A distributed quotation data collaborative pushing method and system

Abstract

The application provides a distributed quotation data collaborative pushing method and system. Wherein, through collecting transmission path environment fluctuation data, a dynamic correlation model with network congestion trend is established to generate environment network correlation parameters; real-time quotation data is cut in dual dimensions of geographic grid boundary and commodity classification level, dynamic binding of data blocks and storage nodes is realized through a scalable hash ring; based on environment parameter energy integration, geographic grid coverage density and commodity level update rate, a dynamic routing strategy is generated, transmission path priority is calculated in combination with geographic expansion direction and data volume growth trend; a path state evolution model is constructed to evaluate node failure probability and spectrum energy attenuation, when the path correction factor exceeds the threshold, a multi-channel pushing mechanism is triggered. The technical scheme provided by the application enables the distributed quotation data pushing to have network state adaptive multi-path collaborative capability.

Description

Technical Field

[0001] This application relates to the field of distributed network communication technology, and in particular to a method and system for collaboratively pushing distributed quotation data. Background Technology

[0002] In cross-regional businesses such as financial transactions and logistics tracking, the system needs to support real-time sharded storage and millisecond-level multi-node synchronization of massive dynamic price data, for scenarios such as global commodity price fluctuations and cross-border exchange rate updates. These businesses require data sharding to balance regional relevance and load balancing, while also ensuring strong data consistency and low latency through cross-data center collaborative push mechanisms.

[0003] Currently, some enterprises adopt a centralized master node sharding and batch broadcast synchronization mechanism. This solution statically divides data into a fixed number of storage nodes through predefined rules, deploys a master node in each region to receive local write requests, and relies on a centralized control node to maintain a global sharding routing table.

[0004] However, the core problem with this solution lies in the coupling defects between the centralized architecture and static policies. Predefined sharding rules cannot dynamically detect changes in business load; sudden traffic surges can easily overload a single master node, and shard expansion requires downtime to migrate data, causing service interruptions. The basic message queue lacks priority scheduling and adaptive compression capabilities, leading to severe network bandwidth contention in high-concurrency scenarios. Summary of the Invention

[0005] This application provides a distributed quotation data collaborative push method and system to solve the problem of low network transmission efficiency in the prior art.

[0006] Firstly, this application provides a distributed quotation data collaborative push method, including:

[0007] Collect environmental fluctuation data of the data transmission path, and dynamically correlate the environmental fluctuation data with the changing trend of the network congestion index to generate environmental network correlation parameters;

[0008] The real-time updated price data is divided into independent data blocks according to the boundaries of the geographic grid and the affiliation of the product category level nodes. The independent data blocks are dynamically bound to the storage nodes through a scalable hash ring to generate a redundant storage topology.

[0009] Based on the energy integration results of the environmental network association parameters, combined with the coverage density of the geographic grid and the update rate of the commodity classification level, a distributed routing strategy is generated.

[0010] According to the communication weight distribution in the distributed routing strategy, in combination with the boundary expansion direction of the geographic grid and the data volume growth trend of the commodity classification hierarchy, the transmission path selection priority of each independent data block is calculated in coordination;

[0011] A path state evolution model is established based on the node failure probability of the redundant storage topology and the spectrum energy decay rate of the environmental network association parameter, and a path correction factor is generated according to the path state evolution model. When the path correction factor exceeds a preset threshold, the independent data block is pushed to a plurality of parallel transmission channels in a preset geographic direction according to the transmission path selection priority.

[0012] Optionally, a set of communication weight distributions is extracted from the storage nodes in the distributed routing strategy, a set of main direction components is generated by vector decomposition of the boundary expansion direction of the geographic grid, and a trend slope parameter is calculated based on the adjacent time window data increment sequence of the commodity classification hierarchy;

[0013] The amplitude of the set of main direction components and the trend slope parameter are input into a distributed collaborative computing node, a set of dynamic superposition coefficients is generated by product operation, and the set of dynamic superposition coefficients is superimposed into the set of communication weight distributions according to the cell identifier of the geographic grid to form a dynamically adjusted communication weight distribution;

[0014] Based on the dynamically adjusted communication weight distribution, in combination with the coverage density, the set of dynamic superposition coefficients, the depth of the commodity classification hierarchy node, and the real-time update frequency of the independent data block, a transmission path selection priority is generated through the distributed collaborative computing node.

[0015] Optionally, a mapping relationship table of the cell identifier of the geographic grid and the set of communication weight distributions is obtained, and the original communication weight value corresponding to the cell identifier of the geographic grid is extracted based on the mapping relationship table;

[0016] According to the inverse relationship between the coverage density of the geographic grid and the depth of the commodity classification hierarchy node, the merging ratio of the dynamic superposition coefficient and the original communication weight value is determined;

[0017] The cell identifier in the set of dynamic superposition coefficients is matched with the cell identifier of the geographic grid in the mapping relationship table. For the cell identifier that matches successfully, the corresponding dynamic superposition coefficient and the original communication weight value are superimposed in proportion according to the merging ratio;

[0018] The communication weight value after superposition processing is written into the set of communication weight distributions according to the cell identifier of the geographic grid, and a dynamically adjusted communication weight distribution containing the associated features of geographic expansion direction and data growth trend is generated.

[0019] Optionally, a communication density parameter is generated by performing a product operation on the communication weight value of the geographic grid unit and the coverage density, and a hierarchical depth parameter is generated by performing an inverse proportional conversion on the dynamic superposition coefficient and the commodity classification hierarchical node depth;

[0020] A dynamic synthesis parameter is generated by fusing the communication density parameter, the hierarchical depth parameter, and an update intensity parameter generated by the real-time update frequency of the independent data block in a preset proportion;

[0021] A transmission path selection priority of the independent data block in the geographic grid unit is generated based on the composite ranking of the dynamic synthesis parameter in the distributed collaborative computing node.

[0022] Optionally, a real-time failure probability value of each storage node in the redundant storage topology is obtained, and a spectrum energy attenuation rate value of the corresponding transmission path in the environmental network association parameter is obtained;

[0023] The superposition weight of the failure probability value is dynamically allocated according to the ratio of the historical online duration of the storage node to the current spectrum energy value;

[0024] The failure probability value is dynamically combined with the spectrum energy attenuation rate value according to the allocated superposition weight, to generate a path evolution parameter sequence of each transmission path;

[0025] Based on the numerical distribution characteristics of the path evolution parameter sequence, a path state evolution model is established by a preset evolution parameter interval division rule, and the path state evolution model divides the numerical interval into a stable state evolution zone, a fluctuation state evolution zone, and a failure state evolution zone;

[0026] When the numerical value of the path evolution parameter sequence falls into the fluctuation state evolution zone or the failure state evolution zone, a path correction factor is generated according to the deviation amplitude of the numerical value from the critical value of the corresponding evolution zone.

[0027] Optionally, the current value of the path correction factor is monitored, and when the current value is greater than a preset correction threshold, the push order of the independent data block to be pushed is determined according to the ranking in the transmission path selection priority list;

[0028] According to a preset coverage range of the geographic direction, a set of storage nodes associated with the preset geographic direction is extracted from the redundant storage topology, and at least one transmission channel is allocated to each storage node in the set of storage nodes;

[0029] The independent data block to be pushed is segmented to generate segmented data blocks equal in number to the transmission channels, and the segmented data blocks are sequentially marked with corresponding segment numbers according to the push order;

[0030] The segment number is dynamically bound with a channel identifier of a transmission channel, a segment channel mapping table is generated, and according to the segment channel mapping table, multiple segment data blocks of the same independent data block are synchronously pushed to multiple parallel transmission channels in a preset geographical direction through corresponding transmission channels.

[0031] Optionally, according to the rectangular boundary coordinates of the geographical grid, the center point coordinates of each geographical grid and the path depth of the commodity classification hierarchical node are extracted;

[0032] When the geographical coordinates of the real-time updated quotation data fall within the rectangular boundary of any geographical grid, the product of the center point coordinates of the geographical grid and the path depth of the commodity classification hierarchical node is calculated, and if the product value is less than a preset threshold, the quotation data is attributed to the independent data block corresponding to the current geographical grid and commodity classification hierarchical node;

[0033] A segment identifier code is generated for each independent data block, a data structure of the scalable hash ring is constructed, and the segment identifier code is input into a key value mapping function of the scalable hash ring to calculate a mapping angle value of the segment identifier code on the scalable hash ring;

[0034] The storage node corresponding to the first virtual mapping point containing the mapping angle value in the clockwise direction is selected as the primary storage node, and at least two storage nodes separated from the primary storage node by a fixed angle on the scalable hash ring are selected as the secondary storage nodes, to generate a redundant storage topology bound by the primary and secondary storage nodes.

