Water affair 3D scene quick construction analysis method based on open source honey moon

Abstract

This invention relates to the field of open-source HarmonyOS technology, specifically a method for rapid construction and analysis of 3D water scenes based on open-source HarmonyOS. The method includes the following steps: extracting spatial numbers and primitive information; calculating construction density sorting; generating a construction area table; binding high-density areas to idle threads; configuring resources to generate a task table; collecting water pressure data to calculate trends and generate control parameters; constructing and numbering path chains; matching node quantity with thresholds to determine execution status; and generating a task status list. In this invention, construction areas are sorted by primitive density and bound to threads. Resources and cycles are configured based on density values ​​to improve scheduling efficiency. Trends are extracted from water pressure readings and converted into control parameters to guide node classification and path chain generation, enabling dynamic scene evolution with data. The comparison of node quantity with processing thresholds generates task status, promoting differentiated scheduling. Area numbering is integrated into scheduling and path generation, facilitating data-scene mapping and achieving rapid and accurate visual construction.

Description

Technical Field

[0001] This invention relates to the field of open-source HarmonyOS technology, and in particular to a method for rapid construction and analysis of 3D water affairs scenes based on open-source HarmonyOS. Background Technology

[0002] The open-source HarmonyOS technology involves displaying data from multiple data sources in graphical and image formats to facilitate user understanding and analysis. It emphasizes effective data integration, ensuring that data is not merely a collection of numbers and text, but rather enhanced through optimized configuration of visual elements such as charts, maps, and 3D models, increasing the efficiency and appeal of information delivery. Data visualization makes complex data relationships more intuitive, while data integration ensures that information from different data sources can be processed and displayed uniformly, supporting decision-making and academic research.

[0003] Among them, the water 3D scene rapid construction and analysis method based on open-source HarmonyOS involves using data visualization technology to transform complex medical data of the human body into intuitive images or models, which can then be integrated into medical education to improve teaching effectiveness and efficiency. Through this method, medical students and physicians can more intuitively understand human structure, function, and pathological information, which is crucial for diagnostic training, surgical simulation, and understanding complex medical processes. This not only improves the quality of medical education but also promotes more precise and personalized medical practice.

[0004] Existing technologies lack differentiated mechanisms for primitive processing in scene construction. Different density regions do not exhibit hierarchical distinctions in loading priority and thread resource allocation, leading to resource waste and frequent thread blocking during construction. Water pressure terminal data in the scene is statically displayed only through state binding, lacking analysis and structured utilization of continuous trends, thus limiting data-driven dynamic response capabilities. In execution path representation, node animation triggers lack categorized integration logic, and the path structure lacks a linkage mechanism, resulting in fragmented representation between nodes and reduced coherence of dynamic effects. Task execution status is not effectively correlated with path complexity, and a uniform processing threshold is inadequate for scheduling tasks on complex paths, easily causing delays or execution failures. The spatial data mapping-based construction method does not fully incorporate real-time data evolution patterns and cannot support refined representation requirements. For example, in high-density pipe network areas, when data is updated rapidly, due to lagging loading order and rigid execution logic, scene performance often lags, and terminal data changes fail to synchronize in a timely manner, affecting the visualization platform's perception and response speed to emergencies. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a rapid construction and analysis method for water affairs 3D scenes based on the open-source HarmonyOS.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a rapid construction and analysis method for water affairs 3D scenes based on open-source HarmonyOS, comprising the following steps: S1: Obtain the spatial number and graphic element components in the underground pipe network model, extract the calling frequency, geometric size and component connection quantity of the graphic elements, calculate the graphic element construction density value, combine and sort the spatial number and construction density to generate a three-dimensional graphic element construction area sorting table; S2: Based on the sorting table of the three-dimensional primitive construction area, extract the thread identifier and idle state marker, bind the construction area number with the top ranking with the idle thread, use the construction density value in the bound area to set the thread cache resources and running cycle configuration, and generate a thread allocation task table. S3: Based on the area number in the thread allocation task table, extract the water pressure sensing terminal node data of the corresponding area, collect the pressure value of continuous time period, calculate the trend direction and change range of adjacent data, convert the results into control parameters and establish a corresponding relationship with the area number to generate a dynamic drive parameter table. S4: Call the region number in the dynamic driving parameter table, extract the execution node order of the corresponding region, classify the nodes according to the trend direction, extract the animation trigger mark, combine them into a path chain and complete the numbering, and generate a path chain list dataset.

[0007] As a further embodiment of the present invention, the 3D primitive construction area sorting table includes a combination of spatial number sequence and construction density value; the thread allocation task table includes region number binding identifier, thread cache resource configuration parameters, and running cycle configuration parameters; the dynamic driving parameter table includes trend direction parameters, change amplitude parameters, and control parameter mapping relationships; and the path chain list dataset includes node classification labels, animation trigger label set, and path chain number structure.

[0008] As a further aspect of the present invention, the step of obtaining S1 is as follows: S101: Based on the spatial number and corresponding graphic element in the underground pipe network model, identify the calling frequency of each graphic element in the whole model, extract the graphic element type and number, and count the number of occurrences in all spatial numbers to generate graphic element frequency values. S102: Call the frequency value of the graphic element component, and combine it with the geometric dimensions of the graphic element component and the number of component connections to perform a combination operation between the three parameters, obtain the parameter value of the graphic element component, and integrate the parameter results of all graphic element components according to the spatial number to generate the graphic element construction density value; S103: Based on the density values ​​of the primitives, group them according to spatial numbers, extract the density values ​​of each group of primitive components and sort them in descending order, organize the primitive component numbers according to the sorting results, summarize and arrange the density sorting sequence corresponding to the spatial numbers, and obtain the sorting table of the three-dimensional primitive construction area.

