An emulation data interaction strategy optimization method

Abstract

The application discloses a simulation data interaction strategy optimization method, relates to the simulation interaction field, and comprises the following steps: obtaining corresponding user interaction data and interaction simulation data; performing hierarchical classification processing on the obtained interaction simulation data according to the user interaction data, and setting a hierarchical storage space; performing classification processing on the interaction simulation data in the hierarchical storage space, setting a classification storage space to perform sorting storage on the interaction simulation data; performing comprehensive connection processing on the interaction simulation data in each hierarchical storage space and the classification storage space, and setting a pyramid interaction storage database; respectively obtaining corresponding user interaction feature data according to the user interaction data, and generating an adaptive pyramid interaction storage database according to the user interaction feature data; performing interaction strategy optimization on the obtained user interaction data according to the corresponding adaptive pyramid interaction storage database, and obtaining corresponding interaction simulation data; and the application improves the efficiency in the interaction strategy optimization process.

Description

Technical Field

[0001] This invention relates to the field of simulation interaction technology, and in particular to a method for optimizing simulation data interaction strategies. Background Technology

[0002] Current simulation data interaction processes typically employ the traditional request-response model. However, with the expansion of business scale, the surge in user volume, and the access of multiple terminals, the original interaction strategies can no longer meet the business requirements of high efficiency, low latency, and high reliability. Problems such as data transmission redundancy, slow interface response, insufficient data consistency, excessive resource consumption, and imperfect exception handling are becoming increasingly prominent, thereby affecting the smoothness of business processes and user experience. Systematic optimization is urgently needed to improve the efficiency, stability, and security of data interaction.

[0003] A search revealed Chinese invention patent CN111147284A, which discloses a data-centric distributed real-time simulation system data interaction strategy. This strategy primarily involves designing a data-centric data exchange architecture, including selecting an applicable data protocol standard, constructing a simulation-driven clock synchronization mechanism, and designing a data exchange strategy. Specifically, the selected data protocol standard is a publish / subscribe-based data interaction protocol; the constructed simulation-driven clock synchronization mechanism is a priority-based dynamic clock synchronization mechanism; and the designed data exchange strategy involves dividing the data exchanged by all devices in the system into data blocks and topics, optimizing transmission quality strategies, and restricting data transmission formats. This data-centric distributed real-time simulation system data interaction strategy meets the real-time and synchronization requirements of data interaction, adapts to diverse application scenarios, reduces network burden, and can be applied to efficient and reliable data transmission between various platforms and tools.

[0004] Compared with existing technologies, the Chinese invention patent with publication number CN111147284A satisfies the requirements of real-time, synchronous and diverse data interaction through a data-centric distributed real-time simulation system data interaction strategy.

[0005] However, in actual use, the above method divides the data of all devices in the system into data blocks and main parts to implement the strategy of optimizing transmission quality. To a certain extent, it ignores the personalized needs of different users in the data interaction process, thus reducing the optimization efficiency in the simulated data interaction process to some extent. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of low efficiency in interaction optimization in existing technologies, and to propose a simulation data interaction strategy optimization method.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for optimizing simulation data interaction strategies includes the following steps: Step S1: Set up the data acquisition terminal and acquire the corresponding user interaction data and interaction simulation data through the data acquisition terminal; Step S2: Set up the data processing terminal, perform hierarchical classification processing on the obtained interactive simulation data according to the user interaction data, store the interactive simulation data in layers according to the hierarchical classification processing results, and set the hierarchical storage space; Step S3: Classify the hierarchically stored interactive simulation data according to the interactive actions, set up separate category storage spaces, and sort and store the interactive simulation data according to the interaction popularity within the category storage space; Step S4: Perform comprehensive connection processing based on the sorting results of each interactive simulation data in each level of storage space and category storage space, and set up the pyramid interactive storage database; Step S5: Set up the interaction optimization end, extract features from the obtained user interaction data, obtain user interaction feature data, and dynamically adjust the interaction simulation data in the pyramid interaction storage database according to the user interaction feature data to generate an adaptive pyramid interaction storage database. Step S6: Optimize the interaction strategy of the obtained user interaction data according to the corresponding adaptive pyramid interaction storage database, and obtain the interaction simulation data corresponding to the user interaction data based on the interaction strategy optimization results.

[0008] The above technical solution further includes: the process of acquiring user interaction data and interaction simulation data includes: A data acquisition terminal is set up, which includes a user acquisition terminal and a simulation acquisition terminal. User interaction data corresponding to different user accounts is obtained through user collection terminals. The user interaction data includes user interaction request data and user interaction behavior log data. Interactive simulation data from different data sources is acquired through a simulation acquisition terminal. The interactive simulation data includes basic simulation model data, simulation interaction parameter data, and historical simulation interaction data.