[0035] Optionally, for each geographical grid, the energy integral result of the environmental network associated parameter is accumulated and calculated, and the coverage density value of the geographical grid is superimposed, to generate the routing weight of each geographical grid;

[0036] The routing weight is multiplied by the update rate of the same commodity classification hierarchy to obtain a dynamic weight coefficient of each commodity classification hierarchy, and the routing level is divided according to the numerical range of the dynamic weight coefficient;

[0037] A preset number of candidate routing paths are allocated for each routing level, and a transmission direction set of the candidate routing path is determined according to the geographical distribution direction of the storage nodes in the redundant storage topology and the boundary expansion direction of the geographical grid;

[0038] The routing level, transmission direction set and corresponding dynamic weight coefficient are associated and stored to generate a distributed routing strategy.

[0039] Optionally, the physical interference intensity data and network delay fluctuation data in the environmental fluctuation data are collected, the fluctuation peak time of both is extracted according to the same time window, and the time matching degree is calculated;

[0040] The fluctuation amplitude of the physical interference intensity data and the sudden increase amplitude of the network delay fluctuation data are proportionally adjusted based on the time matching degree, so that the fluctuation magnitudes of both are mapped to a unified numerical interval;

[0041] The adjusted physical interference fluctuation amplitude and network delay sudden increase amplitude are input into a dynamic weight allocator, the fusion proportional weight of both is automatically adjusted according to the density distribution of the matching events in the time window, and the environmental network correlation parameter is generated.

[0042] In a second aspect, the application provides a distributed quotation data collaborative pushing system, comprising:

[0043] A collection module collects environmental fluctuation data of a transmission path, dynamically correlates the environmental fluctuation data with a network congestion index change trend, and generates an environmental network correlation parameter;

[0044] A division module divides real-time updated quotation data into independent data blocks according to the boundary of a geographic grid and the attribution relationship of a commodity classification hierarchical node, and dynamically binds the independent data blocks to storage nodes through a scalable hash ring to generate a redundant storage topology;

[0045] A generation module generates a distributed routing strategy based on the energy integral result of the environmental network correlation parameter, in combination with the coverage density of the geographic grid and the update rate of the commodity classification hierarchy;

[0046] A calculation module calculates the transmission path selection priority of each independent data block in a distributed manner according to the communication weight distribution in the distributed routing strategy, in combination with the boundary extension direction of the geographic grid and the data volume growth trend of the commodity classification hierarchy;

[0047] A pushing module establishes a path state evolution model based on the node failure probability of the redundant storage topology and the spectral energy decay rate of the environmental network correlation parameter, generates a path correction factor according to the path state evolution model, and pushes the independent data blocks to multiple parallel transmission channels in a preset geographic direction according to the transmission path selection priority when the path correction factor exceeds a preset threshold.

[0048] The embodiment of the application collects environmental fluctuation data of a data transmission path, dynamically correlates the environmental fluctuation data with a change trend of a network congestion index to generate an environmental network correlation parameter, divides real-time updated quotation data into independent data blocks according to boundaries of a geographical grid and attribution of a commodity classification level node, dynamically binds the independent data blocks to storage nodes through a scalable hash ring to generate a redundant storage topology, generates a distributed routing strategy based on an energy integration result of the environmental network correlation parameter, in combination with a coverage density of the geographical grid and an update rate of the commodity classification level, cooperatively calculates a transmission path selection priority of each independent data block according to a communication weight distribution in the distributed routing strategy, in combination with a boundary expansion direction of the geographical grid and a data volume growth trend of the commodity classification level, establishes a path state evolution model based on a node failure probability of the redundant storage topology and a spectral energy decay rate of the environmental network correlation parameter, generates a path correction factor according to the path state evolution model, and pushes the independent data blocks to multiple parallel transmission channels in a preset geographical direction according to the transmission path selection priority when the path correction factor exceeds a preset threshold.

[0049] The application has the following beneficial effects:

[0050] By dynamically correlating network environmental fluctuation and congestion trend, the real-time adaptability and congestion avoidance capability of the transmission path are improved; based on data block fragmentation of the geographical grid and the commodity classification and scalable hash ring binding, the elastic expansion and load balancing of the storage node are realized, and the local disaster recovery capability of regional hotspots is enhanced; by fusing environmental parameters and business characteristics to generate a dynamic routing strategy, the priority scheduling of cross-domain transmission is optimized, and the cumulative delay of multi-hop transmission is reduced; in combination with the path state evolution model and the correction factor, the fault prediction and self-healing switching of the transmission channel are realized, and the push success rate and timeliness stability in the high-concurrency scenario are ensured.

[0051] Further, by extracting principal component vectors of the communication weight distribution and the geographical grid boundary expansion direction, in combination with the incremental trend slope parameter of the commodity classification level, a superposition coefficient is dynamically generated and the communication weight distribution is corrected, and finally multi-dimensional parameters such as coverage density, level depth and update frequency are fused to realize cooperative calculation of the transmission path priority. This mechanism can dynamically perceive the business growth direction and network resource distribution, adaptively adjust the path priority through weight superposition and multi-factor fusion, realize bandwidth tilt allocation in hot regions and redundancy avoidance in low active regions in cross-domain transmission, thereby improving the path selection accuracy in the high-concurrency scenario, reducing the network congestion probability, and reducing the end-to-end delay fluctuation caused by path ossification.

[0052] These and other aspects of the application will become more apparent in the following description of embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0054] Figure 1 A flow chart of a distributed quotation data collaborative pushing method provided by the present application is shown;

[0055] Figure 2 A structural schematic diagram of a distributed quotation data collaborative pushing system provided by the present application is shown. DETAILED DESCRIPTION

[0056] In order to enable the personnel in the technical field to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application.

[0057] In some processes described in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed or in parallel without the order in which they appear in this text. 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 can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the "first", "second", etc. in this text are used to distinguish different messages, devices, modules, etc., and do not represent the order. Also, "first" and "second" are not different types.

[0058] Researchers found that the sharding strategy of the existing distributed quotation system is out of touch with network environment fluctuations, and cross-domain pushing relies on fixed routing rules, resulting in poor dynamic load adaptability, high synchronization delay, and insufficient disaster recovery switching efficiency. Based on this, a distributed quotation data collaborative pushing method is provided, which can dynamically correct the transmission path through real-time association modeling of environment fluctuations and network congestion, and integrate multi-dimensional sharding rules of geographic grid and commodity classification to realize elastic storage of data blocks and low-delay pushing driven by priority.

[0059] The technical solutions of the present application can be applied to distributed data collaborative scenarios such as financial transactions, cross-border e-commerce, etc. which need to support high-frequency quotation updates, cross-regional strong consistency and sudden traffic scheduling.

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

[0061] Figure 1 A flowchart of a distributed quotation data collaborative pushing method is provided for the embodiments of the present application, as shown in Figure 1 The method comprises the following steps.

[0062] 101. Collecting environmental fluctuation data of a data transmission path, dynamically correlating the environmental fluctuation data with a change trend of a network congestion index to generate an environmental network correlation parameter;

[0063] The environmental fluctuation data is a set of indicators reflecting the real-time state of the transmission path collected by a distributed network probe, including bandwidth fluctuation rate, link delay jitter amplitude, data packet loss rate peak value, etc., which are used to characterize the dynamic changes of the network transmission environment. The network congestion index is a quantitative indicator trained based on a long short-term memory network, which predicts the congestion degree of the current network by analyzing the load rate, queue buffer overflow probability and routing hop number change trend in the historical traffic data. The environmental network correlation parameter is a parameter matrix generated by correlating and analyzing the environmental fluctuation data and the network congestion index using a dynamic graph convolution network, which is used to describe the dynamic relationship between network environment changes and congestion states.

[0064] In the implementation process of step 101, first, the intelligent probe cluster deployed at the key nodes of the network continuously monitors the target path in a sliding time window manner, extracts the high-order statistical features of the bandwidth fluctuation rate using the wavelet packet decomposition algorithm, and uses the improved Kalman filter to denoise the original delay data to generate a time-labeled standardized environmental fluctuation data set. Then, the data set is input into the pre-trained spatio-temporal graph neural network, the dynamic time warping technology is used to align it with the congestion index time series data provided by the network control plane, and the correlation coefficients in the time domain and frequency domain are calculated respectively through the double-channel attention mechanism. Finally, based on the joint learning model constructed by the gated recurrent unit, the above correlation features and path topology features are fused, and the environmental network correlation parameter matrix containing the path stability score, the congestion correlation strength and the fluctuation propagation factor is output through the multilayer perception, which will be used as the core input for subsequent route strategy formulation.