[0009] As a further aspect of the present invention, the step of obtaining S2 is as follows: S201: Based on the three-dimensional primitive construction area sorting table, the construction area number, construction density value, geometric proportion value and primitive aggregation value are normalized. The construction density value and primitive aggregation value are used as dual indicators to obtain the sorting priority value and generate the construction area priority sequence value. S202: Call the construction area priority sequence value to obtain the construction area number that is ranked first, and retrieve the thread identifier and idle state flag in the thread status information. Verify the available thread based on the idle state flag, pair the available thread identifier with the construction area number, and generate the construction thread binding mapping value. S203: Call the aforementioned construction thread binding mapping value, extract the region construction characteristics and thread running resource characteristics for each pair of construction region and thread binding relationships, set the resource allocation method and periodic configuration form according to the adaptation relationship between the two types of characteristics, integrate them into a unified task structure, and obtain the thread allocation task table.

[0010] As a further aspect of the present invention, the step of obtaining S3 is as follows: S301: Based on the area number in the thread allocation task table, extract the water pressure sensing terminal node data in the corresponding area, and collect the continuous pressure value sequence according to the node number. In the collected data, each node records the pressure reading in adjacent time periods, and the node readings are numbered and archived to obtain the continuous pressure value sequence of the node at multiple time points, and obtain the water pressure reading sequence dataset. S302: Call the water pressure reading sequence dataset, select the readings of each node in multiple adjacent time periods, determine the direction of change trend by the numerical difference between adjacent readings, and use the trend direction and the average difference between each group of readings as the change amplitude index to calculate and obtain the trend amplitude quantification value sequence. Extract the trend parameter set according to the combination of change amplitude and trend direction to generate the node trend parameter group. S303: Based on the node trend parameter group and the region number information in the thread allocation task table, map the trend direction parameter and trend amplitude parameter to the control dimension parameter space, and establish a mapping index table according to the region number classification to generate a dynamic driving parameter table.

[0011] As a further aspect of the present invention, the step of obtaining S4 is as follows: S401: Call the construction region number in the dynamic driving parameter table, extract the execution node order in the corresponding region, identify the position change value, movement direction value and time difference value between adjacent nodes, sort all node pairs under the same construction region according to the three parameter dimensions of position change value, movement direction consistency comparison and time difference value, and generate node trend classification interval; S402: Based on the node trend classification interval, filter the animation trigger mark value, trigger weight value, trigger direction factor and spatial offset angle value corresponding to the node in the interval, call the trend direction benchmark identifier value under the construction area number, calculate the fitting degree based on the cosine value of the angle between the trigger direction factor and the trend direction benchmark identifier value, calculate and obtain the node direction fitting degree coefficient, sort according to the fitting degree coefficient and set the fitting degree threshold, extract the nodes above the threshold and their corresponding trigger mark values, and obtain the trend direction mark extraction set; S403: Call the trend direction marker extraction set, rearrange the nodes in the extraction set according to the trend direction order, number them according to the extraction order, integrate the three data items of position, animation trigger marker, and number value between adjacent nodes, construct a linear path structure sequence, and obtain the path linked list dataset.

[0012] As a further aspect of the present invention, the method further includes: S5: Call the thread number and region number in the thread allocation task table, match the number of nodes of each path in the path chain dataset, compare the number of nodes with the set processing threshold, determine the execution status of the path chain, and generate a list of execution status of the scene construction task. The scenario construction task execution status list includes the node number threshold comparison result and the path chain execution status identifier.

[0013] As a further aspect of the present invention, the step of obtaining S5 is as follows: S501: Call the thread number and region number in the thread allocation task table, combine each path in the path linked list dataset, detect the number of nodes in the path, read the cumulative count value of the nodes in the path structure, record the number of path nodes, and obtain the set of path node counts. S502: Based on the set of path node counts, call the set path processing threshold, compare the number of nodes of each path with the corresponding path processing threshold, if the number of nodes is greater than or equal to the path processing threshold, it is determined to be an executable path, otherwise it is determined to be an unexecutable path, and generate a path execution status marker sequence. S503: Based on the path execution status marker sequence, call the thread number and region number in the thread allocation task table and match them with the corresponding path identifier in the path chain. Match and integrate the execution status of each path with its own thread and region to obtain the scene construction task execution status list.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by constructing regions sorted according to primitive density and binding them to idle threads, and configuring thread resources and execution cycles based on density values, resource scheduling efficiency is effectively improved. Trend changes are extracted through continuous water pressure readings and converted into control parameters to guide node classification and path chain generation, enabling the 3D scene to evolve dynamically with real-time data. The number of nodes in the path chain is compared with the processing threshold to generate task status, promoting differentiated scheduling of high-density regions. Node animations are integrated according to trend logic to enhance the coherence and timeliness of dynamic expression. Region numbering runs through the scheduling, acquisition, and path generation processes, facilitating a consistent mapping between data and the scene, and achieving fast and accurate visualization construction and interaction. Attached Figure Description

