A Multi-Source Heterogeneous Spatial Data Retrieval Method and System Improved Based on R-Tree

Abstract

This application provides a method and system for retrieving multi-source heterogeneous spatial data improved based on the R-tree. Among them, the data packets carrying geographical tags in the network data stream are processed. These data packets contain at least two different types of spatial information, and the priority of the R-tree splitting dimension is adjusted by analyzing their spatio-temporal aggregation degree. The genetic algorithm is used to generate the initial splitting path, and co-evolution is performed at the network edge nodes to optimize the cross-source retrieval efficiency. The network topology adaptation parameters are updated in each iteration, and an adaptability evaluation function is constructed based on the dynamic priority parameters and transmission delays. The preloading of heterogeneous data packets is triggered based on the path offset, and the gene expression template is used to verify the compatibility. Finally, the optimization parameters are dynamically adjusted according to the cross-source retrieval conflict index and the preloading data verification results to improve the retrieval efficiency. The technical solution provided by this application can improve the efficiency and accuracy of retrieving multi-source heterogeneous spatial data.

Description

Technical Field

[0001] This application relates to the technical field of multi-source heterogeneous spatial data retrieval, and particularly to a method and system for multi-source heterogeneous spatial data retrieval improved based on the R-tree. Background Art

[0002] With the popularization of the Internet of Things and intelligent devices, the generation volume of multi-source heterogeneous spatial data has shown an explosive growth. These data come from different sensor networks, mobile devices, and various geographic information systems, and each data source may adopt different coordinate systems, sampling frequencies, or data formats. In application scenarios such as smart cities, environmental monitoring, and disaster response, how to efficiently and accurately retrieve and integrate spatial information from different sources has become a key technical requirement. Especially in scenarios with high real-time requirements, such as dynamic path planning in emergency rescue operations or instant environmental change monitoring, the ability to quickly obtain cross-source heterogeneous spatial data is particularly important.

[0003] Currently, in the scenario of processing multi-source heterogeneous spatial data, a targeted existing solution is to adopt a spatial data management and query system based on a cloud computing platform. This system uploads spatial data from various sources to the cloud, and uses the powerful computing capabilities and elastic resource allocation mechanisms of cloud service providers to store and process these data. Users can access these data through application programming interfaces or specific application software, and perform complex query operations. This solution supports input and output of multiple data formats, can provide a certain degree of data integration ability, and has good scalability for large-scale data sets.

[0004] However, this method also has some significant defects. First, since all data needs to be transmitted to the cloud for processing, this leads to a high dependence on network bandwidth. Especially in application scenarios with high real-time requirements, network latency may become a bottleneck. Second, although the cloud computing platform provides powerful computing resources, it lacks efficient retrieval optimization for heterogeneous data. Especially when facing data with different coordinate systems and sampling frequencies, it lacks effective spatio-temporal aggregation analysis and is difficult to achieve the optimal data retrieval path planning. In addition, the cloud-based centralized processing mode may cause privacy and security problems. Especially when it comes to sensitive geographical information, the transfer of data ownership and control to third-party service providers will bring additional risks. These problems limit its applicability and reliability in certain specific application scenarios. Summary of the Invention

[0005] This application provides a method and system for multi-source heterogeneous spatial data retrieval improved based on the R-tree, to solve the problems of low retrieval efficiency and poor accuracy of multi-source heterogeneous spatial data in the prior art.

[0006] In a first aspect, the present application provides a method for retrieving multi-source heterogeneous spatial data improved based on an R-tree, including:

[0007] Analyze multi-source heterogeneous data packets carrying geographical tags in the network data stream. Each data packet contains spatial information in at least two different coordinate systems, sampling frequencies, or data formats, and generate dynamic priority parameters for constraining the splitting dimension of the R-tree according to the spatio-temporal aggregation degree of each data packet on the transmission path;

[0008] Extract the reference encoding of the splitting path corresponding to the cross-source query pattern in the historical record of the network data stream. The reference encoding is generated by matching the spatio-temporal coverage complementarity of different data packet transmission paths, and use the reference encoding as the gene expression template of the splitting path in the initial population of the genetic algorithm;

[0009] Perform co-evolution of the splitting path at the network edge node, drive the splitting path to iterate in the direction of minimizing cross-source retrieval conflicts, generate a path offset each time an iteration is performed, and update the network topology adaptation parameters of the gene expression template, and construct an adaptability evaluation function based on the correlation between the dynamic priority parameter and the data packet transmission delay;

[0010] Trigger preloading of heterogeneous data packets according to the direction and amplitude of the path offset. The preloaded data is a set of data packets in adjacent nodes of the network topology that match the spatio-temporal coverage area of the current splitting path, and verify the compatibility of the data packets with the network transmission protocol through the format conversion rules in the gene expression template;

[0011] Generate feedback parameters through the cross-source retrieval conflict index output by the adaptability evaluation function and the preloading data verification result, and dynamically correct the mapping relationship between the dynamic priority parameter and the gene expression template.

[0012] Optionally, the performing co-evolution of the splitting path at the network edge node, driving the splitting path to iterate in the direction of minimizing cross-source retrieval conflicts, generating a path offset each time an iteration is performed, and updating the network topology adaptation parameters of the gene expression template includes:

[0013] Encode the splitting path into a multi-dimensional vector, where each dimension corresponds to an adjustable interval of the network topology adaptation parameter, and calculate the topology overlap degree based on the spatio-temporal region covered by the current splitting path;

[0014] Construct a conflict neighborhood table according to the topology overlap degree, record the heterogeneous data packet transmission paths with spatio-temporal coverage overlap with the current splitting path and the differences in network topology adaptation parameters, and generate candidate offsets;

[0015] A dual - probability selection mechanism is adopted to select a target offset from the candidate offsets. The first probability distribution is based on the success rate of reducing conflicts of the candidate offsets in historical iterations, and the second probability distribution is based on the compatibility constraint between the target offset and the format conversion rules in the gene expression template;

[0016] Apply the target offset to the current splitting path, dynamically adjust the parameter values of the corresponding dimensions in the multi - dimensional vector, and at the same time update the topological overlap degree threshold of the conflict neighborhood table to trigger the elimination of low - priority paths;

[0017] Recalculate the spatio - temporal coverage matching degree between the updated splitting path and the pre - loaded data packet set, and feedback the change amount of the spatio - temporal coverage matching degree to the weight calculation of the dynamic priority parameter to achieve an iterative closed - loop.

[0018] Optionally, the adopting a dual - probability selection mechanism to select a target offset from the candidate offsets includes:

[0019] Based on the success rate of reducing conflicts of the candidate offsets in historical iterations, count the ratio of the number of successful times to the number of times of path priority improvement of each candidate offset within a preset time window, and normalize it to the initial weight of the first probability distribution;

[0020] Extract the protocol fields bound by the format conversion rules in the gene expression template, calculate the overlapping ratio between the corresponding parameter dimensions of the candidate offset and the preset field value range, and map it to the constraint weight of the second probability distribution;

[0021] Non - linearly superimpose the initial weight of the first probability distribution and the constraint weight of the second probability distribution. The superimposition coefficient is dynamically adjusted according to the ratio of low - priority paths in the conflict neighborhood table, so that the compatibility constraint occupies a higher weight in the conflict - intensive area;

[0022] Perform roulette wheel screening based on the comprehensive selection probability, verify the conflict - free property of the candidate offset and the core dimension parameters in the gene expression template. If there is a conflict, backtrack to the sub - optimal candidate offset until the constraint is met and then select the target offset.

[0023] Optionally, the non - linearly superimposing the initial weight of the first probability distribution and the constraint weight of the second probability distribution, and the superimposition coefficient is dynamically adjusted according to the ratio of low - priority paths in the conflict neighborhood table includes:

[0024] Based on the comparison between the proportion of low - priority paths in the conflict neighborhood table and the historical maximum path capacity, calculate the conflict density index through a piece - wise function;

[0025] Generate a dynamic superposition coefficient according to the conflict density index, adopt an adjustment mechanism triggered by segmented thresholds, and combine the historical moving average as a dynamic decay factor to suppress the mutation of the dynamic superposition coefficient, where the growth rate of the dynamic superposition coefficient is adjusted with the change of the conflict density index in the threshold interval;

[0026] Associate the initial weight of the first probability distribution with the dynamic superposition coefficient, and superimpose the association result with the constraint weight of the second probability distribution, and perform logarithmic weighted summation and truncation processing on the superposition coefficient;

[0027] Adjust the rate of the dynamic decay factor according to the difference between the superposition coefficient and the historical moving average. If it exceeds the tolerance interval, increase the rate, and apply the updated rate to the calculation of the superposition coefficient in the next iteration.

[0028] Optionally, constructing an adaptability evaluation function based on the association relationship between the dynamic priority parameter and the data packet transmission delay includes:

[0029] Based on the spatio-temporal distribution of the dynamic priority parameter, calculate the spatio-temporal coupling coefficient between the data packet transmission delay and the geographical distribution density;

[0030] Generate a dynamic weight factor according to the spatio-temporal coupling coefficient, associate the spatio-temporal aggregation degree of high-delay regions and low-density regions, and constrain the dynamic weight factor based on the network topology adaptation parameters of the gene expression template;

[0031] Fuse the dynamic priority parameter, the dynamic weight factor and the spatio-temporal coupling coefficient to construct an adaptability evaluation function, and introduce a penalty term related to the difference in network topology adaptation parameters, where the dynamic priority parameter is used as the main decision variable and the dynamic weight factor is used as the conflict sensitivity adjustment coefficient.