[0065] Optionally, in order to solve the problem that the correlation between environmental fluctuation and network congestion is difficult to quantify, in some embodiments, the step of dynamically correlating the environmental fluctuation data with the change trend of the network congestion index to generate the environmental network correlation parameter in step 101 comprises:

[0066] 1011. Collect the physical interference intensity data and network latency fluctuation data from the environmental fluctuation data, extract the peak fluctuation times of the two in the same time window and calculate the time matching degree;

[0067] 1012. Based on the time matching degree, the fluctuation amplitude of the physical interference intensity data and the sudden increase amplitude of the network latency fluctuation data are proportionally adjusted so that the fluctuation magnitudes of the two are mapped to a unified numerical range.

[0068] 1013. Input the adjusted physical interference fluctuation amplitude and network latency surge amplitude into the dynamic weight allocator, and automatically adjust the fusion ratio weight of the two according to the density distribution of matching events within the time window to generate environmental network association parameters.

[0069] In step 1011, a distributed sensor cluster deployed at the physical layer is used to synchronously collect physical interference intensity data (such as RSSI fluctuations and signal-to-noise ratio changes) and network delay fluctuation data using a sliding time window mechanism. Wavelet transform algorithm is used to extract the peak time sequence of the two fluctuations. Then, dynamic time warping algorithm is applied to align the time axes of the two peak sequences. The time matching degree is generated by calculating the proportion of the number of matching peak pairs to the total peaks. The higher the value, the more significant the causal relationship between physical interference and network delay.

[0070] Step 1012 adjusts the proportions of the two types of data based on the time matching degree: when the time matching degree is higher than the threshold, the maximum-minimum normalization method is used to scale the magnitude of physical interference and the magnitude of sudden increase in network latency to the 0-1 range respectively; if the matching degree is lower than the threshold, the scaling factor is adjusted according to the matching degree ratio by linear interpolation. For example, when the time matching degree is 0.6, the scaling factor of physical interference magnitude is 0.6 × standard normalization factor, ensuring that the difference in the magnitude of fluctuation of low-correlation data is compressed.

[0071] In step 1013, the adjusted physical interference fluctuation amplitude and network latency surge amplitude are input into the dynamic weight allocator. This module uses an event density-aware algorithm to statistically analyze the distribution density of matching events (i.e., events where physical interference and latency surge occur simultaneously) within the current time window: when the density is higher than the historical average, a higher weight is assigned to physical interference (e.g., 0.7); when the density is lower, a higher weight is assigned to network latency (e.g., 0.6). Finally, a multidimensional correlation parameter matrix is ​​generated using the weighted formula: Environmental Network Correlation Parameter = W_Physical × Physical Interference Amplitude + W_Latency × Network Latency Surge Amplitude (W_Physical + W_Latency = 1), which serves as the input for subsequent routing strategies.

[0072] 102、divide the real-time updated quotation data into independent data blocks according to the boundary of the geographical grid and the attribution relationship of the commodity classification hierarchical nodes, and dynamically bind the independent data blocks to storage nodes through a scalable hash ring to generate a redundant storage topology;

[0073] wherein the rectangular boundary coordinates of the geographical grid are a set of coordinate ranges of a regular rectangular grid divided according to longitude and latitude of a geographical area, for discretizing the physical space into computable units; the path depth of the commodity classification hierarchical nodes refers to the hierarchical distance of a commodity from a root node to a current node in a classification tree structure, reflecting the position depth of the commodity in the classification system; the scalable hash ring is a distributed storage structure improved based on a consistent hash algorithm, and the dynamic binding of data blocks to storage nodes is achieved through a virtual node mapping mechanism; the redundant storage topology refers to a data storage architecture constructed through a primary-secondary storage node binding strategy, ensuring that each data block exists in multiple physical nodes to improve disaster recovery capability.

[0074] The specific process of step 102 includes: extracting the geographical grid center coordinates and the commodity classification path depth, determining the data attribution through a product threshold value, generating a segmented identification code and mapping it to a hash ring, and finally selecting primary and secondary storage nodes according to the virtual node position.

[0075] 103、generate a distributed routing strategy based on the energy integral result of the environmental network correlation parameter, in combination with the coverage density of the geographical grid and the update rate of the commodity classification hierarchy;

[0076] wherein the energy integral result of the environmental network correlation parameter is a cumulative energy value obtained by numerically integrating the spectral energy in the parameter matrix in the time and space dimensions, reflecting the comprehensive strength of the network environment stability in a specific area; the coverage density of the geographical grid refers to the number of data nodes contained in a unit geographical area, representing the intensive degree of data distribution; the update rate of the commodity classification hierarchy is the frequency of data update under a certain commodity classification node in a unit time; the distributed routing strategy is a set of dynamic path selection rules generated by integrating the network environment state, data distribution characteristics and business demand, for optimizing data transmission efficiency and reliability.

[0077] Step 103 generates a dynamic routing strategy by integrating the environmental network energy integral, the geographical grid density and the commodity classification update rate. The specific process includes: calculating the routing weight of the geographical grid, obtaining the dynamic weight coefficient in combination with the commodity update rate and dividing the routing level, allocating candidate routing paths and determining the transmission direction, and finally generating a strategy associated with storage.

[0078] 104. According to the communication weight distribution in the distributed routing strategy, in combination with the boundary expansion direction of the geographic grid and the data volume growth trend of the commodity classification hierarchy, the transmission path selection priority of each independent data block is calculated cooperatively;

[0079] Wherein, the communication weight distribution is a set of numerical values reflecting the transmission priority of the geographic grid in the distributed routing strategy, which is calculated comprehensively by the environmental network state and business demand; the boundary expansion direction of the geographic grid is a vector direction generated by analyzing the historical expansion trajectory, representing the main trend of geographic area expansion; the data volume growth trend of the commodity classification hierarchy is the data increment change rate per unit time calculated based on time series analysis; the transmission path selection priority is a path optimization level sequence generated by cooperative calculation of network state, geographic expansion direction and business growth demand.

[0080] Step 104 dynamically adjusts the weight by fusing the communication weight distribution, the geographic expansion direction and the data growth trend, and generates the transmission path priority. The specific process includes: decomposing the geographic expansion direction into vector components, calculating the data growth slope, generating a dynamic superposition coefficient to adjust the communication weight, and finally generating the priority by comprehensively considering multiple parameters.

[0081] 105. Based on the node failure probability of the redundant storage topology and the spectral energy decay rate of the environmental network associated parameter, a path state evolution model is established, and a path correction factor is generated according to the path state evolution model. When the path correction factor exceeds a preset threshold, the independent data block is pushed to multiple parallel transmission channels in the preset geographic direction according to the transmission path selection priority.

[0082] Wherein, the node failure probability of the redundant storage topology is the service interruption possibility of the storage node predicted by the Markov chain model, which is calculated based on the hardware failure rate and network fluctuation historical data; the spectral energy decay rate is an index reflecting the decay rate of the transmission path signal strength with time in the environmental network associated parameter, which is calculated by frequency domain energy difference; the path state evolution model is a mathematical model describing the stability of the transmission path changing with time, which maps the path parameters to discrete state intervals; the path correction factor is a dynamic adjustment coefficient generated according to the degree of deviation of the path state from the stable interval, which is used to trigger the path switching decision; the segmented channel mapping table is a metadata table recording the binding relationship between data segment number and transmission channel, which realizes the cooperative control of multi-channel parallel transmission.

[0083] Step 105 builds a path state evolution model by analyzing the storage node failure probability and spectral energy attenuation, and generates a correction factor. When the correction factor exceeds the limit, the data is segmented and pushed to the parallel channel in the preset direction according to the priority. The specific process includes: collecting node failure probability and spectral attenuation data, dynamically combining to generate path evolution parameters, dividing state intervals, calculating correction factors, and monitoring threshold triggering segmented transmission.

