Software integration monitoring system based on modular architecture

Abstract

The invention discloses a software integration monitoring system based on a modular architecture, and particularly relates to the field of software integration situation awareness, which comprises a data input module, a task distribution module, a path tracking module, a voting module and an evaluation module, the data input module inputs data contained in software as input variables by setting a data access pipeline, converts the data into a standardized input block, and establishes redundant data mapping at the same time, so that the same data is reused for a plurality of calculation paths. Through redundant data mapping and a task hash mechanism, the same data is multiplexed in a plurality of calculation paths, and hash consistency verification is carried out when the calculation paths are executed, compared with a traditional external verification mode, the scheme utilizes redundant information generated in the calculation process to carry out real-time error detection, and the calculation efficiency is improved. Therefore, the calculation deviation and the data pollution risk are effectively reduced in the high-concurrency calculation and distributed storage environment, and the stability of the whole system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software integration degree situation awareness. More specifically, the present invention relates to a software integration degree monitoring system based on a modular architecture. Background Art

[0002] In current technologies, software integration degree refers to the tightness of data sharing and functional coupling among modules, that is, an index for measuring the internal collaborative efficiency of a system; situation awareness is the ability of a system to monitor and identify the states of itself and the external environment in real time, for timely capturing abnormal information; software integration degree situation awareness refers to the real-time monitoring of the data interaction and collaborative integration state among software modules in a system, and by dynamically evaluating interface stability and data fluency, etc., potential abnormalities and integration risks are timely discovered, so as to provide a basis for early warning;

[0003] In high-concurrency computing, distributed storage or real-time data processing, a system usually relies on software integration to ensure the fluency and accuracy of its data operation. Since the dependence of the software layer on data consistency and error detection mainly focuses on external verification mechanisms, rather than directly using redundant information in software operations itself, this results in that some errors cannot be captured in real time. Especially in systems with high throughput requirements, the lack of operation redundancy as an error detection means for software integration degree will cause data pollution, calculation deviation or system crash, affecting the overall reliability. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a software integration degree monitoring system based on a modular architecture. Through a redundant data mapping and task hashing mechanism, the same data is reused in multiple calculation paths, and hash consistency verification is performed when the calculation paths are executed, so as to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solution: A software integration degree monitoring system based on a modular architecture, including a data input module, a task distribution module, a path tracking module, a voting module, and an evaluation module;

[0006] The data input module inputs the data included in the software as input variables by setting up a data access pipeline, converts it into a standardized input block, and at the same time establishes a redundant data mapping to reuse the same data in multiple calculation paths;

[0007] A unique task hash is generated for each input block by the data input module, and an input parsing stack is constructed, which matches the calculation algorithm according to the data type and distributes it to heterogeneous computing pools;

[0008] The task distribution module distributes the standardized input blocks to multiple different algorithm models for calculation by setting up a heterogeneous computing distribution index table; at each algorithm model computing node, a multi-layer computing buffer is established to store the original input data, intermediate results during the calculation, and redundant calculation data respectively;

[0009] The path tracking module is used to record the calculation execution process and establish a calculation path traceability model based on this;

[0010] The voting module is used to store the calculation outputs of each algorithm model and establish a voting mechanism based on the comparison of its redundant calculation results to optimize the credibility of the calculation results through the voting mechanism;

[0011] The evaluation module quantifies the integration level of the software based on the calculation history of the algorithm model, the credibility calculated by the voting mechanism, and the situational awareness stability of the calculation path in the calculation path traceability model.