[0009] Furthermore, the process of setting up hierarchical storage space includes: A data processing terminal is provided, which includes a data preprocessing terminal and a hierarchical processing terminal. The data preprocessing terminal performs data cleaning and redundancy removal on the obtained interactive simulation data and user interaction data respectively, and then inputs the processed interactive simulation data and user interaction data into the hierarchical processing terminal. The terminal performs resolution feature extraction on the obtained interactive simulation data and user interaction data through hierarchical processing, obtains corresponding types of interactive resolution feature data, presets hierarchical classification standards, performs hierarchical classification processing on the interactive simulation data and user interaction data corresponding to different interactive resolution feature data according to the hierarchical classification standards, sets hierarchical storage space according to the hierarchical classification processing results, and stores the obtained data through the hierarchical storage space.

[0010] Furthermore, the process of setting up categorized storage spaces includes: Interaction features are extracted from the interaction simulation data and user interaction data stored in each level of storage space. The interaction action types corresponding to the interaction simulation data and user interaction data are obtained respectively. The level storage space is classified according to the interaction action type. The corresponding level storage space is adaptively divided into regions according to the classification results. The classified storage space is set according to the adaptive region division results. The interactive simulation data and user interaction data in the categorized storage space are stored and sorted for management. The interactive simulation data in each categorized storage space are evaluated for interaction popularity based on the interaction frequency of user interaction data, and the interaction popularity evaluation data corresponding to the interactive simulation data is obtained. The obtained interactive simulation data is sorted according to the interactive resolution feature data. The sorted interactive simulation data is visualized according to the interactive popularity evaluation data. The visualization results are then used for auxiliary sorting and encoding. The auxiliary sorting codes are mapped to the interactive simulation data for sorting and storage, and the corresponding classification storage space is obtained.

[0011] Furthermore, the process of setting up the pyramid interactive storage database includes: Obtain the storage space at each level and the corresponding category storage space within it. Sort the storage space at each level from top to bottom according to the hierarchical classification criteria. Then, connect the storage spaces at each level vertically according to the sorting results. Based on the connection results of each level of storage space, the categorized storage spaces within the level of storage space are connected respectively. Based on the user interaction data corresponding to different interactive action types within each level of storage space, interaction correlation analysis is performed to obtain the interaction correlation data between different interactive action types within each level of storage space. Based on the corresponding interaction correlation data, the categorized storage spaces of each interactive action type are connected horizontally in a ring and distributed in the corresponding positions within the level of storage space. Cross-connect the corresponding interactive action types of the vertically connected storage spaces at each level, and set up the pyramid interactive storage database based on the results of the vertical, horizontal circular, and cross-connections of the storage spaces at each level and category.

[0012] Furthermore, the process of acquiring user interaction feature data includes: The interaction optimization section is set up to personalize the user interaction data in different user accounts and set up personalized analysis nodes according to different user accounts. By using personalized analysis nodes, user interaction behavior log data and user interaction reference data corresponding to user interaction data within the corresponding user account are classified and processed to obtain heterogeneous classification data, which includes operation behavior data and query retrieval logs. Multidimensional association feature extraction is performed on the obtained heterogeneous classification data. The multidimensional association feature extraction process includes user-dimensional association feature extraction and operation sequence association feature extraction. Based on the multidimensional association feature extraction results, user interaction feature data corresponding to the corresponding user account is obtained.

[0013] Furthermore, the process of the adaptive pyramid interactive storage database includes: The pyramid interactive storage database is sent to each personalized analysis node, and the personalized analysis node maps the user interaction data corresponding to the user account to the corresponding hierarchical storage space and category storage space location in the pyramid interactive storage data. Based on the user interaction feature data corresponding to user interaction data at different locations, the auxiliary sorting codes and connection results between the various categories of interactive simulation data in the pyramid interactive storage database are dynamically adjusted. The dynamic adjustment results of each interactive simulation data in the pyramid interactive storage database within the corresponding user account are obtained, and an adaptive pyramid interactive storage database corresponding to the corresponding user account is generated.

