A security investment risk early warning and holding optimization management method and system

Abstract

The present application relates to the technical field of data processing of data prediction and risk supervision, and discloses a security investment risk early warning and holding optimization management method and system, comprising: extracting transaction price variation characteristic scalar and storing it in a cache area; calculating its instantaneous fluctuation variance value and comparing it with a dynamic gating threshold to regulate and control the suspension state of data flow; when the variance value exceeds the threshold, activating a shunt decoupling operator to accumulate and calculate the characteristic vector, generating a convergent timing chip distribution characteristic tensor; inputting a timing state machine to determine a phase code to constrain the holding proportion optimization allocation vector, and when the transaction price reaches the safety boundary line scalar and is in the risk exit decision phase, reducing the allocation vector output to give an early warning result; the present application uses the price instantaneous variance to adaptively deploy the feature matrix update clock, eliminates instruction blocking and computing power yielding under high concurrency, reduces the time delay and improves the real-time convergence degree of risk supervision.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology for data prediction and risk monitoring, and particularly relates to a method and system for securities investment risk early warning and portfolio optimization management. Background Technology

[0002] Currently, within the framework of large-scale, high-concurrency data monitoring server clusters, multi-dimensional topology matrices and time-series correlation channels constitute the signal processing infrastructure. This infrastructure is used to collect high-frequency trading price time-series data and calculate the spatial distribution topology of long-term asset holdings with high throughput. Within large-scale concurrent data processing systems, it undertakes the core functions of monitoring operational status and predicting trends. Under the condition of a short-term surge of massive transaction detail streams, due to the use of static single synchronous clock update rules in traditional solutions, the server simultaneously recalculates the massive time-series chip state feature tensor while solving the ultra-high-frequency price displacement feature scalar. This leads to high-frequency stacking of computational tasks within the prediction server, causing instruction queue blocking and computational power allocation concessions. Ultimately, the processing structure solidifies the inherent constraint that high real-time data refresh and high-load matrix solving are mutually exclusive, resulting in time delays in early warning signals.

[0003] To address the signal latency caused by the aforementioned calculations, conventional linear scaling in the industry involves increasing the processor's clock speed or expanding the number of computing nodes. However, such approaches incur high node synchronization overhead, and asynchronous clock drift between heterogeneous nodes can lead to data ambiguity. Furthermore, the failure to adjust the matrix and recalculate the clock from the internal control flow chain means that not only does the aforementioned linear scaling of hardware clusters for multi-dimensional topology signal processing bases suffer from physical limitations such as node synchronization overhead and clock drift, but software-level control methods and risk identification architectures also have shortcomings. For example, Chinese invention patent application CN118967131A discloses a method, device, computer equipment, and storage medium for identifying risks in programmed abnormal securities trading. This paper constructs graph structure data of securities trading in the form of spatiotemporal data and uses graph neural networks in conjunction with unsupervised and supervised time series neural network models to carry out risk prediction. However, when faced with the short-term influx of massive high-concurrency data streams, the graph structure construction, graph comparison learning enhancement of graph node features, and multi-model fusion calculation still rely on the full or high-frequency feature recalculation mechanism. It lacks the underlying control flow decoupling and flow gating feedforward control loop. When there is a sudden increase in transient flow or high-frequency price noise interference, it causes computational entropy accumulation and core instruction queue blockage in the graph network construction and multi-model fusion stacking calculation stage, causing the warning signal to lag. Moreover, the open-loop graph feature processing chain cannot suppress the non-convergence anomaly of optimization iteration caused by unilateral price changes of the target.

[0004] Therefore, how to construct a timing feature feedforward decoupling mechanism to adaptively adjust the clock for solving the feature matrix, eliminate computational entropy accumulation under high concurrency conditions, and reduce response latency has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention aims to solve the problems of instruction blocking and warning signal delay in data processing systems caused by the full recalculation of high-dimensional feature matrices under high-concurrency data flow conditions.

[0006] This technical solution provides a method for securities investment risk warning and portfolio optimization management, comprising the following steps: Step S1: The data processor extracts the transaction price and volume of the underlying asset from the real-time received anonymized financial asset transaction details stream, calculates the price change characteristic scalar for the continuous control period, and stores the price change characteristic scalar into the first-in-first-out buffer. Step S2: The data processor performs mean centering and second-order central moment calculation on the price change feature scalar in the first-in-first-out buffer to generate a transient fluctuation variance value. The transient fluctuation variance value is compared with the dynamic gate threshold. When the transient fluctuation variance value does not exceed the dynamic gate threshold, the data stream of the price change feature scalar is kept suspended. Step S3: When the transient fluctuation variance value exceeds the dynamic gate threshold, the data processor calls the abnormal data diversion and decoupling operator to release the data stream from the suspended state, stops the global update of the asset weight topology matrix, and only extracts the feature vector corresponding to the price change feature scalar of the current control period for accumulation and superposition to generate the convergent time series chip distribution feature tensor. In step S4, the data processor inputs the convergence time series chip distribution feature tensor into the position optimization time series state machine, combines the safety boundary line scalar and the risk warning line scalar to determine the phase transition condition, and outputs the phase code to limit the position ratio optimization allocation vector. When the transaction price touches the safety boundary line scalar and the phase code is a risk exit decision phase, the position ratio optimization allocation vector is reduced and the position risk warning result is output.

[0007] Preferably, based on step S1, the threshold dynamic fine-tuning process is performed in step S21: every 50 clock cycles, the data processor reads the transient bandwidth utilization rate of the current network and the real-time power consumption index of the data processor; based on the linear weighted sum of the transient bandwidth utilization rate and the real-time power consumption index, the dynamic gate threshold is finely adjusted in segments. When the transient bandwidth utilization rate increases by 10%, the dynamic gate threshold is adjusted upward by 0.0012, so that the dynamic gate threshold is stabilized within the adjusted range, in order to offset the bottleneck of the system's computing resources.

[0008] Preferably, in step S1, the anonymized financial asset transaction details stream is received in real time via a gigabit Ethernet interface; the price change feature scalar is calculated from the transaction prices of the underlying assets over 20 consecutive control periods and is cyclically moved into the first-in-first-out buffer at the current clock determination node.

[0009] Preferably, in step S2, calculating the transient variance value composed of price change feature scalars in the first-in-first-out buffer includes the following sub-steps: Step S22, extract the price change feature scalars of the current time and historical control period in the first-in-first-out buffer to construct a time-series price sliding window sequence; Step S23, perform mean centering operation on the time-series price sliding window sequence and calculate the second-order central moment to obtain the transient variance value.

[0010] Preferably, in step S3, the corresponding feature vector is obtained by mapping the current price change feature scalar; the accumulation calculation is completed within 15μs, so as to control the calculation latency of the management method within 2.4ms, thereby eliminating the instruction blocking bottleneck under high concurrency.

