Deep semantic understanding-based investment and research report processing method and system

Abstract

The invention relates to the technical field of investment and research report semantic processing, and discloses an investment and research report processing method and system based on deep semantic understanding. The method comprises the following steps: analyzing an investment and research report through a deep semantic understanding technology, extracting semantic feature elements, and calculating a resource allocation difference to generate a resource allocation scheme in combination with a task demand quantity and available resource quantity matching result; according to the scheme, key task starting time and interval are determined, a resource calling sequence is arranged, and efficiency and influence factors are evaluated to generate a task priority sequence; analyzing a semantic constraint condition and a resource supply period, identifying resource conflict and shortage tasks, and recombining a task timeline and resource allocation to generate a progress arrangement; environment and resource states are monitored in real time, deviation is measured, consumption is counted, resource allocation and time plans are dynamically adjusted, and a deviation correction result is output. According to the method, accurate adaptation and dynamic management and control of investment and research report processing are realized, the method is adaptive to complex scenes, and the processing systematicness and flexibility are improved.

Description

Technical Field

[0001] This invention relates to the field of semantic processing technology for investment research reports, specifically to a method and system for processing investment research reports based on deep semantic understanding. Background Technology

[0002] Investment research reports, as crucial information carriers in finance and investment, contain a wealth of key content including industry trends, market analysis, and project evaluations. The quality and efficiency of their processing directly impact the effectiveness of subsequent related work. With the rapid development of financial markets and the continuous expansion of investment fields, the number of investment research reports has exploded, and the professionalism and complexity of their content have also increased, placing higher demands on the accuracy and efficiency of report processing. Currently, investment research report processing largely relies on manual operation or traditional automated tools, which have many obvious limitations.

[0003] At the semantic parsing level, traditional methods struggle to deeply extract core semantic information from reports, often only able to extract surface keywords or structured data. They fail to accurately capture the implicit logical connections and semantic features within the report, resulting in a lack of comprehensive and reliable information support for subsequent processing. Regarding resource allocation, existing methods often employ fixed allocation models, failing to fully consider the dynamic matching between the actual demand for processing tasks and the amount of available resources. This can easily lead to resource oversupply or shortage, causing both resource waste and potential task delays.

[0004] In the task sequencing stage, the lack of a comprehensive assessment of task importance, resource utilization efficiency, and the impact of task intervals leads to a chaotic task execution order, preventing critical tasks from being prioritized and resulting in overall low processing efficiency. Furthermore, the existing processing flow lacks a holistic analysis of semantic constraints and resource supply cycles, leading to frequent resource allocation conflicts and difficulty in effectively identifying and promptly addressing resource-scarce tasks, further impacting the stability of processing progress.

[0005] In the process of processing investment research reports, there are many uncertainties such as environmental changes and fluctuations in resource status. Traditional processing methods lack real-time monitoring and dynamic adjustment mechanisms, making it impossible to promptly detect deviations between task time and environmental changes. Furthermore, it is difficult to optimize resource allocation and time plans based on resource consumption and remaining material quantities, leading to the continuous accumulation of processing deviations and ultimately affecting the overall quality of report processing. These problems make existing investment research report processing methods ill-suited for large-scale, complex report processing needs, hindering the efficient transformation and utilization of investment research information. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for processing investment research reports based on deep semantic understanding, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, this invention provides a method for processing investment research reports based on deep semantic understanding, the method comprising: The deep semantic understanding technology is used to parse the investment research report, extract the semantic feature elements in the report, and statistically analyze the demand and available resources of the processing task. The task requirements and resource capacity are matched, the resource allocation difference is calculated, and a resource allocation plan is generated. Based on the resource allocation scheme, determine the start time and task interval of key processing tasks, arrange the order of calling processing resources, evaluate the efficiency of task resource utilization and the impact factor of interval, and generate a task priority sequence. Based on the task priority sequence, analyze semantic constraints and resource supply cycles, identify resource allocation conflicts and resource shortage tasks, adjust task execution time intervals, and generate a set of constraint parameters. Based on the set of constraint parameters, the time nodes and task distribution differences of the report processing are summarized, resource utilization and resource availability time are matched, the task time adjustment range is calculated, the task timeline and resource allocation are reorganized, and a report processing schedule is generated. The system monitors environmental changes and resource status during the report processing, measures the deviation between task time and environmental changes, calculates resource and man-hour consumption and material surplus, adjusts resource allocation and time plans, and generates processing deviation correction outputs.

