Internet advertisement targeting distribution and service platform based on real-time bidding logic

Abstract

The application relates to the technical field of commercial data processing, and discloses an internet advertisement targeted distribution and service platform based on real-time bidding logic, which comprises the following steps: analyzing high-dimensional features of a transaction package and mapping the high-dimensional features to a topological grid space to determine an addressing quadrant; determining a quadrant stiffness coefficient according to a commercial variance statistical table; calculating an address offset of a boundary vertex according to the stiffness coefficient; expanding an envelope surface by modifying coordinates to shrink a low-value traffic throughput aperture, or keeping a high-value prediction channel open by locking a write permission to construct an asymmetric feature filtering surface; and triggering a preloading or resource release instruction based on a topological state of the filtering surface. The application converts computing power distribution into topological grid deformation operation, alleviates the contradiction between processing time delay and estimation accuracy in a high-concurrency environment, and improves system efficiency.

Description

Technical Field

[0001] This invention belongs to the field of commercial data processing technology, and in particular relates to an internet advertising targeted distribution and service platform based on real-time bidding logic. Background Technology

[0002] Currently, in business forecasting and related data matching, real-time trading systems in high-concurrency environments have high requirements for data processing latency. Taking online bidding scenarios as an example, the system needs to complete the parsing, feature extraction, and depth estimation of massive requests within a short time window of less than 100ms in order to determine the optimal resource allocation strategy. Mainstream technical solutions usually adopt serial and fixed-length computing pipelines, performing full feature vector construction and matrix multiplication and addition operations on all incoming data packets without discrimination. This design logic has good stability when handling low-load requests, but implicitly assumes that all requests are homogeneous in terms of computing power consumption and business value distribution.

[0003] However, when the data processing bus faces peak concurrency, this unified computing mechanism based on processor arithmetic logic units performing algebraic deduction reveals significant underlying physical limitations. Not only are there bottlenecks at the hardware computing level, but existing software-level data scheduling logic fails to address the issue of unbalanced traffic characteristics. For example, Chinese invention patent CN114979263B discloses a high-concurrency gateway SIP proxy method and device based on the Gin framework. It utilizes the multi-coroutine mechanism of the Golang language and the asynchronous processing capabilities of the Gin framework to improve the gateway service's concurrent capacity. However, it essentially still belongs to a general request-response algebraic calculation method, relying on standard routing functions to perform full protocol parsing and logical matching of all access data packets. When facing extremely high throughput and highly heterogeneous value distribution conditions, such as real-time bidding in internet advertising, this existing technology... The technology lacks topological awareness of feature space distribution. When processing massive amounts of low-value redundant traffic, processor resources are indiscriminately occupied, making it difficult to achieve rapid short-circuit interception of heterogeneous value fluctuations at the physical addressing level. This causes high-value requests to exceed queuing latency limits under peak concurrency. As feature prediction tasks stack exponentially per unit time, processor instruction queues become congested, leading to a rapid increase in system queuing latency. Under the hard constraints of millisecond-level limits, requests at the back of the queue are very likely to be physically dropped by the system due to processing timeouts. These requests include a large number of potential data nodes with high conversion potential. The industry has tried to alleviate the load pressure by increasing the scale of hardware computing power or using static rules to force flow interception. However, such methods cannot fundamentally eliminate the loss of instruction cycles due to algebraic operations, and the isotropic filtering boundary causes uncontrollable commercial value loss in the flow interception process.

[0004] Therefore, the technical problem to be solved by this invention is how to reconstruct the data flow path from the bottom layer so that the system can complete the short-circuit interception of massive redundant data with a low clock cycle when facing extreme concurrency shocks, and achieve native adaptive screening of heterogeneous business value fluctuations. Summary of the Invention

[0005] This invention provides an internet advertising targeted distribution and service platform based on real-time bidding logic, comprising the following steps: Step S1: Obtain transaction data packets and extract high-dimensional business features, map the high-dimensional business features to a preset logical topology grid space to determine the addressing quadrant of the data to be processed in the memory space, and identify the boundary of the logical topology grid space as the logical envelope surface. Step S2: Query the business status variance statistics table according to the addressing quadrant to obtain the logic stiffness coefficient corresponding to the addressing quadrant. The logic stiffness coefficient is a topology constraint parameter calculated based on the historical conversion rate fluctuation variance within the addressing quadrant. Step S3: Based on the logical stiffness coefficient and the preset mesh deformation model, calculate the address offset of the boundary control vertex within the logical addressing quadrant. Step S4: Modify the memory addressing coordinates of the boundary control vertex according to the address offset. When the logical stiffness coefficient is higher than the first preset threshold, drive the logical envelope surface to perform geometric expansion along the target dimension and simultaneously shrink the calculation throughput of the transaction data packet. Step S5: When the logic stiffness coefficient is lower than the second preset threshold, the write permission lock of the corresponding memory register is executed to restrict the shape change of the logic envelope surface and form an asymmetric feature filtering surface. Step S6: Based on the topological geometric features of the asymmetric feature filtering surface, trigger an asynchronous preloading instruction for the associated feature attributes, or trigger a computational resource release instruction for the current feature inference task.