[0011] Preferably, in step S4, the periodic phase switching determination includes a state transition step: when the transaction price crosses the risk warning line scalar, the position optimization timing state machine is triggered to switch between the risk warning phase and the risk exit decision phase, and the corresponding phase code is output.

[0012] Preferably, in step S4, lowering the position ratio optimization allocation vector includes a two-way feedback control step: the phase code output by the position optimization timing state machine restricts the optimization search space of the position ratio optimization allocation vector in reverse; when the transaction price touches the safety boundary scalar and the phase code is a risk exit decision phase, the upper limit of the position ratio optimization allocation vector is forcibly restricted to prevent the non-convergence anomaly of the optimization iteration caused by a one-sided continuous unidirectional market.

[0013] Preferably, in the state where the position risk warning result is output in step S4, the position evolution trend tracking processing is further performed in step S41, which includes the following sub-steps: Step S411, continuously record the adjusted position ratio optimization allocation vector within the historical control period to generate a position time series evolution dataset; Step S412, calculate the rate of change of the first derivative of the position time series evolution dataset, and use it as a quantitative indicator to characterize the position evolution trend; Step S413, when the quantitative indicator exceeds the safety threshold of 0.05, output a risk warning signal for rapid position changes.

[0014] Preferably, when calculating the convergence time-series chip distribution feature tensor in step S3, the feature vector is read and written in situ at high speed by calling the internal data register of the processor in the prediction server, so as to eliminate the cache read and write latency in the high-speed data stream processing.

[0015] A securities investment risk warning and portfolio optimization management system, used to implement a securities investment risk warning and portfolio optimization management method, comprising: The system includes a detailed flow receiving and change extraction module, a variance calculation and state gating module, a feature splitting and incremental superposition module, and a state determination and optimization early warning module. Among them, the detailed flow receiving and change extraction module is used to receive the anonymized financial asset transaction detailed flow and extract the transaction price and transaction volume of the underlying asset, calculate the price change feature scalar of the continuous control period and store the price change feature scalar in the first-in-first-out buffer. The variance calculation and state gating module is used to calculate the transient fluctuation variance value of the time series composed of price change feature scalars in the first-in-first-out buffer and compare it with the dynamic gating threshold. When the transient fluctuation variance value does not exceed the dynamic gating threshold, the data stream of the price change feature scalar is kept suspended. The feature splitting and incremental superposition module has an embedded abnormal data splitting decoupling operator, which is used to release the data stream from the suspended state when the transient fluctuation variance value exceeds the dynamic gate threshold, stop the global update of the asset weight topology matrix, and only extract the feature vector corresponding to the current price change feature scalar for cumulative superposition to generate a convergent time series chip distribution feature tensor. The state determination and optimization early warning module embeds a position optimization time-series state machine. It inputs the convergence time-series chip distribution feature tensor into the position optimization time-series state machine, combines the safety boundary line scalar and the risk warning line scalar to determine the phase transition condition, and outputs the phase code to limit the position ratio optimization allocation vector. When the transaction price touches the safety boundary line scalar and the phase code is a risk exit decision phase, the position ratio optimization allocation vector is reduced and the position risk warning result is output.

[0016] Compared with existing technologies, the securities investment risk warning and portfolio optimization management method and system of this invention have the following advantages: 1. In securities investment risk warning and position optimization management, the system operates an abnormal data diversion and decoupling operator, uses a sampling window to extract price displacement feature scalars and calculates the transient fluctuation variance value of the current clock node. When the variance value does not exceed the dynamic gating threshold calibrated according to the system operating conditions, the control flow cuts off the update link of the time series chip state feature tensor, forcing the high-dimensional chip space distribution topology matrix to enter a data rest suspension state, so that the high-dimensional chip transition matrix solving module is in a data rest suspension state. The system maintains the phase code output of the previous control cycle, reducing the computational load by 78% and avoiding the computational power allocation retreat caused by the data processing system blindly recalculating high-dimensional features in volatile market conditions.

[0017] 2. When the transient fluctuation variance exceeds the dynamic gating threshold, the system activates the feedforward decoupling operator, releases the data from the quiescent state, and performs local incremental correction. At this time, the processor rejects the global update of the topology matrix and only extracts the local feature vector corresponding to the current price scalar for accumulation and superposition. Within 15μs, it produces the updated low-entropy time-series chip state feature tensor and inputs it into the position optimization state machine, which reduces the overall calculation latency of the risk control response chain from 45ms to 2.4ms. This eliminates the instruction blocking and computing power concession caused by high-throughput matrix recalculation under extreme volatility conditions, and realizes real-time feedforward hedging control of high-concurrency abnormal data in the financial forecasting processing system.

[0018] 3. The system establishes a two-way dependency and collaboration mechanism between the safety boundary line scalar, the risk warning line scalar, and the position optimization state machine. When the price crosses the risk warning line scalar, the state machine is triggered to perform a periodic phase switching judgment. At the same time, the phase code output by the state machine restricts the position ratio optimization allocation logic in reverse. This causes the algorithm to lower the position ratio optimization allocation vector when the asset price touches the safety boundary line and the state machine determines that it is in the panic exit phase. This closes the loop at the data level to block the non-convergence anomaly of the optimization control iteration caused by one-sided continuous unidirectional market conditions, and avoids the prediction data processing system from computational overflow and crash due to data mutations under extreme conditions. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method for securities investment risk warning and position allocation in this invention; Figure 2 This is the decision phase transition diagram for the timing state machine of the position optimization method of the present invention. Detailed Implementation

[0020] 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.

[0021] A method for securities investment risk warning and portfolio optimization management includes the following steps: Step S1: The data processor extracts the transaction price and volume of the underlying asset from the real-time received anonymized financial asset transaction details stream, calculates the price change characteristic scalar for the continuous control period, and stores the price change characteristic scalar into the first-in-first-out buffer. Step S2: The data processor performs mean centering and second-order central moment calculation on the price change feature scalar in the first-in-first-out buffer to generate a transient fluctuation variance value. The transient fluctuation variance value is compared with the dynamic gate threshold. When the transient fluctuation variance value does not exceed the dynamic gate threshold, the data stream of the price change feature scalar is kept suspended. Step S3: When the transient fluctuation variance value exceeds the dynamic gate threshold, the data processor calls the abnormal data diversion and decoupling operator to release the data stream from the suspended state, stops the global update of the asset weight topology matrix, and only extracts the feature vector corresponding to the price change feature scalar of the current control period for accumulation and superposition to generate the convergent time series chip distribution feature tensor. In step S4, the data processor inputs the convergence time series chip distribution feature tensor into the position optimization time series state machine, combines the safety boundary line scalar and the risk warning line scalar to determine the phase transition condition, and outputs the phase code to limit the position ratio optimization allocation vector. When the transaction price touches the safety boundary line scalar and the phase code is a risk exit decision phase, the position ratio optimization allocation vector is reduced and the position risk warning result is output.