[0008] Preferably, the steps for obtaining the resource allocation scheme specifically include: collecting semantic feature elements and processing resource information from investment research reports; comprehensively statistically analyzing the resource demand and availability across multiple task areas; comparing the matching degree between resource capacity and task requirements; calculating the deviation between total resource demand and available resources to form a resource demand deviation set; analyzing the resource demand deviation set; adjusting the allocation ratio of resources and manpower based on the availability of manual processing capabilities to establish a resource and manpower allocation adjustment set; and comparing the resource and manpower allocation based on the resource and manpower allocation adjustment set to obtain the resource allocation scheme.

[0009] Preferably, the step of obtaining the task priority sequence specifically includes: according to the resource allocation scheme, parsing the resource configuration of each key processing task, calculating the task start time, and using an optimization algorithm to predict the optimal task sequence to generate a start timetable for each key task; using the start timetable of each key task, prioritizing the processing resources using a priority sorting algorithm, adjusting resource allocation based on task urgency, and establishing a task priority list; using the task priority list, and combining the interval requirements in actual task execution, calculating the task resource utilization rate and task interval weight of the key tasks to generate a task priority sequence.

[0010] Preferably, the step of obtaining the constraint parameter set specifically includes: extracting the start time and resource cycle of each task from the task priority sequence, analyzing resource usage, applying a resource allocation algorithm to determine the resource requirements of each task, and generating resource requirement analysis results; based on the resource requirement analysis results, marking all time conflicts between resource supply and demand, as well as conflicts and resource shortages that occur in resource allocation, and creating a conflict and shortage index table; using the conflict and shortage index table, recalculating the execution interval of each task, optimizing the task execution plan, and generating the constraint parameter set.

[0011] Preferably, the steps for obtaining the report processing schedule specifically include: extracting the time nodes and task distribution differences for report processing from the constraint parameter set; analyzing the task execution order and resource utilization efficiency based on the comparison of task time and resource distribution, and generating a time node and task difference distribution table; using the time node and task difference distribution table, matching resource utilization rate with resource availability time; based on the matching analysis of resource allocation and task requirements, identifying time conflict and resource shortage tasks, and creating resource conflict and shortage task results; based on the resource conflict and shortage task results, calculating the adjustment range of task execution and time node correction values, optimizing task time distribution, and generating a report processing schedule.

[0012] Preferably, the step of obtaining the processing deviation correction output specifically includes: extracting monitoring data from the report processing schedule, including changes in the processing environment and resource status; combining task time nodes, using data analysis methods to determine the impact of environmental and resource status on the processing progress, and generating environmental and resource status analysis results; using the environmental and resource status analysis results to calculate the deviation between task time and environmental changes, and to statistically analyze the working hours and remaining materials of each resource; using quantitative analysis methods to identify the resource allocation and time nodes that need to be adjusted, and creating a task and environment deviation table; based on the task and environment deviation table, adjusting resource allocation and time nodes to match actual processing environment changes, optimizing task allocation, and generating processing deviation correction output.

[0013] Preferably, the resource allocation scheme further includes resource allocation ratio, manual allocation matching results, and resource and task requirement difference analysis; the task priority sequence further includes key task priority, task resource utilization efficiency, and task interval optimization order; the constraint parameter set further includes resource allocation time parameters, resource shortage task identifiers, and task execution interval correction values; the report processing schedule further includes processing time node arrangements, resource allocation scheme, and task execution time adjustment; and the processing deviation correction output further includes resource operation status adjustment, processing time deviation correction, and material surplus balance parameters.

[0014] Preferably, the resource allocation ratio is determined by dynamically adjusting the resource allocation weights based on the complexity of the semantic feature elements and the historical performance of the processing resources, so as to ensure a balance between the resource allocation scheme and the task requirements.

[0015] Preferably, the method for generating the task interval optimization order is as follows: based on task priority and resource availability, time series analysis technology is used to optimize the interval between tasks in order to maximize resource utilization efficiency.

[0016] Preferably, the present invention also includes an investment research report processing system based on deep semantic understanding. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the investment research report processing method based on deep semantic understanding as described above.

[0017] Compared with the prior art, the beneficial effects of the present invention are: By leveraging deep semantic understanding technology to analyze investment research reports, we can overcome the limitations of traditional methods in semantic mining, accurately extract semantic features from the reports, and capture the implicit logical connections and core information, providing a comprehensive and reliable information foundation for subsequent processing stages. By statistically analyzing the demand for processing tasks and the availability of resources, we can perform targeted matching of demand and resource capacity, accurately calculate resource allocation discrepancies, and generate resource allocation plans. This achieves dynamic adaptation between resource supply and task requirements, avoiding resource waste or insufficient supply issues under fixed allocation models, and making resource allocation more aligned with actual processing scenarios.