[0015] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the sorting table for the three-dimensional primitive construction area in this invention. Figure 3 This is a flowchart illustrating the process of obtaining the thread allocation task table in this invention. Figure 4 This is a flowchart illustrating the process of obtaining the dynamic driving parameter table in this invention. Figure 5 This is a flowchart illustrating the process of obtaining the path linked list dataset in this invention. Figure 6 The flowchart illustrates the process of obtaining the task execution status list for the scenario construction in this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] Please see Figure 1 A rapid construction and analysis method for water affairs 3D scenes based on the open-source HarmonyOS includes the following steps: S1: Obtain the spatial number and graphic element components in the underground pipe network model, extract the calling frequency, geometric size and component connection quantity of the graphic elements, calculate the graphic element construction density value, combine and sort the spatial number and construction density to generate a three-dimensional graphic element construction area sorting table; S2: Based on the 3D primitive construction area sorting table, extract the thread identifier and idle state flag, bind the top-ranked construction area number with the idle thread, use the construction density value in the bound area to set the thread cache resources and runtime configuration, and generate a thread allocation task table; S3: Based on the area number in the thread allocation task table, extract the water pressure sensing terminal node data in the corresponding area, collect the continuous pressure value sequence of the terminal node, select the pressure readings of multiple adjacent time periods, calculate the trend change between adjacent readings, convert the trend direction and change amplitude into control parameters, and establish a parameter mapping table in combination with the area number to generate a dynamic driving parameter table. S4: Call the construction region number in the dynamic drive parameter table, extract the execution node order in the corresponding region, classify the nodes according to the trend direction, extract the animation trigger mark in each category, integrate them into a path chain structure and complete the numbering, and generate a path chain list dataset. S5: Call the thread number and region number in the thread allocation task table, match the number of nodes for each path in the path chain dataset, compare the number of nodes with the set processing threshold, determine the execution status of the path chain, and generate a list of execution statuses for the scene building task.

[0019] The 3D primitive construction area sorting table includes a combination of spatial number sequence and construction density value; the thread allocation task table includes region number binding identifier, thread cache resource configuration parameters, and running cycle configuration parameters; the dynamic driving parameter table includes trend direction parameters, change amplitude parameters, and control parameter mapping relationships; the path chain dataset includes node classification labels, animation trigger label set, and path chain number structure; and the scene construction task execution status list includes node number threshold comparison results and path chain execution status identifiers.

[0020] Please see Figure 2 The steps to obtain S1 are as follows: S101: Based on the spatial number and corresponding graphic element in the underground pipe network model, identify the calling frequency of each graphic element in the whole model, extract the graphic element type and number, and count the number of occurrences in all spatial numbers to generate graphic element frequency values. Based on the spatial numbers and corresponding graphic elements in the underground pipeline network model, a structural scan of the model's structural data is first performed. The spatial number field is read sequentially, and the graphic element number information under the corresponding spatial unit is extracted. An index mapping table between graphic element numbers and spatial numbers is then established. Subsequently, the frequency of each graphic element in all spatial numbers is statistically analyzed. For example, if graphic element T101 is identified in spaces S001, S002, and S004, its call frequency is 3; graphic element T203 exists only in S002, and its call frequency is 1. This process requires traversing a structured dataset to establish a unique identifier for each component number and record its occurrence frequency. During statistical analysis, duplicate component numbers are avoided to prevent frequency aggregation errors. To ensure recognition accuracy, non-graphical entity component records must be removed during the data filtering stage, retaining only those with clear geometric boundaries and topological significance. The frequency statistics results are then structurally integrated with other parameters such as spatial number, graphic element number, geometric dimensions, and connection quantity to form the data foundation for subsequent calculations. Table 1 shows the structural information extracted during the actual recognition process. Table 1. Primitive Construction Parameters

[0021] The structural data listed in Table 1 clearly corresponds to the frequency of each primitive component in the model and its physical parameters, laying a data foundation for subsequent primitive construction density processing operations and ultimately generating primitive component frequency values.

[0022] S102: Call the frequency value of the graphic element component, and combine it with the geometric dimensions of the graphic element component and the number of component connections to perform a combination operation between the three parameters, obtain the parameter value of the graphic element component, and integrate the parameter results of all graphic element components according to the spatial number to generate the graphic element construction density value; The frequency of a graphical element component is called, and combined with the component's geometric dimensions and the number of connections, a logical expression is constructed for these three parameters. In this process, the "Frequency of Call," "Geometric Dimensions," and "Number of Connections" columns in Table 1 are first read and assigned the values ​​fᵢ, gᵢ, and cᵢ respectively to obtain their combined expression. Then, the three parameters of each graphical element component are combined and calculated to determine its expression intensity, thus constructing the basic data value for the element's construction density. For example, element T101 has a frequency of 3, a geometric dimension of 1.2 meters, and a number of connections of 3. Therefore, the basic expression value of this element's parameter combination can be obtained by calculating the difference, ratio, and other parameters between these three values. The density parameters are established using weighted product and other combination methods, and the corresponding density parameter fields are recorded in the component dataset. After performing the same operation on all primitive numbers, the basic density values ​​of each group of components are classified and integrated according to the spatial number to form a density data set of primitives within the spatial partition. For example, for T150 (fᵢ=4, gᵢ=1.1, cᵢ=5), it is converted into a density index and compared with other components such as T203 (fᵢ=1, gᵢ=1.5, cᵢ=2) under the same spatial number. Those with higher density expression values ​​will be prioritized. This process plays a decisive role in obtaining the density values ​​of primitive components under each spatial number, and finally, the density values ​​of primitive construction are generated.

[0023] S103: Based on the density values ​​of the primitives, group them according to their spatial numbers, extract the density values ​​of each group of primitive components and sort them in descending order. Organize the primitive component numbers according to the sorting results, summarize and arrange the density sorting sequence corresponding to the spatial numbers, and obtain the sorting table of the three-dimensional primitive construction area. Based on the primitive construction density values, the obtained primitive density indices are categorized by spatial number. First, an index mapping relationship between spatial number and primitive construction density value is established. In this mapping, all primitive density values ​​under each spatial number are sorted in descending order. For example, space S001 contains primitives T101 and T150, where the construction density value of T150 is 4.5 and that of T101 is 2.8, so the sorting order is T150>T101. During the sorting process, the data type of the density value field needs to be converted to ensure that no type conflict occurs during the sorting operation. For the primitive number in the sorting result, it is uniformly recorded as the priority primitive under that spatial number. Finally, all spatial number sorting structures are integrated into a structured output document and saved in the form of a table or list to clarify the primitive construction order under the three-dimensional spatial partition and obtain the three-dimensional primitive construction area sorting table.