[0022] Preferably, based on step S1, the threshold dynamic fine-tuning process is performed in step S21: every 50 clock cycles, the data processor reads the transient bandwidth utilization rate of the current network and the real-time power consumption index of the data processor; based on the linear weighted sum of the transient bandwidth utilization rate and the real-time power consumption index, the dynamic gate threshold is finely adjusted in segments. When the transient bandwidth utilization rate increases by 10%, the dynamic gate threshold is adjusted upward by 0.0012, so that the dynamic gate threshold is stabilized within the adjusted range, in order to offset the bottleneck of the system's computing resources.

[0023] Preferably, in step S1, the anonymized financial asset transaction details stream is received in real time via a gigabit Ethernet interface; the price change feature scalar is calculated from the transaction prices of the underlying assets over 20 consecutive control periods and is cyclically moved into the first-in-first-out buffer at the current clock determination node.

[0024] Preferably, in step S2, calculating the transient variance value composed of price change feature scalars in the first-in-first-out buffer includes the following sub-steps: Step S22, extract the price change feature scalars of the current time and historical control period in the first-in-first-out buffer to construct a time-series price sliding window sequence; Step S23, perform mean centering operation on the time-series price sliding window sequence and calculate the second-order central moment to obtain the transient variance value.

[0025] Preferably, in step S3, the corresponding feature vector is obtained by mapping the current price change feature scalar; the accumulation calculation is completed within 15μs, so as to control the calculation latency of the management method within 2.4ms, thereby eliminating the instruction blocking bottleneck under high concurrency.

[0026] Preferably, in step S4, the periodic phase switching determination includes a state transition step: when the transaction price crosses the risk warning line scalar, the position optimization timing state machine is triggered to switch between the risk warning phase and the risk exit decision phase, and the corresponding phase code is output.

[0027] Preferably, in step S4, lowering the position ratio optimization allocation vector includes a two-way feedback control step: the phase code output by the position optimization timing state machine restricts the optimization search space of the position ratio optimization allocation vector in reverse; when the transaction price touches the safety boundary scalar and the phase code is a risk exit decision phase, the upper limit of the position ratio optimization allocation vector is forcibly restricted to prevent the non-convergence anomaly of the optimization iteration caused by a one-sided continuous unidirectional market.

[0028] Preferably, in the state where the position risk warning result is output in step S4, the position evolution trend tracking processing is further performed in step S41, which includes the following sub-steps: Step S411, continuously record the adjusted position ratio optimization allocation vector within the historical control period to generate a position time series evolution dataset; Step S412, calculate the rate of change of the first derivative of the position time series evolution dataset, and use it as a quantitative indicator to characterize the position evolution trend; Step S413, when the quantitative indicator exceeds the safety threshold of 0.05, output a risk warning signal for rapid position changes.

[0029] Preferably, when calculating the convergence time-series chip distribution feature tensor in step S3, the feature vector is read and written in situ at high speed by calling the internal data register of the processor in the prediction server, so as to eliminate the cache read and write latency in the high-speed data stream processing.

[0030] A securities investment risk warning and portfolio optimization management system, used to implement a securities investment risk warning and portfolio optimization management method, comprising: The system includes a detailed flow receiving and change extraction module, a variance calculation and state gating module, a feature splitting and incremental superposition module, and a state determination and optimization early warning module. Among them, the detailed flow receiving and change extraction module is used to receive the anonymized financial asset transaction detailed flow and extract the transaction price and transaction volume of the underlying asset, calculate the price change feature scalar of the continuous control period and store the price change feature scalar in the first-in-first-out buffer. The variance calculation and state gating module is used to calculate the transient fluctuation variance value of the time series composed of price change feature scalars in the first-in-first-out buffer and compare it with the dynamic gating threshold. When the transient fluctuation variance value does not exceed the dynamic gating threshold, the data stream of the price change feature scalar is kept suspended. The feature splitting and incremental superposition module has an embedded abnormal data splitting decoupling operator, which is used to release the data stream from the suspended state when the transient fluctuation variance value exceeds the dynamic gate threshold, stop the global update of the asset weight topology matrix, and only extract the feature vector corresponding to the current price change feature scalar for cumulative superposition to generate a convergent time series chip distribution feature tensor. The state determination and optimization early warning module embeds a position optimization time-series state machine. It inputs the convergence time-series chip distribution feature tensor into the position optimization time-series state machine, combines the safety boundary line scalar and the risk warning line scalar to determine the phase transition condition, and outputs the phase code to limit the position ratio optimization allocation vector. When the transaction price touches the safety boundary line scalar and the phase code is a risk exit decision phase, the position ratio optimization allocation vector is reduced and the position risk warning result is output.

[0031] Example 1: In the multi-dimensional high-concurrency monitoring server cluster operating environment of the data processing system, the system is designed for real-time monitoring and prediction tasks of large-scale concurrent securities transaction details under structural cyclical evolution. Because the traditional architecture uses static synchronous update rules, the data center, while calculating high-frequency price displacement feature scalars, fully recalculates the spatial distribution topology of long-term asset holdings. This leads to the stacking of the core instruction queue and the yielding of computing resources within the prediction server when massive transaction flows surge in a short period, resulting in a technical problem where data refresh real-time performance and high-throughput matrix solving are mutually constrained. The method claimed in this invention addresses this issue in the multi-dimensional high-concurrency monitoring server cluster... During deployment and operation within the cluster, the general-purpose central processing unit (CPU) receives anonymized financial asset transaction details streams processed by the data gateway via a gigabit Ethernet physical interface. It extracts the transaction price and volume of the underlying assets from these streams. Internal logic units continuously calculate price change characteristic scalars for 20 consecutive control cycles and cyclically move these scalars into a first-in-first-out (FIFO) buffer. The data processor performs mean-centering and second-order central moment calculations on the price change characteristic scalars in the FIFO buffer to generate a transient variance value. At the judgment node of each clock cycle, it calls the abnormal data diversion and decoupling operator to compare the transient variance value with the dynamic gating threshold. The algebraic relationship between the magnitudes of the transient fluctuations, when the variance of the transient fluctuations does not exceed the dynamic gating threshold. When the conditional control flow cuts off the global update link of the asset weight topology matrix, it forces the chip space distribution topology matrix into a data resting suspension state and overwrites the current update weights to 0, so that the high-dimensional matrix solving module is in a zero-power operation state to maintain the phase code output of the previous control cycle, thereby reducing the computational entropy output of the data processing system by 78% and eliminating computational load loss; conversely, when the transient fluctuation variance value exceeds the dynamic gating threshold... At that time, the abnormal data diversion and decoupling operator releases the data from the quiescent suspension state and initiates a local incremental correction process. The general-purpose central processing unit rejects the global update of the chip space distribution topology matrix, and only extracts the feature vectors corresponding to the price change feature scalar of the current control cycle to complete the accumulation and superposition within 15μs, generating a convergent time series chip distribution feature tensor. When generating the convergent time-series chip distribution feature tensor, the specific mapping rules and superposition process are as follows: The data processor establishes a preset benchmark mapping matrix, linearly expanding the one-dimensional price change feature scalar into a price anomaly feature vector containing 20 dimensions, where the value of each dimension represents the distribution weight of the current price interval; the construction of the benchmark mapping matrix is ​​based on the statistical discrete quantitative mapping principle. The data processor obtains the highest and lowest intraday volatility rates of the underlying asset over the past 250 trading days. The difference between the highest and lowest volatility rates is divided into 20 consecutive and non-overlapping numerical intervals, establishing a mapping relationship between the one-dimensional price change feature scalar and the 20-dimensional feature vector. When the scalar value of the price change feature of the control cycle falls into the 5th value interval, the data processor executes the lookup table transformation logic, sets the value of the 5th dimension of the 20-dimensional feature vector to 1.0, and sets the values ​​of the other 19 dimensions to 0.0, outputting a 20-dimensional price anomaly feature vector. The general-purpose central processing unit only retrieves the feature vector corresponding to the current control cycle, and performs overlapping and accumulation with the historical time-series feature vectors stored in the first-in-first-out buffer at the corresponding positions. When the sum of the absolute values ​​of the rate of change of the feature tensor elements after three consecutive accumulations is less than the preset 0.005, it is determined that the chip distribution feature tensor has reached a mathematical convergence state, thereby locking and outputting the converged time-series chip distribution feature tensor.