[0032] Optionally, generating a dynamic weight factor according to the spatio-temporal coupling coefficient and associating the spatio-temporal aggregation degree of high-delay regions and low-density regions includes:

[0033] Based on the spatio-temporal coupling coefficient and the inverse correlation between the transmission path fluctuation and the geographical distribution gradient change, calculate the spatio-temporal aggregation degree difference index between high-delay regions and low-density regions;

[0034] Generate an initial dynamic weight factor according to the spatio-temporal aggregation degree difference index, associate the path conflict sensitivity of the high-delay region with the spatio-temporal coverage sparsity of the low-density region, and constrain the growth rate of the initial dynamic weight factor through the network topology adaptation parameters of the gene expression template;

[0035] Apply a dynamic decay factor to the initial dynamic weight factor based on the historical correction records of the network topology adaptation parameters to suppress the asymmetric correlation overload in the high-latency region and the low-density region, and generate a dynamic weight factor.

[0036] Optionally, generating a feedback parameter from the cross-source retrieval conflict index output by the adaptability evaluation function and the preloaded data verification result, including:

[0037] Calculate the conflict and compatibility coupling coefficient based on the cross-source retrieval conflict index output by the adaptability evaluation function and the protocol compatibility ratio in the preloaded data verification result;

[0038] Generate an initial feedback parameter according to the conflict and compatibility coupling coefficient, associate the protocol compatibility missing in the high-conflict region with the path offset, and apply a dynamic decay constraint to the initial feedback parameter based on the historical correction records of the gene expression template;

[0039] Based on the initial feedback parameter, fuse the conflict and compatibility coupling coefficient, the cross-source retrieval conflict index, and the protocol compatibility ratio, construct a multi-dimensional feedback model, and introduce a penalty term related to the difference in dynamic priority parameters to generate a feedback parameter, where the cross-source retrieval conflict index serves as the main adjustment variable of the multi-dimensional feedback model.

[0040] In a second aspect, the present application provides a multi-source heterogeneous spatial data retrieval system improved based on an R-tree, including:

[0041] A parsing module that parses multi-source heterogeneous data packets carrying geographic tags in network data streams. Each data packet contains spatial information in at least two different coordinate systems, sampling frequencies, or data formats, and generates a dynamic priority parameter that constrains the R-tree splitting dimension according to the spatio-temporal aggregation degree of each data packet on the transmission path;

[0042] A matching module that extracts the reference encoding of the splitting path corresponding to the cross-source query pattern in the historical records of the network data stream. The reference encoding is generated by matching the spatio-temporal coverage complementarity of different data packet transmission paths, and uses the reference encoding as the gene expression template of the splitting path in the initial population of the genetic algorithm;

[0043] An updating module that performs co-evolution of the splitting path at the network edge node, drives the splitting path to iterate in the direction of minimizing the cross-source retrieval conflict, generates a path offset each time it iterates, updates the network topology adaptation parameters of the gene expression template, and constructs an adaptability evaluation function based on the correlation between the dynamic priority parameter and the data packet transmission delay;

[0044] The triggering module triggers the preloading of heterogeneous data packets according to the direction and amplitude of the path offset. The preloaded data is a set of data packets in adjacent nodes of the network topology that match the spatio-temporal coverage area of the current split path, and performs compatibility verification of the network transmission protocol on the data packets through the format conversion rules in the gene expression template;

[0045] The generating module generates feedback parameters based on the cross-source retrieval conflict index output by the adaptability evaluation function and the preloading data verification result, and dynamically corrects the mapping relationship between the dynamic priority parameter and the gene expression template.

[0046] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-source heterogeneous spatial data retrieval method based on the improvement of the R-tree as described in the first aspect above.

[0047] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a multi-source heterogeneous spatial data retrieval method based on the improvement of the R-tree as described in the first aspect.

[0048] In the embodiment of the present application, multi-source heterogeneous data packets carrying geographical tags in the network data stream are parsed. Each data packet contains spatial information of at least two different coordinate systems, sampling frequencies, or data formats. A dynamic priority parameter for constraining the split dimension of the R-tree is generated according to the spatio-temporal aggregation degree of each data packet on the transmission path; the reference code of the split path corresponding to the cross-source query pattern in the network data stream history is extracted. The reference code is generated by matching the spatio-temporal coverage complementarity of different data packet transmission paths, and the reference code is used as the gene expression template of the split path in the initial population of the genetic algorithm; co-evolution of the split path is performed at the network edge node, driving the split path to iterate in the direction of minimizing the cross-source retrieval conflict. Each iteration generates a path offset and updates the network topology adaptation parameters of the gene expression template, and constructs an adaptability evaluation function based on the correlation between the dynamic priority parameter and the data packet transmission delay; the preloading of heterogeneous data packets is triggered according to the direction and amplitude of the path offset. The preloaded data is a set of data packets in adjacent nodes of the network topology that match the spatio-temporal coverage area of the current split path, and performs compatibility verification of the network transmission protocol on the data packets through the format conversion rules in the gene expression template; feedback parameters are generated based on the cross-source retrieval conflict index output by the adaptability evaluation function and the preloading data verification result, and the mapping relationship between the dynamic priority parameter and the gene expression template is dynamically corrected.

[0049] The technical solution of the present application has the following beneficial effects:

[0050] This application parses data packets with geographical tags, identifies spatial information in different coordinate systems, sampling frequencies, or data formats, generates dynamic priority parameters based on spatio-temporal aggregation, thereby optimizing the selection of the splitting dimension of the R-tree and improving the retrieval efficiency. Extract the splitting path reference encoding corresponding to the cross-source query pattern from the historical records and use it as the initial population template for the genetic algorithm to promote the effective initialization of the splitting path. Execute the co-evolution of the splitting path at the network edge nodes, iteratively drive the splitting path to develop in the direction of minimizing cross-source retrieval conflicts, update the network topology adaptation parameters each iteration, optimize the adaptability evaluation function, and enhance the retrieval accuracy and speed. Trigger the preloading of heterogeneous data packets based on the direction and amplitude of the path offset, and verify the compatibility through the gene expression template to ensure the consistency of the data transmission protocol and improve the data access efficiency. Generate feedback parameters according to the cross-source retrieval conflict index output by the adaptability evaluation function and the preloading data verification result, dynamically correct the mapping relationship between the dynamic priority parameters and the gene expression template, and achieve continuous optimization.

[0051] Furthermore, encode the splitting path as a multi-dimensional vector, calculate the topological overlap degree to construct a conflict neighborhood table, adopt a dual probability selection mechanism to select the target offset, dynamically adjust the value of the network topology adaptation parameter, and at the same time update the threshold of the conflict neighborhood table to eliminate low-priority paths. Recalculate the spatio-temporal coverage matching degree between the updated splitting path and the preloading data packet set, and feedback its change amount to the weight calculation of the dynamic priority parameter to form an iterative closed loop. The effect of this mechanism is to significantly improve the accuracy and efficiency of multi-source heterogeneous spatial data retrieval, reduce cross-source retrieval conflicts by continuously optimizing the splitting path, and enhance the overall performance and response speed of the system.

[0052] These aspects or other aspects of this application will be more clearly understood in the following description of the embodiments. Brief Description of the Drawings

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 Shows the flowchart of a method for retrieving multi-source heterogeneous spatial data based on the improvement of the R-tree provided by this application;

[0055] Figure 2 Shows the structural schematic diagram of a system for retrieving multi-source heterogeneous spatial data based on the improvement of the R-tree provided by this application;

[0056] Figure 3 The figure shows a schematic structural diagram of a computing device provided by the present application. Specific embodiments

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

[0058] In some processes described in the specification, claims and above-mentioned drawings of the present application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0059] The solution analyzes multi-source heterogeneous data packets carrying geographical tags in the network data stream, and generates dynamic priority parameters that constrain the splitting dimension of the R-tree based on the spatio-temporal aggregation degree of each data packet on the transmission path. This method aims to optimize the spatial data retrieval efficiency. Its core lies in identifying and utilizing the relationships between spatial information in different coordinate systems, sampling frequencies, or data formats to improve the retrieval speed and accuracy.

[0060] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0061] Figure 1 The figure is a flowchart of a method for retrieving multi-source heterogeneous spatial data based on an improved R-tree provided for an embodiment of the present application. As Figure 1 shown, the method includes:

[0062] 101. Analyze multi-source heterogeneous data packets carrying geographical tags in the network data stream. Each data packet contains spatial information in at least two different coordinate systems, sampling frequencies, or data formats, and generate dynamic priority parameters that constrain the splitting dimension of the R-tree based on the spatio-temporal aggregation degree of each data packet on the transmission path;

[0063] In this step, the multi-source heterogeneous data packets with geographical tags refer to the data units transmitted in the network. These data contain geographical location information and may use different coordinate systems, sampling frequencies, or data formats.

[0064] The sampling frequency refers to the time interval of data acquisition, that is, the number of data points obtained per unit time.

[0065] The data format refers to the representation form or encoding method of the data.

[0066] The spatio-temporal aggregation degree is used to measure the concentration degree of data packets within a specific time and space range by analyzing the transmission delay and geographical distribution density of the data packets.

[0067] The R-tree splitting dimension is a data structure used for spatial access methods, and its splitting dimension refers to the key factor that determines how to split nodes to maintain balance and efficiency.

[0068] The dynamic priority parameter is an important weight for adaptively adjusting the importance of the R-tree splitting dimension based on the spatio-temporal aggregation degree to optimize the storage and retrieval efficiency.

[0069] In the embodiment of the present application, first, parse the geographical tag data packets in the network data stream to identify their coordinate systems, sampling frequencies, or data formats. Then, calculate the spatio-temporal aggregation degree of each data packet, which requires combining the transmission delay of the data packet and its geographical distribution. Next, generate the dynamic priority parameter that restricts the R-tree splitting dimension using the spatio-temporal aggregation degree. This process involves using spatial data analysis algorithms to evaluate the spatial concentration trend of the data packets and adjusting the R-tree structure according to the results. Finally, through this series of steps, the R-tree structure is optimized and the data retrieval efficiency is improved.