[0024] Please see Figure 3 The steps to obtain S2 are as follows: S201: Based on the 3D primitive construction area sorting table, the construction area number, construction density value, geometric proportion value and primitive aggregation value are normalized. The sorting priority value is obtained based on the construction density value and primitive aggregation value as dual indicators, and the construction area priority sequence value is generated. Based on the 3D primitive construction area sorting table, the construction area numbers are first extracted from the BIM data of prefabricated building components. The construction density is then calculated according to the primitive coverage of each area. Geometric complexity is measured by the non-linear proportion of the boundary formed by the primitives in that area. Primitive aggregation is assessed based on the number of primitives per unit area. For example, for area A1, the construction density is measured to be medium, the proportion of boundary contour polylines is 65%, and the primitive unit density is 0.7. Area A2 has high construction density, a boundary morphological complexity of 85%, and a primitive aggregation degree of 0.95. These indicators can be used to construct the regional sorting relationship. The sorting logic is set with construction density level as the primary sorting item. If two areas have the same construction density level, a secondary sort is performed based on the primitive aggregation index value. The sorting results are mapped as follows: Table 2 Reference Table for Sorting Construction Regions

[0025] As shown in Table 2, region A2 is prioritized because it has a high construction density level and the largest primitive aggregation. The next highest priority is A1 and A4, and the lowest is A3. This ranking is used for subsequent idle thread binding priority allocation, and finally the construction region priority sequence value is obtained.

[0026] S202: Call the build region priority sequence value, obtain the build region number that is ranked first, and retrieve the thread identifier and idle status flag in the thread status information. Verify the available thread based on the idle status flag, match the available thread identifier with the build region number, and generate the build thread binding mapping value. After calling the build region priority sequence value, the build region numbers with the highest priority are extracted in sequence, and the status list of currently available threads is obtained from the thread management module. The status table records the thread number and its idle status identifier. For example, the idle statuses of three threads T1, T2, and T3 are 1, 0, and 1 respectively, indicating that threads T1 and T3 are idle and available, and thread T2 is currently occupied. By filtering the threads with the idle status marked as 1, they are paired one by one with the build region numbers. The binding order is that the regions with the highest priority are bound to the idle threads, forming the following mapping relationship: A2 is bound to T1, A1 is bound to T3. If the number of region numbers exceeds the number of idle threads, the remaining regions are temporarily suspended until the threads are released before binding. This process removes all non-idle threads, obtains the mapping structure, and generates the build thread binding mapping value.

[0027] S203: Call the build thread binding mapping value, extract the region build characteristics and thread running resource characteristics for each pair of build region and thread binding relationships, set the resource allocation method and periodic configuration form according to the adaptation relationship between the two types of characteristics, integrate them into a unified task structure, and obtain the thread allocation task table; The thread binding mapping value is invoked. For each binding pair, indicators such as build density and build operation unit load are extracted from the build area attribute set, and cache resource capacity and runtime configuration are extracted from the thread information set. For example, in binding pair A2-T1, the density level of region A2 is high, the corresponding number of unit primitives is 280, the cache capacity of thread T1 is 32MB, and the runtime is set to 5ms. In binding pair A1-T3, the density level of region A1 is medium, the number of unit primitives is 180, the cache capacity of thread T3 is 24MB, and the runtime is set to 7ms. The build density and primitive unit number are mapped to the number of cache blocks and the runtime length required for thread configuration. For each binding pair, a corresponding thread resource scheduling configuration item is set. The setting structure includes three categories of content: the number of required cache blocks, the corresponding time interval, and the number of synchronization instruction groups. All binding results are summarized and organized into a structured allocation table, which serves as the final allocation and scheduling task list, and the thread allocation task table is obtained.

[0028] Please see Figure 4 The steps to obtain S3 are as follows: S301: Based on the area number in the thread allocation task table, extract the water pressure sensing terminal node data in the corresponding area, and collect the continuous pressure value sequence according to the node number. In the collected data, each node records the pressure reading in adjacent time periods, and the node readings are numbered and archived to obtain the continuous pressure value sequence of the node at multiple time points, and obtain the water pressure reading sequence dataset. Based on the region number in the thread allocation task table, the explicitly identified region number field is first extracted from the table. For example, after reading the thread task field in the system call, region identifiers such as A01, A02, A03, and A04 are obtained. The extracted results are initialized with a number mapping. Then, the water pressure sensing terminal nodes associated with each region number are traversed. The corresponding mapping relationship is recorded in the monitoring device configuration file or node topology. Taking region A01 as an example, its bound monitoring node is N1. N1 is a water pressure monitoring device set at the end of the main supply pipeline. It continuously monitors pressure data with a sampling interval of 5 seconds. The initial sampling time is 0 seconds. From this moment, a pressure value is collected every 5 seconds to form a complete time series. The first four sampled values ​​of this node are read as 310. 4, 309.1, 307.8, 308.3 kPa. Each data record is numbered and archived, and its timestamp sequence is bound. For example, 310.4 kPa corresponds to t0=0 seconds, 309.1 corresponds to t1=5 seconds, and so on. The data is packaged into a continuous time pressure reading structure for that node. During the execution process, this process is repeated for nodes in other regions. For example, for node N2 in region A02, its first four sample values ​​are recorded as 287.6, 289.0, 288.2, and 287.4 kPa. The archiving order is also in ascending order of time, forming the time series structure of N2. After obtaining the node pressure time data for all regions, the overall dataset structure is formed. The following data records show the readings of the nodes in each region at the initial time: Table 3 Initial pressure gauges at monitoring points

[0029] As shown in Table 3, the initial readings corresponding to each node are extracted and included in the dataset, which is used as the basic data for generating continuous pressure monitoring sequences. The data structure binds the node number with the area number and saves the complete pressure change curve sequence information to obtain the water pressure reading sequence dataset.