[0084] To solve the problem of low data storage and retrieval efficiency in the distributed quotation system, in some embodiments, the real-time updated quotation data in step 102 is divided into independent data blocks according to the boundaries of the geographical grid and the attribution relationship of the commodity classification hierarchical nodes, and the independent data blocks are dynamically bound to the storage nodes through the scalable hash ring to generate a redundant storage topology, including:

[0085] 1021. According to the rectangular boundary coordinates of the geographical grid, the center point coordinates of each geographical grid and the path depth of the commodity classification hierarchical nodes are extracted;

[0086] 1022. When the geographical coordinates of the real-time updated quotation data fall within the rectangular boundary of any geographical grid, the product value of the center point coordinates of the geographical grid and the path depth of the commodity classification hierarchical nodes is calculated. If the product value is less than a preset threshold, the quotation data is attributed to the independent data block corresponding to the current geographical grid and commodity classification hierarchical node;

[0087] 1023. A segment identification code is generated for each independent data block, a data structure of the scalable hash ring is constructed, and the segment identification code is input into the key value mapping function of the scalable hash ring to calculate the mapping angle value of the segment identification code on the scalable hash ring;

[0088] 1024. The first storage node containing the mapping angle value in the clockwise direction is selected as the primary storage node, and at least two storage nodes separated by a fixed angle from the primary storage node on the scalable hash ring are selected as the secondary storage nodes, and a redundant storage topology bound by the primary and secondary storage nodes is generated.

[0089] Wherein, the center point coordinates of the geographical grid are the geometric center coordinates determined by calculating the intersection of the diagonal lines of the rectangular grid; the path depth of the commodity classification hierarchical node is the hierarchical number obtained by recursively traversing the classification tree; the segment identification code is a unique code generated based on the data block attributes; the virtual mapping point is a logical storage node identifier distributed at a fixed angle on the scalable hash ring.

[0090] In step 1021, first generate rectangular boundary coordinates based on a geographic grid division algorithm such as GeoHash, calculate the center point coordinates of each grid by the geometric center formula, and simultaneously traverse the commodity classification tree structure to use the depth-first search algorithm to count the path depth of each classification node to the root node, forming a mapping table of grid center coordinates and classification path depth.

[0091] After entering step 1022, when new offer data arrives, determine its belonging geographic grid through a geographic coordinate matching algorithm such as R-tree spatial index, obtain the corresponding center point coordinates and commodity classification path depth, and calculate the product value thereof; if the product value is less than a preset threshold (obtained by training historical data), classify the data to the independent data block corresponding to the current geographic grid and commodity classification level based on the K-means clustering algorithm, otherwise trigger an abnormal processing procedure.

[0092] In step 1023, generate a segment identification code for the metadata (including grid ID and classification path hash value) of the independent data block using the SHA-256 hash algorithm, and then build a data structure of a scalable hash ring: uniformly distribute virtual nodes on the ring at an angle of 2^N, each virtual node corresponds to the logical mapping of a physical storage node, and map the segment identification code to a specific angle value on the ring through a consistent hash algorithm.

[0093] Finally, execute step 1024 to find the first virtual mapping point greater than the angle on the hash ring clockwise according to the mapping angle value of the segment identification code, and set the physical storage node corresponding to the virtual mapping point as the primary node; then select two virtual mapping points corresponding to the physical nodes separated by 120 degrees in the clockwise direction as the secondary nodes according to the redundancy strategy (such as N=3 copies), and complete the binding of the primary and secondary nodes through a distributed lock mechanism to form a redundant storage topology containing data copy distribution relationship.

[0094] In order to solve the problem of insufficient data transmission path optimization in the distributed offer system, in some embodiments, the energy integral result of the environmental network association parameter in step 103 is combined with the coverage density of the geographic grid and the update rate of the commodity classification level to generate a distributed routing strategy, including:

[0095] 1031, for each geographic grid, accumulate and calculate the energy integral result of the environmental network association parameter, and superimpose the coverage density value of the geographic grid to generate the routing weight of each geographic grid;

[0096] 1032, multiply the routing weight by the update rate of the same commodity classification level to obtain the dynamic weight coefficient of each commodity classification level, and divide the routing level according to the numerical range of the dynamic weight coefficient;

[0097] 1033、assign a preset number of candidate routing paths to each of the routing levels, and determine a transmission direction set of the candidate routing paths according to a geographical distribution direction of the storage nodes in the redundant storage topology and a boundary expansion direction of the geographical grid;

[0098] 1034、store the routing levels, the transmission direction sets and the corresponding dynamic weight coefficients in association to generate a distributed routing strategy.

[0099] wherein the routing weight is a path selection priority quantization value calculated by weighting the environmental energy integral and the coverage density in the geographical grid; the dynamic weight coefficient is a product of the routing weight and the commodity classification update rate, used for dynamically adjusting the routing priority; the routing level is a priority level divided according to the range of the dynamic weight coefficient; the candidate routing path is a transmission path set preconfigured for each routing level; and the transmission direction set is a path direction constraint condition generated based on the distribution of the storage nodes and the geographical expansion direction.

[0100] In step 1031, first, the environmental network associated parameter matrix in each geographical grid is discretely integrated and calculated, the trapezoidal integration algorithm is used to accumulate the time-frequency energy value, and the kernel density estimation algorithm is used to calculate the coverage density of the data nodes in the grid; then the energy integral result and the coverage density are weighted and superimposed according to a preset proportion (such as 6:4), and the routing weight value in the range of 0-1 is generated by normalization processing, and the higher the weight, the higher the transmission priority of the grid.

[0101] After entering step 1032, the real-time update rate (such as the number of updates per second) of the commodity classification hierarchical node is extracted, which is multiplied by the routing weight of the corresponding geographical grid to obtain the dynamic weight coefficient; based on the preset hierarchical threshold (such as 0.3, 0.6, 0.9), the interval division algorithm is used to map the coefficient to three routing levels of high, medium and low, and a priority label (such as level 1 for the highest priority) is assigned to each level.

[0102] When step 1033 is executed, according to the latitude and longitude coordinates of the storage nodes in the redundant storage topology, the vector direction analysis algorithm is used to calculate the distribution direction angle of the storage nodes relative to the center point of the geographical grid, and the direction similarity matching algorithm is used to screen out the storage nodes with an included angle less than 15 degrees with the expansion direction (predicted by fitting the historical expansion trajectory) in combination with the boundary expansion direction of the geographical grid; a preset number (such as 5 for level 1) of candidate routing paths is assigned to each routing level, and the path direction set is composed of the matched storage node direction angle set.

[0103] Finally, in step 1034, the routing level, transmission direction set, and dynamic weight coefficient are associated and stored in a key-value database (such as Redis). A Bloom filter is used to optimize fast retrieval capabilities and generate a distributed routing strategy that includes a priority mapping table, direction constraint rules, and weight update mechanism. This strategy is then deployed by synchronizing it to all routing nodes through ZooKeeper.

[0104] To address the issues of delayed transmission path decisions and inaccurate communication weight adaptation caused by the decoupling of the geographic grid expansion direction and the product category data growth trend, in some embodiments, step 104, which involves collaboratively calculating the transmission path selection priority for each independent data block based on the communication weight distribution in the distributed routing strategy and considering the boundary expansion direction of the geographic grid and the data volume growth trend of the product category level, includes:

[0105] 1041. Extract the communication weight distribution set from the storage nodes in the distributed routing strategy, perform vector decomposition on the boundary extension direction of the geographic grid to generate the main direction component set, and calculate the trend slope parameter based on the adjacent time window data increment sequence of the commodity classification level.

[0106] 1042. Input the magnitude of the main direction component set and the trend slope parameter into the distributed collaborative computing node, generate a dynamic superposition coefficient set through product operation, and superimpose the dynamic superposition coefficient set into the communication weight distribution set according to the unit identifier of the geographic grid to form a dynamically adjusted communication weight distribution;

[0107] 1043. Based on the dynamically adjusted communication weight distribution, combined with the coverage density, the dynamic overlay coefficient set, the depth of the product category level nodes, and the real-time update frequency of the independent data blocks, a transmission path selection priority is generated through the distributed collaborative computing nodes.

[0108] Among them, the main direction component set is a set of multiple orthogonal vector components decomposed into the geographic grid expansion direction, used to quantify the spatial distribution of the expansion trend; the trend slope parameter is the rate of change of data increment in adjacent time windows calculated by linear regression; the dynamic overlay coefficient is the product of the main direction component amplitude and the trend slope, used to dynamically adjust the communication weight; the communication density parameter is the product of the communication weight and the coverage density, characterizing the matching degree between transmission resources and data distribution; the hierarchy depth parameter is a value calculated inversely proportional to the depth of the commodity classification node and the dynamic overlay coefficient, reflecting the influence weight of the classification hierarchy on the routing.

[0109] In step 1041, first extract the communication weight distribution set from the storage node of the distributed routing strategy, and use the principal component analysis algorithm to perform vector decomposition on the boundary expansion direction of the geographic grid to generate a main direction component set containing east-west and south-north components. At the same time, based on the time series data increment sequence of the commodity classification hierarchical node, the least squares method is used to fit the data amount change curve of the adjacent time window, and the trend slope parameter is calculated to form a slope and classification level mapping table.