[0006] Preferably, in step S2, the logic stiffness coefficient Determined by the following formula: ,in, Here, k is the logic stiffness coefficient, k is the preset linear adjustment gain, and V is the statistical variance of the business conversion rate for the corresponding addressing quadrant. As a smoothing factor, the statistical variance V of the business conversion rate is obtained by performing variance calculation on historical business conversion samples in the corresponding addressing quadrant.

[0007] Preferably, step S3 includes: establishing an energy functional model of the logical topology grid space, mapping the input intensity of high-dimensional business features to numerical load pressure acting on the logical topology grid space; solving the energy functional model using the displacement variational principle to determine the displacement response vector of each boundary control vertex under the constraint of the logical stiffness coefficient; converting the displacement response vector into the relative addressing word length of the memory space to generate the address offset.

[0008] Preferably, step S4 includes: step S401, identifying low-frequency sub-regions in the logical topology grid space where the variance of the business conversion state is lower than a preset steady-state threshold; step S402, when the low-frequency sub-region overlaps with the logical envelope surface, reducing the stiffness constraint of the boundary control vertex within the low-frequency sub-region, so that the logical envelope surface performs geometric expansion towards the low-frequency sub-region under numerical load pressure, thereby severing the feature calculation link of the data flow in the low-frequency sub-region.

[0009] Preferably, the process of forming the asymmetric feature filter surface in step S5 includes: step S501, identifying high-value sub-regions in the logical topology grid space where the commercial conversion rate fluctuation is higher than the fluctuation threshold; step S502, maintaining the geometric stability of the logical envelope surface in the tangential direction of the high-value sub-region by controlling the write arbitration signal of the memory bus, so that the flow inlet diameter of the asymmetric feature filter surface matches the sample distribution characteristics of the high-value sub-region.

[0010] Preferably, in step S6, the process of triggering the asynchronous preloading instruction includes: while executing the first-level feature detection operator, initiating a memory-ready query for the deep sparse features associated with the transaction data packet; if the asymmetric feature filtering surface determines that the transaction data packet is located in a high-value sub-region, then the asynchronous preloading instruction is placed in a high-priority task queue.

[0011] Preferably, after step S6, the method further includes: step S701, allocating a fixed proportion of transaction data packets to the bypass detection path for full-scale depth inference; step S702, calculating the error gradient between the feedback samples generated by the full-scale depth inference and the filtering decision of the asymmetric feature filtering surface; step S703, rewriting the corresponding address data in the business state variance statistics table according to the error gradient, and achieving dynamic convergence of the asymmetric feature filtering surface by adjusting the local topological stiffness.

[0012] Preferably, the process of parsing high-dimensional business features in step S1 includes: using a streaming processing bus to perform protocol parsing on the transaction data packets, extracting the original sequence containing source node attributes, prediction engine identifiers and timestamp information; performing dimensionality reduction mapping on the original sequence to generate scalar feature values ​​that characterize the entropy of the business information of the data to be processed, as high-dimensional business features.

[0013] Preferably, the process of triggering the computing resource release instruction in step S6 includes: monitoring the queue backlog depth of the feature inference task in the task scheduling framework; if the asymmetric feature filtering surface determines that the transaction data packet is located in a low-frequency sub-region, then the elastic time-series quota is calculated based on the queue backlog depth, and the corresponding computing resource occupation is revoked.

[0014] Preferably, the method is applied to a distributed data processing environment consisting of a streaming data processing bus and an online-updated business forecasting model, by converting the computing power allocation for transaction data packets into addressing and displacement operations in the logical topology grid space to meet the business response latency constraint of less than 100ms.