[0012] In a preferred implementation, when the data input module is executed, an input buffer is set up for the data access pipeline, the original data in the software system is encapsulated into input data blocks, and a timestamp index is attached. The input data blocks are parsed by the standardized input conversion layer and converted into standardized input blocks according to the data format and category;

[0013] During the creation of the standardized input block, the redundant data mapping construction layer of the data input module introduces a redundant data identification table to track the reuse relationship of the same data in multiple calculation paths through hash mapping;

[0014] The input parsing stack of the data input module parses the type, characteristics, and calculation requirements of the input data and constructs an input parsing stack index table. The input parsing stack index table stratifies the input blocks according to the data attributes; the redundant calculation path distribution distributes multiple copies of the same standardized input block along different algorithm paths according to the index information of the input parsing stack, so that the same data is redundantly calculated in multiple calculation processes;

[0015] By setting up a calculation path index table in the data input module, the distribution trajectory of the standardized input block at different computing nodes is recorded to form a traceable calculation path mapping; according to the calculation path index table, tasks are dynamically allocated in the heterogeneous computing pool, and the data input module performs data consistency verification. Before the calculation task starts, a task index table is constructed to compare the task hash of the current input block to make the calculation of redundant data consistent in each path.

[0016] In a preferred embodiment, when a computing task enters the path tracking unit, a computing path index table is created and stores the task identifier, computing branch information, and timestamp in a hash index manner; the path tracking unit constructs a computing path traceability model, and the computing path traceability model uses three execution vectors constructed based on the task execution trajectory, computing result deviation, and algorithm stability as inputs to evaluate the integrity and anomalies of the computing path;

[0017] When the computing task in the path tracking unit is executed, a computing path snapshot is generated, and the computing path snapshot includes the input hash, computing status, and computing output hash of the current computing step, and is stored in the path traceability index table.

[0018] In a preferred embodiment, the task execution trajectory included in the path tracking unit is recorded in the form of a computing step linked list, and each computing step is associated with the input data hash, execution algorithm identifier, and output data hash; the computing result deviation is quantified based on the computing error matrix, the "rows" of the computing error matrix represent the results of the same task on different computing paths, and the "columns" of the computing error matrix represent the computing deviations of the task at different computing time points, and based on this, the computing error evolution trend across time dimensions is constructed;

[0019] The algorithm stability included in the path tracking unit is evaluated by the repeatability and error convergence of historical computing paths; if the results presented in different execution batches of the same computing task are highly consistent, it is determined that the stability score of the corresponding algorithm is increased, otherwise it is marked as an abnormal computing path.

[0020] In a preferred embodiment, the stability score of the computing path is output based on the results of the computing path traceability model. If the acceleration rate of the decay trend of the stability score of a certain computing path is greater than the decay trend acceleration rate threshold preset by the system, it is determined that the trust level of the computing path in the system is abnormal;

[0021] When it is determined that the trust level of the computing path is abnormal, the redundant computing path is activated, the standby algorithm is triggered to execute the same task, and the result is compared with the result of the original computing path; the analysis result of the computing path traceability model is finally fed back to the evaluation module, and the software integration level is analyzed based on the situational awareness stability of the computing path.

[0022] In a preferred embodiment, after the computing task is completed, the computing outputs of each algorithm model are stored in the voting module and indexed and stored according to the task identifier; when storing the computing results, the corresponding redundant computing data is stored at the same time, and the redundant computing data includes the intermediate variables, error ranges, and execution times calculated by different algorithms under the same input conditions to construct a redundant computing comparison benchmark;

[0023] The input data structure of the voting mechanism of the voting module includes calculation results, redundant calculation comparison values, and historical calculation path records; a voting matrix is constructed based on the voting module to perform redundant comparison on all calculation outputs, and based on the calculation path index table, the consistency of redundant data in different calculation paths is quantified, and a multi-dimensional voting decision matrix is output.

[0024] In a preferred embodiment, a voting decision layer is constructed based on the voting mechanism. The voting decision layer performs weighted voting on the calculation results, and assigns different weights to different calculation results according to factors such as historical calculation stability factors, redundant data consistency factors, and calculation path integration score factors; an anomaly detection mechanism is established. When the deviation between the calculation result of a certain algorithm model and the redundant comparison data exceeds the preset deviation threshold, the voting weight of this calculation path is reduced, and the anomaly information is stored in the calculation path index table;

[0025] Based on the calculation result consistency, redundant data deviation, and historical calculation trajectory, evaluate and output the credibility score of the final voting decision.