[0018] Based on the resource allocation plan, the start time and task intervals of critical processing tasks are determined, and the order of resource calls is rationally arranged. Simultaneously, the resource utilization efficiency and interval impact factors are comprehensively evaluated to generate a scientifically sound task priority sequence. This ensures that critical tasks receive priority support, while non-critical tasks proceed in an orderly manner, optimizing the overall processing flow's execution rhythm. Based on the task priority sequence, semantic constraints and resource supply cycles are analyzed holistically. Tasks with resource allocation conflicts and shortages are proactively identified. By adjusting task execution intervals, a set of constraint parameters is generated to proactively avoid potential process bottlenecks, reduce the additional costs of conflict resolution, and ensure smooth processing.

[0019] The system summarizes the differences in time nodes and task distribution for report processing, precisely matches resource utilization with available resource time, calculates the range of task time adjustments, and reorganizes task timelines and resource allocation to generate a clear and feasible report processing schedule. This ensures that each stage of the processing flow is predictable and controllable, facilitating operators' understanding of the overall progress. Throughout the report processing process, environmental changes and resource status are monitored in real time. Deviations between task time and environmental changes are measured promptly, and resource and man-hour consumption and material surplus are accurately calculated. Based on this, resource allocation and time plans are dynamically adjusted, and processing deviation correction results are output. This allows the processing flow to quickly adapt to changes in the external environment and internal resources, preventing the accumulation of deviations from impacting processing quality and efficiency. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the working principle of the investment research report processing method based on deep semantic understanding described in this invention. Figure 2 A flowchart for obtaining a resource allocation scheme; Figure 3 The flowchart obtained for the set of constraint parameters; Figure 4 A distribution chart of resource node health scores; Figure 5 Comparison chart showing the effects of optimizing task intervals. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 This invention provides a method for processing investment research reports based on deep semantic understanding, the method comprising: This process utilizes deep semantic understanding technology to parse investment research reports, extract semantic features, and statistically analyze the demand and available resources for processing tasks. It matches task requirements with resource capacity, calculates resource allocation discrepancies, and generates a resource allocation plan. Based on the plan, it determines the start time and intervals for key processing tasks, arranges the order of resource calls, evaluates resource utilization efficiency and interval impact factors, and generates a task priority sequence. Based on this priority sequence, it analyzes semantic constraints and resource supply cycles, identifies resource allocation conflicts and resource shortages, adjusts task execution intervals, and generates a set of constraint parameters. Using this parameter set, it summarizes the differences in report processing time nodes and task distribution, pairs resource utilization rates with available time, calculates the task time adjustment range, reorganizes the task timeline and resource allocation, and generates a report processing schedule. Finally, it monitors environmental changes and resource status during report processing, measures the deviation between task time and environmental changes, statistically analyzes resource and man-hour consumption and material surplus, adjusts resource allocation and time plans, and generates a processing deviation correction output.

[0023] Example 1: See Figure 2 In practical implementation, the deep semantic understanding-based investment research report processing method involves the generation of resource allocation schemes and task priority sequences. Resource allocation schemes are obtained by collecting semantic feature elements and processing resource information from the investment research report. Semantic feature element extraction utilizes natural language processing technology, such as deploying pre-trained language models to perform word segmentation, entity recognition, and relation extraction on the investment research report text to obtain key concepts and logical structures. Processing resource information covers various types, including computing resources, storage resources, and human resources, and this information is collected and updated in real time through an integrated resource monitoring system. When comprehensively calculating the resource demand and availability across multiple task areas, resource demand is predicted based on historical processing data and task classification models, while availability is dynamically tracked through a resource pool management module. The matching degree between resource capacity and task demand is compared using similarity calculation algorithms, such as cosine similarity or Euclidean distance algorithms, to quantify the difference between resource supply and demand. The deviation between total resource demand and available resources is calculated using mathematical difference calculations or percentage deviation methods, forming a structured set of resource demand deviations for subsequent resource allocation analysis.