[0030] S302: Call the water pressure reading sequence dataset, select the readings of each node in multiple adjacent time periods, determine the direction of change trend by the numerical difference between adjacent readings, and obtain the trend amplitude quantification value sequence based on the trend direction and the average difference between each group of readings. Extract the trend parameter set according to the combination of change amplitude and trend direction, and generate the node trend parameter group. When accessing the water pressure reading sequence dataset, the water pressure readings at multiple consecutive time points are extracted sequentially for each pressure monitoring node. For example, for nodes numbered P1, P2, and P3, readings are obtained over a 5-hour period from 8:00 AM to 12:00 PM on July 1st. The readings for node P1 are 4.8, 5.1, 4.9, 5.2, and 5.0; for node P2, they are 5.0, 5.3, 5.4, 5.5, and 5.6; and for node P3, they are 4.9, 4.7, 4.6, 4.5, and 4.4. Then, the differences between adjacent time points are calculated progressively for each node's time series. The adjacent differences for P1 are 0.3 and -0.2, respectively. The differences were initially set to 0.3, -0.2, and then recorded as positive or negative to indicate the trend direction, forming a direction sequence of +1, -1, +1, -1 for node P1. The absolute values ​​of these differences were then calculated and averaged to obtain an average change of 0.25 for node P1 over 5 hours. Further analysis of the proportion of positive and negative directions was conducted; in P1, the positive direction occurred twice (50%), and the negative direction occurred twice (50%). These proportions were then combined with the average magnitude to form a set of trend characteristic values: 0.5, 0.5, 0.25. This method was then used to analyze node P... 2. The differences are processed as follows: 0.3, 0.1, 0.1, 0.1, with a direction sequence of +1, +1, +1, +1. The average absolute value is 0.15, with 100% positive and 0% negative. Similarly, the differences for node P3 are -0.2, -0.1, -0.1, -0.1, with a direction sequence of -1, -1, -1, -1. The average absolute value is 0.125, with 0% positive and 100% negative. During the above calculations, each difference is compared to a minimum change threshold of 0.05. Differences less than this threshold are set to zero, while differences greater than or equal to this threshold retain the actual difference direction for subsequent trend analysis. To determine the trend direction, if the pressure change between two points is only 0.04, it is considered to have no significant change and is recorded as 0 in the direction sequence because it is less than 0.05. The positive and negative counts are not counted when calculating the trend direction. When comparing the mean amplitude with the threshold, if it is less than 0.1, it is judged as a weak fluctuation; between 0.1 and 0.3, it is judged as a moderate fluctuation; and greater than or equal to 0.3, it is considered a significant fluctuation. According to this standard, nodes P1 and P2 are judged as moderate fluctuations, and node P3 is also classified as a moderate fluctuation. After obtaining the above trend parameters, the trend parameters of all nodes are summarized to form a node trend parameter group and recorded.

[0031] S303: Based on the node trend parameter group and the region number information in the thread allocation task table, map the trend direction parameter and trend amplitude parameter to the control dimension parameter space, and establish a mapping index table according to the region number to generate a dynamic driving parameter table; Based on the trend direction and amplitude data of each node recorded in the node trend parameter group, and using the region number as the classification basis, the results of all nodes are summarized and aggregated. In the data structure, a key-value pair format is constructed: Region Number → Parameter Pair Set, where the parameter pair structure is (trend direction value, trend amplitude quantification value). For example, the result for region A01 is (-1, -1.097), and if the node trend changes for region A02 are (+1, +0.843), a complete region mapping set is formed. Then, the parameter pairs of all nodes under each region are integrated into a region control parameter matrix. Based on this, the matrix is ​​indexed and sorted according to the region number to form the master control mapping structure. The trend direction in the above control matrix is ​​used as the sign parameter item, and the trend amplitude is used as the control intensity item. Finally, a mapping table of region number → control parameter set is constructed, called the dynamic driving parameter table. This table structure is adapted to multi-region parallel computing mode and is used as the control mapping base table in subsequent parameter allocation strategies. Its data all come from the trend parameter structure formed in the above analysis process, and it has continuous update capability and region switching compatibility.

[0032] Please see Figure 5 The steps to obtain S4 are as follows: S401: Call the construction region number in the dynamic driving parameter table, extract the execution node order in the corresponding region, identify the position change value, movement direction value and time difference value between adjacent nodes, sort all node pairs under the same construction region according to the three parameter dimensions of position change value, movement direction consistency comparison and time difference value, and generate node trend classification intervals. To call the build region number in the dynamic drive parameter table, you first need to locate the corresponding build module number field. For example, the number "R03" represents the "Southern Structural Unit" in the 3D build model. Then, obtain the list of execution nodes by querying its node sequence field. For example, the node numbers are N1, N2, N3, and N4. Next, extract the spatial coordinate values ​​of adjacent nodes one by one. For example, node N1 is (2.0, 3.5, 1.0), and N2 is (3.2, 4.1, 1.0). Calculate the positional change value ΔP_ij between the nodes using the 3D spatial Euclidean distance formula, i.e.:

[0033] After obtaining the position change value, the action time difference ΔT_ij of the same node pair is calculated. For example, if the execution time of N1 is 2.0 seconds and that of N2 is 4.1 seconds, then ΔT_12 = 2.1 seconds. Simultaneously, the motion direction vectors between adjacent nodes are obtained. A vector is constructed using the offset direction of adjacent frames of the nodes. For example, the vector from node N1 to N2 is (1.2, 0.6, 0). The cosine value of the direction consistency is calculated as follows:

[0034] The system sets the directional consistency criterion to 0.90. When the cosine value is ≥0.90, it is judged as "same direction". If ΔP_ij satisfies increasing, ΔT_ij steadily increasing, and consistent direction, then adjacent nodes are grouped into a trend set, and the following trend classification information table is finally constructed. Table 4 Node Trend Classification Information Table

[0035] As shown in Table 4, the three consecutive node segments under the construction area number R03 all meet the conditions of increasing spatial distance, distinguishable action time difference and high directional consistency (cosθ ≥ 0.95), which meet the trend classification conditions. Therefore, they are uniformly classified into the trend direction node interval TQ1 for subsequent steps.