[0032] The data processor will generate a convergent time-series chip distribution feature tensor. The input is fed into the position optimization time-series state machine, which combines the risk warning line scalar output by the boundary calculus operator in real time. Scalar of safety boundary line The phase transition condition determination is carried out, and the calculation formulas for the risk warning line scalar and the safety boundary line scalar are expressed as follows: and ,in, This serves as a metric for the risk warning line. For the safety boundary line scalar, The closing price of the previous trading period. For the state matrix The dimensionless dynamic hedging correction coefficient is adjusted synchronously with the real-time results. For the state matrix The dimensionless dynamic hedging correction coefficient is adjusted synchronously with the real-time results. For the state matrix, when the transaction price crosses the risk warning line scalar When the control flow triggers the position optimization timing state machine to switch between the risk warning phase and the risk exit control phase and outputs the corresponding phase code, this phase code in turn restricts the position ratio optimization allocation vector. The optimal search space, when the transaction price touches the safety boundary scalar Furthermore, when the phase code is a risk exit control phase, the data processor forcibly limits the position ratio to optimize the allocation vector. The upper limit of the position ratio is set, and the optimized allocation vector is overwritten into the trading control register as a control instruction, reducing the overall calculation latency from 45ms to within 2.4ms. The specific construction of the state matrix and the synchronous adjustment mechanism of the hedging correction coefficient are as follows: The state matrix is ​​a 2-row, 2-column dimensionless matrix. Its four elements correspond to the normalized ratios of the buy order volume, sell order volume, active buy volume, and active sell volume of the underlying asset in the current control period. At each clock node, the data processor performs eigenvalue solving on the state matrix to obtain its maximum eigenvalue as a quantitative representation of the current market microstructure. The dimensionless dynamic hedging correction coefficient has a definite proportional mapping relationship with this maximum eigenvalue. Specifically, for every 0.1 increase in the maximum eigenvalue, the dimensionless dynamic hedging correction coefficient on the buy side is simultaneously increased by 0.005, and the dimensionless dynamic hedging correction coefficient on the sell side is simultaneously decreased by 0.005, thereby achieving adaptive hedging adjustment based on the micro-trading state.

[0033] During the continuous operation of the data processing system, the data processor reads the transient bandwidth utilization of the data network and the real-time power consumption of the central processing unit every 50 clock cycles. Based on the linear weighted sum of the transient bandwidth utilization and real-time power consumption, a dynamic gating threshold is applied. Segmented fine-tuning is performed; when the transient bandwidth utilization increases to 10%, the data processor adjusts the dynamic gating threshold. The value is adjusted upwards by 0.0012 to stabilize within the adjusted range, thereby reducing the risk of computational resource congestion in the data processing system. Combined with a high-frequency disturbance dynamic drift compensation step, the data processor calculates the local information entropy increment of the capital inflow data over 20 consecutive periods. When the local information entropy increment exceeds a preset confidence threshold of 0.85, high-frequency noise interference is identified, and the data processor automatically triggers the drift compensation rule to adjust the dimensionless dynamic hedging correction coefficient. Applying damping coefficient To reduce the scale of the risk warning line The frequency of change, of which The damping coefficient, dimensionless and ranging from 0.5 to 0.75, is used to suppress high-frequency interference and ensure the system stability of data center servers in complex environments. Through this bidirectional dependent feedforward control loop, which deeply interlocks the high-frequency price displacement characteristic scalar with the low-dimensional incremental time-series chip distribution characteristic tensor, the entire data processing system completes the capture of transient asset price anomalies and real-time feedforward hedging of position ratio control flow within standard computing nodes. This not only blocks the non-convergence anomaly caused by unilateral, continuous, one-way market trends but also enables the commercial financial supervision and prediction system to possess a monotonic deterministic control loop when facing extreme high-concurrency data flow surges. It transforms conventional hardware and software expansion conflicts into data flow link logic reconstruction, achieving system... The system improves the timeliness of prediction and the convergence of risk monitoring at the foundational architecture level. Specifically, the calculation procedure for the local information entropy increment of capital inflow data over 20 consecutive periods is as follows: The data processor calculates the proportion of buy volume to total volume in each of the 20 control periods within the current sliding window, constructing a probability distribution function for capital inflow. The information entropy formula is used to discretely sum this probability distribution to obtain the absolute information entropy value for the current control period. The local information entropy increment is calculated by subtracting the absolute information entropy value of the previous control period from the current period's absolute information entropy value. When this difference is greater than 0.85, it indicates that the capital flow exhibits extremely high disorder and random noise characteristics, leading the system to determine the presence of high-frequency false positive interference.