[0024] When analyzing the resource demand deviation set, adjustments are made based on the availability of human processing capacity. The availability of human processing capacity is assessed through the human resource management system, such as by querying employee work hour records and skill matching databases. Allocation ratio adjustments are executed based on preset priority rules, such as prioritizing automated resources for high-urgency tasks and allocating highly skilled human resources for high-complexity tasks. The adjustment process employs optimization algorithms, such as linear programming or genetic algorithms, with the objective function of minimizing the resource demand deviation. A resource and human allocation adjustment set is established, containing the adjusted resource allocation scheme, human allocation scheme, and adjustment logic explanation, ensuring operational transparency and traceability. Based on the resource and human allocation adjustment set, resource and human allocation are compared. The comparison process uses a consistency check mechanism, such as verifying whether the allocation scheme conforms to business strategies through a rule engine, resulting in a resource allocation scheme. The resource allocation scheme includes a resource allocation list, time allocation plan, and fault tolerance strategy. After the scheme is generated, its feasibility is tested in a simulated execution environment, and parameters are fine-tuned based on test feedback.

[0025] In practical implementation, the acquisition of task priority sequences is based on resource allocation schemes. When analyzing the resource configuration of each critical processing task, resource configuration analysis involves task dependency analysis. For example, a directed acyclic graph (DAG) model is used to model the logical flow between tasks. The task start time is calculated based on resource availability and task priority data. The critical path method is used to determine the earliest and latest start times of tasks. Optimization algorithms, including greedy algorithms or dynamic programming algorithms, are used to predict the optimal task sequence, with the goal of minimizing the total processing time. A startup schedule for each critical task is generated, detailing the planned start time, planned end time, and resource usage for each task. Based on the startup schedule of each critical task, a priority ranking algorithm is used to prioritize processing resources. This algorithm operates based on a weighted scoring model, considering factors such as task complexity, resource requirements, and task deadlines. Resource allocation is adjusted based on task urgency, defined through a business rule base. For example, higher-priority tasks may be allocated more resource quotas. A task priority list is established, including task identifiers, priority scores, and resource allocation suggestions.

[0026] Using a task priority list and considering the interval requirements in actual task execution, the resource utilization rate and task interval weight of critical tasks are calculated. Interval requirements include the minimum and maximum interval times between tasks to avoid resource usage conflicts. Task resource utilization is calculated as the ratio of resource occupancy time to total available time. The task interval weight is assigned based on the impact of the interval on the overall progress, generating a task priority sequence. The task priority sequence is output as an ordered task list to guide the execution order of the actual processing flow. In some embodiments, the extraction of semantic feature elements can integrate multiple natural language processing models to improve the accuracy of feature extraction. The collection of resource information can be extended to a cloud-based resource monitoring platform to achieve cross-platform resource management. It can be understood that the analysis of resource demand deviation sets can incorporate machine learning models to predict deviation trends and optimize the adjustment process.

[0027] In implementation, the resource demand statistics for multi-task areas employ a sliding window technique, processing historical data to identify demand patterns. Dynamic updates to resource availability are achieved through a heartbeat mechanism to ensure real-time data accuracy. The similarity algorithm in matching degree calculation can be configured with threshold parameters to filter low-matching results and improve comparison efficiency. The deviation calculation between total resource demand and available resources can incorporate weighting factors to differentiate the importance of different resource types; the deviation set is stored in a database table for fast querying. The availability assessment of manual processing capacity is combined with a skills certification system to ensure professional matching in manual allocation. The optimization algorithm in allocation ratio adjustment can be constrained, such as with maximum working hours limits, to prevent overload. The establishment of resource and manual allocation adjustment sets uses a version control mechanism to record the history of each adjustment for easy backtracking.

[0028] In practical implementation, the resource allocation scheme comparison process integrates automated verification tools to check resource conflicts and allocation rationality. Fault tolerance mechanisms include redundant resource allocation and failover strategies. The simulated execution environment uses sandbox technology to isolate the impact of testing. Graph theory algorithms can be introduced into the resource configuration analysis of critical processing tasks to optimize task dependency analysis. The calculation of task start times is combined with a calendar system to eliminate interference from non-working days. The objective function in the optimization algorithm can be extended to multi-objective optimization, balancing time and cost factors. The generation of the start schedule uses visualization tools to enhance readability. The weighted scoring model of the priority ranking algorithm can dynamically adjust weight parameters to adapt to business changes. The definition of task urgency is based on a rule engine and supports real-time updates.

[0029] In practical implementation, efficiency indicators such as throughput ratio are introduced into the calculation of task resource utilization. The assignment of task interval weights uses an impact factor model to quantify the contribution of intervals to resource utilization. A conflict resolution algorithm can be incorporated into the task priority sequence generation process to automatically handle priority conflicts. It is understood that interval requirements can be set with reference to industry standards to ensure compliance, and the output format of the ordered task list supports integration with multiple scheduling systems. Optionally, the deployment of the natural language processing model can adopt a distributed computing framework to process large-scale report data. The data collection frequency of the resource monitoring system is configurable, balancing performance and overhead. In some embodiments, resource demand forecasting can be combined with time series analysis to improve accuracy, and manual allocation logic can be embedded in the workflow engine to achieve automated allocation.