[0110] After entering step 1042, the amplitude of the main direction component set is calculated by vector length, and the trend slope parameter is input into the distributed collaborative calculation framework to perform scalar multiplication operation in the calculation node to generate a dynamic superposition coefficient set. Then a mapping relationship table of geographic grid cell identification and communication weight distribution set is established, and the weight adjustment proportion is determined by the inverse ratio of coverage density and commodity classification node depth, and the specific formula is that the merging proportion is equal to 1 divided by 1 plus the product of coverage density and depth. Finally, through the distributed hash table matching unit identification, the dynamic superposition coefficient is weighted and superimposed with the original communication weight according to the merging proportion, for example, the original weight is multiplied by 0.7 plus the dynamic coefficient multiplied by 0.3, to generate a dynamic adjustment communication weight distribution containing the associated characteristics of geographic expansion and data growth.

[0111] In step 1043, first, based on the dynamically adjusted communication weight distribution, the communication density parameter is calculated, which is the communication weight multiplied by the coverage density. At the same time, the hierarchical depth parameter is calculated, which is the dynamic superposition coefficient divided by the commodity classification node depth plus 1. In addition, the update strength parameter is generated by multiplying the real-time update frequency of the independent data block with the preset gain coefficient. Then in the distributed collaborative calculation node, the communication density parameter, the hierarchical depth parameter and the update strength parameter are fused according to the preset proportion, for example, with a weight ratio of 4:3:3, to generate a dynamic synthesis parameter. Finally, based on the composite sorting result of the dynamic synthesis parameter, the transmission path of all independent data blocks in the geographic grid is prioritized through a multi-objective decision algorithm, and a global routing decision table containing path priority labels is output and pushed to the edge routing node for path selection.

[0112] To solve the problem of weight distribution imbalance caused by static coupling of geographic grid expansion and commodity classification data growth trend in traditional communication weight adjustment, in some embodiments, the step 1042 described that the dynamic superposition coefficient set is superimposed into the communication weight distribution set according to the cell identification of the geographic grid to form a dynamically adjusted communication weight distribution, comprising:

[0113] 201, obtain the mapping relationship table of the cell identification of the geographic grid and the communication weight distribution set, and extract the original communication weight value corresponding to the cell identification of the geographic grid based on the mapping relationship table;

[0114] 202. Determine the merging ratio of the dynamic superposition coefficient and the original communication weight value according to the inverse ratio relationship between the coverage density of the geographic grid and the node depth of the commodity classification hierarchy;

[0115] 203. Match the unit identifier in the dynamic superposition coefficient set with the unit identifier of the geographic grid in the mapping relationship table. For the unit identifier that matches successfully, proportionally superimpose the corresponding dynamic superposition coefficient and the original communication weight value according to the merging ratio;

[0116] 204. Re-write the communication weight value after superposition processing according to the unit identifier of the geographic grid into the communication weight distribution set to generate the dynamically adjusted communication weight distribution containing the correlation characteristics of the geographic expansion direction and the data growth trend.

[0117] Wherein, the unit identifier is the unique code of the geographic grid, used for accurately associating data with geographic units; the mapping relationship table is a distributed data structure storing the correspondence between the unit identifier and the communication weight value; the merging ratio is a weight adjustment coefficient dynamically calculated according to the inverse ratio relationship between the coverage density and the node depth of the commodity classification hierarchy; the dynamic superposition coefficient is a correction parameter generated by combining the geographic expansion direction component and the data growth trend, used for optimizing the communication weight distribution.

[0118] In step 201, the mapping relationship table of the pre-stored geographic grid unit identifier and the communication weight distribution set is obtained by querying the distributed key-value database, and the corresponding original communication weight value is quickly located and extracted based on the hash index of the unit identifier.

[0119] After entering step 202, the merging ratio is calculated using the formula according to the inverse ratio relationship between the coverage density and the node depth of the commodity classification hierarchy, and the specific formula is merging ratio = 1 / (1+coverage density x depth). This ratio is used to dynamically adjust the fusion weight of the superposition coefficient and the original weight.

[0120] When step 203 is executed, the unit identifier in the dynamic superposition coefficient set is matched with the unit identifier in the mapping relationship table, and the identifier consistency is quickly compared by the hash table matching algorithm. For the unit identifier that matches successfully, the dynamic superposition coefficient and the original communication weight value are linearly superimposed according to the merging ratio, for example, the adjusted communication weight value = 0.7W_original + 0.3C_dynamic (where W_original is the original communication weight value, and C_dynamic is the dynamic superposition coefficient), to generate the adjusted communication weight value.

[0121] Finally, in step 204, the adjusted communication weight value is re-written into the distributed storage communication weight distribution set according to the geographical grid unit identification, and the data consistency is ensured through the distributed transaction mechanism, and finally a dynamic adjustment communication weight distribution containing the geographical expansion direction and the data growth trend associated characteristics is generated, which provides input for subsequent priority calculation.

[0122] To solve the decision distortion problem caused by the linear superposition of communication weight, geographical density and commodity level depth parameters in traditional transmission path priority calculation, in some embodiments, the transmission path selection priority is generated by the distributed collaborative computing node based on the dynamic adjusted communication weight distribution, combined with the coverage density, the dynamic superposition coefficient set, the depth of the commodity classification level node and the real-time update frequency of the independent data block in step 1043, including:

[0123] 301. Perform product operation on the communication weight value of the geographical grid unit and the coverage density to generate a communication density parameter, and perform inverse proportional conversion on the dynamic superposition coefficient and the depth of the commodity classification level node to generate a level depth parameter;

[0124] 302. Generate a dynamic synthesis parameter by fusing the communication density parameter, the level depth parameter and the update intensity parameter generated by the real-time update frequency of the independent data block according to a preset proportion;

[0125] 303. Generate the transmission path selection priority of the independent data block in the geographical grid unit based on the composite sorting of the dynamic synthesis parameter in the distributed collaborative computing node.

[0126] Among them, the update intensity parameter is a dynamic factor generated by the product of the real-time update frequency of the independent data block and the preset gain coefficient, which is used to quantify the real-time demand of data update on path selection; the dynamic synthesis parameter is a comprehensive decision index generated by nonlinear proportion fusion of the communication density parameter, the level depth parameter and the update intensity parameter, which is used to avoid the priority distortion problem caused by traditional linear superposition.

[0127] In step 301, first, based on the dynamically adjusted communication weight distribution, the communication weight value of each geographical grid unit is extracted, and the multiplication operation is performed on the communication weight value and the coverage density of the corresponding grid to generate a communication density parameter = communication weight x coverage density, which reflects the adaptation degree of transmission resources and data distribution. At the same time, through the level depth inverse proportional conversion formula level depth parameter = dynamic superposition coefficient / (commodity classification node depth+1), the dynamic superposition coefficient is corrected by depth attenuation, and the interference of deep classification nodes on routing decision is weakened.

[0128] After entering step 302, the real-time update frequency of the independent data block is obtained from the distributed storage, which is multiplied by the preset gain coefficient (set to 1.2 according to business requirements) to generate an update intensity parameter = update frequency x 1.2, which is used to amplify the priority of high-frequency update data. Subsequently, a nonlinear fusion strategy is adopted, and the communication density parameter, the hierarchical depth parameter, and the update intensity parameter are dynamically synthesized by a weighted summation formula according to a preset ratio (such as 4:3:3) to generate a parameter = 0.4 x communication density + 0.3 x hierarchical depth + 0.3 x update intensity for fusion, avoiding decision bias caused by linear superposition of a single parameter.

[0129] In step 303, the dynamically synthesized parameter is input into the distributed collaborative computing node (such as Apache Fl ink stream processing engine), and the transmission path of all independent data blocks in the same geographic grid is prioritized based on a multi-objective composite ranking algorithm (such as TOPSIS superior-inferior solution distance method). By calculating the Euclidean distance between the dynamically synthesized parameter of each data block and the ideal solution, a priority ranking result is generated, and a global routing decision table containing path priority labels (such as P0-P3 levels) is finally output, which is pushed to the edge routing node through a message queue (such as Kafka) for real-time path selection.

