It is the attenuation constant, which controls the effect of the difference between viewpoint data and time threshold. This indicates the number of interactions, yielding detailed requirements analysis results for the visual focus area.

[0181] S313: Based on the detail level requirement analysis results for each visual focus area, determine the required rendering detail level for each visual focus area, and obtain the detail level requirement data. .

[0182] Assuming there are three interactions, that is ;

[0183]

[0184]

[0185]

[0186] set up ,but

[0187] For each ,calculate :

[0188] When:

[0189]

[0190] When:

[0191]

[0192] When:

[0193]

[0194] Calculate the cumulative weighted value:

[0195]

[0196]

[0197]

[0198] Calculate :

[0199]

[0200] The average value representing the detail level requirement analysis result of the visual focus area reflects the average visual focus requirement based on the user's point of view and interaction data, where a lower value indicates that the detail requirement in these point of view areas is not extremely high, and the rendering detail level of the corresponding area can be adjusted according to this result.

[0201] Please refer to Figure 6 , the adaptive detail level adjustment table acquisition step is:

[0202] S321: According to the detail level requirement data , adjust the detail level of each object one by one to ensure that each item matches the user's demand, and obtain the adjusted object detail data ;

[0203] S322: According to the adjusted object detail data , calculate the rendering precision of each object to ensure that each detail level matches the user's demand, and apply the formula, ;

[0204] Where, represents the rendering precision data of the object , and is the adjusted detail data of the object , and represents the detail level data point, N represents the total number of data points, is the detail level requirement data is the weight in the detail level requirement data, set according to the visual focus area detail requirement analysis result, to obtain the final rendering precision data ;

[0205] S323: Adjust the rendering precision of each object in the water resources allocation engineering simulation real-time rendering process in combination with the final rendering precision data and the scene requirements,

[0206]

[0207] wherein, is the scene factor, reflecting the scene requirements, including the intensity of light, the visual complexity or the frequency of user interaction, to obtain the adaptive detail level adjustment table T.

[0208] Suppose for an object , there are three detail level data:

[0209]

[0210]

[0211] Calculate the numerator part :

[0212]

[0213]

[0214]

[0215] Calculate the denominator part :

[0216]

[0217] Calculate :

[0218]

[0219] represents the rendering precision of the object , which shows the average detail level of the object after considering the detail requirement weight. A higher value indicates that the object needs a higher level of detail to match the user's visual requirements, which helps to prioritize resource allocation to the visual focus area during the rendering process to improve the overall visual effect.

[0220] Suppose there are three objects in the scene, and the final rendering precision is:

[0221] Object 1, an element near the visual focus.

[0222] Object 2, further away, but still important.

[0223] Object 3, in the background, less noticeable.

[0224] Setting Then, calculate the adjusted detail level for each object:

[0225] Adjusted detail level for Object 1:

[0226]

[0227] Adjusted detail level for Object 2:

[0228]

[0229] Adjusted detail level for Object 3:

[0230]

[0231] T = 0.5 * (Object 1 + Object 2 + Object 3) Presented in list format.

[0232] Calculated values indicate that the detail level of each object has been adjusted according to the scene requirements. Object 1, as an element near the visual focus, maintains a high level of detail even under the influence of scene factors. The detail levels of Object 2 and Object 3 are also adjusted accordingly to ensure efficient use of resources while maintaining the overall visual effect.

[0233] Dynamic adjustment helps ensure that the system can effectively respond to changes under different scene conditions, optimize rendering performance, and provide high-quality images that meet user visual needs.

[0234] Please refer to Figure 7 , the allocation step of the rendering task is:

[0235] S411: Analyze the dynamic resource configuration table, use the formula

[0236]

[0237] Calculate the total capacity of the resource of the node , where is the resource item of the node in the configuration table, is the number of resource types, and the node resource overview is obtained ;

[0238] S412: Calculate the resource demand of each task in the real-time rendering process of water resources allocation project simulation according to the adaptive detail level adjustment table

[0239]

[0240] wherein, is the detail demand of the th object of the task , is the expected visual impact coefficient of the target task , is the number of objects, and the task resource demand analysis result is obtained.