[0070] In an intelligent transportation system, the position updates sent by vehicle sensors are received and parsed. The system analyzes the spatio-temporal aggregation degree of these data packets, and then optimizes the storage structure so that the vehicle data passing through the same section frequently can be stored and queried more efficiently. This not only improves the query speed but also enhances the response ability of the system.

[0071] 102. Extract the reference encoding of the splitting path corresponding to the cross-source query pattern in the historical record of the network data stream. The reference encoding is generated by matching the spatio-temporal coverage complementarity of different data packet transmission paths, and use the reference encoding as the gene expression template of the splitting path in the initial population of the genetic algorithm;

[0072] In this step, the historical record of the network data stream refers to the historical records of all data packets passing through during the network transmission process. These records contain information such as the source, destination, transmission time, and content summary of the data packets, which are used for subsequent analysis and optimization.

[0073] The splitting path corresponding to the cross - source query mode refers to that when data needs to be obtained from multiple different sources, the system will select the optimal data acquisition path according to different query requirements. The splitting path means that in this process, different strategies or path selections are adopted to improve efficiency.

[0074] The benchmark encoding is a coding method used to describe the spatio - temporal coverage complementarity of different data packet transmission paths and serves as the basic template for the splitting paths in the initial population of the genetic algorithm.

[0075] The spatio - temporal coverage complementarity of different data packet transmission paths refers to whether the time and space coverage ranges of different data packets on their transmission paths can complement each other.

[0076] The initial population of the genetic algorithm refers to a set composed of a group of individuals randomly generated or selected based on certain rules at the beginning. Each individual represents a potential solution, and in this context, it refers to the initial set of splitting paths composed of benchmark encodings.

[0077] The gene expression template contains the format conversion rules of multi - source heterogeneous data packets and network topology adaptation parameters, guiding the optimization of path selection in the cross - source query mode.

[0078] In the embodiments of this application, first, the splitting paths related to the cross - source query mode are extracted from the historical network data stream, and the corresponding benchmark encodings are generated. The spatio - temporal coverage analysis technology is used in this process to ensure the full utilization of the spatio - temporal coverage complementarity between different data packets. Then, taking the benchmark encoding as the starting point of the genetic algorithm, path optimization iteration is carried out. Each iteration adjusts the format conversion rules and network topology adaptation parameters according to the effectiveness of the current path, and finally forms an optimized set of splitting paths.

[0079] Continuing with the example of the intelligent transportation system above, when integrating traffic data from multiple sources, the system, based on the previously mentioned benchmark encoding, uses the genetic algorithm to find the best data integration path to ensure data consistency and accuracy. As new data is added, the system continuously optimizes the path selection to reduce query errors caused by data source differences.

[0080] 103. Execute co - evolution of splitting paths at the network edge node, drive the splitting path to iterate towards minimizing cross - source retrieval conflicts, generate a path offset amount in each iteration and update the network topology adaptation parameters of the gene expression template, and construct an adaptability evaluation function based on the correlation between the dynamic priority parameter and the data packet transmission delay;

[0081] In this step, co - evolution refers to the process of jointly improving the splitting paths executed at the network edge node, aiming to minimize cross - source retrieval conflicts.

[0082] Minimizing cross - source retrieval conflicts means reducing the possible conflicts or inconsistencies that may occur when retrieving data simultaneously from multiple different sources by optimizing the data acquisition path, ensuring data consistency and accuracy.

[0083] The path offset represents the direction and magnitude of the split path change.

[0084] In a genetic algorithm, a gene expression template is a template structure that contains the basic building blocks of a solution (such as format conversion rules and network topology adaptation parameters) and is used to guide mutation and crossover operations during the search process.

[0085] Network topology adaptation parameters are used to describe the characteristics of the network structure, helping to adjust the algorithm to meet the requirements of a specific network environment, such as how data packets are routed in the network.

[0086] The adaptability evaluation function is constructed based on the relationship between dynamic priority parameters and data packet transmission delay and is used to evaluate the performance of the split path.

[0087] In the embodiments of this application, first, a co - evolutionary algorithm is run on the network edge nodes to gradually adjust the split path to reduce cross - source retrieval conflicts. This process uses mutation and crossover operations in the evolutionary algorithm, generates a path offset after each iteration, and updates the network topology adaptation parameters. Then, the adaptability evaluation function calculates the effectiveness of the split path according to the relationship between dynamic priority parameters and data packet transmission delay, and finally determines the optimal split path.

[0088] In an intelligent transportation system, as new data is continuously added, the system continuously optimizes the data processing path, reduces query errors caused by data source differences, and improves the overall data processing efficiency. For example, during peak hours, the system automatically adjusts the data processing path to handle the increased data traffic and ensure real - time response.

[0089] 104. Trigger heterogeneous data packet pre - loading according to the direction and magnitude of the path offset. The pre - loaded data is a set of data packets in network topology adjacent nodes that match the spatio - temporal coverage area of the current split path, and the compatibility of the data packets with the network transmission protocol is verified through the format conversion rules in the gene expression template;

[0090] In this step, heterogeneous data packet pre - loading refers to downloading in advance the data packets that may be needed to the cache according to a prediction model for quick access, which is particularly important when dealing with data packets from different sources and in various formats.

[0091] The pre - loaded data is a set of data packets pre - acquired to accelerate queries. These data packets come from network topology adjacent nodes and match the spatio - temporal coverage area of the current split path.

[0092] The current split path spatio-temporal coverage area refers to the time period and geographical area that can be covered by the currently selected data acquisition path, and is used to evaluate the effectiveness and applicability of this path.

[0093] The format conversion rules are used to verify the compatibility of data packets under the network transmission protocol.

[0094] The network transmission protocol defines a set of rules for how data is encapsulated, addressed, transmitted, routed, and received on the network.

[0095] Compatibility verification is the process of ensuring that data packets conform to the expected format and protocol standards, and usually involves checking whether the data packets meet specific format requirements and technical specifications.

[0096] In the embodiments of this application, first, the preloading operation of heterogeneous data packets is triggered according to the path offset. This process involves the analysis and prediction of the network topology. Machine learning algorithms are used to predict the data packets that may be accessed in the future for a period of time and preload them into the cache. At the same time, these data packets are verified using the format conversion rules to ensure that they meet the requirements of the network transmission protocol. Finally, through the above measures, the data access speed is accelerated and the system performance is improved.

[0097] In the intelligent transportation system, to cope with the upcoming peak hours, the system preloads the traffic flow data that may be used to ensure the real-time response speed. In addition, by verifying the format conversion rules of the data packets, it is ensured that all data can be seamlessly integrated into the existing system to provide a consistent service experience.

[0098] 105. Generate a feedback parameter from the cross-source retrieval conflict index output by the adaptability evaluation function and the preloading data verification result, and dynamically correct the mapping relationship between the dynamic priority parameter and the gene expression template.

[0099] In this step, the cross-source retrieval conflict index is used to measure the degree of retrieval conflict between different data sources and is an important indicator for evaluating system performance. A low index means higher data consistency.

[0100] The preloading data verification result is the inspection result of the correctness and availability of the preloaded data to ensure that these data can be used normally in actual applications.

[0101] The feedback parameter is jointly generated by the cross-source retrieval conflict index output by the adaptability evaluation function and the preloading data verification result, and is used to dynamically correct the mapping relationship between the dynamic priority parameter and the gene expression template to achieve continuous optimization.

[0102] In the embodiments of the present application, first, based on the results of the adaptability evaluation function, combined with the cross-source retrieval conflict index and the preloading data verification situation, the association between the dynamic priority parameter and the gene expression template is dynamically adjusted. This process adopts a closed-loop feedback mechanism, which ensures that the system always maintains the optimal state through continuous evaluation and adjustment. Finally, through this dynamic correction mechanism, the continuous optimization of the entire data processing flow is achieved.

[0103] In the intelligent transportation system, the system continuously monitors its own performance, adjusts the internal mechanism according to the latest data, and ensures that the most accurate and timely traffic information service is always provided. For example, by continuously adjusting the data processing path and optimizing the preloading strategy, the response speed and accuracy of the system to emergencies are improved.

[0104] In summary, steps 101 to 105 greatly improve the data processing efficiency and query accuracy, and enhance the user experience by effectively managing and optimizing multi-source heterogeneous data packets in the network data stream, especially in the scenario of the intelligent transportation system. Each step is closely connected to form a complete optimization loop, ensuring that the system can operate efficiently in a dynamic environment.

[0105] To further improve the split path optimization of network edge nodes, the scheme encodes the split path as a multi-dimensional vector and calculates the topological overlap degree to identify conflicts, constructs a conflict neighborhood table to generate candidate offsets, adjusts the split path parameters using the selected offsets, and eliminates low-priority paths. Finally, the change in the spatio-temporal matching degree of the preloaded data packets is evaluated to form an iterative closed loop, continuously reducing the cross-source retrieval conflict. In some embodiments, the co-evolution of the split path executed in step 103 drives the split path to iterate in the direction of minimizing the cross-source retrieval conflict, and each iteration generates a path offset and updates the network topology adaptation parameters of the gene expression template, including:

[0106] 201. Encode the split path as a multi-dimensional vector, where each dimension corresponds to the adjustable range of the network topology adaptation parameters, and calculate the topological overlap degree based on the spatio-temporal region covered by the current split path;

[0107] In step 201, the multi-dimensional vector is a mathematical representation method used to describe the various attributes of the split path; the network topology adaptation parameters refer to various settings for adjusting the network structure to meet specific requirements, such as bandwidth, delay, etc. The topological overlap degree measures the degree of overlap of different data packet transmission paths in time and space, and is used to evaluate the possibility of conflicts. This step helps to identify potential data conflict areas and provides a basis for subsequent optimization.