[0015] Compared to existing technologies, the internet advertising targeted distribution and service platform based on real-time bidding logic of this invention has the following advantages: 1. In targeted distribution of internet advertising, by translating the feature evaluation process from algebraic calculation to spatial topology memory addressing operations, the dependence of the early screening stage on the processor's arithmetic logic unit is eliminated. The system pre-builds and maintains a low-conversion-rate topological envelope surface generated by mapping historical data feature coordinates in the static storage area. The matrix multiplication and addition operations that originally required a large number of clock cycles are reduced to memory address pointer offset detection. This mechanism enables the system to complete the short-circuit interception of massive redundant requests with a near constant hardware clock cycle when facing extreme concurrency impacts. The throughput bottleneck is smoothly transferred from the uncontrollable congestion of the instruction queue to the highly deterministic and low-latency memory read and write bus, ensuring the deterministic response of the business prediction center under millisecond constraints.

[0016] 2. A non-uniform parameterized deformation mechanism for topological boundaries based on the variance of transformation states is introduced to achieve a native adaptive response to the heterogeneity of value distribution in a multi-dimensional feature space. Unlike the traditional isotropic boundary expansion strategy, this scheme extracts the structural stiffness coefficient of specific feature dimensions to drive the envelope surface to undergo deep geometric expansion in the low-frequency space where the historical transformation state is stable, while maintaining boundary residence in the potential high-value space where the transformation rate fluctuates drastically. This asymmetric physical filtering path ensures that the system can avoid physically killing long-tail high-potential traffic in the exploration phase when performing concurrent interception and pressure relief, thus decoupling the contradiction between improving system throughput and maintaining the accuracy of business insights from the underlying architecture level.

[0017] 3. By deeply collaborating adaptive gating logic with asynchronous preloading scheduling of high-dimensional features, the system eliminates temporal discontinuities in the multi-level cascaded inference process. While the first-level operator performs topology probing, the system simultaneously initiates memory-ready instructions for deep sparse features and triggers the interruption, destruction, or activation of preloading tasks in real time based on the probing results. This cross-module dynamic linkage mechanism allows core computing resources to be isolated and directed to request packets with high uncertainty. Combined with elastic temporal quota recalculation based on queue backlog depth, the system has the ability to protect itself and degrade under complex operating conditions, improving the overall operational efficiency of the data flow network when processing unbalanced commercial data streams. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the core operation of the advertising distribution based on topology addressing and feature filtering in this invention. Figure 2 This is a system deployment architecture diagram of the physical node array and prediction engine of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0021] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0022] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] An internet advertising targeted distribution and service platform based on real-time bidding logic includes the following steps: Step S1: Obtain transaction data packets and extract high-dimensional business features, map the high-dimensional business features to a preset logical topology grid space to determine the addressing quadrant of the data to be processed in the memory space, and identify the boundary of the logical topology grid space as the logical envelope surface. Step S2: Query the business status variance statistics table according to the addressing quadrant to obtain the logic stiffness coefficient corresponding to the addressing quadrant. The logic stiffness coefficient is a topology constraint parameter calculated based on the historical conversion rate fluctuation variance within the addressing quadrant. Step S3: Based on the logical stiffness coefficient and the preset mesh deformation model, calculate the address offset of the boundary control vertex within the logical addressing quadrant. Step S4: Modify the memory addressing coordinates of the boundary control vertex according to the address offset. When the logical stiffness coefficient is higher than the first preset threshold, drive the logical envelope surface to perform geometric expansion along the target dimension and simultaneously shrink the calculation throughput of the transaction data packet. Step S5: When the logic stiffness coefficient is lower than the second preset threshold, the write permission lock of the corresponding memory register is executed to restrict the shape change of the logic envelope surface and form an asymmetric feature filtering surface. Step S6: Based on the topological geometric features of the asymmetric feature filtering surface, trigger an asynchronous preloading instruction for the associated feature attributes, or trigger a computational resource release instruction for the current feature inference task.

[0024] Preferably, in step S2, the logic stiffness coefficient Determined by the following formula: ,in, Here, k is the logic stiffness coefficient, k is the preset linear adjustment gain, and V is the statistical variance of the business conversion rate for the corresponding addressing quadrant. As a smoothing factor, the statistical variance V of the business conversion rate is obtained by performing variance calculation on historical business conversion samples in the corresponding addressing quadrant.

[0025] Preferably, step S3 includes: establishing an energy functional model of the logical topology grid space, mapping the input intensity of high-dimensional business features to numerical load pressure acting on the logical topology grid space; solving the energy functional model using the displacement variational principle to determine the displacement response vector of each boundary control vertex under the constraint of the logical stiffness coefficient; converting the displacement response vector into the relative addressing word length of the memory space to generate the address offset.

[0026] Preferably, step S4 includes: step S401, identifying low-frequency sub-regions in the logical topology grid space where the variance of the business conversion state is lower than a preset steady-state threshold; step S402, when the low-frequency sub-region overlaps with the logical envelope surface, reducing the stiffness constraint of the boundary control vertex within the low-frequency sub-region, so that the logical envelope surface performs geometric expansion towards the low-frequency sub-region under numerical load pressure, thereby severing the feature calculation link of the data flow in the low-frequency sub-region.