[0130] To solve the transmission path stability problem caused by node failure and network attenuation in the distributed storage environment, in some embodiments, step 105 establishes a path state evolution model based on the node failure probability of the redundant storage topology and the spectral energy decay rate of the environmental network associated parameters, generates a path correction factor according to the path state evolution model, and when the path correction factor exceeds a preset threshold, pushes the independent data block to multiple parallel transmission channels in a preset geographic direction according to the transmission path selection priority, including:

[0131] 1051、Obtain the real-time failure probability value of each storage node in the redundant storage topology, and the spectral energy decay rate value of the corresponding transmission path in the environmental network associated parameters;

[0132] 1052、Dynamically allocate the superposition weight of the failure probability value according to the ratio of the historical online duration of the storage node to the current spectral energy value;

[0133] 1053、Dynamically combine the failure probability value with the spectral energy decay rate value according to the allocated superposition weight to generate a path evolution parameter sequence of each transmission path;

[0134] 1054、Based on the numerical distribution characteristics of the path evolution parameter sequence, a path state evolution model is established by a preset evolution parameter interval division rule, and the path state evolution model divides the numerical interval into stable state evolution zone, fluctuation state evolution zone, and failure state evolution zone.

[0135] 1055、When the value of the path evolution parameter sequence falls within the fluctuation state evolution zone or the failure state evolution zone, a path correction factor is generated according to the deviation amplitude of the value from the critical value of the corresponding evolution zone.

[0136] 1056、The current value of the path correction factor is monitored, and when the current value is greater than a preset correction threshold, the pushing order of the independent data block to be pushed is determined according to the ranking in the transmission path selection priority list;

[0137] 1057、According to the coverage range of the preset geographical direction, a set of storage nodes associated with the preset geographical direction is extracted from the redundant storage topology, and at least one transmission channel is allocated to each storage node in the set of storage nodes;

[0138] 1058、The independent data block to be pushed is segmented to generate segmented data blocks equal in number to the transmission channels, and the segmented data blocks are sequentially marked with corresponding segment numbers in the pushing order;

[0139] 1059、The segment number is dynamically bound to the channel identifier of the transmission channel to generate a segment channel mapping table, and according to the segment channel mapping table, multiple segmented data blocks of the same independent data block are synchronously pushed through corresponding transmission channels into multiple parallel transmission channels in the preset geographical direction.

[0140] Wherein, the real-time failure probability value is a quantitative indicator of the service availability of the storage node monitored in real time; the spectral energy decay rate value is the decay amount of the transmission path signal energy per unit time; the superposition weight is a weight coefficient dynamically allocated according to the dynamic proportion of the node historical online duration and the current spectral energy; the path evolution parameter sequence is a time sequence parameter set generated by weighting combination of the failure probability and the spectral decay rate; the stable state evolution zone, the fluctuation state evolution zone and the failure state evolution zone are stability level intervals defined in the path state model; the deviation amplitude is the difference between the path evolution parameter value and the evolution zone critical value; the segmented data block is an equal amount of data unit obtained by splitting the independent data block according to the number of transmission channels; the channel identifier is the unique logical identifier of the transmission channel; the segment channel mapping table is a distributed metadata table recording the binding relationship between the segmented data and the channel.

[0141] In step 1051, the hardware state logs (such as disk health, CPU load) and network packet loss rate of each storage node in the redundant storage topology are collected in real time by the distributed monitoring system, and the node failure probability value is calculated using the exponential smoothing algorithm; at the same time, the spectral energy time series data of each transmission path is extracted from the environmental network association parameters, and the energy decay rate value per unit time is calculated through first-order difference to form a key-value pair set of failure probability and decay rate.

[0142] Step 1052 dynamically allocates the superposition weight of the failure probability value according to the historical online duration (continuous service time within the statistical period) of the storage node and the current spectrum energy value (normalized to the 0-1 interval) using the proportional allocation formula superposition weight = historical online duration / (historical online duration + current spectrum energy). The long online high energy node is allocated a low weight to reduce its negative impact on path stability.

[0143] When step 1053 is executed, the failure probability value is dynamically combined with the spectrum energy decay rate according to the superposition weight, and the calculation formula is path evolution parameter = a x failure probability + (1-a) x decay rate (where a is the superposition weight). The evolution parameter sequence of each transmission path is generated and stored in the time series database (such as InfluxDB).

[0144] Step 1054 divides the evolution interval based on the numerical distribution of the evolution parameter sequence using the three quantile statistical method: stable state (such as path evolution parameter ≤ Q1), fluctuation state (such as Q1 < path evolution parameter ≤ Q3), and failure state (such as path evolution parameter > Q3), where Q1 and Q3 are the first and third quartile values of the sequence, and a three-state path evolution model is constructed.

[0145] In step 1055, when the path evolution parameter falls into the fluctuation state or the failure state, the absolute deviation deviation = |parameter value - critical value| of the parameter value from the nearest critical value is calculated. Through the S-shaped function path correction factor = 1 / (1+e^(-k x deviation)) (k is the slope coefficient), it is mapped to the correction factor in the 0-1 interval. The larger the deviation, the closer the correction factor is to 1.

[0146] Step 1056 continuously monitors the correction factor, and when the value exceeds the preset threshold (such as 0.7), the independent data blocks to be pushed are extracted from the transmission path selection priority list according to the order, and a push order queue is generated (such as data blocks with high priority are placed at the front end of the queue).

[0147] Step 1057 selects matching storage nodes from the redundant storage topology according to the latitude and longitude range of the preset geographical direction (such as the eastern region), and uses a load balancing algorithm (such as a round-robin strategy) to assign at least one transmission channel to each node to form a channel-node binding list.

[0148] Step 1058 divides the independent data blocks to be pushed into equal segments according to the number of transmission channels, for example, 3 channels are divided into 3 segmented data blocks, each segment is attached with an incremental segment number (such as Block_001 to Block_003), and the sending order is marked according to the push order queue.

[0149] Finally, in step 1059, a mapping relationship between the segment number and the channel identifier is established, such as Block_001→Channel_A, Block_002→Channel_B, and a segment channel mapping table is generated; the mapping table is broadcast to all related nodes through a distributed message queue (such as RabbitMQ), and each channel synchronously transmits the corresponding segment data block according to the mapping table, ensuring that it is pushed to the target geographical direction of the storage node in parallel.

[0150] The following are some practical use cases provided for steps 101-105:

[0151] For example, after deploying the scheme in a certain e-commerce logistics center, first, through the intelligent probe cluster deployed at the edge of the warehouse network, the probe is realized based on the Prometheus monitoring tool, which collects the bandwidth fluctuation rate of the transmission path in real time. Specifically, it samples once per second and calculates the standard deviation as the fluctuation rate index, measures the link delay jitter data, uses the ICMP protocol to obtain the end-to-end delay, and calculates the jitter variance within a 10-second time window. The wavelet packet decomposition algorithm is used to extract the third moment feature of the bandwidth fluctuation, and the pre-trained LSTM model is used to calculate the network congestion index. This model is trained based on historical traffic data for 3 months, with input parameters including traffic load rate and queue buffer overflow rate in the past 1 hour, and the model output value range is set to 0 to 1. When the output value exceeds 0.8, it is determined to be severe congestion.

[0152] Subsequently, real-time logistics quotation data is divided into 10 km by 10 km geographical grids according to latitude and longitude, and the grid uses GeoHash encoding. The center coordinates of each grid are calculated, specifically taking the average of the latitude and longitude of the grid diagonal intersection points, and multiplying them by the product of the commodity classification level depth. The commodity classification level depth is defined as the path length from the root node "all categories" to the last classification. The preset threshold is 500, which is determined by fitting historical order distribution. For example, the classification depth of a certain clothing category commodity is 4 layers, and the grid center coordinate product value is 450, which is lower than the threshold 500, triggering the attribution determination logic, and classifying the data to the corresponding block. Dynamic binding is achieved through a scalable hash ring, with a virtual node number of 2 to the power of 16, using the MurmurHash3 algorithm to bind the data block to 3 storage nodes in the eastern region. These nodes are built based on AWS S3 storage instances, forming a three-redundant topology.

[0153] Then, a dynamic routing strategy is generated based on the energy integration result of the environmental network correlation parameter. The energy integration is calculated by integrating the spectral energy of the parameter matrix in a 0.5-second time window, while combining the grid coverage density and the commodity update rate. The grid coverage density is defined as the number of active devices per square kilometer, and the commodity update rate is the number of price updates per minute. For example, the commodity update rate of the home appliance category is as high as 50 times per minute, which is assigned to a high-priority routing strategy, and a low-delay private line channel with a delay of less than 50 milliseconds is preferentially selected. When the failure probability of a certain storage node is monitored to exceed the threshold value of 0.65, the system automatically triggers the path correction mechanism. The failure probability is predicted based on the historical failure rate of the node and the current CPU load, and the correction mechanism divides the data into 4 segments with a size of 4 megabytes each, for a total data size of 16 megabytes. The data is transmitted synchronously through 4 parallel transmission channels in the southeast direction, with a bandwidth of 1 gigabit per second for each channel, ensuring that the order data pushing delay during the big promotion period is stable within 20 milliseconds.