[0241] S413: Combine the node resource overview and the task resource demand analysis result , and use the resource allocation algorithm to allocate tasks to all nodes

[0242]

[0243] wherein, is the priority coefficient of the task , is the total number of tasks, is the number of resource types, is the adjustment factor for balancing resource allocation, and the task allocation table is obtained.

[0244] Suppose node has three types of resources (CPU, GPU, Memory), and the resource amounts are as follows:

[0245] CPU: 32 cores;

[0246] GPU: 8 units;

[0247] Memory: ;

[0248] Then,

[0249]

[0250] The total resource amount of node is 104 units, which is the key indicator for determining how many and what type of tasks the node can handle.

[0251] Suppose task ​The detail requirement of the three objects are as follows:

[0252] Object 1: 20 units;

[0253] Object 2: 30 units;

[0254] Object 3: 50 units;

[0255] Set the expected visual impact coefficient , then,

[0256]

[0257] Indicate the task 10 units of resources are required to meet its detail requirements, which is a key factor to consider when allocating resources.

[0258] Suppose the node has three tasks, and the resource requirement and priority coefficient of each task are as follows:

[0259] Task 1: 10 units, ;

[0260] Task 2: 5 units, ;

[0261] Task 3: 15 units, ;

[0262] Total resources , set , then,

[0263] ;

[0264] Indicate the task allocation index of the node is 0.184, which reflects the efficiency of task allocation under given resources, and the higher the value, the more efficiently the resources are utilized.

[0265] See Figure 8 , the steps to obtain the final rendering result are as follows:

[0266] S421: Perform real-time data consistency monitoring according to the task allocation table using the formula,

[0267] ;

[0268] where, indicates the data consistency score of the node , and is the monitoring data on the node , is expected data, is data item importance weight, represents data item quantity, resulting in consistency monitoring record ;

[0269] S422: Collect and analyze digital twin platform execution logs using the formula,

[0270] ;

[0271] where, represents the task execution efficiency on node , is the execution time of the task on node , is the efficiency weight of the task , is a small constant of stability, avoiding division by zero error, is the number of tasks, resulting in task execution efficiency analysis results ;

[0272] S423: According to the consistency monitoring record and the task execution efficiency analysis results , adjust the resource configuration using the formula,

[0273]

[0274] where, represents the final rendering effect and response speed score, and are adjustment coefficients, balancing rendering efficiency and data consistency, represents the number of nodes, outputting the final rendering result.

[0275] Suppose there are three data items on node , each with monitoring and expected values, and weights as follows:

[0276] Data 1: Monitoring value , expected value , weight ;

[0277] Data : Monitoring value , expected value , weight ;

[0278] Data : Monitoring value , expected value , weight ;

[0279] Then,

[0280]

[0281]

[0282] Data consistency score for node is 37.5, the lower the value the better the data consistency. Higher scores indicate significant differences between data, which need further checking or adjustment.

[0283] Assume node has three tasks, each with execution time and efficiency weights as follows:

[0284] Task 1: Execution time , weight ;

[0285] Task 2: Execution time , weight ;

[0286] Task 3: Execution time , weight ;

[0287] Assume , then,

[0288]

[0289]

[0290] Task execution efficiency score on node , the higher the value the more efficient the task execution, which can be used to identify performance bottlenecks and optimize resource allocation.

[0291] Assume the system has two nodes, respectively ( , ) and respectively ( , ), assume adjustment coefficients and .

[0292] Sum ;

[0293] Sum ;

[0294] Then,

[0295]

[0296] The rendering effect and response speed score of the system as a whole. A negative value indicates that data consistency issues have a greater impact on system performance, and further optimization of data processing or adjustment of resource allocation strategies is needed.