[0014] Furthermore, the process of optimizing the interaction strategy based on the obtained user interaction data according to the corresponding adaptive pyramid interaction storage database includes: Obtain user interaction data corresponding to user accounts, input the obtained user interaction data into the corresponding adaptive pyramid interaction storage database for traversal and retrieval, set corresponding interaction strategy nodes and interaction strategy sub-nodes according to each level of storage space and the corresponding category storage space within the level of storage space, connect each interaction strategy node to generate an interaction connection link. Based on the interactive connection link, the corresponding interactive strategy sub-nodes in each interactive strategy node are sequentially sorted to obtain the strategy sorting data of the corresponding interactive strategy sub-nodes in each interactive strategy node in the interactive connection link. Based on user interaction data, sequentially select the interaction simulation data within the corresponding interaction strategy sub-nodes of the corresponding interaction strategy nodes within the interaction connection link. Based on the interaction simulation data obtained by the corresponding interaction strategy sub-nodes within the current interaction strategy node, perform interaction strategy optimization processing on the strategy sorting data of the corresponding interaction strategy sub-nodes within the subsequent interaction strategy nodes within the interaction connection link using an adaptive pyramid storage database, and obtain the corresponding interaction strategy optimization data within the interaction connection link. Based on the interaction strategy optimization data obtained within the interactive connection link, the interaction simulation data corresponding to the user interaction data is obtained.

[0015] The present invention has the following beneficial effects: 1. In this invention, the obtained interactive simulation data and user interaction data are processed by hierarchical classification and interactive action classification. Based on the classification results, hierarchical storage space and category storage space are obtained respectively. The interactive simulation data in the category storage space is sorted and stored according to the interaction popularity corresponding to the user interaction data. Based on the sorting storage results, comprehensive connection processing is performed to set up a corresponding pyramid interactive storage database, which can improve the efficiency of obtaining the corresponding interactive simulation data in the interactive simulation process to a certain extent.

[0016] 2. In this invention, the interaction patterns of different interaction simulation data in the pyramid interaction storage database are dynamically adjusted based on the user interaction data corresponding to different user accounts to obtain an adaptive pyramid interaction storage database suitable for different user accounts. The interaction strategy is optimized based on the adaptive pyramid interaction storage database corresponding to different user accounts, which can improve the flexibility and optimization efficiency of the interaction simulation process. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of a simulation data interaction strategy optimization method proposed in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 like Figure 1 As shown, the simulation data interaction strategy optimization method proposed in this invention includes the following steps: Step S1: Set up the data acquisition terminal and acquire the corresponding user interaction data and interaction simulation data through the data acquisition terminal; Step S2: Set up the data processing terminal, perform hierarchical classification processing on the obtained interactive simulation data according to the user interaction data, store the interactive simulation data in layers according to the hierarchical classification processing results, and set the hierarchical storage space; Step S3: Classify the hierarchically stored interactive simulation data according to the interactive actions, set up separate category storage spaces, and sort and store the interactive simulation data according to the interaction popularity within the category storage space; Step S4: Perform comprehensive connection processing based on the sorting results of each interactive simulation data in each level of storage space and category storage space, and set up the pyramid interactive storage database; Step S5: Set up the interaction optimization end, extract features from the obtained user interaction data, obtain user interaction feature data, and dynamically adjust the interaction simulation data in the pyramid interaction storage database according to the user interaction feature data to generate an adaptive pyramid interaction storage database. Step S6: Optimize the interaction strategy of the obtained user interaction data according to the corresponding adaptive pyramid interaction storage database, and obtain the interaction simulation data corresponding to the user interaction data based on the interaction strategy optimization result. In the specific implementation process, the step S1 of setting up a data acquisition terminal and acquiring the corresponding user interaction data and interaction simulation data through the data acquisition terminal includes: S11: Set up a data acquisition terminal, which includes a user acquisition terminal and a simulation acquisition terminal; S12: Obtain user interaction data corresponding to different user accounts through the user collection terminal. The user interaction data includes user interaction request data and user interaction behavior log data, wherein: User interaction requirement data includes user operation command data, user input parameter data, and user query and retrieval data, etc. User interaction behavior log data includes operation timestamps, operation sequences, operation paths, etc., corresponding to historical user interaction request data; S13: Acquire interactive simulation data corresponding to different data sources through the simulation acquisition terminal. The interactive simulation data includes basic simulation model data, simulation interaction parameter data, and historical simulation interaction data, wherein: The basic data of the simulation model includes 3D model geometric data, topology results, component relationships, physical properties, etc. The simulation interaction parameter data consists of interaction parameters in different simulation interaction processes, including resolution data, simulation interaction process parameter data, and simulation result output data.