[0154] For example, in the urban traffic monitoring scenario, the system collects physical interference data through electromagnetic sensors deployed at intersections. The sensor model is RS-EMF-01, with a sampling rate of 1 kilohertz, measuring electromagnetic noise intensity and recording it in decibel-milliwatts. At the same time, through network probes, end-to-end delay fluctuation data from cameras to the center node are captured based on the NetFlow protocol, and the jitter value is calculated every 5 seconds. The dynamic time warping algorithm is used to align the physical interference peak and the delay surge sequence, with a window width of 2 seconds and a step size of 0.1 second. The physical interference peak is identified by threshold detection, and the delay surge is defined as a fluctuation that exceeds the baseline value by 30%. The time matching degree is calculated to be 0.78, which means that the matching peak accounts for 78% of the total peak. Then, the interference amplitude and the delay surge amplitude are normalized. The interference amplitude is normalized to the range of 0 to 1 by the difference between the maximum and minimum values, and the delay surge amplitude is expressed as a percentage of the surge value relative to the baseline. The interference amplitude is adjusted to 0.663, and the delay amplitude is adjusted to 0.484, with a matching degree of 0.78. The environmental network correlation parameter is generated by fusing the interference amplitude and the delay amplitude with a weight ratio of 6 to 4, and the final value is 0.581. This weight ratio is determined based on historical data analysis, and the optimal solution is when the influence of physical interference on delay accounts for 60%.

[0155] The real-time video stream is then divided into a 500-meter by 500-meter geographical grid, with the grid being divided based on the latitude and longitude of the intersection, and the video stream being encoded using H.264 at a rate of 4 megabits per second. In combination with the classification level of the traffic hub, the main road is defined as level 1, the secondary road as level 2, and the branch road as level 3. The data block is dynamically bound to the 5 edge nodes on the scalable hash ring, and the nodes use Huawei Atlas 500 intelligent edge servers. The distribution density is determined by the number of cameras in the grid. Based on the energy integral and grid density of the environmental network correlation parameters, a three-level routing strategy is generated. The energy integral is calculated by integrating the parameter value over a 10-second window. For example, the energy integral of a certain road monitoring video is 7.1, and the grid density is an average of 6 cameras per grid, which is marked as the highest routing level. When the spectral energy decay rate of a certain node is detected to exceed the threshold value of 0.2 decibels per millisecond, the path correction factor is increased to 0.75. This correction factor is calculated based on the S-shaped function, and the preset threshold is 0.7. The system immediately divides the video stream into 6 data blocks of 150 megabytes each, with a total video stream of 900 megabytes, and pushes it through the preset 6 transmission channels in the northwest direction in parallel. The channels are based on the 5G slice network and each has a bandwidth of 200 megabits per second. In combination with the TOPSIS algorithm, the path selection is dynamically optimized. The ideal solution is defined as a combination of a delay of less than 50 milliseconds and a bandwidth of more than 1 gigabit per second. The actual solution is sorted by Euclidean distance. The final key road video transmission delay is reduced from an average of 120 milliseconds to 45 milliseconds, and the packet loss rate is reduced from 5% to 0.3%.

[0156] The present application collects and dynamically correlates environmental fluctuation data and network congestion index in real time, accurately perceives the trend of network transmission quality change, divides data blocks based on geographical grid and commodity classification level, and constructs a redundant storage topology, which significantly improves data storage reliability and partition management efficiency. Based on multi-dimensional parameter fusion, a dynamic routing strategy is generated, and the transmission priority is calculated using communication weight, coverage density, and update rate, which effectively optimizes the real-time and accuracy of path selection decision. Through the path state evolution model, the node failure risk and spectral decay are monitored in real time, and the abnormal path is dynamically corrected by using the segmented parallel transmission mechanism. In a complex network environment, high fault tolerance and low delay data pushing are realized, and finally the comprehensive optimization effect of improving global resource utilization, reducing transmission interruption rate, and millisecond-level real-time response in multi-service scenarios is achieved.

[0157] Figure 2 A structure diagram of a distributed quotation data collaborative pushing system is provided for the embodiments of the present application, as shown in Figure 2 The system comprises:

[0158] The acquisition module 21 acquires the environmental fluctuation data of the transmission path, dynamically correlates the environmental fluctuation data with the network congestion index change trend, and generates environmental network correlation parameters.

[0159] The division module 22 divides the real-time updated quotation data into independent data blocks according to the boundaries of the geographical grid and the attribution of the commodity classification level nodes, and dynamically binds to the storage nodes through the scalable hash ring to generate a redundant storage topology;

[0160] The generation module 23 generates a distributed routing strategy based on the energy integral result of the environmental network correlation parameter, in combination with the coverage density of the geographical grid and the update rate of the commodity classification level;

[0161] The calculation module 24 calculates the transmission path selection priority of each independent data block in a distributed manner according to the communication weight distribution in the distributed routing strategy, in combination with the boundary expansion direction of the geographical grid and the data volume growth trend of the commodity classification level;

[0162] The push module 25 establishes a path state evolution model based on the node failure probability of the redundant storage topology and the spectral energy decay rate of the environmental network correlation parameter, generates a path correction factor according to the path state evolution model, and pushes the independent data block to multiple parallel transmission channels in a preset geographical direction according to the transmission path selection priority when the path correction factor exceeds a preset threshold.

[0163] Figure 2 The distributed quotation data cooperative pushing system can perform Figure 1 The implementation principle and technical effects of the distributed quotation data cooperative pushing method described in the embodiments are not described again. The specific operation of each module and unit in the distributed quotation data cooperative pushing system described in the above embodiments has been described in detail in the embodiments related to the method, and will not be described in detail here.

[0164] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part 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 the embodiments of the present application.

Claims

1. A distributed quotation data collaborative push method, characterized in that, The method comprises the following steps: Collecting environmental fluctuation data of the data transmission path, dynamically correlating the environmental fluctuation data with the change trend of the network congestion index to generate an environmental network correlation parameter; Dividing real-time updated quotation data into independent data blocks according to the boundaries of the geographical grid and the attribution of the commodity classification level nodes, and dynamically binding the independent data blocks to storage nodes through a scalable hash ring to generate a redundant storage topology; Based on the energy integral result of the environmental network correlation parameter, combining the coverage density of the geographical grid and the update rate of the commodity classification level, a distributed routing strategy is generated, wherein the energy integral result is the cumulative energy value obtained by numerically integrating the spectral energy in the environmental network correlation parameter in the time-space dimension, reflecting the comprehensive strength of the network environment stability in a specific area; According to the communication weight distribution in the distributed routing strategy, combining the boundary expansion direction of the geographical grid and the data volume growth trend of the commodity classification level, the transmission path selection priority of each independent data block is calculated, wherein the communication weight distribution is a numerical set reflecting the transmission priority of the geographical grid in the distributed routing strategy, which is obtained by comprehensive calculation of the environmental network state and business demand; Based on the node failure probability of the redundant storage topology and the spectral energy decay rate of the environmental network correlation parameter, a path state evolution model is established, and a path correction factor is generated according to the path state evolution model. When the path correction factor exceeds a preset threshold, the independent data blocks are pushed to multiple parallel transmission channels in the preset geographical direction according to the transmission path selection priority, wherein the spectral energy decay rate of the environmental network correlation parameter is an index reflecting the decay rate of the transmission path signal strength with time in the environmental network correlation parameter, which is calculated by frequency domain energy difference.

2. The method of claim 1, wherein, According to the communication weight distribution in the distributed routing strategy, combining the boundary expansion direction of the geographical grid and the data volume growth trend of the commodity classification level, the transmission path selection priority of each independent data block is calculated, including: Extracting a communication weight distribution set from the storage nodes in the distributed routing strategy, performing vector decomposition on the boundary expansion direction of the geographical grid to generate a main direction component set, and calculating a trend slope parameter based on the adjacent time window data increment sequence of the commodity classification level; Input the amplitude of the main direction component set and the trend slope parameter into the distributed collaborative calculation node to generate a dynamic superposition coefficient set through product operation, and superimpose the dynamic superposition coefficient set on the geographical grid unit identifier to form a dynamically adjusted communication weight distribution in the communication weight distribution set; Based on the dynamically adjusted communication weight distribution, combining the coverage density, the dynamic superposition coefficient set, the depth of the commodity classification level node and the real-time update frequency of the independent data block, and generating a transmission path selection priority through the distributed collaborative calculation node.