[0297] The above is only a preferred embodiment of the present application, and does not limit the form of the present application in other forms. Any skilled person in the art can use the disclosed technical content to make changes or modifications as equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made on the basis of the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still belongs to the protection scope of the technical solution of the present application.

Claims

1. A real-time rendering method for water resource allocation engineering simulation based on digital twins, characterized in that, Includes the following steps: Based on the digital twin platform, real-time network monitoring is used to collect current load capacity data, network status data, and user device performance data of all rendering nodes. The data is then aggregated, and real-time load analysis is performed based on the aggregated data to determine the resource allocation requirements of all nodes and generate load prediction analysis results. Based on the load prediction analysis results, resource quotas are adjusted and network bandwidth allocation is optimized. Combined with the adjustment results, the resource configuration of all rendering nodes is adjusted on the digital twin platform, the resource allocation strategy is updated in real time, and a dynamic resource configuration table is generated. Based on user viewpoint movement and interaction behavior data, we collect and analyze the level of detail requirements of the visual focus area, adjust the objects in the scene one by one, adjust the level of detail and rendering precision of each object, match the user's needs, and generate an adaptive level of detail adjustment table. Based on the dynamic resource configuration table and the adaptive level of detail adjustment table, rendering tasks are assigned to all nodes, data consistency and rendering task execution efficiency are monitored synchronously, the real-time rendering quality and system response speed of water resource allocation engineering simulation are optimized, and the final rendering result is output. The adjustment steps for the resource quota are as follows: Extract key performance indicators from the load prediction analysis results to generate a prediction performance indicator dataset. ; Based on the predicted performance metric dataset Calculate the difference between the current water resource allocation quota and the predicted demand in real-time simulation rendering of the engineering simulation. ; in, Indicates the predicted resource allocation. This represents the current resource quota, resulting in a demand difference dataset. ; Based on the aforementioned demand difference dataset Resource quota adjustments are made using the following formula: ; in, , and These are all adjustment coefficients. Used to enhance adjustment strength Used to ensure basic adjustments, Used for smooth adjustment to obtain new resource quotas ; The steps for obtaining the dynamic resource allocation table are as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] When applied to a digital twin platform, the resource configuration of each rendering node is updated to obtain the updated node configuration. ; Based on the updated node configuration Calculate the contribution ratio of each node to the total resource demand and adjust the resource allocation strategy accordingly. ； in, This is the updated node configuration. It is the total number of nodes. This is the total demand for node resources. It is a moderating factor that affects the sensitivity to configuration differences, thus leading to the resource allocation strategy. Apply the updated resource allocation strategy. and node configuration Combining the formula, ; in, It is the total amount of resource allocation. and These are adjustment parameters used to adjust the weights of configurations and strategies, and output a dynamic resource configuration table.

2. The real-time rendering method for water resource allocation engineering simulation based on digital twins according to claim 1, characterized in that, The steps for obtaining the load prediction analysis results are as follows: Load data for each rendering node of the digital twin platform is collected through real-time network monitoring. Network status data and user equipment performance data ; Regarding the load capacity data Network status data and user equipment performance data To summarize, use a formula. ;in, These are weighting coefficients that adjust the influence of load capacity data, network status data, and user equipment performance data in the aggregated data, generating the aggregated dataset. ; for the summarized dataset Perform real-time load analysis and calculate the weighted average deviation. ;in, It is the load imbalance score. It is the first Data points in the dataset The value in It is a dataset The average value, These are weights, reflecting the importance of the disparate data points. The spectral density is used to adjust for the sensitivity of deviation. This represents the total number of data points, generating real-time analysis results. Based on the real-time analysis results, the simulated resource allocation requirements and future load forecasts for water resource allocation engineering are calculated for all nodes. ;in, It is a resource category The load imbalance score, It is a resource category The relative importance weights, It is the total number of resource types. These are adjustment parameters used to control the load imbalance fraction. The strength of the impact on the prediction results It is a constant, generating load forecasting analysis results. .