[0020] In the specific implementation process, the step S2 of setting up the data processing terminal, classifying the obtained interactive simulation data according to user interaction data, and storing the interactive simulation data in layers according to the classification results, includes: S21: Set up a data processing terminal, which includes a data preprocessing terminal and a hierarchical processing terminal; S22: The obtained interactive simulation data and user interaction data are cleaned and deredundanted through the data preprocessing terminal, and the processed interactive simulation data and user interaction data are input into the hierarchical processing terminal. S23: The obtained interactive simulation data and user interaction data are classified and processed hierarchically through the hierarchical processing terminal, and the hierarchical storage space is set according to the hierarchical classification and processing results; S231: Extract resolution features from the obtained interactive simulation data and user interaction data to obtain corresponding types of interactive resolution feature data. The process includes: S2311: Obtain the simulation interaction points, simulation interaction timestamps, and simulation interaction operations involved in the interactive simulation data and user interaction data; S2312: Perform density statistics on the simulation interaction points involved to obtain the corresponding spatial resolution data KF; perform data update frequency statistics on the simulation interaction timestamps obtained from the corresponding simulation interaction points to obtain temporal resolution data SF; perform view scale evaluation on the simulation interaction operations obtained from the corresponding simulation interaction points within the corresponding simulation interaction timestamps to obtain view resolution data TF. S2313: Construct corresponding resolution feature vectors based on the spatial resolution data, temporal resolution data, and view resolution data obtained from interactive simulation data and user interaction data. ; S2314: Construct the weight vector corresponding to the resolution feature vector , The obtained resolution feature vectors are then weighted and fused based on the constructed weight vectors. Obtain the corresponding resolution feature data bf; S232: Preset hierarchical classification standards, perform hierarchical classification processing on interactive simulation data and user interaction data corresponding to different interactive resolution feature data according to the hierarchical classification standards, determine the hierarchical classification interval to which each interactive simulation data and user interaction data belongs, and obtain the corresponding hierarchical classification processing results; S233: Set up hierarchical storage space based on the hierarchical classification processing results, and store the obtained data through the hierarchical storage space.

[0021] In the specific implementation process, the step S3 of classifying the hierarchically stored interactive simulation data according to the interactive actions, setting up separate category storage spaces, and sorting and storing the interactive simulation data according to the interaction popularity within the category storage spaces includes: S31: The process of classifying and processing the hierarchically stored interactive simulation data according to the interactive actions, and setting up separate storage spaces for each category, includes: S311: Extract interaction features from the interaction simulation data and user interaction data stored in each level of storage space, and obtain the corresponding interaction action types of the interaction simulation data and user interaction data respectively; S312: Set spatial point index information according to the corresponding simulation interaction points in each level of storage space, classify the interactive simulation data and user interaction data involved in the corresponding spatial point index information according to the corresponding interactive action type, and obtain the interactive dataset of interactive simulation data and user interaction data corresponding to different interactive action types. S313: Based on the amount of data in each interactive dataset obtained from each spatial point index information, the hierarchical storage space is adaptively divided into regions, and the classification storage space is set according to the adaptive region division result. The adaptive region division process is based on the storage space size at the corresponding spatial point index information. S32: Perform storage sorting management on the interactive simulation data and user interaction data in the classified storage space, evaluate the interaction heat of the interactive simulation data in each classified storage space according to the interaction frequency of the user interaction data, and obtain the interaction heat evaluation data corresponding to the interactive simulation data. S321: Obtain the interaction dataset corresponding to each category storage space, process the interaction simulation data and user interaction data in the interaction dataset accordingly, that is, associate the interaction simulation data to which the user interaction data belongs, and obtain the corresponding processing result of the user interaction data corresponding to each interaction simulation data. S322: Evaluate the interaction popularity of the corresponding processing results of user interaction data at each interactive simulation data location, and obtain the interaction popularity evaluation factors for the interactive simulation data respectively. The interaction popularity evaluation factors include access frequency factor f, repeated access factor r, and concurrent access factor c, wherein: The access frequency factor f is the total number of times the corresponding processing results of each user interaction data at the corresponding interaction simulation data location are interacted with. The repeated access factor r is the number of times the same user or multiple users repeatedly access the same corresponding simulation interaction data; The concurrent access factor c is the number of concurrent interactive accesses corresponding to multiple users simultaneously accessing different simulation interactive data. S323: Calculate the overall popularity based on each interaction popularity evaluation factor to obtain the interaction popularity evaluation data H, where... ,in , and These are the weight data for the corresponding interaction popularity evaluation factors, and their sum is 1; S33: The process of sorting and storing interactive simulation data according to the interaction popularity within the categorized storage space includes: S331: Obtain the interactive simulation data in each category storage space, sort the obtained interactive simulation data according to the interactive resolution feature data, and segment the sorting results corresponding to each interactive simulation data. S332: The segmented processing results corresponding to each interactive simulation data are visualized based on the interactive heat evaluation data. The visualization process sets RGB values ​​according to the interactive heat evaluation data, and uses the color corresponding to the RGB values ​​from dark to light to represent the heat from high to low. The corresponding segmented processing is visualized and rendered to obtain the visualization processing results of each interactive simulation data. S333: The visualization processing results of each interactive simulation data are combined with the corresponding interactive resolution feature data and sorted normally from high to low. The auxiliary sorting code is processed according to the normal distributed sorting result. The auxiliary sorting code is the sorting position identifier after the corresponding interactive simulation data is the corresponding visualization processing result. The obtained auxiliary sorting code is mapped to the interactive simulation data for sorting and storage, and the corresponding classification storage space is obtained. It should be further explained that, in the specific implementation process, the auxiliary sorting code is used to describe the distribution of the classification storage space of each interactive simulation data. The interactive simulation data is dynamically sorted and managed according to the auxiliary sorting code, which makes it easier for the interactive simulation data to change sorting according to the changes in the corresponding interactive popularity evaluation data, thereby improving the efficiency of the intelligent sorting dynamic change process of the classification storage space.

