A project risk identification and early warning management and control method and system

Abstract

This invention relates to the field of data processing and discloses a method and system for project risk identification, early warning, and control. The method includes: acquiring state parameters of logical state nodes in a multi-dimensional data logical network, constructing state evolution trajectories, and calculating decay acceleration within adjacent time windows; identifying parallel monitoring node pairs sharing a common underlying resource pool and calculating the correlation of decay acceleration; when the correlation is negative and the difference exceeds a threshold, generating directed edges pointing to asymmetric resource offset occupancy in the topology graph and outputting early warning and control instructions. This invention identifies the asymmetry in the decay rate of evolution between parallel nodes, constructs asynchronous decay scissor difference judgment rules, achieves reverse mapping of resource scheduling imbalance, eliminates the blind spots of cross-path resource dependency perception in traditional monitoring systems, locks down the risk source before local fluctuations trigger global cascading failures, and improves the timeliness of early warning.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a method and system for project risk identification, early warning and control. Background Technology

[0002] Current project management data processing systems typically employ a monitoring approach that combines business flow topology with time-series progress indicators. The system collects status update data from each business node, calculates the difference between it and the planned baseline, and generates an alarm signal when the difference exceeds a set threshold. This logic is based on the assumption that the evolution trajectories of each business node are independent of each other, and maintains monitoring sensitivity by setting a static progress threshold.

[0003] In project management with high-density concurrent tasks and complex cross-node resource dependencies, horizontal resource coupling is common among business nodes. When a specific high-priority node experiences state lag due to external interference, the underlying scheduling mechanism tilts resources to compensate that node. This behavior creates a horizontal resource siphon effect, crowding out underlying general execution resources. Consequently, parallel business nodes without explicit temporal dependencies become stagnant due to resource starvation. Existing technologies for monitoring vertical process paths lack cross-validation logic for the temporal fluctuations in the state evolution rate of multiple nodes, making it impossible to represent this cross-path resource contention state at the data layer. Simply relying on shortening the sampling period or tightening the single-point warning threshold can induce high-frequency redundant instructions, causing the system to lose an effective pre-intervention window. In scenarios with high-density concurrent tasks and complex resource dependencies, the system is limited by the physical constraints of the monitoring sensors and hardware cabling infrastructure in terms of topology sensing depth. The limitations make it difficult to penetrate the black box of the underlying physical resource pool operation. The upper-level software control logic also has defects. For example, Chinese invention patent with authorization announcement number CN121523865B discloses a commercial resource optimization management method and system based on computing power demand prediction. It uses graph attention network and neural ordinary differential equations to predict computing power demand in time and space and guides multi-agent collaborative decision-making. It belongs to the positive prediction-pre-allocation control method. The scheme is highly dependent on the fitting degree of the prediction model to historical patterns. Due to the convergence lag of the model, it cannot perceive instantaneous resource siphoning. Such schemes lack the ability to reverse map the asymmetric decay of evolution rate between nodes in the shared resource pool that have no logical dependence. The evolution rate scissors difference caused by cross-path contention of underlying resources is easily judged as random load disturbance by the prediction algorithm before it manifests as a substantial progress deviation. This causes the early warning signal to be delayed and cannot lock the source of risk before the local risk evolves into a global cascading failure.

[0004] Therefore, the technical problem to be solved by this invention is how to eliminate the delay in early warning signals caused by cross-node implicit resource contention in a project management data processing system with multi-node resource coupling by reconstructing the time-series evolution calculation path of node states. Summary of the Invention

[0005] This invention aims to solve the problem of delayed early warning signals caused by cross-node implicit resource contention in a multi-node resource coupled project management data processing system by reconstructing the time-series evolution calculation path of node states.

[0006] This technical solution provides a method for project risk identification, early warning, and control, comprising the following steps: Step 101: Read the state parameters of each logical state node in the multidimensional data logic network within the preset sampling period, and calculate the state evolution trajectory of each logical state node with time offset. Step 102: Calculate the state evolution rate of each logical state node within the adjacent first and second time windows based on the state evolution trajectory. Step 103: Calculate the change in the evolution rate of each state between the first time window and the second time window to obtain the decay acceleration of each logic state node; Step 104: parse the resource occupancy identifiers of each logical state node, identify the first logical state node and the second logical state node that share the same underlying physical resource pool and have no direct logical dependency path, and construct a parallel monitoring node pair; Step 105: Calculate the correlation of decay acceleration between the first logic state node and the second logic state node; Step 106: When the correlation is negative and the difference in decay acceleration between the first logical state node and the second logical state node exceeds a preset threshold, generate an asymmetric resource offset occupancy directed edge pointing to the node with larger decay acceleration in the topology graph of the multidimensional data logic network. Step 107: Output the operation risk warning and control command based on the connected domain of the topology graph containing the directed edges occupied by asymmetric resource offsets.