[0027] Preferably, the process of forming the asymmetric feature filter surface in step S5 includes: step S501, identifying high-value sub-regions in the logical topology grid space where the commercial conversion rate fluctuation is higher than the fluctuation threshold; step S502, maintaining the geometric stability of the logical envelope surface in the tangential direction of the high-value sub-region by controlling the write arbitration signal of the memory bus, so that the flow inlet diameter of the asymmetric feature filter surface matches the sample distribution characteristics of the high-value sub-region.

[0028] Preferably, in step S6, the process of triggering the asynchronous preloading instruction includes: while executing the first-level feature detection operator, initiating a memory-ready query for the deep sparse features associated with the transaction data packet; if the asymmetric feature filtering surface determines that the transaction data packet is located in a high-value sub-region, then the asynchronous preloading instruction is placed in a high-priority task queue.

[0029] Preferably, after step S6, the method further includes: step S701, allocating a fixed proportion of transaction data packets to the bypass detection path for full-scale depth inference; step S702, calculating the error gradient between the feedback samples generated by the full-scale depth inference and the filtering decision of the asymmetric feature filtering surface; step S703, rewriting the corresponding address data in the business state variance statistics table according to the error gradient, and achieving dynamic convergence of the asymmetric feature filtering surface by adjusting the local topological stiffness.

[0030] Preferably, the process of parsing high-dimensional business features in step S1 includes: using a streaming processing bus to perform protocol parsing on the transaction data packets, extracting the original sequence containing source node attributes, prediction engine identifiers and timestamp information; performing dimensionality reduction mapping on the original sequence to generate scalar feature values ​​that characterize the entropy of the business information of the data to be processed, as high-dimensional business features.

[0031] Preferably, the process of triggering the computing resource release instruction in step S6 includes: monitoring the queue backlog depth of the feature inference task in the task scheduling framework; if the asymmetric feature filtering surface determines that the transaction data packet is located in a low-frequency sub-region, then the elastic time-series quota is calculated based on the queue backlog depth, and the corresponding computing resource occupation is revoked.

[0032] Preferably, the method is applied to a distributed data processing environment consisting of a streaming data processing bus and an online-updated business forecasting model, by converting the computing power allocation for transaction data packets into addressing and displacement operations in the logical topology grid space to meet the business response latency constraint of less than 100ms.

[0033] Example 1: In high-concurrency real-time bidding internet advertising data matching scenarios where the business response latency constraint is less than 100ms, when the data processing bus experiences peak concurrency, the traditional fixed process based on the processor's arithmetic logic unit performing full feature tensor algebra deduction will cause instruction queue congestion, resulting in transaction data packets at the back of the queue being physically discarded due to processing timeouts. The system acquires transaction data packets and extracts high-dimensional business features, maps these features to a preset logical topology grid space to determine the addressing quadrant of the data to be processed in memory space, and identifies the boundary of the logical topology grid space as the logical envelope surface. When determining the initial mapping benchmark of the logical topology grid space, the data packets containing... The system takes N feature vectors as input, where N is the total number of feature vector dimensions. A hash mapping function converts these feature vectors into specific memory offsets. This hash mapping function uses a prime-number modulo hash bucket allocation method to ensure that the results of different feature dimension combinations are evenly distributed within the memory addressing quadrant. During system initialization, the logical topology grid step size is set according to the processor cache line length, ensuring that the physical storage boundary of each grid cell matches the burst transfer length of the memory controller. When calculating the address offset, the topology displacement response is directly converted into the physical memory address bus address increment. The system queries the business state variance statistics table based on the addressing quadrant to obtain the logical stiffness coefficient corresponding to the addressing quadrant. The logical stiffness coefficient Through formula Where k is the preset linear adjustment gain, and V is the statistical variance of the historical business conversion rate for the corresponding addressing quadrant. The smoothing factor; the system is based on the logic stiffness coefficient. The address offset of the boundary control vertices within the logical addressing quadrant is calculated using the preset mesh deformation model, and the memory addressing coordinates of the boundary control vertices are modified based on the address offset; when the logical stiffness coefficient... When the value exceeds the first preset threshold, the driving logic envelope surface performs geometric expansion along the target dimension and simultaneously shrinks the computational throughput for the transaction data packet. When the logic stiffness coefficient... When the value is below the second preset threshold, the write permission of the corresponding memory register is locked to restrict the morphological change of the logical envelope surface, forming an asymmetric feature filtering surface. In this execution path, the spatial mapping of high-dimensional business features determines the coordinate benchmark for addressing, and the logical stiffness coefficient based on the historical business conversion rate fluctuation constrains the address offset of the control vertex. The two work together to complete the transformation from abstract data parsing to dynamic reshaping of physical memory boundaries.