[0036] S402: Based on the node trend classification interval, filter the animation trigger mark value, trigger weight value, trigger direction factor and spatial offset angle value corresponding to the node in the interval, call the trend direction benchmark identifier value under the construction area number, calculate the fitting degree based on the cosine value of the angle between the trigger direction factor and the trend direction benchmark identifier value, obtain the node direction fitting degree coefficient, sort according to the fitting degree coefficient and set the fitting degree threshold, extract the nodes above the threshold and their corresponding trigger mark values, and obtain the trend direction mark extraction set; Based on the node trend classification interval, the spatial coordinates of each node within 10 consecutive frames are first calculated sequentially to obtain the inter-frame distance increment and combine it with the time interval to form a rate sequence. Each rate value is obtained by dividing the Euclidean distance between two adjacent frames by the frame time difference. For example, if the distance between the first and second frames is 0.25 m and the frame interval is 0.02 s, the rate is 12.5 m / s. The rate sequence of 10 frames is stored in an array, such as [12.5, 13.0, 11.2, 14.6, 13.9, 12.0, 10.5, 15.2, 11.8, 13.3]. Then, the array is averaged to obtain 12.8 m / s. The sequence is then iterated through for single-point threshold comparison. When the sequence shows 5 consecutive values ​​greater than 0.15 m / s, it is classified as a fast-rising interval; if it shows consecutive values ​​less than 0.05 m / s, it is classified as a fast-rising interval. If the speed is m / s, it is classified as a low-motion interval; otherwise, it is classified as a medium-motion interval. For example, if the speed of this node is greater than 0.15 m / s for all 10 frames, it is classified as a fast-rising interval. Then, the animation trigger marker values ​​recorded by the node within this interval are filtered. The marker array recorded by the corresponding frame timestamp is traversed, such as [2,3,2,4,5,2,3,4,3,5], and its average value of 3.3 is calculated as the trigger weight value. Then, the trend direction reference angle value corresponding to the node's region number is retrieved. For example, if the stored array is [45°, 60°, 90°], the direction factor array extracted within the node's time period is [50°, 58°, ...]. [93°], then the difference angle between each direction factor and the corresponding reference angle is calculated and its absolute value is taken. For example, the difference between 50° and 45° is 5°, the difference between 58° and 60° is 2°, and the difference between 93° and 90° is 3°. The cosine values ​​are obtained by approximate table lookup, which are 0.996, 0.999, and 0.998, forming a nodal direction fitting coefficient sequence [0.996, 0.999, 0.998]. This sequence is then sorted in descending order, resulting in [0.999, 0.998, ...]. [0.996], then set the fit threshold to 98%, filter out coefficients greater than 0.98 in the sequence to obtain all three items that meet the requirements, extract the corresponding node index and its animation trigger mark value [4,5,2], and combine them to form a trend direction mark extraction set. Through this set, the same process can be continued to be performed on subsequent frame data for cumulative updates, continuously rolling and filtering high-fit nodes in the new data stream, and finally summarizing in multiple cycles to form a complete trend direction mark library for the input data basis for subsequent stages of dynamic pattern matching and direction prediction.

[0037] S403: Call the trend direction marker extraction set, rearrange the nodes in the extraction set according to the trend direction order, number them according to the extraction order, integrate the three data items of position, animation trigger marker, and number value between adjacent nodes, construct a linear path structure sequence, and obtain the path linked list dataset; The trend direction marker extraction set is called, and the nodes are arranged in ascending order of their included angle difference to form an execution chain structure, numbered sequentially as P1, P2, P3... The path sequence is reconstructed, such as N2→P1, N3→P2, N4→P3 after sorting. For each node pair, path segment attributes are constructed. For example, if the difference in node number between P1 and P2 is 1, the animation trigger value change Δv is 4→2, and the spatial offset is 1.8m, then the path segment attributes are recorded as {Start point: P1, End point: P2, ΔN=1, Δv=2, ΔP=1.8}. Following this rule, the entire group of trend nodes is traversed, and the path segments are structured in a chain, forming a path linked list. Each linked list node records its own number, start point identifier, animation value, offset value, and the pointer field to the next node. Finally, all path segments are summarized to form a chained data structure path table, as shown below: Linked list structure diagram: P1(N2, Animation 4)→P2(N3, Animation 2)→P3(N4, Animation 3) Fields per node: {Number: P} i Animation value: v j Offset value: d ij Sequence number difference: ΔN, Next pointer: P {i+1} Ultimately, a path structure set of path linked list files is formed, which is used as the data-driven input for subsequent execution stages such as model-driven, animation rendering, and path control, to obtain the path linked list dataset.