[0007] Preferably, step 105 includes the following sub-steps: Step 1051, obtaining the resource quota occupancy index and logical scheduling priority of each logical state node in the parallel monitoring node pair within the first time window and the second time window; Step 1052, calculating the product weight of the resource quota occupancy index and the logical scheduling priority with respect to each decay acceleration, and generating normalized evolution deviation features; Step 1053, calculating the covariance of the normalized evolution deviation features between different logical state nodes; Step 1054, when the covariance is less than 0 and its absolute value exceeds a preset correlation threshold, determining the correlation as negative.

[0008] Preferably, step 107 includes the following sub-steps: step 1071, calculating the timing lag deviation of each logical state node connected by the directed edge of asymmetric resource offset; step 1072, updating the resonance warning threshold by subtracting the timing lag deviation from the resonance warning threshold after the sampling period; step 1073, generating an operation risk warning and control instruction when the magnitude of the deviation of the state parameter from the baseline reaches the updated resonance warning threshold.

[0009] Preferably, in step 101, the state parameters consist of the data processing progress of the logical state node, the load of the underlying computing resources, and the communication throughput latency; the state evolution trajectory is generated by performing feature space mapping on each state parameter and extracting the temporal smoothing mean.

[0010] Preferably, in step 102, the state evolution rate is obtained by calculating the average slope of the tangent line of the state evolution trajectory within the corresponding time window. The state evolution rate is used to characterize the state migration intensity of the logical state node per unit time.

[0011] Preferably, step 104 includes the following sub-steps: step 1041, retrieve the explicit dependency paths between each logical state node in the multidimensional data logical network; step 1042, use the depth-first search algorithm to traverse the directed paths between each logical state node; step 1043, determine two logical state nodes that do not have a direct logical dependency path and have the same resource occupancy identifier as a pair of parallel monitoring nodes.

[0012] Preferably, after generating the operational risk warning and control instruction, the following resource reset sub-step is also included: Step 1074, locate the starting node of the directed edge occupied by asymmetric resource offset and identify it as a source of resource overload; Step 1075, impose a retry frequency limit on the non-critical requests of the resource overload source and reclaim the underlying computing resources occupied by them; Step 1076, reset the reclaimed underlying computing resources to the pointing node of the directed edge occupied by asymmetric resource offset.

[0013] Preferably, in step 106, the preset threshold is dynamically calculated based on the data processing concurrency density of the multidimensional data logic network; when the data processing concurrency density increases, the sensitivity factor of the preset threshold is increased to shorten the response time to the directed edge occupied by the asymmetric resource offset.

[0014] Preferably, after step 107, the following topology optimization steps are also included: Step 1077, counting the generation frequency of directed edges occupied by asymmetric resource offsets within a preset period; Step 1078, identifying logical state nodes whose generation frequency exceeds a preset frequency threshold as structural conflict node pairs; Step 1079, setting logical isolation buffers for structural conflict node pairs in the multidimensional data logical network to adjust the topology of the multidimensional data logical network.

[0015] A project risk identification and early warning management system, which implements a project risk identification and early warning management method, includes: The parameter acquisition module is used to read the state parameters of each logical state node in the multidimensional data logic network within a preset sampling period. The trajectory modeling module is used to calculate the state evolution trajectory of each logical state node over time. The rate analysis module is used to calculate the state evolution rate of each logical state node within the adjacent first and second time windows based on the state evolution trajectory. The acceleration determination module is used to calculate the change in the evolution rate of each state between the first time window and the second time window, and to obtain the decay acceleration of each logic state node. The topology identification module is used to parse the resource occupancy identifiers of each logical state node, identify the first logical state node and the second logical state node that share the same underlying physical resource pool and have no direct logical dependency path, so as to construct a pair of parallel monitoring nodes. The correlation measurement module is used to calculate the correlation of decay acceleration between the first logic state node and the second logic state node in the parallel monitoring node pair. The siphon mapping module is used to generate an asymmetric resource offset occupancy directed edge pointing to the one with larger decay acceleration in the topology graph of the multidimensional data logic network when the correlation is negative and the difference in decay acceleration between the first logical state node and the second logical state node exceeds a preset threshold; and the control instruction module is used to output operation risk warning control instructions based on the connected domain of the topology graph containing the asymmetric resource offset occupancy directed edge.

[0016] Compared with existing technologies, the project risk identification and early warning management method of the present invention has the following advantages: 1. In project risk identification and early warning management, by utilizing the state dependency topology between business nodes, and by identifying the competitive constraints of parallel nodes on shared execution resources, the originally isolated progress statistics data are transformed into an evolution network with causal relationships. This enables the horizontal resource siphoning phenomenon hidden outside the conventional vertical planning path to have deterministic data representation, eliminating the monitoring blind spots caused by the lack of perception of cross-path resource dependencies in traditional monitoring systems.

[0017] 2. Based on the difference in the rate of state evolution within adjacent sampling periods, this invention captures the asymmetry of rate decay between parallel nodes and constructs an asynchronous decay scissor difference judgment logic to achieve reverse mapping of the resource scheduling imbalance state of the underlying management system. This enables the system to identify the negative correlation deviation trend of resource allocation before local business fluctuations evolve into substantial schedule deviations, effectively extending the response window for risk warning.