[0030] The construction of the weighted scoring model begins with determining the scoring factors, including key indicators such as task complexity, resource requirements, and task deadlines. Task complexity is assessed based on semantic features extracted using deep semantic understanding technology. For example, a complexity score is calculated by analyzing the syntactic structure, entity density, and logical relationship depth of investment research report text. Resource requirements are predicted based on resource consumption patterns of similar tasks in historical data. Task deadlines are assigned a time urgency score according to the urgency level defined in the business rule base. The weight allocation for each scoring factor employs a dynamic adjustment mechanism. The weight values ​​are calculated using business priority rules and real-time feedback data. For example, high-priority task categories are assigned higher weights to complexity factors, while resource-intensive tasks have a higher weight ratio for resource requirements factors. The comprehensive score calculation uses a weighted summation formula, multiplying each factor's score by its corresponding weight and summing the results to obtain the score for each task. The score results are used in the priority ranking algorithm to determine the task execution order by comparing scores, ensuring that high-scoring tasks are allocated resources first. In practice, the parameters of the weighted scoring model can be adjusted through the configuration interface to adapt to the processing needs of different types of investment research reports. The model does not rely on external databases during operation, and all calculations are completed in memory to improve response speed.

[0031] The interval weighting process is another crucial step in generating the task priority sequence, aiming to quantify the impact of task execution intervals on overall processing progress. Interval weighting defines influencing factors, including parameters such as interval duration, resource dependencies, and task chain continuity. Interval duration is measured based on planned intervals in the task initiation schedule; resource dependencies are assessed by analyzing the probability of conflicts in the use of shared resources between tasks; and task chain continuity is determined based on path length and critical path analysis in the task dependency graph. The quantification of influencing factors employs numerical mapping methods; for example, interval duration is mapped to standardized coefficients, resource dependencies are converted into conflict risk scores, and task chain continuity is calculated as dependency strength values. Weighting is achieved by integrating these quantified values, using a multivariate function to calculate the weight of each interval. The function parameters are trained based on historical processing data, for example, by fitting the relationship between interval weights and progress delays through regression analysis. The assigned interval weights are used to adjust the task priority sequence, prioritizing tasks corresponding to higher-weight intervals to minimize the negative impact of intervals. During runtime, the interval weighting module is tightly integrated with the task scheduler, monitoring interval changes in real time and dynamically updating weights. It is understandable that the interval weight assignment process can incorporate machine learning models to adaptively optimize parameters and improve assignment accuracy; optionally, intermediate results can be cached during weight calculation to reduce redundant computation overhead. In some embodiments, the selection of influencing factors can be extended to environmental variables, such as system load fluctuations, to enhance robustness.

[0032] Example 2: See Figure 3 In practical implementation, the deep semantic understanding-based investment research report processing method involves the generation of a constraint parameter set. The constraint parameter set is obtained by extracting the start time and resource cycle start of each task from the task priority sequence. The start time is read from the task start time field of the task priority sequence. The resource cycle is calculated by analyzing the resource occupation duration pattern in the historical execution data of the task. The analysis of resource usage is achieved by deploying a resource monitoring agent. The resource monitoring agent collects CPU usage, memory usage, and network bandwidth data in real time. The resource allocation algorithm is applied to determine the resource requirements of each task. The resource allocation algorithm adopts a dynamic quota allocation mechanism based on load prediction, and calculates the resource requirements by combining task type characteristics and historical resource consumption curves. The resource requirements analysis results are stored in the form of a structured data table, which includes task number, resource type, minimum resource requirement, maximum resource threshold, and recommended resource configuration parameters.