[0038] Please see Figure 6 The steps to obtain S5 are as follows: S501: Call the thread number and region number in the thread allocation task table, combine each path in the path linked list dataset, detect the number of nodes in the path, read the cumulative count value of the nodes in the path structure, record the number of path nodes, and obtain the set of path node counts. The thread and region numbers in the task allocation table are called. The process involves sequentially parsing each record. First, the thread and region numbers are read and matched against the path numbers in the constructed path linked list to determine which thread and region each path belongs to. For example, path P001 matches thread T01 and region R01, and path P002 matches thread T02 and region R02. After clarifying the mapping relationship, the number of nodes in each path is checked. Path node counting is performed using pointer traversal, relying on the path linked list structure. Starting from the beginning node of each path, the number of nodes in the path is accumulated by continuously accessing the next node pointer. The process continues up to the last node of the path. For example, path P001 contains nodes N001 to N016, totaling 16 nodes; path P002 connects nodes N021 to N029, totaling 9 nodes. During the accumulation of node counts, every node access operation must be recorded to prevent missed jump nodes or double counting due to circular structures. For potential structural breaks or pointer loss in the path, such as a path interrupted at the 6th node, the logic and pointer status should be checked to confirm if it is a true termination point. If it is an abnormal breakpoint, it is not included in the node count. After obtaining the node count for each path, a node count set is generated. The collected data results are shown below: Table 5. Number of Path Nodes Collected

[0039] As shown in Table 5, after clarifying the corresponding thread and region for each path, the number of nodes was traversed and counted, and finally a set of path numbers and node numbers was formed, resulting in the set of path node counts.

[0040] S502: Based on the set of path node counts, call the set path processing threshold, compare the number of nodes in each path with the corresponding path processing threshold. If the number of nodes is greater than or equal to the path processing threshold, it is determined to be an executable path; otherwise, it is determined to be an unexecutable path, and a path execution status marker sequence is generated. Based on the set of path node counts, a predefined path processing threshold is invoked, and the number of nodes for each path is assessed to determine its executable status. This threshold is determined based on historical operational pressure and resource scheduling tests, with a standard of 12 nodes. That is, a path is considered executable if its number of nodes is greater than or equal to 12; otherwise, it is considered inexecutable. The execution process iterates using the path number as an index. For example, P001, with 16 nodes (greater than the threshold), is marked as executable; P002, with 9 nodes (less than the threshold), is marked as inexecutable; P003 (13 nodes) and P004 (20 nodes) are both greater than the threshold. The value is used to determine whether a path is executable. This determination is achieved by setting a Boolean condition: if the number of path nodes is greater than or equal to a threshold, the execution status is assigned a value of 1; otherwise, it is assigned a value of 0. The execution status of each path is recorded as a path tag sequence. The threshold setting process referenced multiple sets of path execution test data and comprehensively evaluated the average execution time, CPU utilization, and memory allocation ratio under different numbers of nodes. In the experiment, the average memory usage in the scenario with 12 execution paths was 65%, and there was no congestion in resource scheduling. Therefore, 12 nodes were used as the standard threshold for executable execution. The data is shown in the table below: Table 6 Path Execution Status Flag Table

[0041] As shown in Table 6, each path is processed by comparing the number of nodes with the threshold, and a clear execution status mark is output, forming a path execution status mark sequence.

[0042] S503: Based on the path execution status marker sequence, call the thread number and region number in the thread allocation task table and match them with the corresponding path identifier in the path chain. Match and integrate the execution status of each path with its own thread and region to obtain the scene construction task execution status list. Based on the path execution status marker sequence, task thread and region information are further integrated. First, using the path number as the primary key, the execution status of each path is matched and combined with the thread number and region number to form a path task allocation status record. This operation requires traversing the task table and path status marker table, extracting entries with the same path number, and then extracting the thread number and region number fields. These three data items are combined into a task execution record entry. For example, if path P001 has an execution status of 1, thread number T01, and region number R01, the combination is record T01-R01-1. During execution, it is necessary to determine... To avoid confusion, the path number is checked for duplicates in multiple tables. Unmatched entries are marked as missing. Finally, all path records are merged to form a task execution list. In this list, the total number of execution paths can be counted by region number for subsequent thread load balancing and scheduling optimization analysis. For example, in this case, region R01 has two paths, P001 and P003, both of which are executable, for a total of 2 execution paths. Region R02 only has P002, which is not executable. Region R03 has P004, which is an executable path. The above processing yields the scenario construction task execution status list.

[0043] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for quickly constructing and analyzing a water affair 3D scene based on open-source Hongmeng, characterized in that, The method comprises the following steps: S1: obtaining the spatial number and the graphic element component in the underground pipe network model, extracting the calling frequency, geometric size and component connection number of the graphic element, calculating the graphic element construction density value, combining and sorting the spatial number and the construction density, and generating a three-dimensional graphic element construction area sorting table; S2: based on the three-dimensional graphic element construction area sorting table, extracting the thread identifier and the idle state marker, binding the construction area number with high ranking with the idle thread, using the construction density value in the bound area to set the thread cache resource and the running cycle configuration, and generating a thread allocation task table; S3: according to the area number in the thread allocation task table, extracting the water pressure sensing terminal node data of the corresponding area, collecting the pressure value in the continuous time period, calculating the trend direction and the change amplitude of the adjacent data, converting the result into a control parameter and establishing a corresponding relationship with the area number, and generating a dynamic driving parameter table; S4: calling the area number in the dynamic driving parameter table, extracting the execution node sequence of the corresponding area, classifying the nodes according to the trend direction, extracting the animation trigger marker, combining it into a path chain and completing the numbering, and generating a path chain table dataset.

2. The open-source-HM-based water affair 3D scene rapid construction analysis method according to claim 1, characterized in that, The three-dimensional graphic element construction area sorting table comprises a spatial number sequence and a construction density value combination, the thread allocation task table comprises a region number binding identifier, a thread cache resource configuration parameter and a running cycle configuration parameter, the dynamic driving parameter table comprises a trend direction parameter, a change amplitude parameter and a control parameter mapping relationship, and the path chain table dataset comprises a node classification marker, an animation trigger marker set and a path chain numbering structure.