[0018] 3. This invention dynamically generates implicit resource siphon directed edges in the state-dependent topology graph and adaptively lowers the resonance threshold by combining time-series lag deviation. This constructs a dynamic fitting mechanism between risk perception sensitivity and global deviation, which not only avoids false alarms and false alarms under complex working conditions by fixing the threshold, but also cuts off the cross-path cascading failure chain caused by resource starvation by reconstructing the topology connected domain, thus ensuring the robustness of the project flow data network. Attached Figure Description

[0019] Figure 1 This is a flowchart of the project risk identification and early warning control method based on node state evolution analysis of the present invention; Figure 2 This is the logical interaction topology diagram of the project risk early warning and control system under the multidimensional data network of this 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] 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, low, lateral, 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 indicating the number of technical features indicated.

[0022] 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.

[0023] 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.

[0024] A method for project risk identification, early warning, and control includes the following steps: Step 101: Read the state parameters of each logical state node in the multidimensional data logic network within the preset sampling period, and calculate the state evolution trajectory of each logical state node with time offset. Step 102: Calculate the state evolution rate of each logical state node within the adjacent first and second time windows based on the state evolution trajectory. Step 103: Calculate the change in the evolution rate of each state between the first time window and the second time window to obtain the decay acceleration of each logic state node; Step 104: parse the resource occupancy identifiers of each logical state node, identify the first logical state node and the second logical state node that share the same underlying physical resource pool and have no direct logical dependency path, and construct a parallel monitoring node pair; Step 105: Calculate the correlation of decay acceleration between the first logic state node and the second logic state node; Step 106: When the correlation is negative and the difference in decay acceleration between the first logical state node and the second logical state node exceeds a preset threshold, generate an asymmetric resource offset occupancy directed edge pointing to the node with larger decay acceleration in the topology graph of the multidimensional data logic network. Step 107: Output the operation risk warning and control command based on the connected domain of the topology graph containing the directed edges occupied by asymmetric resource offsets.

[0025] Preferably, step 105 includes the following sub-steps: Step 1051, obtaining the resource quota occupancy index and logical scheduling priority of each logical state node in the parallel monitoring node pair within the first time window and the second time window; Step 1052, calculating the product weight of the resource quota occupancy index and the logical scheduling priority with respect to each decay acceleration, and generating normalized evolution deviation features; Step 1053, calculating the covariance of the normalized evolution deviation features between different logical state nodes; Step 1054, when the covariance is less than 0 and its absolute value exceeds a preset correlation threshold, determining the correlation as negative.

[0026] Preferably, step 107 includes the following sub-steps: step 1071, calculating the timing lag deviation of each logical state node connected by the directed edge of asymmetric resource offset; step 1072, updating the resonance warning threshold by subtracting the timing lag deviation from the resonance warning threshold after the sampling period; step 1073, generating an operation risk warning and control instruction when the magnitude of the deviation of the state parameter from the baseline reaches the updated resonance warning threshold.

[0027] Preferably, in step 101, the state parameters consist of the data processing progress of the logical state node, the load of the underlying computing resources, and the communication throughput latency; the state evolution trajectory is generated by performing feature space mapping on each state parameter and extracting the temporal smoothing mean.

[0028] Preferably, in step 102, the state evolution rate is obtained by calculating the average slope of the tangent line of the state evolution trajectory within the corresponding time window. The state evolution rate is used to characterize the state migration intensity of the logical state node per unit time.

[0029] Preferably, step 104 includes the following sub-steps: step 1041, retrieve the explicit dependency paths between each logical state node in the multidimensional data logical network; step 1042, use the depth-first search algorithm to traverse the directed paths between each logical state node; step 1043, determine two logical state nodes that do not have a direct logical dependency path and have the same resource occupancy identifier as a pair of parallel monitoring nodes.

[0030] Preferably, after generating the operational risk warning and control instruction, the following resource reset sub-step is also included: Step 1074, locate the starting node of the directed edge occupied by asymmetric resource offset and identify it as a source of resource overload; Step 1075, impose a retry frequency limit on the non-critical requests of the resource overload source and reclaim the underlying computing resources occupied by them; Step 1076, reset the reclaimed underlying computing resources to the pointing node of the directed edge occupied by asymmetric resource offset.

[0031] Preferably, in step 106, the preset threshold is dynamically calculated based on the data processing concurrency density of the multidimensional data logic network; when the data processing concurrency density increases, the sensitivity factor of the preset threshold is increased to shorten the response time to the directed edge occupied by the asymmetric resource offset.

[0032] Preferably, after step 107, the following topology optimization steps are also included: Step 1077, counting the generation frequency of directed edges occupied by asymmetric resource offsets within a preset period; Step 1078, identifying logical state nodes whose generation frequency exceeds a preset frequency threshold as structural conflict node pairs; Step 1079, setting logical isolation buffers for structural conflict node pairs in the multidimensional data logical network to adjust the topology of the multidimensional data logical network.