[0033] Based on the resource demand analysis results, all time conflicts in resource supply and demand, as well as conflicts and shortages arising during resource allocation, are marked. Time conflict detection is achieved through a time overlap checker, which compares whether there is an overlap in the resource occupancy time windows of different tasks. Time conflict identification for resource supply and demand employs a multi-level conflict detection strategy, including hard and soft conflict classification mechanisms. Conflicts and shortages arising during resource allocation are identified through a resource gap analysis module. This module calculates the difference between real-time resource supply and planned demand, sets warning thresholds, and creates a conflict and shortage index table. This index table uses a hash index structure to store conflict task numbers, conflict type codes, conflict occurrence timestamps, resource shortage magnitudes, and resolution status flags. Using the conflict and shortage index table, the execution interval of each task is recalculated. The recalculation process employs an interval adjustment algorithm based on constraint satisfaction theory. This algorithm considers the dynamic changes in task dependencies and resource availability time windows. Optimizing the task execution plan is achieved through a rescheduling engine. This engine integrates multiple scheduling strategies and supports real-time strategy switching, generating a constraint parameter set. This set includes adjusted task execution interval parameters, a list of resource constraints, optimized task sequences, and conflict resolution records. In practical implementation, the calculation of resource cycles can incorporate a moving average algorithm to smooth fluctuating data and improve the accuracy of cycle estimation. It can be understood that the time overlap checker can be extended to support multi-timezone task scheduling scenarios, enhancing system adaptability.

[0034] In practical implementation, the dynamic quota allocation mechanism of the resource allocation algorithm is implemented through a machine learning model. The training data for the machine learning model comes from historical task execution logs and resource monitoring records. Load prediction uses time series analysis to analyze resource usage patterns. Task type characteristics are mapped to resource demand patterns by semantic feature elements extracted through deep semantic understanding technology. Historical resource consumption curves are identified for peak and average characteristics through data mining methods. The generation process of resource demand analysis results includes a data verification step, which checks the rationality and consistency of resource demand parameters to prevent outliers from affecting subsequent processing. In some embodiments, the deployment of the resource monitoring agent can adopt a distributed architecture to achieve cross-node data collection, and the data table structure of the resource demand analysis results can support multi-dimensional queries and analysis.

[0035] In practical implementation, the multi-level conflict detection strategy defines hard conflicts as those caused by absolute resource shortages, detected by comparing total resource demand with the system's maximum supply capacity. Soft conflicts refer to temporary resource shortages or competitive conflicts, detected by analyzing the overlap and priority differences of resource usage time windows. The resource gap analysis module's warning threshold settings employ a dynamic adjustment mechanism, with thresholds set hierarchically based on resource importance and business impact. The hash index structure of the conflict and shortage index table supports fast lookup and update operations. Optionally, the time overlap check program can integrate visualization tools to display the conflict time distribution, and the conflict and shortage index table can be configured with automatic alarm functions to notify system administrators.

[0036] In practical implementation, the interval adjustment algorithm based on constraint satisfaction theory models the task interval optimization problem as a constraint satisfaction problem. Constraints include resource availability constraints, task dependency constraints, and time window constraints. The interval adjustment algorithm uses a backtracking search algorithm to find the feasible solution space. The rescheduling engine's scheduling strategy library includes various algorithms such as earliest deadline first, minimum relaxation time first, and resource balancing strategies. Real-time strategy switching is dynamically triggered based on system load status and business priority. The generation process of the constraint parameter set includes a parameter verification module, which verifies the validity and feasibility of the constraint parameters to ensure that the parameters meet the requirements of the actual execution environment. It can be understood that the backtracking search of the interval adjustment algorithm can be set with a maximum number of iterations to prevent infinite loops, and the strategy switching of the rescheduling engine can record audit logs for subsequent analysis. In some embodiments, the storage format of the constraint parameter set can support version management, facilitating backtracking and comparison of parameter settings in different versions.

[0037] In implementation, the data collection frequency of the resource monitoring agent is configurable, dynamically adjusted based on task criticality and system load. CPU utilization data is obtained through the operating system interface, memory usage is queried through the system memory management module, and network bandwidth data is collected through network traffic monitoring tools. The machine learning model training for the resource allocation algorithm combines offline training and online updates. Offline training uses historical large datasets to build the basic model, while online updates fine-tune model parameters based on real-time feedback data. The mapping from task type features to resource demand patterns is achieved through feature engineering, which extracts key indicators from task semantic features as model input. The rule base for data verification can be updated as business rules change, and multi-dimensional query functions can be provided externally through a structured query language interface. In implementation, hard and soft conflicts are handled using different strategies. Hard conflicts trigger resource expansion or task reallocation processes, while soft conflicts are mitigated through priority negotiation and time offset techniques. The automatic alarm function for the conflict and shortage index table can be configured with multiple notification channels, including email, SMS, and internal system messages. The modeling process for constraint satisfaction problems defines the variable domain, constraints, and optimization objective. The variable domain represents the adjustable range of task intervals, constraints encode business rules and system limitations, and the optimization objective is set as minimizing total processing time or maximizing resource utilization. The parameter validation module's validation logic includes boundary value checks, logical consistency checks, and resource feasibility checks to ensure that the generated set of constraint parameters can be directly used in the production environment. Optionally, the conflict detection process can introduce a fuzzy matching algorithm to handle uncertain time intervals, and the rescheduling engine can support a manual intervention mode to handle special business scenarios.