[0034] Based on the topological geometric features of the asymmetric feature filtering surface, the system triggers asynchronous preloading instructions for associated feature attributes or releases computational resources for the current feature inference task. At the system's underlying execution logic, low-frequency sub-regions with a business transformation state variance below a preset steady-state threshold within the logical topological grid space are identified. When a low-frequency sub-region overlaps topologically with the logical envelope surface, the stiffness constraint of the boundary control vertices within the low-frequency sub-region is reduced. This causes the logical envelope surface to geometrically expand towards the low-frequency sub-region under the numerical load pressure of the input features, thereby halting feature computation for the data flow in the low-frequency sub-region. The system identifies high-value sub-regions where the commercial conversion rate fluctuates above the fluctuation threshold. By controlling the write arbitration signal of the memory bus, it maintains the geometric stability of the logical envelope in the tangential direction of the high-value sub-region, so that the traffic inlet diameter of the asymmetric feature filtering surface matches the sample distribution characteristics of the high-value sub-region. By translating the feature interception process into spatial topology memory addressing and geometric deformation operations, the core inference computing power avoids the indiscriminate accumulation of instruction queues. At the physical hardware execution level, it separates the system resource allocation path between concurrent traffic depressurization and interception and the exploration of high-potential commercial value.

[0035] Example 2: When the system faces a high-concurrency real-time bidding test with a peak of 150,000 requests per second, the test platform uses a commercial bidding open-source log dataset as the basic data source. A 20ms latency jitter following a Poisson distribution and discrete traffic spikes are superimposed at the data bus input to construct a matching environment with background noise. The hardware verification platform uses a server with 256GB of physical memory and a 3.2GHz processor to match the system's memory addressing step size and clock cycle to the constraints of the industrial scenario. A preset basic expansion constant is used. The calibration needs to achieve a technical balance between the geometric resolution of the feature filtering boundary and the hardware latency caused by memory register rewriting. When the concurrent growth rate of the test traffic is greater than or equal to 10,000 times / second, the excessively high boundary deformation frequency consumes memory bus bandwidth. Based on this physical constraint, the system uses the upper limit of memory read and write throughput as a boundary condition to determine the preset basic expansion constant. It is 16.

[0036] The system initiates the streaming data processing bus and ingests noisy service request data packets. The first-level lightweight inference operator extracts shallow feature vectors. And mapped to the memory space addressing quadrant; the test settings include the sample group of the present invention using the scheme of the present invention, the first comparison sample group using fixed addressing boundaries, and a preset basic expansion constant. The second comparison sample was set with an absolute upper limit of 128 and a preset basic expansion constant. The third comparative sample group was set with an absolute lower limit of 2. Core intermediate parameters intercepted by the monitoring system showed that when the sample group of this invention faced low-frequency sub-region traffic with a historical business conversion rate statistical variance V of 0.002, the controller, according to the formula... The calculated logic stiffness coefficient Maintaining a low impedance of 0.15, the system operates according to the formula... Generate the corresponding address offset This drives the logic envelope to generate a geometric expansion of 24 addressable words; in high-value sub-regions with a historical business conversion rate statistical variance V of 0.85, the calculated logic stiffness coefficient... The value climbs to 45.8. The system locks the write permission of the corresponding memory register based on a very small address offset, thus limiting the deformation of the envelope surface.

[0037] Final monitoring indicators show that the sample group of this invention maintained a P99 response latency of 82.4ms under a peak concurrency of 150,000 transactions / second, and achieved a high-value transaction data packet retention rate of 98.7% for historical business conversion rate statistical variance V greater than 0.8. In contrast, the P99 response latency of sample group one deteriorated to 215.6ms, and queue overflow caused the retention rate of the aforementioned high-value transaction data packets to drop to 41.2%. The data trend confirms the address offset. The driven geometric expansion and write permission locking mechanism physically separates the computing power allocation paths for low-frequency traffic interception and high-potential feature exploration. Parameter gradient tests show that, compared to sample group two, the high-value retention rate is 98.9%, encountering a gain plateau, and the dense rewriting of boundary vertex coordinates consumes memory bandwidth, causing the overall P99 response latency to rise to 180.5ms. Compared to sample group three, the excessively large envelope deformation stiffness fails to effectively filter low-frequency traffic, resulting in a P99 response latency of 145.2ms. These nonlinear performance inflection points verify the preset basic expansion constant. The engineering uniqueness of the optimal value range confirms that the system solves the physical constraints of high concurrency throughput and feature preservation through the collaborative mechanism of spatial memory topology addressing and geometric deformation.