[0022] In the specific implementation process, the step S4 of setting up the pyramid interactive storage database by performing comprehensive connection processing based on the sorting results of each interactive simulation data in each level of storage space and category storage space includes: S41: Obtain the storage space at each level and the corresponding category storage space therein. Sort the storage space at each level from top to bottom according to the hierarchical classification criteria. Connect the storage space at each level vertically according to the sorting results, so that the storage space at each level is connected in a progressive manner. S42: The process of connecting the categorized storage spaces within each level of storage space includes: S421: Based on the connection results of each level of storage space, the interactive simulation data of the classified storage space in each level of storage space are sequentially subjected to interactive association extraction. The interactive association extraction process is to extract the interactive simulation data corresponding to different interactive action types at each spatial point index information in the level of storage space according to their corresponding user interaction data, and obtain the corresponding interactive association datasets respectively. S422: Perform interaction correlation analysis on the obtained interaction correlation datasets respectively. Label the interaction simulation data corresponding to different interaction action types and different interaction resolution feature data at different spatial point retrieval information locations corresponding to user interaction data as x and y respectively. Label the interaction correlation data as r, where: Where i is the data label for x and y, and These are the mean data of the interactive simulation data and another interactive simulation data, respectively. S423: Preset an interaction association threshold. Compare the interaction association data corresponding to each interaction association dataset with the corresponding interaction association threshold. If the interaction association data is greater than or equal to the interaction association threshold, mark the corresponding interaction association dataset as having an association. Otherwise, there is no association. S424: Obtain the associated interactive datasets and their corresponding interactive data, and sort the associated interactive datasets at each spatial point index information in descending order of interactive data. S425: Based on the sorting order of the corresponding interactive data in each interactive data set, the classification storage spaces corresponding to different interactive action types are horizontally connected in a ring. The horizontal ring connection process is as follows: the classification storage space with the largest interactive data is selected first. Taking this classification storage space as the center, the other two classification storage spaces with the largest interactive data corresponding to this classification storage space are obtained respectively. The other two classification storage spaces are distributed on both sides of this classification storage space. Based on the other classification storage spaces, the classification storage spaces with the largest interactive data with them are obtained and connected. If the connection has been completed, it is removed until all classification storage spaces are connected and distributed. The distribution of all classification storage spaces is horizontally connected in a ring, so that there is a large amount of interactive data between each adjacent classification storage space. S43: Cross-connect the corresponding interactive action type category storage spaces in each vertically connected storage level, and set up the pyramid interactive storage database based on the vertical connection, horizontal ring connection and cross connection results of each level storage space and category storage space; S431: Obtain the connection results of different category storage spaces at each spatial point retrieval information in each level of storage space, and cross-correspond the corresponding category storage spaces according to the interaction association data corresponding to different interaction action types between adjacent spatial point retrieval information, and obtain the cross-correspondence relationship between different category storage spaces between adjacent spatial point retrieval information. S432: Cross-connect the category storage spaces corresponding to the same spatial point retrieval information in the vertically connected storage spaces for each interactive action type, and obtain the cross-connection results between the category storage spaces corresponding to the same interactive action type. S433: Set up a pyramid interactive storage database based on the vertical connection, horizontal ring connection and cross connection results of each level of storage space and category storage space. The pyramid interactive storage database is used to sequentially connect and store the corresponding interactive simulation data according to the interaction resolution feature data and the relationship between different interactive action types.