[0038] In specific implementations, a multi-level conflict detection strategy is the core method for accurately identifying resource supply and demand time conflicts. The strategy operates based on a layered diagnostic architecture, which divides conflicts into two main levels: hard conflicts and soft conflicts, and handles them differently. Hard conflict detection focuses on situations of absolute resource insufficiency. This is achieved by establishing a system resource total list and a task resource demand ledger. The system resource total list dynamically records the physical upper limit capacity of various resources (such as computing units, memory blocks, and storage space), while the task resource demand ledger accumulates the resource requests submitted by all pending tasks at the same time. When the resource request volume in the task resource demand ledger at a certain time exceeds the physical upper limit capacity of the corresponding resource in the system resource total list, the hard conflict determination condition is met, and the system immediately marks it as a hard conflict event and triggers a high-level alarm. Soft conflict detection targets scenarios with temporary resource shortages or competitive conflicts. It employs a time window sliding comparison algorithm and a priority cross-validation mechanism. The time window sliding comparison algorithm converts the resource occupancy period of each task into a standard time window object, quantifying the conflict severity by calculating the overlapping area and duration of different task time window objects. The priority cross-validation mechanism uses task priority data from the task priority sequence. When the time windows of high-priority and low-priority tasks overlap, the system classifies the conflict into different levels of soft conflict based on the priority difference. The execution flow of the multi-level conflict detection strategy is driven by the conflict detection engine, which periodically scans task plans and resource status, applying the aforementioned rules to generate a diagnostic report containing conflict type, conflict task identifier, conflict time range, and conflict severity level.

[0039] In a specific implementation, the real-time policy switching function of the rescheduling engine is a key mechanism for dynamically optimizing task execution plans. Real-time policy switching is achieved through the collaborative work of the policy management module, the environment monitoring module, and the decision-making module. The policy management module maintains a pluggable policy library, which contains various pre-built task scheduling policy algorithms (such as the earliest deadline first algorithm, the minimum relaxation time first algorithm, and the resource balancing algorithm). Each policy algorithm is encapsulated as an independent policy component and equipped with a performance indicator descriptor. The environment monitoring module is responsible for tracking preset trigger conditions in real time. Trigger conditions include fluctuations in the overall system load rate, sudden changes in the availability of critical resources, emergency insertion events of high-priority tasks, or changes in the flag bits of specific business rules. The environment monitoring module quantifies these conditions into a series of measurable monitoring indicators. The decision-making module, acting as the switching hub, operates the core decision-making logic. It continuously receives monitoring metrics from the environment monitoring module and matches these metrics with the activation conditions of various strategy components in the strategy library. The matching process combines rule evaluation and weighted scoring. Rule evaluation is based on a predefined "condition-action" rule table (e.g., triggering a switch to a resource balancing algorithm when the system load rate consistently exceeds threshold A). Weighted scoring normalizes multiple monitoring metrics and calculates a comprehensive score to select the optimal strategy. Once the decision-making module determines a switch is necessary, the strategy management module performs a hot-swap operation. This includes pausing the scheduling decision thread of the current strategy component, loading the context data of the new strategy component, verifying the consistency between the new strategy and the current task state, and activating the new strategy component to take over scheduling work after successful verification. The entire switching process is logged in detail, including the switching timestamp, switching reason, old and new strategy identifiers, and system state snapshots before and after the switch. The strategy component design supports dynamic loading and unloading, facilitating the expansion of new strategies. The trigger threshold for real-time strategy switching can be configured through the management interface to adapt to different operating environments.

[0040] Example 3: In specific implementation, the research report processing method based on deep semantic understanding involves the generation process of processing deviation correction output. The acquisition of processing deviation correction output begins with extracting monitoring data from the report processing schedule. The monitoring data includes two types of information: changes in the processing environment and resource status. Changes in the processing environment cover dynamic factors such as network latency fluctuations, abnormal interruptions of system services, and timeouts in external data interface responses. Resource status includes real-time indicators such as computing node online rate, storage space utilization ratio, and remaining memory capacity. Combining task time node data, data analysis methods are used to determine the impact of environmental and resource status on the processing progress. The data analysis method adopts multivariate correlation analysis technology. Multivariate correlation analysis technology quantifies the degree of impact by establishing a correlation coefficient matrix between environmental indicators, resource indicators, and task progress delays, generating environmental and resource status analysis results. The environmental and resource status analysis results are presented in the form of an impact report, which clearly lists the key influencing factors, the direction of influence (positive / negative), and the influence weight coefficient.