[0108] In the embodiments of the present application, first, the splitting paths are converted into a multi-dimensional vector form, and each path is represented by a series of numerical values to characterize its properties in the network. Then, the topological overlap degree is calculated based on the spatio-temporal region covered by the current path, and geographic information system technology and algorithms are used to quantify the spatio-temporal overlap between paths. Next, this information is analyzed to determine which paths may cause conflicts, thereby guiding the subsequent optimization work. The final result is a detailed evaluation system for measuring the overlap degree between the splitting paths and the network environment.

[0109] 202. Construct a conflict neighborhood table according to the topological overlap degree, record the heterogeneous data packet transmission paths with spatio-temporal coverage overlap with the current splitting path and the differences in network topology adaptation parameters, and generate candidate offsets;

[0110] In step 202, the conflict neighborhood table is a tool for locating and resolving cross-source retrieval conflicts; the heterogeneous data packet transmission paths and the differences in network topology adaptation parameters refer to the paths used by data packets from different sources during transmission and the differences in the network structure configuration of these paths; the candidate offsets are potential optimization solutions generated from these differences, aiming to reduce conflicts. In this way, the specific paths that need to be optimized can be identified more precisely.

[0111] In the embodiments of the present application, first, a conflict neighborhood table is constructed based on the topological overlap degree, and all relevant transmission paths and parameter differences are recorded. Data analysis techniques are used to process this information, and path combinations that may lead to a high conflict rate are screened out. Subsequently, candidate offsets are generated according to the characteristics of these paths and used as the basis for the next step of optimization. Finally, by evaluating the effects of different solutions, the most suitable offset is selected and applied to the actual path adjustment.

[0112] 203. Select a target offset from the candidate offsets by adopting a dual-probability selection mechanism. The first probability distribution is based on the success rate of reducing conflicts of the candidate offsets in historical iterations, and the second probability distribution is based on the compatibility constraint between the target offset and the format conversion rules in the gene expression template;

[0113] In step 203, the dual-probability selection mechanism is a decision-making algorithm that combines two independent probability distributions to determine the best path adjustment solution; the first probability distribution considers the results of previous adjustment attempts and evaluates the effectiveness of each candidate offset in reducing conflicts in the future; the second probability distribution focuses on the existing format conversion rules and evaluates whether each candidate offset can cooperate well with other system components. This mechanism improves the efficiency and success rate of the optimization process.

[0114] In the embodiments of the present application, first, statistical methods are used to analyze the historical performance of candidate offsets to determine the first probability distribution. At the same time, in combination with format conversion rules, the compatibility of each offset is evaluated to establish the second probability distribution. Then, the selection probabilities of each offset are calculated based on these two distributions. Finally, the best offset is selected and applied to the actual path adjustment to ensure the efficient operation of the split path.

[0115] 204. Apply the target offset to the current split path, dynamically adjust the parameter values of the corresponding dimensions in the multi-dimensional vector, and at the same time update the topological overlap degree threshold of the conflict neighborhood table to trigger the elimination of low-priority paths.

[0116] In step 204, the target offset refers to the selected best path adjustment scheme; dynamically adjusting the parameter values of the corresponding dimensions in the multi-dimensional vector means updating path attributes according to the new offset, such as increasing or decreasing latency, changing bandwidth, etc.; updating the topological overlap degree threshold of the conflict neighborhood table is to modify the standard value used to determine whether there is significant spatio-temporal coverage overlap; eliminating low-priority paths means automatically removing those data packet transmission paths that contribute less to the overall performance. This process continuously optimizes the split path configuration and reduces cross-source retrieval conflicts.

[0117] In the embodiments of the present application, first, the target offset is applied to update the split path parameters, and the relevant thresholds of the conflict neighborhood table are synchronously adjusted. An automated script is used to execute the elimination process of low-priority paths. The data processing and calculations involved in this process all rely on pre-set algorithms and models. Finally, the real-time optimization of the split path configuration is achieved to ensure efficient data transmission.

[0118] 205. Recalculate the spatio-temporal coverage matching degree between the updated split path and the pre-loaded data packet set, and feedback the change amount of the spatio-temporal coverage matching degree to the weight calculation of the dynamic priority parameter to achieve an iterative closed loop.

[0119] In step 205, recalculation means re-evaluating the matching situation between the data packet transmission path and the pre-loaded data packets after applying the new offset; the change amount of the spatio-temporal coverage matching degree takes the change of the matching degree as input to adjust the importance weights of the dynamic priority parameters of different paths to optimize the future data flow allocation; the weight calculation of the dynamic priority parameter uses the change information of the spatio-temporal coverage matching degree to adjust the importance weights of different paths to ensure that the system can optimize the future data flow allocation strategy according to the latest performance; the iterative closed loop means continuously repeating the above process to continuously improve the selection and configuration of the data packet transmission path to reach the best performance state.

[0120] In the embodiments of the present application, first, the spatio-temporal coverage matching degree after the split path update is calculated, and its change situation is analyzed in detail. Then, a machine learning algorithm is used to analyze this information to adjust the weights of the dynamic priority parameters. The whole process emphasizes data-driven decision-making to ensure that each iteration can bring substantial improvements.

[0121] The following is a specific example:

[0122] In an intelligent transportation management system, heterogeneous data streams generated by multiple sensors need to be integrated and analyzed. First, the transmission paths of each data stream are encoded as multi-dimensional vectors, and their topological overlap degree in the network is calculated. Then, a conflict neighborhood table is constructed to identify the data packet paths with spatio-temporal coverage overlap, and candidate offsets are generated. Then, a dual probability selection mechanism is adopted to select the optimal offset for path adjustment, and at the same time, low-priority paths are eliminated. Finally, the spatio-temporal matching degree between the split path and the pre-loaded data packet set is re-evaluated, significantly reducing cross-source retrieval conflicts and improving the response speed and data processing efficiency of the system.

[0123] In summary, steps 201 to 205 achieve effective optimization of the split path on the network edge node, significantly reducing conflicts in cross-source data retrieval and improving the adaptability and flexibility of the data transmission path. Through continuous feedback and adjustment, the system can more accurately meet real-time requirements, enhancing the overall stability and response speed. This method not only improves the user experience but also provides strong support for data management and utilization in complex environments, ensuring efficient data processing and rapid decision-making.

[0124] In order to further improve the selection accuracy of candidate offsets in the split path optimization process, the solution combines historical data with the real-time situation to optimize the selection of offsets in path planning. First, the historical success rate of each candidate offset is evaluated, then its applicability is checked according to the existing rules, then the weights are dynamically adjusted to adapt to the current traffic hotspots, and finally, the optimal offset is determined through roulette wheel screening. This method aims to improve the accuracy and flexibility of decision-making. In some embodiments, the step of using a dual probability selection mechanism to select a target offset from the candidate offsets described in step 203 includes:

[0125] 301. Based on the success rate of reducing conflicts of the candidate offsets in historical iterations, count the ratio of the number of successful times of each candidate offset to the number of times of path priority improvement within a preset time window, and normalize it to the initial weight of the first probability distribution;

[0126] In step 301, the candidate offsets in historical iterations refer to all possible position adjustment values that are tried during path planning to reduce conflicts. The success rate is a probability metric that measures the likelihood that a specific offset can successfully reduce conflicts. The preset time window is a fixed time period set for calculating the success rate, and the data collected during this period will be used to evaluate the performance of each candidate offset. The initial weight of the first probability distribution represents the likelihood of each candidate offset based on its past performance.

[0127] In the embodiments of the present application, first, all the data within the preset time window is analyzed to count the number of successful times and the number of times the path priority is improved for each candidate offset. Here, basic data statistical methods are used. The number of successful times is obtained by recording whether the conflict is reduced after each path adjustment, and the number of times the path priority is improved is obtained based on the change in the priority of the path after optimization. Then, these statistical data are normalized to determine the initial weight of the first probability distribution for each candidate offset. The final result is obtained through in-depth mining and quantitative analysis of historical data, which helps to identify the offset that is most likely to solve the current problem.

[0128] 302. Extract the protocol fields bound by the format conversion rules in the gene expression template, calculate the overlap ratio between the parameter dimension corresponding to the candidate offset and the preset field value range, and map it to the constraint weight of the second probability distribution;

[0129] In step 302, the gene expression template is a data structure used to describe the path planning parameter settings, which contains various rules and protocol fields. The protocol fields bound by the format conversion rules refer to the specific protocol fields associated with the rule set defined in the gene expression template for conversion between different formats. The overlap ratio reflects the matching degree between the parameter dimension corresponding to the candidate offset and the preset field value range. The constraint weight of the second probability distribution measures the applicability of the candidate offset based on this matching degree.

[0130] In the embodiments of the present application, first, the protocol fields specific to the format conversion rules are extracted from the gene expression template. This process involves data structure parsing and mathematical calculation techniques. Then, the overlap ratio between the parameter dimension corresponding to the candidate offset and the value ranges of these fields is calculated to find the best matching degree between the candidate offset and the existing rules. Finally, the calculated overlap ratio is mapped to the constraint weight of the second probability distribution, providing basic data support for subsequent steps.

[0131] 303. Non-linearly superimpose the initial weight of the first probability distribution and the constraint weight of the second probability distribution. The superimposition coefficient is dynamically adjusted according to the proportion of low-priority paths in the conflict neighborhood table, so that the compatibility constraint occupies a higher weight in the conflict-intensive area;

[0132] In step 303, non-linear superposition refers to a weighted summation method that combines multiple factors, where the weights are not fixed but vary dynamically according to the input. The conflict neighborhood table is a data structure that records all possible conflict locations and their severities. The proportion of low-priority paths refers to the proportion of low-priority paths in the total paths within these conflict regions.