[0038] Example 3: In a continuously operating environment supporting internet advertising targeted distribution and service platforms, the prediction engine faces the parallel mapping task of massive business request data packets. If the physical conversion mechanism from feature space to memory addressing is not precisely set, it will cause the topological mesh deformation to lose its deterministic boundary and trigger memory overflow when processing massive feature tensors. This example provides a deterministic operating procedure for the logical addressing coordinate update mechanism of the aforementioned technical solution. To solve the data timeliness deviation in the calculation of the variance of commercial conversion rate statistics, the system divides a ring-shaped buffer storage area containing 128 time slice slots in physical memory to construct a sliding sampling window, with each 10ms system clock cycle... Within the synchronization period, the original Boolean signals of ad clicks and commercial conversions corresponding to each addressing quadrant within the current time slice are extracted and accumulated to calculate the conversion frequency. The system data processing unit calculates the variance of the conversion frequency of all time slices within the sliding sampling window based on the discrete time variance update algorithm, and multiplies it by a forgetting weight factor that decays over time to generate the statistical variance V of the commercial conversion rate for the current period and write it into the commercial status variance statistics table. The base of the forgetting weight factor is set to a constant in the range of 0.95 to 0.99. This operation process ensures that the system retains its responsiveness to recent sudden high-value commercial traffic, while filtering out the trailing data interference of old traffic distribution through decay calculation.

[0039] The system obtains the statistical variance V of the business conversion rate and uses the formula Determine the logical stiffness coefficient Subsequently, for the displacement addressing operation of the boundary control vertices, the system computing unit establishes an energy functional model of the logical topology grid space on the addressing bus. This model uses the product of the arrival frequency of the high-dimensional service features and the modulus of the feature vector as an external input parameter, mapping it to the numerical load pressure acting on the nodes of the logical topology grid space. The system computing unit calls the built-in partial differential equation solving module and uses the displacement variational principle to find the extremum of the energy functional model to establish the stationary condition of the total potential energy of the system. In this operation mechanism, the logical stiffness coefficient... Substituting the core penalty term into the elasticity matrix, the computational unit outputs the dimensionless displacement response vector of each boundary control vertex under a specific stress state; the system then combines this dimensionless displacement response vector with a preset basic expansion constant. Based on the current queue backlog load factor L of the system, the formula is derived. Limited address offset ; Calculate address offset At that time, the grid energy equilibrium state is discretized and solved. Within the calculation period, the concurrent queue backlog length of the system is extracted as the queue backlog load coefficient L, and compared with the preset basic expansion constant. Multiplication, in the formula This represents the address offset, and L represents the queue backlog load factor. This represents the preset basic expansion constant. This represents the logic stiffness coefficient, which is the coefficient of performance when the statistical variance V of the business conversion rate approaches zero. Reduced to smoothing factor Determine the minimum value, where This represents the smoothing factor, at which point the address offset... Increase the maximum addressing limit to the upper limit value, drive the logical envelope to geometrically expand outward in the low-value addressing quadrant, trigger the memory addressing reset direction of the data flow in the corresponding quadrant, and the system determines the address offset based on the upper limit value. Modifying the memory addressing coordinates of the boundary control vertices in the low-frequency addressing quadrant drives the contraction of the logical envelope surface to optimize the computational throughput of redundant traffic. This deterministic procedure translates the interception actions of commercial traffic characteristics into low-level arithmetic logic unit operations based on variational principles, thus resolving the physical constraints between the convergence of dynamic deformation of the topology mesh and the stability of hardware addressing.

[0040] Example 4: In the initial physical node array of a data processing system deployed for large-scale commercial forecasting purposes, the newly connected feature extraction engine lacks baseline data on the current memory read / write bus bandwidth and concurrent request queue carrying capacity limit at startup. The envelope deformation parameters will cause oscillations and lockout states in the computing power allocation during addressing. Before the system accesses the service traffic on the streaming data processing bus, it starts a white noise traffic generator to generate pseudo-random service request data packets that follow a uniform distribution and injects them into the logical topology grid space. The system probe records the instruction cycle time of the arithmetic logic unit, the memory page swapping frequency, and the backlog length of the addressing queue under pseudo-random service traffic. The system data processing unit calls a polynomial fitting algorithm to map the instruction cycle time, memory page swapping frequency, and backlog length to the baseline saturation load coefficient of the current physical node.