3. The open-source-honmng-based water affair 3D scene rapid construction analysis method according to claim 1, characterized in that, The obtaining step S1 is: S101: based on the spatial number and the corresponding graphic element component in the underground pipe network model, identifying the calling frequency of each graphic element component in the whole model, extracting the graphic element type, number and counting the occurrence times in all spatial numbers, and generating a graphic element component frequency value; S102: calling the graphic element component frequency value, combining the geometric size and the component connection number of the graphic element component, performing combination operation among the three parameters, obtaining the parameter value of the graphic element component, and integrating the parameter results of all graphic element components according to the spatial number, and generating a graphic element construction density value; S103: according to the graphic element construction density value, grouping according to the spatial number, extracting the density value of each group of graphic element components for descending order arrangement, arranging the graphic element component number according to the sorting result, summarizing and arranging the density sorting sequence corresponding to the spatial number, and obtaining a three-dimensional graphic element construction area sorting table.

4. The open-source-hyperledger-based water affair 3D scene rapid construction analysis method according to claim 3, characterized in that, The obtaining step S2 is: S201: based on the three-dimensional graphic element construction area sorting table, performing normalization processing on the construction area number, the construction density value, the geometric proportion value and the graphic element aggregation value, taking the construction density value and the graphic element aggregation value as double index basis, obtaining the sorting priority value, and generating a construction area priority sequence value; S202: calling the construction area priority sequence value, obtaining the construction area number with high ranking, and retrieving the thread identifier and the idle state marker in the thread state information; according to the idle state marker, verifying the available thread, pairing the available thread identifier with the construction area number, and generating a construction thread binding mapping value; S203: Call the aforementioned construction thread binding mapping value, extract the region construction characteristics and thread running resource characteristics for each pair of construction region and thread binding relationships, set the resource allocation method and periodic configuration form according to the adaptation relationship between the two types of characteristics, integrate them into a unified task structure, and obtain the thread allocation task table.

5. The open-source-hyperledger-based water affair 3D scene rapid construction analysis method according to claim 4, characterized in that, The steps to obtain S3 are as follows: S301: Based on the area number in the thread allocation task table, extract the water pressure sensing terminal node data in the corresponding area, and collect the continuous pressure value sequence according to the node number. In the collected data, each node records the pressure reading in adjacent time periods, and the node readings are numbered and archived to obtain the continuous pressure value sequence of the node at multiple time points, and obtain the water pressure reading sequence dataset. S302: Call the water pressure reading sequence dataset, select the readings of each node in multiple adjacent time periods, determine the direction of change trend by the numerical difference between adjacent readings, and use the trend direction and the average difference between each group of readings as the change amplitude index to calculate and obtain the trend amplitude quantification value sequence. Extract the trend parameter set according to the combination of change amplitude and trend direction to generate the node trend parameter group. S303: Based on the node trend parameter group and the region number information in the thread allocation task table, map the trend direction parameter and trend amplitude parameter to the control dimension parameter space, and establish a mapping index table according to the region number classification to generate a dynamic driving parameter table.

6. The open-source-HyperJumper-based water affair 3D scene rapid construction analysis method according to claim 5, characterized in that, The steps to obtain S4 are as follows: S401: Call the construction region number in the dynamic driving parameter table, extract the execution node order in the corresponding region, identify the position change value, movement direction value and time difference value between adjacent nodes, sort all node pairs under the same construction region according to the three parameter dimensions of position change value, movement direction consistency comparison and time difference value, and generate node trend classification interval; S402: Based on the node trend classification interval, filter the animation trigger mark value, trigger weight value, trigger direction factor and spatial offset angle value corresponding to the node in the interval, call the trend direction benchmark identifier value under the construction area number, calculate the fitting degree based on the cosine value of the angle between the trigger direction factor and the trend direction benchmark identifier value, calculate and obtain the node direction fitting degree coefficient, sort according to the fitting degree coefficient and set the fitting degree threshold, extract the nodes above the threshold and their corresponding trigger mark values, and obtain the trend direction mark extraction set; S403: Call the trend direction marker extraction set, rearrange the nodes in the extraction set according to the trend direction order, number them according to the extraction order, integrate the three data items of position, animation trigger marker, and number value between adjacent nodes, construct a linear path structure sequence, and obtain the path linked list dataset.

7. The open-source-HyperJumper-based water affair 3D scene rapid construction analysis method according to claim 6, characterized in that, The method further includes: S5: Call the thread number and region number in the thread allocation task table, match the number of nodes in each path in the path chain dataset, compare the number of nodes with the set processing threshold, determine the execution status of the path chain, and generate a task execution status list. The task execution status list includes the node number threshold comparison result and the path chain execution status identifier.

8. The open-source-HyperJumper-based water affair 3D scene rapid construction analysis method according to claim 7, characterized in that, The steps to obtain S5 are as follows: S501: Call the thread number and region number in the thread allocation task table, combine each path in the path linked list dataset, detect the number of nodes in the path, read the cumulative count value of the nodes in the path structure, record the number of path nodes, and obtain the set of path node counts. S502: Based on the set of path node counts, call the set path processing threshold, compare the number of nodes of each path with the corresponding path processing threshold, if the number of nodes is greater than or equal to the path processing threshold, it is determined to be an executable path, otherwise it is determined to be an unexecutable path, and generate a path execution status marker sequence. S503: Based on the path execution status marker sequence, call the thread number and region number in the thread allocation task table and match them with the corresponding path identifier in the path chain. Match and integrate the execution status of each path with its own thread and region to obtain a task execution status list.

Images