[0023] In the specific implementation process, the step S5, which involves setting up an interaction optimization terminal, extracting features from the obtained user interaction data to obtain user interaction feature data, and dynamically adjusting the interaction simulation data in the pyramid interaction storage database based on the user interaction feature data to generate an adaptive pyramid interaction storage database, includes: S51: Set up the interaction optimization end, which is used to personalize the user interaction data in different user accounts and set up personalized analysis nodes according to different user accounts. S52: The user interaction behavior log data and user interaction reference data corresponding to the user interaction data within the relevant user account are classified and processed through personalized analysis nodes to obtain heterogeneous classification data. The heterogeneous classification data includes operation behavior data and query retrieval logs, wherein: The operation behavior data refers to historical operation behavior data of different types of interactive actions involved in the interaction process of the corresponding user account. The query and retrieval log is the historical query and retrieval information of different spatial points involved in the user's interaction within the corresponding user account; S53: Perform multidimensional association feature extraction on the obtained heterogeneous classification data. The multidimensional association feature extraction process includes user dimension association feature extraction and operation sequence association feature extraction. Based on the multidimensional association feature extraction results, obtain the user interaction feature data corresponding to the corresponding user account. S531: Obtain the simulation interaction points, simulation interaction timestamps, and simulation interaction action types corresponding to each heterogeneous category of data within the corresponding user account; S532: Perform data frequency statistics on the types of interactive simulation actions obtained from the simulation interaction timestamps of the corresponding simulation interaction points, and obtain the user dimension association features corresponding to the types of interactive simulation actions at different simulation interaction points of the corresponding user account. S533: Perform data frequency statistics on the sequence of different interactive simulation action types between the simulation interaction timestamps obtained before the corresponding simulation interaction points to obtain the corresponding operation sequence association features. S534: Based on the user dimension association features and the operation sequence association features, preset corresponding association thresholds respectively. If the corresponding data frequency statistics result is greater than or equal to the corresponding association threshold, then perform feature extraction on the interaction simulation action type corresponding to the simulation interaction timestamp obtained at the corresponding simulation interaction point or the order of different interaction simulation action types corresponding to the simulation interaction timestamps obtained before the simulation interaction point. Based on the feature extraction results corresponding to the user dimension association features and the operation sequence association features, obtain the user interaction feature data corresponding to the corresponding user account. The user interaction feature data is used to characterize the personalized interaction habits of the corresponding user account for different interaction simulation action types during the interaction process. S54: Send the pyramid interactive storage database to each personalized analysis node, and the personalized analysis node will map the user interaction data corresponding to the user account to the corresponding hierarchical storage space and category storage space location in the pyramid interactive storage data. S55: The process of dynamically adjusting the interaction simulation data in the pyramid interaction storage database based on user interaction feature data to generate an adaptive pyramid interaction storage database includes: S551: Based on the user interaction feature data corresponding to user interaction data at different locations, dynamically adjust the auxiliary sorting code and the connection results between the various categories of interactive simulation data in the pyramid interactive storage database. S552: The dynamic adjustment process is to adjust the connection and sorting process of each category storage space in the pyramid interactive storage database according to the user interaction feature data, obtain the dynamic adjustment result corresponding to the user account, and generate the adaptive pyramid interactive storage database corresponding to the user account.