[0133] In the embodiment of the present application, first, a non-linear superposition method is used to combine the initial weights of the first probability distribution and the constraint weights of the second probability distribution. This step uses a complex algorithm to dynamically adjust the weights. Then, the superposition coefficient is adjusted according to the proportion of low-priority paths in the conflict neighborhood table to ensure that the compatibility constraint occupies a higher weight in the conflict-intensive region, and finally, a comprehensive selection probability distribution is formed.

[0134] 304. Perform roulette wheel screening based on the comprehensive selection probability, verify the non-conflictiveness of the candidate offset with the core dimension parameters in the gene expression template. If there is a conflict, backtrack to the sub-optimal candidate offset until the target offset is selected after meeting the constraints.

[0135] In step 304, roulette wheel screening is a random selection method, where the probability of each option being selected is proportional to its weight. The core dimension parameters are the most important several parameters in path planning, which determine the basic characteristics of the path. This step aims to verify whether the candidate offset conflicts with these key parameters.

[0136] In the embodiment of the present application, perform roulette wheel screening based on the comprehensive selection probability generated in step 303, and check whether each candidate offset is compatible with the core dimension parameters in the gene expression template. If a conflict is found, automatically backtrack to the sub-optimal candidate offset until the target offset that meets all the constraints is found. This process ensures that the finally selected offset not only conforms to the historical performance but also adapts to the current environment.

[0137] The following is a specific example:

[0138] In an intelligent city traffic management system, the system evaluates the success rate of different offsets in reducing traffic congestion by analyzing data from the past month. Then, the system checks the existing traffic rules and signal control schemes to determine the best match of the candidate offset with these rules. According to the distribution of congestion hotspots in the current road network, the system dynamically adjusts the strategy for selecting offsets to ensure that the compatibility constraint occupies a higher weight in the conflict-intensive region. Finally, verify the effectiveness of the selected offset through the roulette wheel screening method and backtrack to the sub-optimal option until the best solution is found. This greatly improves traffic flow and reduces congestion.

[0139] In summary, steps 301 to 304 effectively select the optimal solution by precisely evaluating the historical performance of candidate offsets and their compatibility with the existing system, significantly reducing cross-source retrieval conflicts. The dual-probability selection mechanism significantly improves the accuracy and efficiency of offset selection in path planning. By combining historical performance and real-time adaptability, it can not only identify the offset most likely to solve the current problem but also flexibly handle various emergencies in the complex and changeable urban traffic environment. This method uses technical means such as data statistics, overlap ratio calculation, and non-linear superposition to effectively enhance the system's response speed and decision-making quality, making traffic management more scientific and reasonable, and greatly improving the overall operation efficiency of urban traffic and user experience.

[0140] To solve the problems of efficiency and accuracy in offset selection for path planning, during the process of selecting the target offset, the scheme determines the weights of the first and second probability distributions according to the success rate of reducing conflicts in historical iterations and the overlap ratio of protocol fields, and adjusts these weights through non-linear superposition, especially increasing the weight of compatibility constraints in conflict-intensive areas. This ensures that the selection of split paths not only reduces conflicts but also guarantees compatibility with network components. In some embodiments, the non-linear superposition of the initial weight of the first probability distribution and the constraint weight of the second probability distribution in step 303, where the superposition coefficient is dynamically adjusted according to the proportion of low-priority paths in the conflict neighborhood table, includes:

[0141] 401. Based on the comparison between the proportion of low-priority paths in the conflict neighborhood table and the historical maximum path capacity, calculate the conflict density index through a piecewise function;

[0142] In step 401, the conflict neighborhood table records all possible conflict locations and their severity. The proportion of low-priority paths refers to the proportion of low-priority paths in the total paths within these conflict areas. The historical maximum path capacity refers to the maximum number of paths that a specific area could carry during a past time period. The conflict density index is an index calculated through a piecewise function and is used to measure the conflict intensity within the current area.

[0143] In the embodiments of this application, first calculate the proportion of low-priority paths based on the data in the conflict neighborhood table and compare it with the historical maximum path capacity. Then use a predefined piecewise function to calculate the conflict density index. This process involves basic data analysis and mathematical operation techniques, and the final result is a value reflecting the conflict situation within the current area.

[0144] 402. Generate a dynamic superposition coefficient according to the conflict density index, adopt an adjustment mechanism triggered by segmented thresholds, and combine the historical moving average as a dynamic attenuation factor to suppress the mutation of the dynamic superposition coefficient, where the growth rate of the dynamic superposition coefficient is adjusted according to the change of the conflict density index within the threshold interval;

[0145] In step 402, the dynamic superposition coefficient is a variable generated according to the conflict density index and is used to adjust the relationship between the initial weight of the first probability distribution and the constraint weight of the second probability distribution. The segmented threshold trigger mechanism is a method for automatically adjusting parameters based on preset conditions. The historical moving average serves as a dynamic attenuation factor to suppress the mutation of the dynamic superposition coefficient and ensure its smooth transition. The growth rate describes the speed at which the dynamic superposition coefficient changes with the conflict density index.

[0146] In the embodiment of the present application, first, according to different intervals of the conflict density index, the growth rate of the dynamic superposition coefficient is adjusted by using a segmented threshold trigger mechanism, and the historical moving average is combined as a dynamic attenuation factor for smooth transition. This step uses algorithm logic and statistical methods to ensure that the change of the dynamic superposition coefficient is both sensitive and stable. Through this mechanism, the weights can be effectively adjusted dynamically to adapt to the changing situation.

[0147] 403. Associate the initial weight of the first probability distribution with the dynamic superposition coefficient, superimpose the association result with the constraint weight of the second probability distribution, and perform logarithmic weighted summation and truncation processing on the superposition coefficient;

[0148] In step 403, the initial weight of the first probability distribution represents the likelihood of the historical performance of each candidate offset. The dynamic superposition coefficient is associated with this weight and superimposed with the constraint weight of the second probability distribution. Logarithmic weighted summation is a mathematical processing method used to integrate probability distributions from different sources. Truncation processing is to limit the result within a reasonable range to prevent extreme values from affecting the overall calculation result.

[0149] In the embodiment of the present application, first, the initial weight of the first probability distribution is associated with the dynamic superposition coefficient, and then superimposed with the constraint weight of the second probability distribution. The logarithmic weighted summation method is used to integrate this information, and the truncation processing is used to ensure the rationality of the result. This step comprehensively uses mathematical models and algorithms to achieve an effective combination of different weights, and finally forms an optimized comprehensive weight for guiding the selection of offsets in path planning.

[0150] 404. Adjust the rate of the dynamic attenuation factor according to the difference between the superposition coefficient and the historical moving average. If it exceeds the tolerance interval, increase the rate, and apply the updated rate to the calculation of the superposition coefficient in the next iteration.

[0151] In step 404, the difference between the superposition coefficient and the historical moving average is used to measure whether there is a mutation in the current dynamic superposition coefficient. The dynamic decay factor is a tuning parameter, and its rate determines the speed and smoothness of the adjustment of the dynamic superposition coefficient. The tolerance interval sets an allowable fluctuation range, and if it exceeds this range, it is considered that a faster adjustment is required to restore stability.

[0152] In the embodiment of the present application, first, the difference between the superposition coefficient and the historical moving average is calculated. If this difference exceeds the preset tolerance interval, it is considered that there is a large fluctuation in the current system state, and the adjustment speed needs to be increased to restore the normal state. At this time, the rate of the dynamic decay factor is increased, and the updated rate is applied to the calculation of the superposition coefficient in the next iteration. In this way, the drastic change of the superposition coefficient caused by unexpected situations can be effectively suppressed, ensuring the stability and smooth transition of the entire system.

[0153] The following is a specific example:

[0154] In an intelligent urban traffic management system, the system first calculates the conflict density index in a certain area by analyzing the data in the conflict neighborhood table. Then, the dynamic superposition coefficient is adjusted according to this index and applied to the calculation of the weights. Then, the adjusted coefficient is applied to the superposition of the initial weight and the constraint weight, the logarithmic weighted summation method is used to integrate the information, and the truncation process is used to ensure the rationality of the result. If it is found that the superposition coefficient exceeds the tolerance range, the dynamic decay factor is quickly adjusted to maintain stability. In this way, the system can effectively cope with traffic congestion and improve the overall operation efficiency.

[0155] In summary, steps 401 to 404 significantly improve the flexibility and accuracy of the offset selection in the path planning process. By dynamically adjusting the superposition coefficient and performing smoothing processing in combination with historical data, not only the response speed of the system is enhanced, but also the decision-making deviation caused by unexpected situations is effectively avoided. This method greatly improves the intelligent level of urban traffic management and dispatching, makes traffic management more scientific and reasonable, improves the user experience, and improves the overall operation efficiency of the city. At the same time, through the adjustment mechanism of the dynamic decay factor, the stability and reliability of the system in the face of unexpected situations are ensured.