[0034] Example 2: When the system runs on a simulation test platform of a multi-dimensional high-concurrency monitoring server cluster, this test platform is deployed in an offline playback environment composed of a historical open market financial asset transaction detail dataset. The anonymized data stream input interface provides a constant throughput of 50,000 records per second and a clock allocation resolution boundary of 1ms. In this system environment, the control cycle length parameter of the time-loop first-in-first-out buffer is... The setting of this parameter is influenced by several key technical factors, including the characteristic fluctuation frequency of anonymous transaction data and the content storage space allocation overhead of the monitoring server node. The settings need to balance the accuracy of capturing short-term price displacement features with the computational load of the server instruction processing queue. Specifically, when the transaction price of the underlying asset fluctuates drastically, the length parameter is adjusted to improve the timeliness of feedforward decoupling. The length parameter tends to be closer to the lower limit of the range to shorten the cache sequence length, while when the market is in a volatile state, the length parameter... To smooth out random noise, the upper limit of the value range is considered. For a high-frequency trading volume of 40,000 transactions per second, the time-cycle first-in-first-out buffer control period length parameter determined by the above control rules is applied. The fixed number is 20.

[0035] To verify the decoupling operator for abnormal data flow and the optimized allocation vector for position ratio, To assess the control response performance, a simulation test platform was constructed to build an experimental verification system containing a multi-dimensional control group. This system included the sample group of this invention employing the complete technical solution, a control group using a conventional static full-scale calculation scheme, a partially missing control group lacking gating threshold filtering features, and a control group with the gating threshold... An out-of-range control group was set outside the 0.50 limit. Simultaneously, the system divided the scalar fluctuation intensity of the price change characteristic, a core variable, into three test gradients: a low fluctuation gradient of 0.05, a medium fluctuation gradient of 0.15, and a high fluctuation gradient of 0.35. To simulate industrial electromagnetic and financial network transmission environments, the data processor actively superimposed Gaussian white noise with a signal-to-noise ratio of 20dB into the anonymous asset transaction details stream at the input end, and injected high-frequency price jitter interference harmonics at a frequency of 50Hz through the data bus. This was used to test the anti-interference performance and operational reliability of the system sample under non-ideal operating conditions.

[0036] Measured data collected from the multidimensional high-concurrency monitoring server cluster shows that under a low volatility gradient of 0.05, the control group experienced a continuous instruction latency of 42.3ms due to frequent full calculations of the high-maintenance matrix, while the partially missing control group experienced a computation latency of 41.8ms. In contrast, the sample group of this invention, relying on conditional control flow to suspend the chip space distribution topology matrix, achieved a computation latency of only 2.1ms, with a synchronous reduction of 78.3% in system computational entropy output, effectively preventing the retreat of computing resources. When the test conditions shifted to a high volatility gradient of 0.35, the out-of-range control group could not activate the local incremental correction procedure due to parameter exceeding limits, resulting in non-convergence of matrix optimization iteration and a data latency of 89.6ms. Conversely, the abnormal data diversion and decoupling operator of the sample group of this invention achieved a latency of 89.6ms when the transient volatility variance exceeded the dynamic gating threshold. The clock node response converges within 14.6 μs for the time-series chip distribution feature tensor. The incremental superposition keeps the overall computation latency at 2.3ms. The position optimization timing state machine determines the phase transition based on the control boundary output of the boundary calculation operator, and the risk warning line scalar. Scalar of safety boundary line The calculation formulas are expressed as follows: and ,in, This serves as a metric for the risk warning line. For the safety boundary line scalar, The closing price of the previous trading period. For the state matrix The dimensionless dynamic hedging correction coefficient is adjusted synchronously with the real-time results. For the state matrix The dimensionless dynamic hedging correction coefficient is adjusted synchronously with the real-time results. As the state matrix, the experimental verification shows that the data evolution trajectory of the system exhibits a nonlinear performance inflection point. Test results indicate that when the dynamic gating threshold... After the fine-tuning step variant exceeds the 0.0025 working window, the optimization efficiency of system response latency slows down and tends to a flat plateau. This indicates that the processor's computing power allocation has reached a local upper limit, and further expanding the threshold range cannot further improve the efficiency of latency reduction. This performance inflection point establishes the rationality of the dynamic gating threshold control boundary. When dealing with extreme concurrency, high noise, and high fluctuation conditions, the data processor captures the sudden increase of 10% in data network bandwidth utilization through a segmented fine-tuning mechanism, and adjusts the dynamic gating threshold accordingly. Adjust upwards by 0.0012, and simultaneously adjust the dimensionless dynamic hedging correction coefficient. Applying a dimensionless damping coefficient Thus, the scalar value of the risk warning line is stabilized. High-frequency temperature drift, where the damping coefficient The measured value of 0.62 at the endpoint of the characteristic range was selected to eliminate false positive warning noise caused by 50Hz high-frequency disturbances, stabilize the output of the corresponding phase code, and optimize the allocation vector of the position ratio. By writing into the transaction control register, this bidirectional dependent feedforward control loop converges concurrent traffic surges into deterministic state machine transitions, establishing a deterministic control defense against financial transaction data blockage at the system level.

[0037] Example 3: When the data processing system runs in a commercial financial forecasting data processing cluster environment containing multiple parallel computing nodes, and faces the server core instruction queue delay triggered by the short-term instantaneous influx of massive concurrent asset transaction flows, the system receives the anonymized asset transaction details stream through the gigabit Ethernet physical interface. After receiving the anonymous data stream, the instruction scheduling unit inside the data processor opens an independent double buffer in the data bus address space, and alternately latches the transaction price data of the current clock node in a pipeline manner. In order to lay out the program actions inside the data processor, the discretized execution steps of mean centering calculation and second-order central moment calculation are as follows: The first arithmetic logic unit inside the data processor The first unit sequentially reads price change feature scalars for 20 consecutive control cycles from the first-in-first-out buffer, sums them, and then right-shifts them to obtain the local price mean scalar. The second arithmetic logic unit is triggered in parallel, subtracting the local price mean scalar from each price change feature scalar at each address unit in the buffer, generating 20 difference symbol feature values. The multiplier array synchronously performs a self-square operation on the difference symbol feature values, and shifts the resulting squares into the summator. Finally, the radix is ​​normalized by the division operator, and the transient fluctuation variance value is output. This data source directly relies on the underlying shift and summation hardware timing, thereby converting the solution cost of multidimensional features from matrix multiplication to one-dimensional linear accumulation.