[0026] The technical effects and advantages of the present invention:

[0027] 1. Through the redundant data mapping and task hashing mechanism, the same data can be reused in multiple calculation paths, and hash consistency verification is performed during the execution of the calculation paths. Compared with the traditional external verification method, this solution uses the redundant information generated during the calculation process for real-time error detection, thereby effectively reducing the calculation deviation and data pollution risks in high-concurrency calculation and distributed storage environments, and improving the stability of the overall system;

[0028] 2. By introducing a path tracking module, a calculation path tracing model is established to dynamically analyze the execution trajectory, error change trend, and algorithm stability of the calculation path, and calculation path snapshots and index tables are used to make the execution process of all calculation tasks traceable;

[0029] 3. By storing the output results of each calculation path and using redundant calculation data comparison, a multi-dimensional voting decision matrix is constructed. The voting mechanism comprehensively considers the calculation history, calculation path score, and redundant comparison results, assigns weights to the calculation results of different paths, realizes dynamic credibility evaluation, and eliminates abnormal calculation results;

[0030] 4. By introducing calculation history analysis in the evaluation module and comprehensively considering the voting mechanism score and calculation path tracing score, it is beneficial to perform long-term trend analysis on the calculation path;

[0031] 5. By combining path tracing, calculation history analysis, and voting credibility scoring in the evaluation module, a situation awareness stability model of the calculation path is constructed to quantify the software integration level, making the calculation management in high-concurrency calculation and distributed storage environments relatively more efficient. Description of the Drawings

[0032] Figure 1 This is the system module diagram of the present invention.

[0033] Figure 2 This is the system execution flow of the present invention Figure 1 。

[0034] Figure 3 This is the system execution flow of the present invention Figure 2 。

[0035] Figure 4 This is the system execution flow of the present invention Figure 3 。

[0036] Figure 5 This is the system execution flow of the present invention Figure 4 。

[0037] Figure 6 This is the overall process architecture diagram of the present invention. Specific embodiments

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0039] Referring to the attached instructions Figures 1-6 , a software integration degree monitoring system based on a modular architecture according to an embodiment of the present invention includes a data input module, a task distribution module, a path tracking module, a voting module, and an evaluation module;

[0040] The data input module inputs the data included in the software as input variables through the establishment of a data access pipeline, converts it into a standardized input block, and simultaneously establishes a redundant data mapping to reuse the same data for multiple calculation paths;

[0041] A unique task hash is generated for each input block through the data input module, which is used to track the data flow and ensure calculation consistency, and an input parsing stack is constructed, which matches a suitable calculation algorithm according to the data type and distributes it to a heterogeneous computing pool;

[0042] The task distribution module distributes the standardized input blocks to multiple different algorithm models for calculation through the establishment of a heterogeneous computing distribution index table; a multi-layer calculation buffer is established at each algorithm model calculation node to store the original input data, intermediate results during the calculation process, and redundant calculation data;

[0043] The path tracking module is used to record the calculation execution process and establish a calculation path traceability model based on this;

[0044] The voting module is used to store the calculation outputs of each algorithm model, establish a voting mechanism based on the comparison of their redundant calculation results, and optimize the credibility of the calculation results through the voting mechanism;

[0045] The evaluation module quantifies the integration level of the software based on the calculation history of the algorithm model, the credibility calculated by the voting mechanism, and the situational awareness stability of the calculation path by the calculation path traceability model.