[0156] To solve the optimization problem between packet transmission delay and network topology structure in path planning, the construction of the adaptability evaluation function is based on the correlation between dynamic priority parameters and packet transmission delay, integrating dynamic weight factors and spatio-temporal coupling coefficients, and at the same time introducing a penalty term related to the difference in network topology adaptation parameters. This process helps to identify the spatio-temporal aggregation degree in high-delay and low-density regions, and further optimize the data retrieval strategy. In some embodiments, constructing the adaptability evaluation function based on the correlation between the dynamic priority parameters and the packet transmission delay in step 103 includes:

[0157] 501. Calculate the spatio-temporal coupling coefficient of packet transmission delay and geographical distribution density based on the spatio-temporal distribution of the dynamic priority parameters;

[0158] In step 501, the dynamic priority parameter refers to the packet processing order index dynamically adjusted according to the real-time network condition and historical data. The spatio-temporal distribution describes the changes of these parameters over time and geographical location. The packet transmission delay is the time required for data to travel from the sender to the receiver. The geographical distribution density refers to the number of nodes or devices in a certain area. The spatio-temporal coupling coefficient is a quantitative index to measure the degree of mutual influence between packet transmission delay and geographical distribution density.

[0159] In the embodiments of the present application, first analyze the spatio-temporal distribution of the dynamic priority parameters. By collecting the packet transmission delay and node density information at different geographical locations over a period of time, use statistical methods to calculate the spatio-temporal coupling coefficient. This process involves data analysis, time series analysis, and mathematical modeling techniques, and finally generates a value reflecting the relationship between the two, which serves as the basis for subsequent steps.

[0160] 502. Generate a dynamic weight factor according to the spatio-temporal coupling coefficient, associate the spatio-temporal aggregation degree of high-delay regions and low-density regions, and constrain the dynamic weight factor based on the network topology adaptation parameters of the gene expression template;

[0161] In step 502, the dynamic weight factor is a variable used to adjust the packet processing priority, and its value is generated based on the spatio-temporal coupling coefficient. The spatio-temporal aggregation degree of high-delay regions and low-density regions represents the concentration degree of these two factors in a specific time and space. The network topology adaptation parameters in the gene expression template are a set of rules used to guide the optimal configuration of the network structure. The dynamic weight factor is constrained on this basis to ensure efficient packet processing when the network topology changes.

[0162] In the embodiments of the present application, first, a dynamic weight factor is generated according to the spatio-temporal coupling coefficient calculated in step 501. This step adopts a regression analysis method based on machine learning, and predicts the optimal weight factor under different conditions by training a model. The model is trained using historical data, including spatio-temporal coupling coefficients, geographical distribution densities, and corresponding transmission delays, etc., to find the best weight factor allocation strategy. Next, the spatio-temporal aggregation degrees of high-delay regions and low-density regions are associated. Here, clustering analysis technology is adopted to identify spatial regions with similar characteristics (such as high delay and low density), and evaluate the spatio-temporal aggregation of these regions. This analysis helps to determine which regions need higher weight factors for priority processing, so as to reduce the delay in the overall network. Finally, all the above information is integrated to form a comprehensive dynamic weight factor adjustment mechanism. This mechanism can not only respond to real-time network condition changes, but also improve its own accuracy through continuous learning and optimization.

[0163] 503. Integrate the dynamic priority parameter, the dynamic weight factor and the spatio-temporal coupling coefficient to construct an adaptability evaluation function, and introduce a penalty term related to the difference in network topology adaptation parameters, where the dynamic priority parameter is used as the main decision variable, and the dynamic weight factor is used as the conflict sensitivity adjustment coefficient.

[0164] In step 503, the adaptability evaluation function is a mathematical model that comprehensively considers various factors and aims to evaluate the system performance under different strategies. The dynamic priority parameter, as the main decision variable, determines the basic processing order of data packets, while the dynamic weight factor, as the conflict sensitivity adjustment coefficient, is used to fine-tune the priority to cope with emergencies. Introducing a penalty term related to the difference in network topology adaptation parameters is to prevent the performance degradation caused by network structure adjustment.

[0165] In the embodiments of the present application, first, the dynamic priority parameter, the dynamic weight factor and the spatio-temporal coupling coefficient are integrated. Specifically, a multivariate regression analysis method is used to quantify the relationships between these factors, and the combination of these parameters is optimized through machine learning algorithms. Next, when constructing the adaptability evaluation function, a penalty term related to the difference in network topology adaptation parameters is introduced. This process involves in-depth analysis of the current network state and historical data to determine the best parameter configuration. In order to ensure that the evaluation function can reflect the actual operation situation, simulation technology is also adopted to simulate the performance under different network conditions. The final result is an evaluation function that can comprehensively consider various influencing factors and provide an optimal solution.

[0166] The following is a specific example:

[0167] In an intelligent urban traffic management system, the system first analyzes the spatio-temporal distribution of dynamic priority parameters in a certain area and calculates the spatio-temporal coupling coefficient of data packet transmission delay and geographical distribution density. Then, based on the spatio-temporal coupling coefficient, a dynamic weight factor is generated and applied to network topology optimization. Next, by integrating dynamic priority parameters, dynamic weight factors, and spatio-temporal coupling coefficients, an adaptability evaluation function is constructed, and a penalty term is introduced to address the challenges brought about by network topology changes. In this way, the system can manage data streams more effectively and improve the overall operation efficiency.

[0168] In summary, steps 501 to 503 significantly improve the flexibility and accuracy of data packet processing in path planning. By integrating multiple factors to construct an adaptability evaluation function and using a verification and feedback adjustment mechanism for continuous optimization, not only the response speed and stability of the system are enhanced, but also performance fluctuations caused by network structure adjustments are effectively avoided. This method greatly improves the intelligent level of data management and scheduling, makes traffic management more scientific and reasonable, improves the user experience, and significantly enhances the overall operation efficiency of the city. At the same time, the dynamic adjustment mechanism ensures the stability and reliability of the system in the face of emergencies.

[0169] To solve the optimization problem between data packet transmission delay and network topology structure in path planning, the solution proposes a method for calculating dynamic weight factors based on spatio-temporal coupling coefficients for the spatio-temporal aggregation degree correlation problem between high-delay areas and low-density areas. By combining the inverse correlation of geographical distribution gradient changes, this method accurately quantifies the spatio-temporal aggregation degree difference index between high-delay and low-density areas and realizes effective control of the dynamic weight factor through network topology adaptation parameters. In some embodiments, generating the dynamic weight factor according to the spatio-temporal coupling coefficient in step 502 and correlating the spatio-temporal aggregation degrees of high-delay areas and low-density areas includes:

[0170] 601. Calculate the spatio-temporal aggregation degree difference index between high-delay areas and low-density areas based on the spatio-temporal coupling coefficient and the inverse correlation between transmission path fluctuations and geographical distribution gradient changes;

[0171] In step 601, the spatio-temporal coupling coefficient is a quantitative index measuring the degree of mutual influence between data packet transmission delay and geographical distribution density. Transmission path fluctuations refer to the path changes encountered by data packets during transmission in the network. Geographical distribution gradient changes describe the density changes of nodes or devices at different geographical locations. The spatio-temporal aggregation degree difference index is an index used to measure the difference in spatio-temporal aggregation between high-delay areas and low-density areas.

[0172] In the embodiments of the present application, first, based on the spatio-temporal coupling coefficient and the inverse correlation between the transmission path fluctuation and the geographical distribution gradient change, this step adopts statistical analysis and mathematical modeling techniques. By collecting data on the transmission path fluctuation, geographical distribution density, and their change trends at different geographical locations over a period of time, the spatio-temporal aggregation degree difference index between the high-delay area and the low-density area is calculated. The final result is a value reflecting the difference in spatio-temporal aggregation between the high-delay area and the low-density area, serving as the basis for subsequent steps.

[0173] 602. Generate an initial dynamic weight factor according to the spatio-temporal aggregation degree difference index, associate the path conflict sensitivity of the high-delay area with the spatio-temporal coverage sparsity of the low-density area, and constrain the growth rate of the initial dynamic weight factor through the network topology adaptation parameters of the gene expression template;

[0174] In step 602, the initial dynamic weight factor is a variable generated based on the spatio-temporal aggregation degree difference index and is used to adjust the packet processing priority. The path conflict sensitivity refers to the degree of increase in packet transmission delay caused by path conflicts in the high-delay area. The spatio-temporal coverage sparsity describes the sparsity of the distribution of nodes or devices in the low-density area. The network topology adaptation parameters are a set of rules used to guide the optimal configuration of the network structure. The growth rate controls the change speed of the initial dynamic weight factor over time.

[0175] In the embodiments of the present application, an initial dynamic weight factor is generated according to the spatio-temporal aggregation degree difference index calculated in step 601. Here, machine learning algorithms such as regression trees or neural networks are adopted. By training on historical data, the optimal weight factor allocation strategy is found. Then, the path conflict sensitivity of the high-delay area is associated with the spatio-temporal coverage sparsity of the low-density area to form a comprehensive evaluation model. Then, based on the network topology adaptation parameters in the gene expression template, the growth rate of the initial dynamic weight factor is constrained to ensure its high efficiency in different network states. This process utilizes optimization algorithms and simulation techniques to ensure the effectiveness and stability of the weight factor.

[0176] 603. Apply a dynamic decay factor to the initial dynamic weight factor based on the historical correction record of the network topology adaptation parameters to suppress the asymmetric association overload between the high-delay area and the low-density area and generate a dynamic weight factor.

[0177] In step 603, the historical correction record of the network topology adaptation parameter contains relevant information about past network topology adjustments. The dynamic decay factor is a tuning parameter used to suppress the asymmetric correlation overload phenomenon of the initial dynamic weight factor, ensuring a smooth transition in its practical application. The finally generated dynamic weight factor is a weight factor adjusted by the dynamic decay factor, which can better adapt to the real-time network conditions.

[0178] In the embodiments of the present application, first, based on the historical correction record of the network topology adaptation parameter, a dynamic decay factor is applied to the initial dynamic weight factor. Specifically, using the adaptive control theory, the optimal decay factor value in the current network state is predicted according to the data in the historical correction record. Then, the dynamic decay factor is used to suppress the asymmetric correlation overload in the high-delay area and the low-density area, ensuring that the weight factor can remain stable under different conditions. Finally, all information is integrated to generate the final dynamic weight factor. This step combines data analysis, adaptive control, and optimization algorithms to ensure that the system can operate efficiently in a complex and changing real environment.