[0038] The data processor compares the transient fluctuation variance with the dynamic gating threshold using an internal digital comparator. The algebraic value of the result is determined when the result does not exceed the dynamic gating threshold. Under low-fluctuation operating conditions, the state latch outputs a high-level blocking signal, forcibly shutting down the global update clock of the asset weight topology matrix, thus putting the subsequent high-dimensional transition matrix solving module into a zero-power steady state with phase suspension; however, when the judgment result exceeds the dynamic gating threshold... At that time, the abnormal data diversion and decoupling operator is activated, extracting feature vectors from the buffer and performing parallel accumulation and superposition calculations for 15μs to generate a convergent time-series chip distribution feature tensor. The tensor is directly mapped and input into the holding optimization timing state machine as a multidimensional control variable. To ensure that the parallel accumulation and superposition calculation of 15 microseconds and the overall calculation latency of less than 2.4 milliseconds are rigidly implemented on the underlying hardware, the general-purpose central processing unit is equipped with a dedicated pipeline hardware acceleration architecture, which includes 20 sets of parallel arithmetic logic units and internal in-situ data registers directly mounted on the high-speed bus. When the abnormal data diversion and decoupling operator is activated, the data scheduling unit directly distributes single-instruction multiple data stream operations to these 20 sets of parallel arithmetic logic units through the instruction set, realizing the synchronous superposition calculation of each dimension of the feature vector in a single clock cycle. The entire data reading and overwriting process is completed in-situ in the data registers inside the processor, refusing to trigger the read and write cycles of the external memory bus, thereby reducing the latency caused by bus conflicts and ensuring that the overall response time is controlled within the preset physical latency range.

[0039] The internal topology of the position optimization time-series state machine is dissected. This state machine includes a feature input layer, a logic jump verification loop, and an output limiting register. The feature input layer receives the convergence time-series chip distribution feature tensor. Furthermore, at the hardware level, it is flattened into a multi-dimensional feature vector containing price change gradients and position change rates. The logical jump verification loop is configured with mutually exclusive risk warning command lines and safety boundary command lines, which are used to receive the risk warning line scalars output in real time by the boundary calculation operator. Scalar of safety boundary line Specifically, the calculation formulas for the risk warning line scalar and the safety boundary line scalar are expressed as follows: and ,in, This serves as a metric for the risk warning line. For the safety boundary line scalar, The closing price of the previous trading period. This is a dimensionless dynamic hedging correction coefficient; This is a dimensionless dynamic hedging correction coefficient. When the current transaction price of the underlying asset crosses the risk warning line upwards... Furthermore, when the volatility intensity shows an upward trend, the hardware status register within the logic jump verification loop is triggered to switch on the next rising edge of the clock. The control instruction pointer unconditionally jumps from the risk warning phase to the risk exit control phase, outputting the corresponding phase code signal. This phase code signal serves as a high-priority chip select enable signal to reversely limit the position ratio optimization allocation vector. To eliminate technical uncertainties in the setting of key parameters, the register addressing bit width within the optimization search space is dynamically controlled by a threshold value in this invention. The step adjustment coefficient of 0.0012 is a causal operator calculated and determined by the system's endogenous dissipative transformation equation during the power-on self-test phase. The system monitors sensors to collect real-time data on the system network bandwidth utilization of multiple parallel computing nodes and the thermal power consumption of the general-purpose CPU. It then calculates the dimensionless impedance residual, reflecting computing power loss. Since the system network bandwidth utilization and the thermal power consumption of the general-purpose CPU exhibit a non-linear positive correlation and monotonically increasing trend under hardware overload, the data processor converts the dimensionless impedance residual into an adaptive gating correction increment using a reference transfer function. Under this control condition, when the system network bandwidth utilization increases to a bottleneck trigger point of 10%, the adaptive gating correction increment corresponds to the number 0.0012. Based on this, the data processor adjusts the dynamic gating threshold. The gating window is automatically widened by an upward step of 0.0012, thus filtering out high-frequency disturbance noise caused by short-term data aggregation. The dimensionless impedance residual is calculated based on a linear weighted resource balancing model. The data processor multiplies the real-time collected system network bandwidth utilization by a first preset weighting coefficient of 0.4 and the central processing unit's thermal power consumption by a second preset weighting coefficient of 0.6. The sum of these two values ​​yields the dimensionless impedance residual. At each clock cycle, the data processor compares the dimensionless impedance residual with a preset system bottleneck threshold. When the dimensionless impedance residual reaches or exceeds the system bottleneck threshold and the system network bandwidth utilization increases by 10%, the data processor determines the adaptive gating correction increment to be 0.0012. When the transaction price further touches the safety boundary scalar... Furthermore, when the phase code remains at the boundary state of the risk exit control phase, the output limit register automatically intercepts the currently calculated position ratio optimization allocation vector. The corresponding control field is forcibly cut off and overwritten to zero. The optimized position control flow instruction, which has been truncated, is directly overwritten into the bottom execution port of the transaction control register as a hard control code. Through the data flow and information control architecture constructed by the hardware double buffer, arithmetic logic unit and adaptive gating correction increment, the entire multi-dimensional high-concurrency monitoring server cluster can transform the software optimization iteration process into deterministic state machine hardware jump and local feature tensor superposition when facing sudden high-concurrency traffic impact. At the time node of the means implementation, it automatically completes the blocking response to the warning delay and memory instruction blocking. At the system level, it achieves the monotonic unity of data processing timeliness and risk supervision convergence, so that the overall calculation latency is always rigidly held and stabilized within the physical boundary of 2.4ms.

[0040] Example 4: In a multi-dimensional high-concurrency monitoring server cluster operating environment, to address the challenges of memory address stacking and system baseline drift that may occur during long-term operation of financial transaction detail stream data processing nodes, the test platform pre-sets a standardized pre-calibration process that includes the transaction stream load intensity and hardware load status at different time periods. In this process, the data processor monitors the unit bit flip rate in the buffer area through cyclic redundancy check codes and sets a baseline value for transient bandwidth utilization to calibrate the real-time computing power level of various computing nodes. When the fluctuation amplitude of the continuous read and write timing of the buffer area is detected to exceed the preset fault tolerance deviation band, the processor automatically triggers the baseline reconstruction instruction to forcibly reset the buffer area address pointer and clear the residual logical fragments. This is intended to ensure that the input data stream is in a deterministic state at the physical level before performing high-dimensional chip space distribution topology matrix operations, eliminating unexpected computing power losses caused by long-term operation.