[0046] When the data input module executes, it sets up an input buffer for the data access pipeline, encapsulates the original data in the software system into input data blocks, and attaches a timestamp index. It parses the input data blocks through a standardized input conversion layer and converts them into standardized input blocks according to the data format and category;

[0047] During the creation of the standardized input block, the redundant data mapping construction layer of the data input module introduces a redundant data identification table and tracks the reuse relationship of the same data in multiple calculation paths through hash mapping;

[0048] The input parsing stack of the data input module parses the type, features, and calculation requirements of the input data and constructs an input parsing stack index table. The input parsing stack index table stratifies the input blocks according to the data attributes; The redundant calculation path distribution distributes multiple copies of the same standardized input block along different algorithm paths according to the index information of the input parsing stack, so that the same data can be redundantly calculated in multiple calculation processes;

[0049] By setting up a calculation path index table in the data input module, record the distribution trajectory of the standardized input block at different calculation nodes to form a traceable calculation path mapping; According to the calculation path index table, tasks are dynamically allocated within the heterogeneous computing pool. Through the data input module, data consistency verification is performed. Before the calculation task starts, a task index table is constructed to compare the task hash of the current input block, so that the calculation of redundant data in each path is consistent.

[0050] When the calculation task enters the path tracking unit, a calculation path index table is created and stores the task identifier, calculation branch information, and timestamp in a hash index manner; Through the path tracking unit, a calculation path traceability model is constructed. The calculation path traceability model takes three execution vectors constructed based on the task execution trajectory, calculation result deviation, and algorithm stability as inputs to evaluate the integrity and abnormal conditions of the calculation path;

[0051] When the calculation task in the path tracking unit executes, a calculation path snapshot is generated. The calculation path snapshot includes the input hash, calculation status, and calculation output hash of the current calculation step and is stored in the path traceability index table for subsequent anomaly detection.

[0052] The task execution trajectory included in the path tracking unit is recorded in the form of a linked list of calculation steps. Each calculation step is associated with an input data hash, an execution algorithm identifier, and an output data hash. The purpose of this is to make the path completely traceable. The calculation result deviation is quantified based on the calculation error matrix. The "rows" of the calculation error matrix represent the results of the same task on different calculation paths, and the "columns" of the calculation error matrix represent the calculation deviations of the task at different calculation time points. Based on this, the evolution trend of the calculation error across the time dimension is constructed.

[0053] The algorithm stability included in the path tracking unit is evaluated by the repeatability of the historical calculation path and the error convergence situation. If the results presented in different execution batches of the same calculation task are highly consistent, it is determined to increase the stability score of the corresponding algorithm. Otherwise, it is marked as an abnormal calculation path.

[0054] Based on the results of the calculation path tracing model, the stability score of the calculation path is output. If the acceleration rate of the decay trend of the stability score of a certain calculation path is greater than the decay trend acceleration rate threshold preset by the system, it is determined that the trust level of the calculation path in the system is abnormal, which means that its calculation results may be affected by abnormal factors.

[0055] When it is determined that the trust level of the calculation path is abnormal, the redundant calculation path is activated, the standby algorithm is triggered to execute the same task, and the result is compared with the result of the original calculation path. The analysis result of the calculation path tracing model is finally fed back to the evaluation module. Based on the situational awareness stability of the calculation path, the software integration level is analyzed, and the scheduling strategy of the calculation task is adjusted.

[0056] After the calculation task is completed, the calculation outputs of each algorithm model are stored in the voting module and indexed and stored according to the task identifier. When storing the calculation results, the corresponding redundant calculation data is also stored. The redundant calculation data includes intermediate variables, error ranges, and execution times calculated by different algorithms under the same input conditions to construct a redundant calculation comparison benchmark.

[0057] The input data structure of the voting mechanism of the voting module includes calculation results, redundant calculation comparison values, and historical calculation path records. Based on the voting module, a voting matrix is constructed to perform redundant comparison on all calculation outputs. Based on the calculation path index table, the consistency of the redundant data in different calculation paths is quantified, and a multi-dimensional voting decision matrix is output.