[0179] The following is a specific example:

[0180] In an intelligent urban traffic management system, the system first calculates the spatio-temporal aggregation degree difference index between the high-delay area and the low-density area based on the spatio-temporal coupling coefficient. Then, an initial dynamic weight factor is generated according to this index and applied to network topology optimization, while considering the path conflict sensitivity and spatio-temporal coverage sparsity. Then, based on the historical correction record of the network topology adaptation parameter, a dynamic decay factor is applied to the initial dynamic weight factor to ensure its stability in practical applications. For example, when it is detected that a certain area has high delay and low density, the system will automatically adjust the weight factor to preferentially process the data packets in this area, thereby alleviating congestion and improving the overall traffic flow.

[0181] In summary, steps 601 to 603 significantly improve the flexibility and accuracy of data packet processing. By dynamically adjusting the weight factor and combining with the changes in the network topology for real-time optimization, not only the response speed and stability of the system are enhanced, but also the performance fluctuations caused by network structure adjustments are effectively avoided. This method greatly improves the intelligent level of data management and scheduling, makes traffic management more scientific and reasonable, improves the user experience, and significantly improves the overall operation efficiency of the city. At the same time, by introducing the dynamic decay factor mechanism, the stability and reliability of the system in the face of emergencies are ensured, ensuring efficient path planning and data management.

[0182] To solve the cross - source retrieval conflict and protocol compatibility problems in data packet transmission during path planning, the solution generates feedback parameters based on the cross - source retrieval conflict index output by the adaptability evaluation function and the pre - loaded data verification result. This method constructs a multi - dimensional feedback model by calculating the conflict and compatibility coupling coefficient, combining the cross - source retrieval conflict index and the protocol compatibility ratio, effectively promoting the system's ability of self - adjustment and optimization. In some embodiments, generating the feedback parameters based on the cross - source retrieval conflict index output by the adaptability evaluation function and the pre - loaded data verification result in step 105 includes:

[0183] 701. Calculate the conflict and compatibility coupling coefficient based on the cross - source retrieval conflict index output by the adaptability evaluation function and the protocol compatibility ratio in the pre - loaded data verification result;

[0184] In step 701, the cross - source retrieval conflict index is a quantitative indicator measuring the degree of retrieval conflict between different data sources. The protocol compatibility ratio in the pre - loaded data verification result refers to the degree of compatibility between different protocols found during data verification. The conflict and compatibility coupling coefficient is an indicator used to measure the degree of mutual influence between conflict and compatibility.

[0185] In the embodiments of the present application, first, based on the cross - source retrieval conflict index output by the adaptability evaluation function and the protocol compatibility ratio in the pre - loaded data verification result, using statistical analysis methods, combining historical data and the current network state, calculate the conflict and compatibility coupling coefficient through data analysis techniques. Specifically, collect the conflict situations between different data sources and the verification results of protocol compatibility over a period of time, and use these data to construct a mathematical model to calculate the conflict and compatibility coupling coefficient. The final result is a value reflecting the intensity of the interaction between conflict and compatibility, serving as the basis for subsequent steps.

[0186] 702. Generate initial feedback parameters according to the conflict and compatibility coupling coefficient, associate the protocol compatibility loss in high - conflict regions with the path offset, and impose dynamic decay constraints on the initial feedback parameters based on the historical correction records of the gene expression template;

[0187] In step 702, the initial feedback parameter is a variable generated based on the conflict and compatibility coupling coefficient, used to adjust the data packet processing priority. The protocol compatibility loss in high - conflict regions refers to data transmission problems caused by protocol incompatibility in high - conflict regions. The path offset is a strategy for adjusting the path to reduce conflicts. The dynamic decay constraint is a mechanism used to suppress the change speed of the initial feedback parameter to ensure its smooth transition.

[0188] In the embodiments of the present application, initial feedback parameters are generated based on the conflict and compatibility coupling coefficients calculated in step 701. Here, machine learning algorithms (such as support vector machines or neural networks) are used to find the optimal feedback parameter allocation strategy through training on historical data. Then, the protocol compatibility loss in high-conflict regions is associated with the path offset to form a comprehensive evaluation model. Subsequently, dynamic decay constraints are imposed on the initial feedback parameters based on the historical correction records of the gene expression templates to ensure their high efficiency and stability in different network states. This process utilizes optimization algorithms and simulation techniques to ensure the effectiveness and stability of the feedback parameters.

[0189] 703. Based on the initial feedback parameters, fuse the conflict and compatibility coupling coefficients, the cross-source retrieval conflict index, and the protocol compatibility ratio to construct a multi-dimensional feedback model, and introduce a penalty term related to the difference in dynamic priority parameters to generate feedback parameters, where the cross-source retrieval conflict index serves as the main adjustment variable of the multi-dimensional feedback model.

[0190] In step 703, the multi-dimensional feedback model is a mathematical model that comprehensively considers various factors and aims to evaluate the system performance under different strategies. The penalty term related to the difference in dynamic priority parameters is to prevent the performance degradation caused by the change of dynamic priority parameters. The cross-source retrieval conflict index, as the main adjustment variable, determines the basic processing order of data packets.

[0191] In the embodiments of the present application, regression analysis and optimization algorithms are first used to fuse the conflict and compatibility coupling coefficients, the cross-source retrieval conflict index, and the protocol compatibility ratio to form a multi-dimensional feedback model. Then, a penalty term related to the difference in dynamic priority parameters is introduced to prevent performance fluctuations caused by changes in dynamic priority parameters. By simulating and testing the performance under different network conditions, the model parameters are adjusted until the best performance is achieved. The final result is a feedback model that can comprehensively consider various factors and provide an optimal solution to guide actual operations.

[0192] The following is a specific example:

[0193] In an intelligent urban traffic management system, the system first calculates the conflict and compatibility coupling coefficients. Then, based on this coefficient, initial feedback parameters are generated and applied to network topology optimization, while considering the protocol compatibility loss and path offset in high-conflict regions. Next, the conflict and compatibility coupling coefficients, the cross-source retrieval conflict index, and the protocol compatibility ratio are fused to construct a multi-dimensional feedback model. For example, when it is detected that a certain area has high conflicts and low protocol compatibility, the system will automatically adjust the feedback parameters to optimize the path selection, thereby improving the overall traffic flow.

[0194] In summary, steps 701 to 703 significantly improve the flexibility and accuracy of data packet processing. By dynamically adjusting the feedback parameters and performing real-time optimization in combination with changes in the network topology, not only the response speed and stability of the system are enhanced, but also performance fluctuations caused by protocol compatibility and cross-source retrieval conflicts are effectively avoided. This method greatly improves the intelligence level of data management and scheduling, makes traffic management more scientific and reasonable, improves the user experience, and significantly improves the overall operation efficiency of the city. At the same time, by introducing a multi-dimensional feedback model and penalty terms, the stability and reliability of the system in the face of complex and changing actual environments are ensured, and efficient path planning and data management are guaranteed.

[0195] Figure 2 The following is a schematic structural diagram of a multi-source heterogeneous spatial data retrieval system improved based on the R-tree provided by an embodiment of the present application. As Figure 2 shown, the system includes:

[0196] A parsing module 21 that parses multi-source heterogeneous data packets carrying geographic tags in the network data stream. Each data packet contains spatial information of at least two different coordinate systems, sampling frequencies, or data formats, and generates dynamic priority parameters that constrain the splitting dimension of the R-tree according to the spatio-temporal aggregation degree of each data packet on the transmission path;

[0197] A matching module 22 that extracts the reference encoding of the splitting path corresponding to the cross-source query pattern in the historical record of the network data stream. The reference encoding is generated by matching the spatio-temporal coverage complementarity of different data packet transmission paths, and uses the reference encoding as the gene expression template of the splitting path in the initial population of the genetic algorithm;

[0198] An updating module 23 that performs co-evolution of the splitting path at the network edge node, drives the splitting path to iterate in the direction of minimizing cross-source retrieval conflicts, generates a path offset each time an iteration is performed, updates the network topology adaptation parameters of the gene expression template, and constructs an adaptability evaluation function based on the correlation between the dynamic priority parameter and the data packet transmission delay;

[0199] A triggering module 24 that triggers preloading of heterogeneous data packets according to the direction and amplitude of the path offset. The preloaded data is a set of data packets in network topology adjacent nodes that match the spatio-temporal coverage area of the current splitting path, and performs compatibility verification of the network transmission protocol on the data packets through the format conversion rules in the gene expression template;

[0200] A generating module 25 that generates feedback parameters through the cross-source retrieval conflict index output by the adaptability evaluation function and the preloading data verification result, and dynamically corrects the mapping relationship between the dynamic priority parameter and the gene expression template.

[0201] Figure 2The described multi-source heterogeneous spatial data retrieval system improved based on the R-tree can execute Figure 1 A method for retrieving multi-source heterogeneous spatial data improved based on the R-tree described in the illustrated embodiment, the implementation principle and technical effects of which will not be elaborated. For the multi-source heterogeneous spatial data retrieval system improved based on the R-tree in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0202] In a possible design, Figure 2 The multi-source heterogeneous spatial data retrieval system improved based on the R-tree in the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0203] The storage component 31 stores one or more computer instructions, where the one or more computer instructions are called and executed by the processing component 32.

[0204] The processing component 32 is used for the Figure 1 Method for retrieving multi-source heterogeneous spatial data improved based on the R-tree in the above embodiment.