[0033] A project risk identification and early warning management system, comprising: The parameter acquisition module is used to read the state parameters of each logical state node in the multidimensional data logic network within a preset sampling period. The trajectory modeling module is used to calculate the state evolution trajectory of each logical state node over time. The rate analysis module is used to calculate the state evolution rate of each logical state node within the adjacent first and second time windows based on the state evolution trajectory. The acceleration determination module is used to calculate the change in the evolution rate of each state between the first time window and the second time window, and to obtain the decay acceleration of each logic state node. The topology identification module is used to parse the resource occupancy identifiers of each logical state node, identify the first logical state node and the second logical state node that share the same underlying physical resource pool and have no direct logical dependency path, so as to construct a pair of parallel monitoring nodes. The correlation measurement module is used to calculate the correlation of decay acceleration between the first logic state node and the second logic state node in the parallel monitoring node pair. The siphon mapping module is used to generate an asymmetric resource offset occupancy directed edge pointing to the one with larger decay acceleration in the topology graph of the multidimensional data logic network when the correlation is negative and the difference in decay acceleration between the first logical state node and the second logical state node exceeds a preset threshold; and the control instruction module is used to output operation risk warning control instructions based on the connected domain of the topology graph containing the asymmetric resource offset occupancy directed edge.

[0034] Example 1: In a project engineering data processing system containing high-density concurrent task sequences and shared computing resource quotas, the business execution logic is divided into demand analysis nodes and module development nodes with mutually independent resource occupancy identifiers. There is no direct directed dependency path between the demand analysis nodes and module development nodes in the explicit business time-series topology. When the demand analysis node triggers the resource compensation response of the underlying scheduling mechanism due to external uncertainties, the underlying scheduling mechanism resets the general computing resource quota originally allocated to the module development node to the demand analysis node. At this time, the demand analysis node maintains a preset state evolution rate, while the module development node experiences unexpected progress stagnation due to resource supply interruption. Since the progress deviation of the module development node has not yet triggered the preset static alarm threshold, the monitoring logic based on isolated node state judgment cannot identify this type of state evolution anomaly caused by resource cross-path siphoning.

[0035] The processing device reads data logs containing the historical operating trajectories of each business node from the data storage unit, and constructs a state topology diagram reflecting the business dependencies between nodes based on logical connection relationships. Before starting the state topology diagram analysis, it calls the physical hardware homogeneity verification procedure. Addressing the common hardware issue of logical resource allocation identifiers drifting across physical hosts in the virtual computing environment, it reads the media access control address of the host machine to which each business node belongs and the motherboard processor physical socket number. Only nodes with the same media access control address and processor physical socket number are grouped into the absolutely same underlying physical computing power domain, generating a multi-dimensional heterogeneous architecture. Parametric dimensionality reduction data, based on the information entropy weighting principle, shows that the information load of system state parameters is positively correlated with the degree of fluctuation and dispersion in the historical time series. Initial state parameters, including data processing progress, underlying computing resource load, and communication throughput latency, are obtained and transformed into dimensionless values ​​using the range standardization formula. For initial state parameters with heterogeneous physical dimensions, the processing device invokes the range standardization procedure. For data processing progress parameters exhibiting positive gain attributes, the difference between the current value and the minimum value within the historical time window is used, divided by the difference between the historical maximum and minimum values. For computation parameters exhibiting negative suppression attributes... Load and communication delay parameters are calculated by subtracting the current value from the historical maximum value, and then dividing by the difference between the historical maximum and minimum values. This maps the millisecond time dimension and the task percentage dimension to a dimensionless closed interval [0,1], ensuring the objective consistency of mathematical processing dimensions. The inverse variance of each dimensionless value within a set sliding time window is calculated and used as the feature weight coefficient for each corresponding state parameter. The sum of the products of each dimensionless value and its corresponding feature weight coefficient is calculated, and the coordinates of the single numerical state evolution trajectory are output. To eliminate the masking of the true computing resource state by communication channel congestion, [further details are needed]. The communication throughput delay parameter is fed into a first-order low-pass filter algorithm to filter transient network delay pulse data higher than the set cutoff frequency. This removes network fluctuation interference caused by non-physical computing power preemption from the input raw state parameters. The processing device identifies the demand analysis node and module development node, which share the same underlying computing power resource pool and have no direct connection in the state topology graph, based on resource occupancy identifiers. These two nodes are then confirmed as a parallel monitoring node pair. The processing device calculates the average tangent slope of the state trajectory of each node within a preset sliding sampling time window to obtain the first state evolution rate of the demand analysis node at the current sampling time. Reference rate of evolution from the previous sampling time Simultaneously, the second state evolution rate of the module development node at the current sampling time is obtained. Reference rate of evolution from the previous sampling time The processing equipment calculates the resource contention gradient value based on the aforementioned rate parameters. The calculation formula is as follows: ,in, To compete for gradient values ​​for resources, This represents the rate of state evolution of the demand analysis node within the current sampling time window. This represents the rate of state evolution of the demand analysis node in the previous sampling time window. The rate of state evolution of the module development node within the current sampling time window. To determine the state evolution rate of the module development node in the previous sampling time window, based on the principle of calculus difference correlation, the relative degradation mutation intensity of the concurrent variable sequence is equivalent to the quotient of the absolute values ​​of their first-order differences. The processing device extracts the ratio of the absolute component of the module development node's rate decay to the absolute component of the demand analysis node's rate decay based on the above formula structure, and calculates the scalarized resource contention gradient value. The resource contention gradient value is written into the comparator logic unit to check whether the deviation from the stationary constant exceeds the set associated tolerance limit. The stationary constant is a system baseline parameter defined by the average rate gradient under stable conditions without concurrency conflicts extracted from the platform's historical operation archives. The associated tolerance limit is a threshold band dynamically mapped by the system's underlying scheduler based on the ratio of the current available physical computing power margin of the processing cluster to the total number of real-time high-priority queue tasks. This allows the allowed judgment tolerance to adaptively tighten as the system's redundant computing power decreases. When the resource contention gradient value exceeds the associated tolerance limit and the single-node independent attenuation judgment condition is simultaneously met, a logical action is generated. When the processing device determines the rate attenuation component of the demand analysis node... The rate decay component of the module development node is below the preset steady-state deviation threshold. When the deviation exceeds the preset threshold, it is confirmed that there is an asynchronous decay scissor difference between the parallel monitoring node pairs. When the correlation coefficient between the first state evolution rate and the second state evolution rate is negatively correlated and the numerical difference exceeds the preset dynamic comparison threshold, the processing device generates an asymmetric resource offset occupancy directed edge from the module development node to the demand analysis node in the state topology diagram.