[0058] Construct a voting decision-making layer based on a voting mechanism. The voting decision-making layer performs weighted voting on the calculation results and assigns different weights to different calculation results according to the historical calculation stability factor, redundant data consistency factor, and calculation path integration score factor; establish an anomaly detection mechanism. When the calculation result of a certain algorithm model deviates from the redundant comparison data by more than the preset deviation threshold, reduce the voting weight of this calculation path and store the anomaly information in the calculation path index table;

[0059] Evaluate and output the credibility score of the final voting decision based on the calculation result consistency, redundant data deviation, and historical calculation trajectory;

[0060] In a further solution, it should be noted that:

[0061] Construct a calculation path tracing model based on the path tracking module. The calculation path tracing model is used to analyze the execution situation of the calculation path, which includes calculation snapshot stability, path deviation, and time decay factors, and constructs a global stability index of the calculation path;

[0062] For the calculation snapshot stability score, through P i to represent the snapshot stability score of algorithm i in the calculation path tracing, reflecting the local consistency of its calculation path;

[0063]

[0064] Among them, M i is the total number of snapshots of algorithm i in the calculation path snapshot, that is, the sampling number of the path record; S i,j is the calculation path score of algorithm i at snapshot index j, and S i,j is used to reflect the stability of this snapshot during the calculation execution process, such as the matching degree of the input hash and the output hash; is the median of the scores of all snapshots of algorithm i. In practical applications can be used as a benchmark value for stability; λ P is the deviation impact factor of the calculation path snapshot, used to control the fluctuation degree of the score; W(S i,j ) represents the weight function used to adjust the importance of different snapshots; W(·) is the LambertW function, which is used to describe the non-linear growth situation in practical applications; δ P is the weight adjustment parameter. More specifically, δ P is used to control the influence of the snapshot score in the calculation path;

[0065] Quantify the comprehensive score of the calculation path tracing model based on the calculation snapshot stability score;

[0066]

[0067] Among them, E P,i is the stability score of a single algorithm i in the calculation path tracing model; κ P is the path score non-linear amplification factor, and κ P is used to affect the convergence rate of the calculation path fluctuation; η P is the path score exponential adjustment parameter, and the path score exponential adjustment parameter is used to affect the influence degree of the snapshot score on the global path stability score; E P is the global calculation path tracing model score, that is, it finally reflects the stability of the entire calculation path in all algorithm models; N is the total number of algorithm models participating in the calculation;

[0068] Describe the calculation history of each algorithm model; analyze the abnormal accumulation, calculation complexity and consistency of each algorithm model in historical tasks to evaluate the impact of long-term calculation behavior on the system;

[0069] Calculate the historical abnormal integral, and designate H i as the historical abnormal integral of algorithm i in the time interval [T0, T]; calculate the calculation data complexity correction, and designate D i as the calculation data complexity correction coefficient of algorithm i;

[0070]

[0071] Among them is the instantaneous error value calculated by algorithm i at time t, and the instantaneous error value comes from calculation path deviation, redundant data comparison results, etc.; δ H is the abnormal data sensitivity factor, and the abnormal data sensitivity factor is used to control the influence degree of the abnormal integral on historical calculations; X i is the complexity measure of the input data; K i is the task consistency score, which is obtained by historical task hash matching; 1F1(μ; ν; X i ) is the confluent hypergeometric function, which is used to measure the influence of the input data on the historical abnormal integral of the calculation;

[0072] Based on H i and D i calculate the historical score;

[0073]

[0074] Among them, E H,i is the calculation historical score of algorithm i; Γ(x) is the gamma function, which is used to describe the historical accumulation effect of non-linearly amplified abnormal data; α H , β H are used to control the weights of the calculation historical data.

[0075] Based on EH,i Describe the historical global score;

[0076]

[0077] where E H is the global calculated historical score; N is the total number of algorithm models participating in the calculation; is used to describe the product of the calculated historical scores of all algorithms, ensuring the non-linear amplification of abnormal scores; is the geometric mean operation exponent, used to balance the value of the comprehensive score and not affect the overall result due to an overly large abnormal score of a certain algorithm;

[0078] The credibility calculated by the voting mechanism provides a credibility score for the calculation task by comprehensively considering the redundant calculation results, time fluctuations, and historical stability;

[0079]