[0041] The system obtains the baseline saturation load coefficient and uses this coefficient value as the initial constant input to the address offset calculation logic as the queue backlog load coefficient L. Within the sandbox memory partition, a parameter traversal optimization loop is initiated. The controller simulates the outward expansion of the logic envelope surface based on a series of parameters within the sandbox memory partition, recording the memory read latency response curve corresponding to each geometric deformation scale. The system calculates the first derivative of the latency response curve to identify the physical inflection point where the latency slope undergoes a step change. The deformation scale corresponding to this physical inflection point is limited as the maximum addressing limit of the current hardware architecture. The system uses the maximum addressing limit as a boundary condition in the formula. Calculate the preset basic expansion constant The system sets throughput boundary limits based on physical measurement data of hardware bandwidth consumption characteristics and calculates the extreme values ​​of control parameters to eliminate the parameter boundary gap between physical computing power and topological mesh deformation mapping. The underlying processing hardware maintains the operation of the Internet advertising targeted distribution platform based on real-time bidding logic in heterogeneous nodes accordingly.

[0042] Example 5: When the system operates continuously across cycles in an internet advertising service environment and faces the objective condition that the overall distribution of commercial traffic changes over time, the fixed stiffness comparison benchmark causes the morphological evolution of the logical envelope surface and the spatial mapping misalignment of high-dimensional business characteristics; the system initiates an adaptive threshold calibration procedure, the streaming data processing bus intercepts the full transaction data packets in the previous complete business cycle, the data processing unit extracts the statistical variance V of the commercial conversion rate of all addressing quadrants in the cycle and constructs a frequency histogram containing discrete variance samples, the system calls the kernel density estimation algorithm to convert the frequency histogram into a continuous probability density distribution curve, the calculation module solves for the zero point of the second derivative of the probability density distribution curve to locate the local minimum point of the curve between the low variance interval and the high variance interval, and the system extracts the variance value corresponding to the local minimum point as the critical variance benchmark used to distinguish between steady-state low-frequency traffic and high-value fluctuating traffic.

[0043] The system obtains the critical variance benchmark and substitutes it into the formula. The system calculates the basic critical stiffness value. Using this value as a base, the controller calculates positive and negative linear offsets based on the tolerance coefficient of the system hardware instruction cycle, generating a first and second preset threshold for determining topology deformation triggering conditions. The system loads the updated first and second preset thresholds into the write arbitration module of the memory bus. When a new round of transaction data packets is mapped to the logical topology grid space and a real-time logical stiffness coefficient is generated... At that time, the arbitration module performs a status comparison based on the updated threshold, and issues the address offset for the boundary control vertex in the case of exceeding the limit. This calibration procedure establishes classification boundaries by extracting the mathematical morphological features of objective data distribution, and builds an adaptive closed loop of the underlying physical computing power allocation model to adapt to changes in the external commercial bidding environment. While maintaining the dynamic stability of the asymmetric feature filtering surface, the system constructs a sliding sampling window by dividing a ring-shaped buffer storage area containing multiple time-slice slots in physical memory. Within a fixed time interval, the variance of the commercial conversion frequency within the window is calculated and used as the statistical variance V of the commercial conversion rate, inputting it into the commercial state variance statistics table. During step S703, when adjusting the local topology stiffness, the corresponding addressing quadrant logic parameters are rewritten by calculating the error gradient between the feedback sample and the filtering decision. When the error gradient exceeds the preset instability threshold, a preset basic expansion constant is triggered. The parameter calibration procedure iterates through memory read / write latencies at different deformation scales within the sandbox memory partition until a parameter combination is determined where the system P99 response latency is less than 100ms and the retention rate of high-value sub-regions is greater than 98%.

[0044] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. An internet advertising targeted distribution and service platform based on real-time bidding logic, characterized in that, Includes the following steps: Step S1: Obtain transaction data packets and extract high-dimensional business features, map the high-dimensional business features to a preset logical topology grid space to determine the addressing quadrant of the data to be processed in the memory space, and identify the boundary of the logical topology grid space as the logical envelope surface. Step S2: Query the business status variance statistics table according to the addressing quadrant to obtain the logic stiffness coefficient corresponding to the addressing quadrant. The logic stiffness coefficient is a topology constraint parameter calculated based on the historical conversion rate fluctuation variance within the addressing quadrant. Step S3: Based on the logical stiffness coefficient and the preset mesh deformation model, calculate the address offset of the boundary control vertex within the logical addressing quadrant. Step S4: Modify the memory addressing coordinates of the boundary control vertex according to the address offset. When the logical stiffness coefficient is higher than the first preset threshold, drive the logical envelope surface to perform geometric expansion along the target dimension and simultaneously shrink the calculation throughput of the transaction data packet. Step S5: When the logic stiffness coefficient is lower than the second preset threshold, the write permission lock of the corresponding memory register is executed to restrict the shape change of the logic envelope surface and form an asymmetric feature filtering surface. Step S6: Based on the topological geometric features of the asymmetric feature filtering surface, trigger an asynchronous preloading instruction for the associated feature attributes, or trigger a computational resource release instruction for the current feature inference task.