[0036] The processing device corrects the connectivity attributes of the state topology graph based on the generated asymmetric resource offset occupancy directed edges, and generates operational risk warning and control instructions before the absolute progress deviation of the module development node reaches the static trigger boundary based on the updated topology connection relationship. The processing device uses the time lag deviation of the demand analysis node to reverse adjust the sensitivity coefficient of risk judgment, so that the system warning logic changes from threshold judgment relying on isolated nodes to dynamic feature mapping based on cross-path resource competition status. This scheme transforms the unobservable underlying resource contention phenomenon into asymmetric fluctuation characteristics of the business node evolution rate. By extracting the negative correlation of the evolution trajectory between parallel nodes, it characterizes the resource scheduling imbalance of the underlying management system. Under the premise of using the existing processors in the system to execute the above logic, the processing device uses the original time-series state data stream of the business nodes to realize the real-time characterization of the implicit resource imbalance status, thereby locking the source of resource conflict before local fluctuations evolve into global cascading failures.

[0037] Example 2: In a distributed project engineering management data processing simulation platform with 32 general-purpose computing nodes, the processing device uses a high-speed data bus to read the status parameter log sequence containing data processing progress, underlying computing resource load, and communication throughput delay. To simulate the uncertainty of data acquisition in real working conditions, the platform superimposes Gaussian white noise with a signal-to-noise ratio of 20dB into the original progress reporting data stream and introduces random communication jitter with a mean of 10ms. The length of the sampling time window is set in the experiment to balance the smoothness of state fluctuations and the transient response delay of risk identification. When the data processing concurrency density in the system is between 500 and 800 concurrent tasks, the processing device sets the sampling period to 500ms to satisfy the sampling theorem and avoid signal aliasing of high-frequency state components. The experimental platform establishes running observation sequences for the requirement analysis node and the module development node, and divides them into an experimental group using the method of this invention, a control group A that removes the asynchronous attenuation scissor difference judgment logic, and a control group B that triggers an early warning based on a progress lag of 20%.

[0038] During the 120th sampling period of the experiment, the processing equipment modified the resource occupancy flag of the demand analysis node to elevate its priority, inducing the node to generate a high frequency of logical re-examination requests; at this time, the observed data showed the first state evolution rate of the demand analysis node in the experimental group. Maintaining at 1.25% / s, its rate decay component The rate is 0.02% / s, which is within the preset steady-state deviation threshold of 0.05% / s. The demand analysis node maintains its preset evolution state under resource scheduling bias. Meanwhile, the second state evolution rate of the module development node, which shares the computing pool but has no explicit dependent paths, is also within the preset steady-state deviation threshold. The rate decreases from 1.22% / s to 0.45% / s, corresponding to a rate attenuation component of 0.77% / s, exceeding the preset abrupt deviation threshold of 0.15% / s. To ensure the engineering objectivity of the above threshold determination boundary, the 0.05% / s and 0.15% / s selected in the embodiment are not custom parameters. Their derivation is based on the resource full-load limit pressure data spectrum collected during the pre-test phase. The steady-state deviation threshold is aligned with the upper limit of fault tolerance fluctuations caused by the retransmission of a single network heartbeat packet at the underlying level, while the abrupt deviation threshold is anchored in an engineering physical sense as the minimum attenuation equivalent of process suspension when the system virtual memory triggers a forced swapping of physical pages. The processing device calculates the resource contention gradient value based on the current data. The correlation coefficient is 38.5, and the Pearson correlation coefficient between the two-node rate sequences is -0.92, which meets the criterion for asynchronous decay scissor difference. The calculation formula is as follows: ,in, To compete for gradient values ​​for resources, This represents the rate of state evolution of the demand analysis node within the current sampling time window. This represents the rate of state evolution of the demand analysis node in the previous sampling time window. The rate of state evolution of the module development node within the current sampling time window. The state evolution rate of the module development node in the previous sampling time window; when obtaining the two-node rate sequences required by the above judgment criteria and calculating their Pearson correlation coefficient and covariance, the processing device executes a discrete sequence extraction procedure, that is, extracting normalized deviation data of 10 consecutive independent sampling periods in reverse from the judgment trigger point to construct a bivariate fixed-length sample set, using the covariance matrix operator to calculate the covariance trend between sample points to filter out transient outliers, ensuring that the extraction of the correlation dimension has a temporal basis and statistical power, and the processing device generates a path in the state topology graph from the module development node to the requirement. The analysis focuses on the asymmetric resource offset of the node occupying the directed edge, and outputs a risk warning and control command when the progress deviation of the module development node reaches 3.5% in the 125th sampling period. In contrast, the control group A, due to the lack of correlation comparison with the rate decay acceleration, cannot identify the implicit resource siphoning feature, and the sampling point for generating the warning signal is delayed until the 148th period. The control group B does not trigger a static warning until the 152nd sampling period, when the progress deviation reaches 20.5%. The above data confirms that the method of the present invention has a lead time of 13.5 seconds compared with the control group B at the risk locking node.