[0041] To address the uneven distribution of computing resources in multi-user, high-frequency bidding scenarios, the data processor executes the convergence time-series chip distribution feature tensor. Before the generation logic, a dynamic scheduling operator distributed by the multi-node load collaborative management module is loaded in real time. This operator, based on the current real-time task backlog length of all computing nodes and the CPU core temperature feedback value, uses a linear weighted allocation function to assign distributed computing weight coefficients to each computing node. For real-time calibration, the calculation relationship of the linear weighted allocation function is as follows: ,in, The weight coefficients for distributed computing are dimensionless. The length of the task stack. CPU core temperature This is the normalization adjustment factor for the task stack length. This is a normalization adjustment factor for the CPU core temperature, which the processor uses to adjust the temperature. The calculation results dynamically adjust the load share of each computing node in the topology matrix solution link. When the weight coefficient of a node is lower than the system's preset communication delay protection threshold, the computing task flow of that node is automatically and smoothly migrated to an adjacent redundant node with a larger load margin. This ensures that the prediction convergence performance of the entire cluster remains at a stable output level when facing complex and ever-changing financial network load environments. The specific calibration and selection criteria for the normalized adjustment coefficients of task backlog length and CPU core temperature are as follows: During system initialization and deployment, the test platform uses a preset standard full-load test flow to test each node. The computing nodes are benchmarked to determine the maximum allowed task queue stack length of 5000 lines and the maximum safe temperature of the processor core of 85 degrees Celsius. In order to perform dimensionless processing and balanced alignment of different physical dimensions, the normalization adjustment factor for the task stack length is taken as 0.5 times the reciprocal of the maximum stack length, that is, precisely set to 0.0001 per line, and the normalization adjustment factor for the CPU core temperature is taken as 0.5 times the reciprocal of the maximum safe temperature, that is, precisely set to 0.0058 per degree Celsius, thereby ensuring that the two indicators have equal logical weight when calculating the distributed computing weight coefficient.

[0042] Example 5: When the system faces the deployment calibration and boundary testing of the securities investment risk warning and position optimization management system, the general-purpose central processing unit reads the anonymized financial asset transaction details stream from the data gateway cache, activates the system hardware dual buffer and pipeline interactive latch logic, verifies the storage status of the price change feature scalars for 20 consecutive control cycles in the cyclic first-in-first-out cache, and the first arithmetic logic unit in the data processor sequentially reads the price change feature scalars for 20 consecutive control cycles from the first-in-first-out cache, sums them, right-shifts them to obtain the local price mean scalar, and the second arithmetic logic unit triggers the difference operation in parallel. Each price change feature scalar is subtracted from the local price mean scalar to generate 20 difference symbol feature values; the multiplier array performs self-square calculation on the difference symbol feature values, and the summator and division operator convert the squared result into a base-normalized value and output the price displacement feature scalar. Transient fluctuation variance, distributed computing weight coefficients The calculation formula for the computing power loss of multi-path parallel computing nodes is as follows: ,in, For distributed computing of weight coefficients, The length of the task stack. This refers to the core temperature of a general-purpose central processing unit. This is the normalization adjustment factor for the task stack length. This is the normalization adjustment factor for the core temperature of a general-purpose central processing unit.

[0043] Position optimization time-series state machine based on price displacement feature scalar Scalar of safety boundary line or risk warning line scalar The numerical comparison results, combined with the proportion of active chip market value, establish state transition gating rules; when the proportion of active chip market value is less than or equal to 15% and the transaction price of the underlying asset touches the safety boundary line, the scalar... At that time, the position optimization timing state machine determines the phase code as the accumulation phase code 01; when the market value of active chips is greater than or equal to 65% and the transaction price crosses the risk warning line scalar. At that time, the position optimization timing state machine determines the phase code as distribution lock-in code 03; when the active chip market value ratio is within the range of 15% to 35% and the transaction price falls below the safety boundary line scalar within 3 consecutive control cycles. At that time, the position optimization time series state machine determines the phase code as risk exit control phase code 04. When the position optimization time series state machine determines the code, the input data source uses discrete quantitative parameters calculated by the data processor. The active chip market value ratio is less than or equal to 15% and the transaction price touches the safety boundary line scalar. At this time, the state machine generates accumulation state code 01; the market value of active chips accounts for more than or equal to 65% and the transaction price crosses the risk warning line. At this time, the state machine generates a deadlock code 03; the active chips' market value ratio is between 15% and 35% and the transaction price is below the safety boundary scalar for three consecutive control cycles. At that time, the state machine generates risk exit control phase code 04. The state transition judgment uses numbers and standard units of measurement to describe parameters, and clears the mapping conflict between graphic text and instruction manual text features. Before the actual execution of the state transition gating rules, the separation and extraction process of the active chip market value ratio from the input convergent time-series chip distribution feature tensor is as follows: The data processor performs dimensionality reduction mapping on the convergent time-series chip distribution feature tensor, associates the multidimensional chip price distribution vector in the tensor with the current real-time transaction price, and extracts the feature matrix element set whose price is within the range of ±5% of the current transaction price; then, the logic unit normalizes and sums all elements in the set, calculates the ratio of the number of chips in this specific price range to the total sum of the chip feature tensor, and this ratio is used as the scalar form of the active chip market value ratio, and is directly output to the input register of the position optimization time-series state machine.

[0044] The data processor is configured with three validation groups with parametric gradients; the first validation group sets the damping coefficient. With a lower limit of 0.5, when the data processor monitors a local information entropy increment of 0.88 in the capital inflow data over 20 consecutive control periods and this increment is greater than the confidence threshold of 0.85, the data processor activates the drift compensation rule to adjust the dimensionless dynamic hedging correction coefficient. Applying damping coefficient Weighted calculation to reduce the scalar value of the risk warning line The frequency of change is such that the system response delay is 2.2ms; the second verification group sets the damping coefficient. With a median of 0.625, when the local information entropy increment rises to 0.92, the data processor uses this median to dampen the recalculation of the feature matrix and optimizes the allocation vector of the position ratio. The computation latency was controlled within 2.4ms, and the computational entropy output of the data center server was reduced by 78%; the third verification group set the damping coefficient. With an upper limit of 0.75, when the local information entropy increment reaches 0.96, the data processor uses the damping coefficient... The dimensionless dynamic hedging correction coefficient for the feedback loop constraint is 0.75. With a fluctuation step size and a system computation latency of 2.4ms, when the computational entropy output of the data center server decreases by 78%, its text-defined computational system and physical reality performance are as follows: The computational entropy is obtained by the data processor through the frequency of occurrence of the full recalculation instruction of the high-dimensional topological matrix in the server instruction queue, combined with the total number of instructions accumulated in the current control cycle, and performing discrete logarithmic summation, which is used to quantitatively evaluate the disordered complexity of the software execution path; when the update link of the feature matrix is ​​successfully cut off by the dynamic gating threshold and enters the resting suspension state, the instruction scheduler at the bottom layer of the server automatically exempts the recalculation of the full matrix solution. The task eliminates high-frequency stacked tasks in the core instruction queue, transforming the instruction execution state into a highly monotonous and deterministic linear flow. After discrete data comparison and measurement by a performance tester, it was confirmed that the computational entropy of the system rigidly decreased by 78% from the baseline value before suspension. This directly corresponds to a significant drop in the real-time thermal power consumption of the server motherboard power supply module. When data network transmission delays occur or data packet loss occurs in the anonymized financial asset transaction details stream, the data processor automatically issues an instruction to clear the cyclic first-in-first-out buffer when it detects that the details stream is missing for three consecutive control cycles, setting the current dynamic gating threshold. Overwrite with a safety constant of 0, and the position ratio will be optimized and allocated to the vector by the trading control register. Limit it to a safety lower limit of 0.15 or below.