3. The real-time rendering method for water resource allocation engineering simulation based on digital twins according to claim 1, characterized in that, The analysis steps for the level of detail requirements of the visual focus area are as follows: User behavior datasets are obtained by collecting viewpoint tracking and interaction data from user devices. ; Based on the user behavior dataset Statistical analysis was conducted to determine the user's visual focus area and analyze detailed level requirements, using formulas. ;in, This represents the average value. Indicates the first User viewpoint data in this interaction It is the threshold for the duration of the viewpoint. It is the weight of the viewpoint data. It is the attenuation constant, which controls the effect of the difference between viewpoint data and time threshold. This indicates the number of interactions, yielding detailed requirements analysis results for the visual focus area. Based on the analysis results of the detail level requirements for the visual focus areas, the required rendering detail level for each visual focus area is determined, thus obtaining the detail level requirement data. .

4. The real-time rendering method for water resource allocation engineering simulation based on digital twins according to claim 3, characterized in that, The steps for obtaining the adaptive level of detail adjustment table are as follows: Based on the detailed level of requirement data Each object's detail level was adjusted one by one to ensure that each item matched the user's needs, resulting in the adjusted object detail data. ; Based on the adjusted object details data The rendering precision of each object is calculated to ensure that every level of detail matches the user's needs, and formulas are applied. ;in, Representation Object Rendering accuracy data, It is an object Adjusted detailed data, Represents data points at the level of detail. This represents the total number of data points. It is detailed level requirement data. The weights in the rendering are set based on the analysis results of the detail requirements of the visual focus area, resulting in the final rendering accuracy data. ; Combined with the final rendering accuracy data Based on the requirements of the scenario, the rendering precision of each object in the real-time rendering process of the water resource allocation engineering simulation was adjusted. ;in, These are scene factors that reflect scene requirements, including light intensity, visual complexity, or the frequency of user interaction, resulting in an adaptive detail level adjustment table T.

5. The real-time rendering method for water resource allocation engineering simulation based on digital twins according to claim 1, characterized in that, The steps for allocating the rendering task are as follows: Analyze the dynamic resource allocation table and use the formula Compute nodes Total resource capacity ,in, It is a node In the resource items of the configuration table, This is the number of resource types, providing an overview of node resources. ; According to the adaptive level of detail adjustment table The resource requirements of each task in the real-time rendering process of water resource allocation engineering simulation are calculated. ;in, It is a task The Detailed requirements of each object The target task The expected visual impact coefficient, The number of objects yields the task resource requirements analysis results. ; Combined with the above node resource overview and task resource requirements analysis results The task is distributed to all nodes using a resource allocation algorithm. ;in, It is a task Priority coefficient, It is the total number of tasks. It refers to the number of resource types. It is a regulating factor used to balance resource allocation and obtain the task allocation table. .

6. The real-time rendering method for water resource allocation engineering simulation based on digital twins according to claim 5, characterized in that, The steps for obtaining the final rendering result are as follows: Real-time data consistency monitoring is performed based on the task allocation table, using the formula. ;in, Represents a node Data consistency score, It is a node The monitoring data on the screen This is expected data. It is a data item Importance weight, This indicates the number of data items, resulting in consistency monitoring records. ; Collect and analyze the execution logs of the digital twin platform using formulas. ;in, Represents a node Task execution efficiency It is a node On the task Execution time, It is a task Efficiency weights It is a small stability constant, avoiding division-by-zero errors. The number of tasks determines the task execution efficiency analysis results. According to the consistency monitoring records And task execution efficiency analysis results Adjust resource allocation and use formulas. ;in, The rating represents the final rendering quality and response speed. and It's an adjustment factor that balances rendering efficiency and data consistency. This indicates the number of nodes and outputs the final rendering result.

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