[0039] To verify the boundary effect of parameter values, the experiment examined the evolution trend of the system state by adjusting the number of concurrent tasks. Observational data showed that when the number of concurrent tasks exceeded 1200, due to the intensified random conflicts in computing resource scheduling, the signal-to-noise ratio of the state evolution trajectory dropped to below 5dB. This caused the instantaneous fluctuation of the resource contention gradient value to exceed the preset dynamic comparison threshold, resulting in unexpected false risk reports. This phenomenon confirms that the concurrency density range defined in this invention is the working window to ensure the coordination of early warning sensitivity and accuracy. This experiment extracted the asymmetric decay characteristics of the evolution trajectory between parallel nodes, mapping the unobservable underlying resource scheduling imbalance state to quantifiable topological graph attribute changes. It captured the implicit cascading failure risk using existing processing equipment without adding physical sensing hardware. The experimental results show that this method eliminates the blind spot of cross-path resource dependency perception by auditing the correlation of the decay component of the state evolution rate, and completes the output of early warning and control instructions before local fluctuations evolve into global systemic risks.

[0040] Example 3: In a commercial engineering data processing system that manages high-density dynamic concurrent loads and involves multi-departmental collaboration, the processing equipment monitors multiple business nodes in real time, including financial auditing, material procurement, and progress reporting. Due to fluctuations in the data migration intensity of each business node under different operating conditions, if the system uses a preset static deviation threshold to implement asynchronous attenuation scissor difference judgment, when the concurrent task density changes abruptly, the underlying random resource contention noise will induce false alarms. This mismatch between the static threshold and the dynamically evolving operating conditions makes it impossible for the monitoring logic to take into account the accuracy of risk identification.

[0041] The processing device reads the node state evolution rate sequence of the target system over the past 24 hours from the data storage unit, and performs classification and statistical analysis on the rate decay component according to different data processing concurrency density intervals. The processing device calculates the average value of each classification and statistical result. and standard deviation This is used to construct a reference noise envelope reflecting the system's inherent fluctuations. Based on the statistical process control laws of Shewhart control charts, and considering the normally distributed dynamic physical characteristics, the probability of measured values ​​exceeding the reference mean plus three times the standard deviation is extremely low. The out-of-limit phenomenon essentially represents a non-random, abnormal, sudden disturbance to the physical operating environment. The statistical process control laws are solidified into the real-time extraction procedure of the system tolerance boundaries. To achieve adaptive calibration of the judgment threshold, the processing equipment adjusts the task concurrency density based on the current sampling period. Retrieve the corresponding classification statistics parameters and determine the mutation bias threshold according to the following formula. ,in, The threshold for mutation deviation. This is a sensitivity coefficient, which is selected as 3.0 in the specific deployment of this embodiment. This represents the average rate decay within the current concurrency density range. This represents the standard deviation of rate decay within the current concurrency density range. After identifying a pair of parallel monitoring nodes that meet the judgment criteria, the processing device locates the starting node of the directed edge occupied by the asymmetric resource offset and marks it as a source of resource overload. The processing device outputs frequency limiting signaling to the underlying scheduling unit and limits the frequency of non-critical data requests from the source of resource overload by modifying the priority weight of the kernel scheduling queue.

[0042] The processing equipment recovers excessive general computing resources from resource overload sources and reassigns them to the nodes pointed to by the directed edges of asymmetric resource offsets, i.e., module development nodes in a resource-starved state, thereby achieving dynamic compensation for the cross-path resource siphon effect. This process establishes a dynamic threshold calibration procedure based on concurrency density, transforming the originally static thresholds that depend on settings into dynamic parameters controlled by the system's endogenous noise characteristics, ensuring that the risk identification logic maintains its identification accuracy in the face of environmental changes. This approach extends risk warning to the substantive control stage through a physical-level resource reassignment mechanism, correcting resource allocation imbalances before asymmetric fluctuations in local evolution rates are transmitted to explicit business results, thus enabling the management and supervision system to proactively intercept project risks without human intervention.