[0080] where E V,i is the voting credibility score calculated by algorithm i, E V,i is used to measure the credibility of this algorithm after redundant calculation comparison; exp(·) is the exponential function, used to ensure that the credibility score is always positive and enhance the influence of high-credibility votes; γ V is the amplification factor of the credibility score, used to control the range of the credibility score, making it easier to distinguish low-credibility scores and more prominent high-credibility scores in practical applications; Ei(·) is the exponential integral function, used to enhance the response of the credibility score to extremely high values and make the credibility score relatively stable; V i is the original voting score of algorithm i, which comes from the matching degree between the calculation result and the redundant comparison; δ V is the exponential adjustment parameter of the voting credibility score, δ V is used to control the sensitivity of the credibility score to the original voting score;

[0081] In addition, the 2F1(ρ, σ; τ; R i ) part in the above formula is the Gaussian hypergeometric function, which is used to measure the non-linear contribution of redundant calculation data to the voting credibility; ρ is the hypergeometric parameter 1, controlling the main influence term of redundant calculation data on the credibility score; σ is the hypergeometric parameter 2, adjusting the change rate of the credibility score under different redundant calculation situations; τ is the hypergeometric parameter 3, used to affect the weight of the calculated credibility on different calculation paths; R i is the redundant calculation data exponent calculated by algorithm i, which includes the weighted sum of redundant intermediate variables, error range, execution time, etc. generated by this algorithm during the calculation process; T i is the execution time fluctuation factor of algorithm i, T iUsed to reflect the computational time instability of the algorithm in historical tasks; lnT i Indicates taking the logarithm of the execution time so that it can still stably affect the credibility score in the case of extreme computational time differences; 1 + |lnT i The added 1 in | is used to avoid the logarithm function approaching 0, ensuring that the formula is computable under all execution time conditions;

[0082] Finally, based on E V,i Calculate the global voting credibility score E V ; E V Describes the non - linear aggregation result of the voting credibility scores calculated by all algorithms;

[0083]

[0084] Where Indicates summing the voting credibility scores of all algorithm models; ξ V Is the power - mean index parameter, used to control the fusion method of the voting credibility scores; Is the power - mean normalization, making the non - linear contributions of different voting scores reasonable;

[0085] In the description of the situation awareness stability of the computational path by the computational path tracing model; through I 最终 To describe the situation awareness stability score of the computational path, comprehensively calculating the final outputs of the computational path tracing model, historical calculations, and voting credibility scores, and measuring the stability of the overall software integration of the system;

[0086]

[0087] Where exp(·) is the exponential function, used to keep the final score always positive and can effectively smooth the calculated credibility score; Φ H Is the computational history conversion index, and the computational history conversion index is used to measure the stability performance of each algorithm model in historical calculations; E H Is the global computational history score; α I Is the non - linear adjustment parameter of the computational history score, used to control the contribution of the computational history score to the overall stability; Φ V Is the voting credibility conversion value, and the voting credibility conversion value is used to measure the impact of redundant calculation results on the credibility; Li3(·) is the trilogarithmic function of the third order, and the trilogarithmic function of the third order in the above formula is used to enhance the non - linear characteristics of the credibility score so that it can stably provide reliable scores under different calculation conditions; β I Is the weight adjustment parameter of the credibility score; Φ P Is the computational path tracing stability factor;

[0088] In addition, for ΦP In the formula, arcsin(·) represents the arcsine function, which is used to keep the calculation path stability score within a reasonable range and avoid the influence of extreme calculation path scores on the overall software integration evaluation; γ I is the weight parameter of the calculation path score, which is used to control the contribution of the calculation path score to the overall software integration; in addition, the 1 + Φ P part is used to ensure that the contribution of the calculation path score is within a controllable range and avoid the final score distortion caused by the calculation path score being too large or too small; is the overall adjustment parameter, which is used to control the output scale of the final score;

[0089] In addition, for this system, it should be noted that the purpose of this solution is to build a system for monitoring software integration, so that the calculation tasks maintain consistency and traceability on different calculation paths; it realizes precise tracking, error detection, and optimization of the calculation process through data input, task distribution, path tracking, voting decision-making, and evaluation feedback;