[0205] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0206] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0207] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0208] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0209] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0210] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server. The above processing component, storage component, etc. can be basic server resources rented or purchased from a cloud computing platform.

[0211] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 A method for retrieving multi-source heterogeneous spatial data based on the improvement of the R-tree shown in the embodiment.

[0212] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0213] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0214] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A multi-source heterogeneous spatial data retrieval method based on an improved R-tree, characterized in that: include: Parse multi-source heterogeneous data packets carrying geographic tags in network data streams. Each data packet contains spatial information of at least two different coordinate systems, sampling frequencies or data formats. Generate dynamic priority parameters that constrain the R-tree splitting dimension based on the spatiotemporal aggregation of each data packet on the transmission path. Extracting a benchmark code of a split path corresponding to a cross-source query pattern in a historical record of a network data flow, wherein the benchmark code is generated by matching the spatiotemporal coverage complementarity of different data packet transmission paths, and using the benchmark code as a gene expression template of the split path in an initial population of a genetic algorithm; Executing the co-evolution of the split path at the network edge node, driving the split path to iterate in the direction of minimizing cross-source retrieval conflicts, generating a path offset and updating the network topology adaptation parameters of the gene expression template in each iteration, and constructing an adaptability evaluation function based on the correlation between the dynamic priority parameter and the data packet transmission delay; Triggering heterogeneous data packet preloading according to the direction and magnitude of the path offset, the preloaded data is a set of data packets in adjacent nodes of the network topology that match the spatiotemporal coverage area of ​​the current split path, and verifying the compatibility of the data packets with the network transmission protocol through the format conversion rules in the gene expression template; Feedback parameters are generated by the cross-source search conflict index output by the adaptability evaluation function and the preloaded data verification result, and the mapping relationship between the dynamic priority parameter and the gene expression template is dynamically corrected.

2. The method according to claim 1, characterized in that The method of executing the co-evolution of the split path at the network edge node, driving the split path to iterate in the direction of minimizing cross-source retrieval conflicts, generating a path offset in each iteration and updating the network topology adaptation parameters of the gene expression template, includes: The split path is encoded into a multi-dimensional vector, where each dimension corresponds to the adjustable interval of the network topology adaptation parameter. The topological overlap is calculated based on the spatiotemporal area covered by the current split path. Constructing a conflicting neighborhood table according to the topological overlap, recording the heterogeneous data packet transmission paths and network topology adaptation parameter differences that overlap with the current split path in time and space, and generating candidate offsets; A dual probability selection mechanism is used to select a target offset from the candidate offsets, wherein a first probability distribution is based on a success rate of the candidate offset in reducing conflicts in historical iterations, and a second probability distribution is based on a compatibility constraint between the target offset and a format conversion rule in a gene expression template; Applying the target offset to the current split path, dynamically adjusting the parameter value of the corresponding dimension in the multidimensional vector, and updating the topological overlap threshold of the conflicting neighbor table to trigger the elimination of low-priority paths; The time-space coverage matching degree between the updated split path and the preloaded data packet set is recalculated, and the change in the time-space coverage matching degree is fed back to the weight calculation of the dynamic priority parameter to achieve an iterative closed loop.

3. The method according to claim 2, characterized in that The adopting a dual probability selection mechanism to select a target offset from the candidate offsets includes: Based on the success rate of reducing conflicts by the candidate offset in historical iterations, the ratio of the number of successes of each candidate offset to the number of path priority enhancements within a preset time window is counted and normalized to an initial weight of the first probability distribution; Extracting the protocol field bound to the format conversion rule in the gene expression template, calculating the overlap ratio between the parameter dimension corresponding to the candidate offset and the preset field value range, and mapping it to the constraint weight of the second probability distribution; Nonlinearly superimposing the initial weight of the first probability distribution and the constraint weight of the second probability distribution, and dynamically adjusting the superposition coefficient according to the proportion of low-priority paths in the conflict neighborhood table, so that the compatibility constraint has a higher weight in the conflict-intensive area; Roulette screening is performed based on the comprehensive selection probability to verify the non-conflict between the candidate offset and the core dimension parameters in the gene expression template. If there is a conflict, the second-best candidate offset is traced back until the target offset is selected after the constraints are met.

4. The method according to claim 3, characterized in that The nonlinear superposition of the initial weight of the first probability distribution and the constraint weight of the second probability distribution, wherein the superposition coefficient is dynamically adjusted according to the proportion of the low priority path in the conflict neighborhood table, comprises: Based on the comparison between the proportion of low-priority paths in the conflict neighborhood table and the historical maximum path capacity, the conflict density index is calculated through a piecewise function; A dynamic superposition coefficient is generated according to the conflict density index, and an adjustment mechanism triggered by a segmented threshold is adopted, and a historical sliding mean is combined as a dynamic attenuation factor to suppress the sudden change of the dynamic superposition coefficient, wherein the growth rate of the dynamic superposition coefficient is adjusted as the conflict density index changes within the threshold interval; Associating the initial weight of the first probability distribution with the dynamic superposition coefficient, and superimposing the association result with the constraint weight of the second probability distribution, performing logarithmic weighted summation and truncation processing on the superposition coefficient; The rate of the dynamic attenuation factor is adjusted according to the difference between the superposition coefficient and the historical sliding mean. If it exceeds the tolerance interval, the rate is increased, and the updated rate is applied to the superposition coefficient calculation of the next iteration.

5. The method according to claim 1, characterized in that The constructing of an adaptability evaluation function based on the association between the dynamic priority parameter and the data packet transmission delay comprises: Based on the spatiotemporal distribution of the dynamic priority parameters, calculating the spatiotemporal coupling coefficient of data packet transmission delay and geographic distribution density; Generating a dynamic weight factor according to the spatiotemporal coupling coefficient, associating the spatiotemporal aggregation of high-delay regions with low-density regions, and constraining the dynamic weight factor based on a network topology adaptation parameter of a gene expression template; The dynamic priority parameter, dynamic weight factor and spatiotemporal coupling coefficient are integrated to construct an adaptability evaluation function, and a penalty term related to the difference in network topology adaptation parameters is introduced, wherein the dynamic priority parameter is used as the main decision variable and the dynamic weight factor is used as the conflict sensitivity adjustment coefficient.

6. The method according to claim 5, characterized in that Generating a dynamic weight factor according to the spatiotemporal coupling coefficient to associate the spatiotemporal aggregation of the high-delay area with the low-density area includes: Based on the spatiotemporal coupling coefficient and the inverse correlation between the transmission path fluctuation and the change in the geographical distribution gradient, the spatiotemporal aggregation difference index between the high-latency area and the low-density area is calculated; Generate an initial dynamic weight factor according to the spatiotemporal aggregation difference index, associate the path conflict sensitivity of the high-delay area with the spatiotemporal coverage sparsity of the low-density area, and constrain the growth rate of the initial dynamic weight factor through the network topology adaptation parameter of the gene expression template; Based on the historical correction record of the network topology adaptation parameter, a dynamic attenuation factor is applied to the initial dynamic weight factor to suppress the asymmetric associated overload between the high-latency area and the low-density area, thereby generating a dynamic weight factor.

7. The method according to claim 1, characterized in that The generating of feedback parameters by using the cross-source search conflict index output by the adaptability evaluation function and the preloaded data verification result includes: Calculate the conflict and compatibility coupling coefficient based on the cross-source retrieval conflict index output by the adaptability evaluation function and the protocol compatibility ratio in the preloaded data verification result; generating an initial feedback parameter according to the conflict and compatibility coupling coefficient, associating the protocol compatibility loss in the high-conflict region with the path offset, and applying a dynamic attenuation constraint to the initial feedback parameter based on the historical correction record of the gene expression template; Based on the initial feedback parameters, the conflict and compatibility coupling coefficient, the cross-source retrieval conflict index and the protocol compatibility ratio are integrated to construct a multidimensional feedback model, and a penalty term related to the dynamic priority parameter difference is introduced to generate feedback parameters, wherein the cross-source retrieval conflict index serves as the main regulating variable of the multidimensional feedback model.

8. A multi-source heterogeneous spatial data retrieval system based on R-tree improvement, characterized in that: include: A parsing module is used to parse multi-source heterogeneous data packets carrying geographic tags in network data streams. Each data packet contains spatial information of at least two different coordinate systems, sampling frequencies or data formats. The dynamic priority parameters constraining the R-tree splitting dimension are generated according to the spatiotemporal aggregation of each data packet on the transmission path. A matching module extracts a benchmark code of a split path corresponding to a cross-source query pattern in a historical record of a network data flow, wherein the benchmark code is generated by matching the spatiotemporal coverage complementarity of different data packet transmission paths, and uses the benchmark code as a gene expression template of the split path in an initial population of a genetic algorithm; An update module executes the co-evolution of the split path at the network edge node, drives the split path to iterate in the direction of minimizing cross-source retrieval conflicts, generates a path offset in each iteration and updates the network topology adaptation parameters of the gene expression template, and constructs an adaptability evaluation function based on the correlation between the dynamic priority parameter and the data packet transmission delay; A trigger module triggers the preloading of heterogeneous data packets according to the direction and magnitude of the path offset, wherein the preloaded data is a set of data packets in adjacent nodes of the network topology that match the spatiotemporal coverage area of ​​the current split path, and verifies the compatibility of the data packets with the network transmission protocol through the format conversion rules in the gene expression template; A generation module generates feedback parameters through the cross-source search conflict index output by the adaptability evaluation function and the preloaded data verification result, and dynamically corrects the mapping relationship between the dynamic priority parameter and the gene expression template.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-source heterogeneous spatial data retrieval method based on an R-tree improvement as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, the multi-source heterogeneous spatial data retrieval method based on the improved R-tree as described in any one of claims 1 to 7 is implemented.

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