3. The method of claim 2, wherein, Superimpose the dynamic superposition coefficient set according to the cell identifier of the geographical grid into the communication weight distribution set to form a dynamically adjusted communication weight distribution, including: Obtain a mapping relationship table of the cell identifier of the geographical grid and the communication weight distribution set, and extract the original communication weight value corresponding to the cell identifier of the geographical grid based on the mapping relationship table; According to the inverse proportional relationship between the coverage density of the geographical grid and the depth of the commodity classification hierarchical node, determine the merging ratio of the dynamic superposition coefficient and the original communication weight value; Match the cell identifier in the dynamic superposition coefficient set with the cell identifier of the geographical grid in the mapping relationship table. For the cell identifier that matches successfully, superimpose the corresponding dynamic superposition coefficient and the original communication weight value according to the merging ratio; Write the superimposed communication weight value according to the cell identifier of the geographical grid into the communication weight distribution set to generate a dynamically adjusted communication weight distribution containing geographical expansion direction and data growth trend associated features.

4. The method of claim 2, wherein, Based on the dynamically adjusted communication weight distribution, combine the coverage density, the dynamic superposition coefficient set, the depth of the commodity classification hierarchical node, and the real-time update frequency of the independent data block, and generate a transmission path selection priority through the distributed collaborative computing node, including: Perform product operation on the communication weight value and the coverage density of the geographical grid cell to generate a communication density parameter, and perform inverse proportional conversion on the dynamic superposition coefficient and the depth of the commodity classification hierarchical node to generate a hierarchical depth parameter; Fuse the communication density parameter, the hierarchical depth parameter, and the update intensity parameter generated by the real-time update frequency of the independent data block according to a preset ratio to generate a dynamic synthesis parameter; Generate the transmission path selection priority of the independent data block in the geographical grid cell based on the composite sorting of the dynamic synthesis parameter in the distributed collaborative computing node.

5. The method of claim 1, wherein, Based on the node failure probability of the redundant storage topology and the spectrum energy decay rate of the environmental network associated parameter, establish a path state evolution model, and generate a path correction factor according to the path state evolution model, including: Obtain the failure probability value of each storage node in the redundant storage topology, and the spectrum energy decay rate value of the corresponding transmission path in the environmental network associated parameter; Dynamically allocate the superposition weight of the failure probability value according to the ratio of the historical online duration of the storage node to the current spectrum energy value; Dynamically combine the failure probability value according to the allocated superposition weight and the spectrum energy decay rate value to generate a path evolution parameter sequence of each transmission path; Based on the numerical distribution characteristics of the path evolution parameter sequence, establish a path state evolution model through a preset evolution parameter interval division rule. The path state evolution model divides the numerical interval into stable state evolution zone, fluctuation state evolution zone and failure state evolution zone; When the numerical value of the path evolution parameter sequence falls into the fluctuation state evolution zone or the failure state evolution zone, generate a path correction factor according to the deviation amplitude of the numerical value from the critical value of the corresponding evolution zone.

6. The method of claim 1, wherein, When the path correction factor exceeds a preset threshold, the independent data blocks are pushed to multiple parallel transmission channels in a preset geographical direction according to the transmission path selection priority, including: Monitoring the current value of the path correction factor, when the current value is greater than a preset correction threshold, determining the pushing order of the independent data blocks to be pushed according to the ranking in the transmission path selection priority; According to the coverage range of the preset geographical direction, extracting a set of storage nodes associated with the preset geographical direction from the redundant storage topology, and assigning at least one transmission channel to each storage node in the set of storage nodes; Segmenting the independent data blocks to be pushed to generate segmented data blocks equal in number to the transmission channels, and sequentially marking the segmented data blocks with corresponding segment numbers according to the pushing order; Binding the segment numbers with the channel identifiers of the transmission channels to generate a segment channel mapping table, and synchronously pushing multiple segmented data blocks of the same independent data block through corresponding transmission channels into multiple parallel transmission channels in a preset geographical direction according to the segment channel mapping table.

7. The method of claim 1, wherein, Divide the real-time updated offer data into independent data blocks according to the boundaries of the geographical grid and the attribution relationship of the commodity classification hierarchical nodes, and dynamically bind the independent data blocks to storage nodes through a scalable hash ring to generate a redundant storage topology, including: According to the rectangular boundary coordinates of the geographical grid, extracting the center point coordinates of each geographical grid and the path depth of the commodity classification hierarchical nodes; When the geographical coordinates of the real-time updated offer data fall within the rectangular boundary of any geographical grid, calculate the product value of the center point coordinates of the geographical grid and the path depth of the commodity classification hierarchical nodes, if the product value is less than a preset threshold, then attribute the offer data to the independent data block corresponding to the current geographical grid and commodity classification hierarchical node; Generate a segment identification code for each independent data block, construct the data structure of the scalable hash ring, and input the segment identification code into the key value mapping function of the scalable hash ring to calculate the mapping angle value of the segment identification code on the scalable hash ring; Select the first virtual mapping point containing the mapping angle value in the clockwise direction as the primary storage node, and select at least two secondary storage nodes separated from the primary storage node by a fixed angle on the scalable hash ring to generate a redundant storage topology bound by the primary and secondary storage nodes.

8. The method of claim 1, wherein, Based on the energy integral result of the environmental network association parameter, combined with the coverage density of the geographical grid and the update rate of the commodity classification hierarchy, a distributed routing strategy is generated, including: For each geographical grid, the energy integral result of the environmental network association parameter is calculated cumulatively, and the coverage density value of the geographical grid is superimposed to generate the routing weight of each geographical grid; Multiply the routing weight by the update rate of the same commodity classification hierarchy to obtain the dynamic weight coefficient of each commodity classification hierarchy, and divide the routing level according to the numerical range of the dynamic weight coefficient. assign a preset number of candidate routing paths to each of the routing levels, and determine a transmission direction set of the candidate routing paths according to a geographical distribution direction of the storage nodes in the redundant storage topology and a boundary expansion direction of the geographical grid; store the routing levels, the transmission direction set and the corresponding dynamic weight coefficient in association to generate a distributed routing strategy.

9. The method of claim 1, wherein, dynamically associate the environmental fluctuation data with a change trend of the network congestion index to generate an environmental network correlation parameter, including: collect physical interference intensity data and network delay fluctuation data in the environmental fluctuation data, extract fluctuation peak time of the physical interference intensity data and the network delay fluctuation data in the same time window and calculate time matching degree; based on the time matching degree, proportionally adjust fluctuation amplitude of the physical interference intensity data and sudden increase amplitude of the network delay fluctuation data, so that the fluctuation amplitudes of the fluctuation amplitude and the sudden increase amplitude are mapped to a unified numerical interval; input the adjusted fluctuation amplitude and sudden increase amplitude into a dynamic weight allocator, automatically adjust the fusion proportional weight of the two according to the density distribution of matching events in the time window, and generate an environmental network correlation parameter.

10. A distributed collaborative push of quote data system, characterized by, including: a collection module that collects environmental fluctuation data of a transmission path, dynamically associates the environmental fluctuation data with a change trend of a network congestion index, and generates an environmental network correlation parameter; a division module that divides real-time updated bid data into independent data blocks according to the boundary of a geographical grid and the attribution relationship of a commodity classification hierarchical node, and dynamically binds the independent data blocks to storage nodes through a scalable hash ring to generate a redundant storage topology; a generation module that generates a distributed routing strategy based on an energy integration result of the environmental network correlation parameter, in combination with a coverage density of the geographical grid and an update rate of the commodity classification hierarchy, wherein the energy integration result is a cumulative energy value obtained by numerically integrating the spectral energy in the environmental network correlation parameter in the time and space dimensions, reflecting the comprehensive strength of network environment stability in a specific area; a calculation module that calculates a transmission path selection priority of each of the independent data blocks in a distributed manner according to a communication weight distribution in the distributed routing strategy, in combination with a boundary expansion direction of the geographical grid and a data volume growth trend of the commodity classification hierarchy, wherein the communication weight distribution is a numerical set reflecting the transmission priority of the geographical grid defined in the distributed routing strategy, and is obtained by comprehensive calculation of the environmental network state and the business demand; a push module that establishes a path state evolution model based on a node failure probability of the redundant storage topology and a spectral energy decay rate of the environmental network correlation parameter, generates a path correction factor according to the path state evolution model, and pushes the independent data blocks to a plurality of parallel transmission channels in a preset geographical direction according to the transmission path selection priority when the path correction factor exceeds a preset threshold, wherein the spectral energy decay rate of the environmental network correlation parameter is an index reflecting the signal strength decay rate of the transmission path over time in the environmental network correlation parameter, and is calculated by frequency domain energy difference.

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