[0045] 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. A method for securities investment risk early warning and portfolio optimization management, characterized in that, Includes the following steps: Step S1: The data processor extracts the transaction price and volume of the underlying asset from the real-time received anonymized financial asset transaction details stream, calculates the price change characteristic scalar for the continuous control period, and stores the price change characteristic scalar into the first-in-first-out buffer. Step S2: The data processor performs mean centering and second-order central moment calculation on the price change feature scalar in the first-in-first-out buffer to generate transient fluctuation variance value. The transient fluctuation variance value is compared with the dynamic gate threshold. When the transient fluctuation variance value does not exceed the dynamic gate threshold, the data stream of the price change feature scalar is kept suspended. Step S3: When the transient fluctuation variance value exceeds the dynamic gating threshold, the data processor calls the abnormal data diversion and decoupling operator to release the data stream from the suspended state, stops the global update of the asset weight topology matrix, and only extracts the feature vector corresponding to the price change feature scalar of the current control period for accumulation and superposition to generate the convergent time series chip distribution feature tensor. In step S4, the data processor inputs the convergence time-series chip distribution feature tensor into the position optimization time-series state machine, combines the safety boundary line scalar and the risk warning line scalar to determine the phase transition condition, and outputs the phase code to limit the position ratio optimization allocation vector. When the transaction price touches the safety boundary line scalar and the phase code is a risk exit decision phase, the position ratio optimization allocation vector is reduced and the position risk warning result is output.

2. The method for securities investment risk early warning and portfolio optimization management according to claim 1, characterized in that, Based on step S1, the threshold is dynamically fine-tuned in step S21: Every 50 clock cycles, the data processor reads the transient bandwidth utilization rate of the current network and the real-time power consumption index of the data processor; based on the linear weighted sum of the transient bandwidth utilization rate and the real-time power consumption index, the dynamic gate threshold is finely adjusted in segments. When the transient bandwidth utilization rate increases by 10%, the dynamic gate threshold is adjusted upward by 0.0012 to stabilize the dynamic gate threshold within the adjusted range, so as to offset the bottleneck of the system's computing resources.

3. The method for securities investment risk early warning and portfolio optimization management according to claim 1, characterized in that, In step S1, the anonymized financial asset transaction details stream is received in real time through a gigabit Ethernet interface; the price change feature scalar is calculated from the transaction price of the underlying asset over 20 consecutive control periods and is cyclically moved into the first-in-first-out buffer at the current clock determination node.

4. The method for securities investment risk early warning and portfolio optimization management according to claim 1, characterized in that, In step S2, calculating the transient variance value composed of price change feature scalars in the first-in-first-out buffer includes the following sub-steps: Step S22, extract the price change feature scalars of the current time and the historical control period in the first-in-first-out buffer to construct a time-series price sliding window sequence; Step S23, perform mean centering operation on the time-series price sliding window sequence and calculate the second-order central moment to obtain the transient variance value.

5. The method for securities investment risk early warning and portfolio optimization management according to claim 1, characterized in that, In step S3, the corresponding feature vector is obtained by mapping the current price change feature scalar; the accumulation and superposition are completed within 15μs, so as to control the calculation latency of the management method within 2.4ms, thereby eliminating the instruction blocking bottleneck under high concurrency.

6. The method for securities investment risk early warning and portfolio optimization management according to claim 1, characterized in that, In step S4, the periodic phase switching determination includes a state transition step: when the transaction price crosses the risk warning line scalar, the position optimization timing state machine is triggered to switch between the risk warning phase and the risk exit decision phase, and outputs the corresponding phase code.

7. The method for securities investment risk early warning and portfolio optimization management according to claim 1, characterized in that, In step S4, the reduction of the position ratio optimization allocation vector includes a two-way feedback control step: the phase code output by the position optimization timing state machine restricts the optimization search space of the position ratio optimization allocation vector in reverse; when the transaction price touches the safety boundary scalar and the phase code is the risk exit decision phase, the upper limit of the position ratio optimization allocation vector is forcibly restricted to prevent the non-convergence anomaly of the optimization iteration caused by the one-sided continuous one-way market.

8. The method for securities investment risk early warning and portfolio optimization management according to claim 1, characterized in that, In the state where the position risk warning result is output in step S4, the position evolution trend tracking process is further carried out in step S41, which includes the following sub-steps: step S411, continuously record the adjusted position ratio optimization allocation vector within the historical control period to generate a position time series evolution dataset; step S412, calculate the rate of change of the first derivative of the position time series evolution dataset, and use it as a quantitative indicator to characterize the position evolution trend. Step S413: When the quantitative indicator exceeds the safety threshold of 0.05, output a risk warning signal for rapid changes in holdings.

9. The method for securities investment risk early warning and portfolio optimization management according to claim 1, characterized in that, When calculating the convergence time-series chip distribution feature tensor in step S3, the feature vector is read and written in place at high speed by calling the internal data register of the processor in the prediction server, so as to eliminate the cache read and write latency in the high-speed data stream processing.

10. A securities investment risk early warning and portfolio optimization management system, used to implement the securities investment risk early warning and portfolio optimization management method as described in claim 1, characterized in that, include: The system includes a detailed stream receiving and change extraction module, a variance calculation and state gating module, a feature splitting and incremental superposition module, and a state determination and optimization early warning module. Among them, the detailed stream receiving and change extraction module is used to receive the anonymized financial asset transaction detailed stream and extract the transaction price and transaction volume of the underlying asset, calculate the price change feature scalar of the continuous control period and store the price change feature scalar into the first-in-first-out buffer. The variance calculation and state gating module is used to calculate the transient fluctuation variance of the time series composed of price change feature scalars in the first-in-first-out buffer and compare it with the dynamic gating threshold. When the transient fluctuation variance does not exceed the dynamic gating threshold, the data stream of price change feature scalars is kept suspended. The feature splitting and incremental superposition module has an embedded abnormal data splitting decoupling operator, which is used to release the data stream suspension state when the transient fluctuation variance value exceeds the dynamic gate threshold, stop the global update of the asset weight topology matrix, and only extract the feature vector corresponding to the current price change feature scalar for accumulation and superposition to generate the convergent time series chip distribution feature tensor. The state determination and optimization early warning module embeds a position optimization time-series state machine. It inputs the convergence time-series chip distribution feature tensor into the position optimization time-series state machine, combines the safety boundary line scalar and the risk warning line scalar to determine the phase transition condition, and outputs the phase code to limit the position ratio optimization allocation vector. When the transaction price touches the safety boundary line scalar and the phase code is a risk exit decision phase, the position ratio optimization allocation vector is reduced and the position risk warning result is output.

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