[0043] Example 4: In a project engineering management data processing platform that includes a heterogeneous data processing subsystem, before starting monitoring, the processing device retrieves the node status update logs of the system under historical operating conditions, constructs a training sample set, and classifies and samples the training sample set according to the data processing concurrency density level. The processing device extracts the detrended residuals of the state evolution rate sequence within the sampling layer to remove the linear offset caused by business growth, calculates the probability density distribution function of the detrended sequence, and extracts the second-order moment parameter corresponding to the normal distribution. ,in, To calculate the rate decay standard deviation, a static baseline envelope table containing multiple sets of density interval features is generated in the local storage unit.

[0044] The processing device adapts the risk identification logic to deployment environments with specific physical communication link constraints. During system initialization, it reads the average communication throughput latency of the current node and determines it as the offset compensation operator for feature space mapping. The processing equipment calculates the original state parameters and The algebraic difference is used to eliminate the initial sampling bias caused by differences in hardware wiring. The processing device uses the mean value from the static reference envelope table. Establish the initial state vector of the moving average filter, and determine the sensitivity coefficient after continuously acquiring N sampling points that satisfy the normal distribution characteristics. Adaptive tuning is performed, among which... The mean rate decay is N, where N is the number of sampling points. For the sensitivity coefficient, the calibration and calibration process eliminates the distortion of state observation caused by differences in hardware performance. The risk judgment threshold is coupled with the background noise characteristics of the engineering environment. To ensure the logical consistency of the above algebraic difference calculation in terms of mathematical basis and physical essence, before actually performing the subtraction operation, the processing device uses the maximum theoretical bandwidth of the accessed communication link to normalize and convert the extracted average communication throughput delay. The original delay parameter with time dimension is forcibly converted into a dimensionless percentage feature in the [0,1] interval that characterizes the degree of communication congestion. Thus, the deviation elimination calculation with the original state parameter is completed under a unified dimension.

[0045] Example 5: In a project management data processing system containing heterogeneous computing units, the processing device retrieves the dynamic determination procedure for the sampling time window length W during the system initialization phase. The processing device reads the average processing time of each business node under the baseline operating state from the data storage unit. This is determined as the minimum time reference for time series feature extraction. The processing device adjusts the sampling frequency in a stepwise manner and collects the signal-to-noise ratio (SNR) characteristics of the state evolution trajectory in real time. When the SNR reaches 15 dB and the root mean square error of the first derivative of the state evolution rate converges to the steady-state region, the current sampling frequency is locked as the optimal sampling frequency, and the optimal sampling period is then calculated. The processing equipment calculates the optimal sampling period. The physical length W of the sliding sampling time window is obtained by multiplying the product with the preset smoothing factor m, where W is the sampling time window length. This represents the average processing time for the task. The optimal sampling period is defined by m, which is a smoothing factor. The length W spans more than two complete business instruction cycles to smooth out random disturbance signals caused by the execution of a single instruction.

[0046] After identifying the parallel monitoring node pair, the processing device retrieves the environment adaptation procedure for the risk judgment threshold. Under system no-load conditions, the processing device retrieves the original resource usage logs of the requirement analysis node and module development node. It then calculates the covariance matrix of the requirement analysis node and module development node under static conditions to characterize the underlying resource usage correlation characteristics of the system. The processing device extracts the trace of the covariance matrix to obtain the system's background noise energy value, and sets twice the background noise energy value as the dynamic comparison threshold. The initial bias component is used to identify the peak response intensity of the asynchronous attenuation scissor difference in a simulated resource siphon environment. The corresponding sensitivity weight is matched according to the peak response intensity to obtain the logical judgment boundary that distinguishes between random load fluctuations and resource preemption behavior. This procedure eliminates the observation offset caused by manually set parameters by establishing a mapping from physical resource occupation fluctuations to logical feature space, so that the risk warning logic can maintain stable recognition accuracy in a heterogeneous hardware environment.

[0047] 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 project risk identification, early warning, and control, characterized in that, Includes the following steps: Step 101: Read the state parameters of each logical state node in the multidimensional data logic network within the preset sampling period, and calculate the state evolution trajectory of each logical state node with time offset. Step 102: Calculate the state evolution rate of each logical state node within the adjacent first and second time windows based on the state evolution trajectory. Step 103: Calculate the change in the evolution rate of each state between the first time window and the second time window to obtain the decay acceleration of each logic state node; Step 104: parse the resource occupancy identifiers of each logical state node, identify the first logical state node and the second logical state node that share the same underlying physical resource pool and have no direct logical dependency path, and construct a parallel monitoring node pair; Step 105: Calculate the correlation of decay acceleration between the first logic state node and the second logic state node; Step 106: When the correlation is negative and the difference in decay acceleration between the first logical state node and the second logical state node exceeds a preset threshold, generate an asymmetric resource offset occupancy directed edge pointing to the node with larger decay acceleration in the topology graph of the multidimensional data logic network. Step 107: Output the operation risk warning and control command based on the connected domain of the topology graph containing the directed edges occupied by asymmetric resource offsets.