[0090] In the data input stage, the system receives the input data through the data access pipeline and converts it into a standardized input block, so as to ensure that all calculation tasks are processed based on data in a unified format; at the same time, the solution introduces a redundant data mapping mechanism, enabling the same data to be reused by multiple calculation paths for subsequent cross-verification in the calculation to improve the consistency of the calculation results;

[0091] In the task distribution stage, each standardized input block is assigned a unique task hash value, which is not only used for data tracking but also enables the calculation consistency of the same data in different calculation paths; at the same time, the system distributes tasks to heterogeneous computing pools according to data types and calculation requirements, and manages the calculation paths through an index table to achieve efficient task scheduling;

[0092] After entering the path tracking stage, the system records the calculation execution process in real time and stores the calculation path for subsequent error analysis and anomaly detection; the calculation path tracing model depends on the task execution trajectory, calculation result deviation, and algorithm stability to evaluate the stability and credibility of the calculation path; in addition, the system also includes generating a calculation snapshot, which is used to store the input hash, calculation status, and calculation output hash to ensure the traceability of the calculation process;

[0093] The voting module is used to optimize the credibility of the calculation results, store the calculation outputs of each calculation path, and establish a voting mechanism based on the comparison of redundant calculation results. The redundant data calculated by different algorithm paths will be compared, and the final calculation results will be optimized through the voting mechanism to ensure that even if some paths are interfered during the calculation process, the overall calculation results still have credibility;

[0094] The evaluation module is the decision-making core part of the system. Based on the algorithm calculation history, the credibility calculated by the voting mechanism, and the calculation path tracing model, it quantifies the software integration level. It not only evaluates the quality of the current calculation task, but also can optimize the scheduling strategy of subsequent calculation tasks based on historical calculation behaviors and anomaly analysis. For example, when the score of a certain calculation path continues to decline, the system can trigger an alternative algorithm for calculation to improve the stability of the calculation.

[0095] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A software integration degree monitoring system based on a modular architecture, comprising a data input module, a task distribution module, a path tracking module, a voting module, and an evaluation module; It is characterized in that: The data input module inputs the data contained in the software as input variables by setting up a data access pipeline, converts it into a standardized input block, and at the same time establishes a redundant data mapping to reuse the same data for multiple calculation paths; A unique task hash is generated for each input block by the data input module, and an input parsing stack is constructed, which matches the calculation algorithm according to the data type and distributes it to the heterogeneous computing pool; The task distribution module distributes the standardized input blocks to multiple different algorithm models for calculation by setting up a heterogeneous computing distribution index table; a multi-layer calculation buffer is established at each algorithm model calculation node to store the original input data, intermediate results and redundant calculation data during the calculation process; The path tracking module is used to record the calculation execution process and establish a calculation path traceability model based on this; The voting module is used to store the calculation outputs of each algorithm model, and establish a voting mechanism based on the comparison of its redundant calculation results to optimize the credibility of the calculation results through the voting mechanism; The evaluation module quantifies the integration degree level of the software based on the calculation history of the algorithm model, the credibility calculated by the voting mechanism, and the situation awareness stability of the calculation path by the calculation path traceability model.

2. The software integration degree monitoring system based on a modular architecture according to claim 1, characterized in that: When the data input module is executing, an input buffer is set up for the data access pipeline, the original data in the software system is encapsulated into input data blocks, and a timestamp index is added. The input data blocks are parsed by the standardized input conversion layer and converted into standardized input blocks according to the data format and category; During the creation of the standardized input block, the redundant data mapping construction layer of the data input module introduces a redundant data identification table to track the reuse relationship of the same data in multiple calculation paths through hash mapping; The input parsing stack of the data input module parses the type, characteristics, and calculation requirements of the input data, and constructs an input parsing stack index table. The input parsing stack index table stratifies the input blocks according to the data attributes; The redundant calculation path distribution distributes multiple copies of the same standardized input block along different algorithm paths according to the index information of the input parsing stack, so that the same data is redundantly calculated in multiple calculation processes; A calculation path index table is set up by the data input module to record the distribution trajectory of the standardized input block at different calculation nodes, forming a traceable calculation path mapping; according to the calculation path index table, tasks are dynamically allocated in the heterogeneous computing pool. The data input module performs data consistency verification. Before the calculation task starts, a task index table is constructed to compare the task hash of the current input block to make the calculation of the redundant data consistent in each path.