[0024] In the specific implementation process, the step S6 of optimizing the interaction strategy based on the obtained user interaction data according to the corresponding adaptive pyramid interaction storage database, and obtaining the interaction simulation data corresponding to the user interaction data based on the interaction strategy optimization result includes: S61: The process of obtaining user interaction data corresponding to a user account and generating an interaction connection link based on the user interaction data includes: S611: Input the obtained user interaction data into the corresponding adaptive pyramid interaction storage database for traversal and retrieval, that is, obtain the location information of the corresponding user interaction requirement data in the adaptive pyramid interaction storage database. S612: Based on the location information of the corresponding hierarchical storage space and the corresponding category storage space within the adaptive pyramid interactive storage database to which the corresponding user interaction request data belongs, set the corresponding interaction strategy node and interaction strategy sub-node respectively. S613: Connect each interaction strategy node and the interaction strategy sub-nodes within the interaction strategy node according to the order of user interaction request data to generate an interaction connection link. S62: Based on the interactive connection link, sequentially sort the corresponding interactive policy sub-nodes within each interactive policy node to obtain the policy sorting data of the corresponding interactive policy sub-nodes within each interactive policy node in the interactive connection link. The process includes: S621: Based on the user interaction requirement data, the interaction connection link is segmented to obtain the interaction link segment, the corresponding interaction strategy node within the interaction link segment, and the interaction strategy sub-node within the interaction strategy node. S622: Based on the location information of the adaptive pyramid storage database to which the interaction strategy sub-node belongs within the interaction link segment, obtain the category storage space connected to the interaction strategy sub-node; sort the interaction association data with the interaction strategy sub-node in the category storage space from high to low; and obtain the interaction sorting data of other interaction strategy sub-nodes within the corresponding interaction link segment. S623: Integrate the interaction sorting data of multiple interaction strategy sub-nodes in each interaction link segment to obtain the strategy sorting data of the corresponding interaction strategy sub-nodes in each interaction strategy node in the interaction connection link. S63: The process of obtaining the corresponding interaction strategy optimization data within the interactive connection link includes: S631: Based on the user interaction data, sequentially obtain the interaction simulation data in the corresponding interaction strategy sub-node of the interaction strategy node corresponding to the interaction link segment where the interaction simulation is completed within the interaction connection link. S632: Based on the interaction simulation data obtained by the corresponding interaction strategy sub-node in the current interaction strategy node, the strategy sorting data of the corresponding interaction strategy sub-node in the interaction strategy node in the subsequent interaction link segment in the interaction connection link is optimized by the adaptive pyramid storage database, and the interaction sorting data of each interaction strategy sub-node in other subsequent interaction link segments is adjusted to obtain the corresponding interaction strategy optimization data in the interaction connection link. S64: Obtain the interaction simulation data corresponding to the user interaction data based on the interaction strategy optimization data obtained within the interaction connection link.

[0025] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing simulation data interaction strategies, characterized in that, Includes the following steps: Step S1: Set up the data acquisition terminal and acquire the corresponding user interaction data and interaction simulation data through the data acquisition terminal; Step S2: Set up the data processing terminal, perform hierarchical classification processing on the obtained interactive simulation data according to the user interaction data, store the interactive simulation data in layers according to the hierarchical classification processing results, and set the hierarchical storage space; Step S3: Classify the hierarchically stored interactive simulation data according to the interactive actions, set up separate category storage spaces, and sort and store the interactive simulation data according to the interaction popularity within the category storage space. Step S4: Perform comprehensive connection processing based on the sorting results of each interactive simulation data in each level of storage space and category storage space, and set up the pyramid interactive storage database; Step S5: Set up the interaction optimization end, extract features from the obtained user interaction data, obtain user interaction feature data, and dynamically adjust the interaction simulation data in the pyramid interaction storage database according to the user interaction feature data to generate an adaptive pyramid interaction storage database. Step S6: Optimize the interaction strategy of the obtained user interaction data according to the corresponding adaptive pyramid interaction storage database, and obtain the interaction simulation data corresponding to the user interaction data based on the interaction strategy optimization results.

2. The simulation data interaction strategy optimization method according to claim 1, characterized in that, The process of acquiring user interaction data and interaction simulation data includes: A data acquisition terminal is set up, which includes a user acquisition terminal and a simulation acquisition terminal. User interaction data corresponding to different user accounts is obtained through user collection terminals. The user interaction data includes user interaction request data and user interaction behavior log data. Interactive simulation data from different data sources is acquired through a simulation acquisition terminal. The interactive simulation data includes basic simulation model data, simulation interaction parameter data, and historical simulation interaction data.

3. The simulation data interaction strategy optimization method according to claim 2, characterized in that, The process of setting up hierarchical storage space includes: A data processing terminal is provided, which includes a data preprocessing terminal and a hierarchical processing terminal. The data preprocessing terminal performs data cleaning and redundancy removal on the obtained interactive simulation data and user interaction data respectively, and then inputs the processed interactive simulation data and user interaction data into the hierarchical processing terminal. The layered processing terminal extracts resolution features from the obtained interactive simulation data and user interaction data, obtains corresponding types of interactive resolution feature data, presets hierarchical classification standards, and performs hierarchical classification processing on the interactive simulation data and user interaction data corresponding to different interactive resolution feature data according to the hierarchical classification standards. Based on the hierarchical classification processing results, a hierarchical storage space is set up, and the obtained data is stored through the hierarchical storage space.