2. The internet advertising targeted distribution and service platform based on real-time bidding logic according to claim 1, characterized in that, In step S2, the logic stiffness coefficient Determined by the following formula: ,in, Here, k is the logic stiffness coefficient, k is the preset linear adjustment gain, and V is the statistical variance of the business conversion rate for the corresponding addressing quadrant. As a smoothing factor, the statistical variance V of the business conversion rate is obtained by performing variance calculation on historical business conversion samples in the corresponding addressing quadrant.

3. The internet advertising targeted distribution and service platform based on real-time bidding logic according to claim 1, characterized in that, Step S3 includes: establishing an energy functional model of the logical topology grid space, mapping the input intensity of high-dimensional business features to numerical load pressure acting on the logical topology grid space; solving the energy functional model using the displacement variational principle to determine the displacement response vector of each boundary control vertex under the constraint of logical stiffness coefficient; converting the displacement response vector into the relative addressing word length of the memory space to generate the address offset.

4. The internet advertising targeted distribution and service platform based on real-time bidding logic according to claim 1, characterized in that, Step S4 includes: Step S401, identifying low-frequency sub-regions in the logical topology grid space where the variance of the business transformation state is lower than a preset steady-state threshold; Step S402, when the low-frequency sub-region overlaps with the logical envelope surface, reducing the stiffness constraint of the boundary control vertex within the low-frequency sub-region, so that the logical envelope surface performs geometric expansion towards the low-frequency sub-region under numerical load pressure, thereby severing the feature calculation link of the data flow in the low-frequency sub-region.

5. The internet advertising targeted distribution and service platform based on real-time bidding logic according to claim 1, characterized in that, The process of forming the asymmetric feature filter surface in step S5 includes: step S501, identifying high-value sub-regions in the logical topology grid space where the commercial conversion rate fluctuation is higher than the fluctuation threshold; step S502, maintaining the geometric stability of the logical envelope surface in the tangential direction of the high-value sub-region by controlling the write arbitration signal of the memory bus, so that the flow inlet diameter of the asymmetric feature filter surface matches the sample distribution characteristics of the high-value sub-region.

6. The internet advertising targeted distribution and service platform based on real-time bidding logic according to claim 1, characterized in that, In step S6, the process of triggering the asynchronous preloading instruction includes: while executing the first-level feature detection operator, starting a memory-ready query for the deep sparse features associated with the transaction data packet; if the asymmetric feature filtering surface determines that the transaction data packet is located in a high-value sub-region, then the asynchronous preloading instruction is placed in the high-priority task queue.

7. The internet advertising targeted distribution and service platform based on real-time bidding logic according to claim 1, characterized in that, Step S6 is followed by: Step S701, allocating a fixed proportion of transaction data packets to the bypass detection path for full-scale depth inference; Step S702, calculating the error gradient between the feedback samples generated by full-scale depth inference and the filtering decision of the asymmetric feature filtering surface; Step S703, rewriting the corresponding address data in the business state variance statistics table according to the error gradient, and achieving dynamic convergence of the asymmetric feature filtering surface by adjusting the local topological stiffness.

8. The internet advertising targeted distribution and service platform based on real-time bidding logic according to claim 1, characterized in that, The process of parsing high-dimensional business features in step S1 includes: using a streaming processing bus to parse the transaction data packets according to the protocol, extracting the original sequence containing source node attributes, prediction engine identifiers and timestamp information; performing dimensionality reduction mapping on the original sequence to generate scalar feature values ​​that characterize the entropy of the business information of the data to be processed, as high-dimensional business features.

9. The internet advertising targeted distribution and service platform based on real-time bidding logic according to claim 1, characterized in that, The process of triggering the computing resource release instruction in step S6 includes: monitoring the queue backlog depth of the feature inference task in the task scheduling framework; if the asymmetric feature filtering surface determines that the transaction data packet is located in a low-frequency sub-region, then the elastic time-series quota is calculated based on the queue backlog depth, and the corresponding computing resource occupation is revoked.

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