3. The software integration degree monitoring system based on a modular architecture according to claim 2, characterized in that: When a computing task enters the path tracking unit, a computing path index table is created, and the task identifier, computing branch information, and timestamp are stored in a hash index manner; a computing path traceability model is constructed by the path tracking unit, and the computing path traceability model takes three execution vectors constructed based on the task execution trajectory, computing result deviation, and algorithm stability as inputs to evaluate the integrity and anomalies of the computing path; When the computing task in the path tracking unit is executed, a computing path snapshot is generated, which includes the input hash, computing status, and computing output hash of the current computing step, and is stored in the path traceability index table.

4. The software integration degree monitoring system based on a modular architecture according to claim 3, characterized in that: The task execution trajectory included in the path tracking unit is recorded in the form of a computing step linked list, and each computing step is associated with the input data hash, execution algorithm identifier, and output data hash; The computing result deviation is quantified based on the computing error matrix. The "rows" of the computing error matrix represent the results of the same task on different computing paths, and the "columns" of the computing error matrix represent the computing deviations of the task at different computing time points. Based on this, the evolution trend of the computing error across the time dimension is constructed; The algorithm stability included in the path tracking unit is evaluated by the repeatability and error convergence of the historical computing path; if the results presented in different execution batches of the same computing task are highly consistent, it is determined to increase the stability score of the corresponding algorithm, otherwise it is marked as an abnormal computing path.

5. The software integration degree monitoring system based on a modular architecture according to claim 4, characterized in that: Based on the result of the computing path traceability model, the stability score of the computing path is output. If the acceleration rate of the attenuation trend of the stability score of a certain computing path is greater than the attenuation trend acceleration rate threshold preset by the system, it is determined that the trust level of the computing path in the system is abnormal; When it is determined that the trust level of the computing path is abnormal, the redundant computing path is activated, the standby algorithm is triggered to execute the same task, and the result is compared with the result of the original computing path; The analysis result of the computing path traceability model is finally fed back to the evaluation module, and the software integration degree level is analyzed based on the situational awareness stability of the computing path.

6. The software integration degree monitoring system based on a modular architecture according to claim 5, characterized in that: After the computing task is completed, the computing outputs of each algorithm model are stored in the voting module and indexed and stored according to the task identifier; when storing the computing results, the corresponding redundant computing data is stored at the same time, and the redundant computing data includes intermediate variables, error ranges, and execution times calculated by different algorithms under the same input conditions to construct a redundant computing comparison benchmark; The input data structure of the voting mechanism of the voting module includes the computing result, redundant computing comparison value, and historical computing path record; Based on the voting module, a voting matrix is constructed to perform redundant comparison on all computing outputs. Based on the computing path index table, the consistency of the redundant data in different computing paths is quantified, and a multi-dimensional voting decision matrix is output.

7. A software integration degree monitoring system based on a modular architecture according to claim 6, characterized in that: A voting decision-making layer is constructed based on a voting mechanism. The voting decision-making layer performs weighted voting on the calculation results, and different weights are assigned to different calculation results according to historical calculation stability factors, redundant data consistency factors, and calculation path integration degree scoring factors; An anomaly detection mechanism is established. When the deviation between the calculation result of a certain algorithm model and the redundant comparison data exceeds a preset deviation threshold, the voting weight of this calculation path is reduced, and the anomaly information is stored in the calculation path index table; Based on calculation result consistency, redundant data deviation, and historical calculation trajectories, the credibility score of the final voting decision is evaluated and output.

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