4. The simulation data interaction strategy optimization method according to claim 3, characterized in that, The process of setting up categorized storage spaces includes: Interaction features are extracted from the interaction simulation data and user interaction data stored in each level of storage space. The interaction action types corresponding to the interaction simulation data and user interaction data are obtained respectively. The level storage space is classified according to the interaction action type. The corresponding level storage space is adaptively divided into regions according to the classification results. The classified storage space is set according to the adaptive region division results. The interactive simulation data and user interaction data in the categorized storage space are stored and sorted for management. The interactive simulation data in each categorized storage space are evaluated for interaction popularity based on the interaction frequency of user interaction data, and the interaction popularity evaluation data corresponding to the interactive simulation data is obtained. The obtained interactive simulation data is sorted according to the interactive resolution feature data. The sorted interactive simulation data is visualized according to the interactive popularity evaluation data. The visualization results are then used for auxiliary sorting and encoding. The auxiliary sorting codes are mapped to the interactive simulation data for sorting and storage, and the corresponding classification storage space is obtained.

5. The simulation data interaction strategy optimization method according to claim 4, characterized in that, The process of setting up the pyramid interactive storage database includes: Obtain the storage space at each level and the corresponding category storage space within it. Sort the storage space at each level from top to bottom according to the hierarchical classification criteria. Then, connect the storage spaces at each level vertically according to the sorting results. Based on the connection results of each level of storage space, the categorized storage spaces within the level of storage space are connected respectively. Based on the user interaction data corresponding to different interactive action types within each level of storage space, interaction correlation analysis is performed to obtain the interaction correlation data between different interactive action types within each level of storage space. Based on the corresponding interaction correlation data, the categorized storage spaces of each interactive action type are connected horizontally in a ring and distributed in the corresponding positions within the level of storage space. Cross-connect the corresponding interactive action types of the vertically connected storage spaces at each level, and set up the pyramid interactive storage database based on the results of the vertical, horizontal circular, and cross-connections of the storage spaces at each level and category.

6. The simulation data interaction strategy optimization method according to claim 5, characterized in that, The process of acquiring user interaction feature data includes: The interaction optimization section is set up to personalize the user interaction data in different user accounts and set up personalized analysis nodes according to different user accounts. By using personalized analysis nodes, user interaction behavior log data and user interaction reference data corresponding to user interaction data within the corresponding user account are classified and processed to obtain heterogeneous classification data, which includes operation behavior data and query retrieval logs. Multidimensional association feature extraction is performed on the obtained heterogeneous classification data. The multidimensional association feature extraction process includes user-dimensional association feature extraction and operation sequence association feature extraction. Based on the multidimensional association feature extraction results, user interaction feature data corresponding to the corresponding user account is obtained.

7. The simulation data interaction strategy optimization method according to claim 6, characterized in that, The process of the adaptive pyramid interactive storage database includes: The pyramid interactive storage database is sent to each personalized analysis node, and the personalized analysis node maps the user interaction data corresponding to the user account to the corresponding hierarchical storage space and category storage space location in the pyramid interactive storage data. Based on the user interaction feature data corresponding to user interaction data at different locations, the auxiliary sorting codes and connection results between the various categories of interactive simulation data in the pyramid interactive storage database are dynamically adjusted. The dynamic adjustment results of each interactive simulation data in the pyramid interactive storage database within the corresponding user account are obtained, and an adaptive pyramid interactive storage database corresponding to the corresponding user account is generated.

8. The simulation data interaction strategy optimization method according to claim 7, characterized in that, The process of optimizing the interaction strategy based on the obtained user interaction data according to the corresponding adaptive pyramid interaction storage database includes: Obtain user interaction data corresponding to user accounts, input the obtained user interaction data into the corresponding adaptive pyramid interaction storage database for traversal and retrieval, set corresponding interaction strategy nodes and interaction strategy sub-nodes according to each level of storage space and the corresponding category storage space within the level of storage space, connect each interaction strategy node to generate an interaction connection link. Based on the interactive connection link, the corresponding interactive strategy sub-nodes in each interactive strategy node are sequentially sorted to obtain the strategy sorting data of the corresponding interactive strategy sub-nodes in each interactive strategy node in the interactive connection link. Based on user interaction data, sequentially select the interaction simulation data within the corresponding interaction strategy sub-nodes of the corresponding interaction strategy nodes within the interaction connection link. Based on the interaction simulation data obtained by the corresponding interaction strategy sub-nodes within the current interaction strategy node, perform interaction strategy optimization processing on the strategy sorting data of the corresponding interaction strategy sub-nodes within the subsequent interaction strategy nodes within the interaction connection link using an adaptive pyramid storage database, and obtain the corresponding interaction strategy optimization data within the interaction connection link. Based on the interaction strategy optimization data obtained within the interactive connection link, the interaction simulation data corresponding to the user interaction data is obtained.

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