2. The project risk identification and early warning management method according to claim 1, characterized in that, Step 105 includes the following sub-steps: Step 1051, obtain the resource quota occupancy index and logical scheduling priority of each logical state node in the parallel monitoring node pair within the first time window and the second time window; Step 1052: Calculate the product weight of resource quota occupancy index and logical scheduling priority with respect to each decay acceleration, and generate normalized evolution deviation characteristics. Step 1053: Calculate the covariance of the normalized evolution deviation characteristics among nodes of different logical states; Step 1054: When the covariance is less than 0 and its absolute value exceeds the preset correlation threshold, the correlation is determined to be negative.

3. The project risk identification and early warning management method according to claim 1, characterized in that, Step 107 includes the following sub-steps: Step 1071, calculate the timing lag deviation of each logical state node connected by the directed edge of asymmetric resource offset; Step 1072, update the resonance warning threshold by subtracting the timing lag deviation from the resonance warning threshold after the sampling period; Step 1073, generate an operation risk warning and control instruction when the magnitude of the deviation of the state parameter from the baseline reaches the updated resonance warning threshold.

4. The project risk identification and early warning management method according to claim 1, characterized in that, In step 101, the state parameters consist of the data processing progress of the logical state node, the load of the underlying computing resources, and the communication throughput latency; the state evolution trajectory is generated by mapping the feature space of each state parameter and extracting the temporal smoothing mean.

5. The project risk identification and early warning management method according to claim 1, characterized in that, In step 102, the state evolution rate is obtained by calculating the average slope of the tangent line of the state evolution trajectory within the corresponding time window. The state evolution rate is used to characterize the state migration intensity of the logical state node per unit time.

6. The project risk identification and early warning management method according to claim 1, characterized in that, Step 104 includes the following sub-steps: Step 1041, retrieve the explicit dependency paths between each logical state node in the multidimensional data logical network; Step 1042, use the depth-first search algorithm to traverse the directed paths between each logical state node; Step 1043, identify two logical state nodes that do not have a direct logical dependency path and have the same resource usage identifier as a pair of parallel monitoring nodes.

7. The project risk identification and early warning management method according to claim 3, characterized in that, After generating the operational risk warning and control instruction, the following resource reset sub-steps are also included: Step 1074, locate the starting node of the directed edge occupied by asymmetric resource offset and identify it as a source of resource overload; Step 1075, impose a retry frequency limit on non-critical requests of the resource overload source and reclaim the underlying computing resources it occupies; Step 1076, reset the reclaimed underlying computing resources to the node pointed to by the directed edge occupied by asymmetric resource offset.

8. The project risk identification and early warning management method according to claim 1, characterized in that, In step 106, the preset threshold is dynamically calculated based on the data processing concurrency density of the multidimensional data logic network; when the data processing concurrency density increases, the sensitivity factor of the preset threshold is increased to shorten the response time to the directed edge occupied by the asymmetric resource offset.

9. The project risk identification and early warning management method according to claim 1, characterized in that, Following step 107, the following topology optimization steps are also included: Step 1077, counting the generation frequency of directed edges occupied by asymmetric resource offsets within a preset period; Step 1078, identifying logical state nodes whose generation frequency exceeds a preset frequency threshold as structural conflict node pairs. Step 1079: Set up logical isolation buffers for structurally conflicting node pairs in the multidimensional data logical network to adjust the topology of the multidimensional data logical network.

10. A project risk identification and early warning management system, used to implement the project risk identification and early warning management method according to claim 1, characterized in that, include: The parameter acquisition module is used to read the state parameters of each logical state node in the multidimensional data logic network within a preset sampling period. The trajectory modeling module is used to calculate the state evolution trajectory of each logical state node over time. The rate analysis module is used to calculate the state evolution rate of each logical state node within the adjacent first and second time windows based on the state evolution trajectory. The acceleration determination module is used to calculate the change in the evolution rate of each state between the first time window and the second time window, and to obtain the decay acceleration of each logic state node. The topology identification module is used to parse the resource occupancy identifiers of each logical state node, identify the first logical state node and the second logical state node that share the same underlying physical resource pool and have no direct logical dependency path, so as to construct a pair of parallel monitoring nodes. The correlation measurement module is used to calculate the correlation of decay acceleration between the first logic state node and the second logic state node in the parallel monitoring node pair. The siphon mapping module is used to generate an asymmetric resource offset occupancy directed edge pointing to the one with larger decay acceleration in the topology map of the multidimensional data logic network when the correlation is negative and the difference in decay acceleration between the first logical state node and the second logical state node exceeds a preset threshold. as well as The control instruction module is used to output operational risk warning and control instructions based on the connected domain of the topology graph containing directed edges occupied by asymmetric resource offsets.

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