[0041] Using the results of environmental and resource status analysis, the deviation between task time and environmental changes is calculated. The deviation calculation employs a dynamic time warping algorithm to align the planned timeline with the actual execution timeline and measures the absolute time difference at corresponding points. The labor consumption and material surplus for each resource are statistically analyzed. Labor consumption is obtained by accumulating the actual working time of each resource instance, and material surplus is obtained by querying the real-time database of the resource inventory management system. Quantitative analysis methods are used to identify resource allocations and time nodes that need adjustment. These methods employ a sensitivity analysis model, which determines adjustment priorities by calculating the partial derivatives of the impact of changes in resource allocation parameters and time nodes on the overall progress. A task-environment deviation table is created, using a two-dimensional relational table structure to record the deviation type, deviation value, associated task number, resource identifier, deviation generation timestamp, and suggested adjustment direction.

[0042] Based on the task-environment deviation table, resource allocation and time nodes are adjusted. The adjustment process employs an adaptive control algorithm, which dynamically calculates the resource redistribution ratio and time offset based on the values ​​in the deviation table. Matching is performed to actual processing environment changes, an environmental pattern recognition engine that performs similarity matching between real-time environmental data and a historical pattern library. Task allocation is optimized using a heuristic search algorithm to find near-optimal solutions in the solution space, generating a processing deviation correction output. This output includes a corrected resource allocation map, revised time node sequence, resource operation status adjustment parameters, and a material surplus balance scheme. In practical implementation, the correlation coefficient matrix of multivariate correlation analysis can incorporate a time decay factor to enhance the influence of recent data. The window size parameter of the dynamic time warping algorithm can be dynamically configured according to the task cycle to improve alignment accuracy.

[0043] In practical implementation, the generation process of environmental and resource status analysis results includes a data cleaning step. This step filters out abnormal sensor readings and incomplete monitoring records to ensure the reliability of the analysis data. The absolute time difference measurement in deviation calculation employs a high-precision time synchronization protocol to guarantee the consistency of time data across nodes. Work time consumption statistics are integrated with the work log analysis system to automatically identify valid working time periods. The sensitivity analysis model for quantitative analysis methods can be defined in the following calculation form: Let the resource allocation parameter vector be... ,in Indicates the first The allocation amount of class resources, The total number of resource types; the time node parameter vector is ,in Indicates the first The planned timeline for each task. The total number of tasks; the overall progress objective function is: , representing the progress assessment value under given resource allocation and time nodes; then the parameter sensitivity coefficient is expressed through partial derivatives. and The first partial derivative is calculated. Indicates the first The impact of changes in resource allocation on the overall schedule is calculated using the finite difference method:

[0044] in: This represents a small change in resource allocation, typically taken as the smallest unit of resource allocation allowed by the system. (The numerator part...) Indicates the first Increase in the allocation of class resources The progress assessment value when other parameters remain unchanged. This represents the baseline progress value under the current resource allocation. During the calculation process, the system maintains the time node parameter vector. Keep it fixed, only change the allocation of specific resources. Let's observe the progress changes. The second partial derivative. Indicates the first The impact of adjustments to individual task time points on the overall progress is calculated using a similar method:

[0045] in: This represents a tiny adjustment at a specific time point, typically expressed in units of time precision used in system scheduling. The numerator... Indicates the first The deadline for each task has been postponed. The progress assessment value when other time points remain unchanged. This represents the baseline progress value under the current schedule. The resource allocation parameter vector must be maintained during calculation. Keep it fixed, only adjust specific time points. A composite index is established in the two-dimensional relationship table of task and environment deviation, supporting fast multi-condition queries. The parameters of the adaptive control algorithm follow an incremental adjustment strategy to avoid large oscillations. The environmental pattern recognition engine uses a dynamic time-warped distance algorithm for similarity matching to identify the most similar historical environmental patterns. It is understood that the data cleaning step can be configured with automatic repair rules to handle common data anomalies, and the convergence condition of the heuristic search algorithm can be set with a maximum number of iterations to prevent infinite loops.

[0046] In practical implementation, monitoring of environmental changes is achieved through a distributed monitoring agent network. Monitoring agents are deployed on each processing node to collect environmental indicators at a fixed frequency. Resource status data is acquired periodically through the application programming interface (API) of the resource management platform to ensure real-time status. Multivariate correlation analysis technology employs a parallel computing framework to accelerate large-scale matrix operations. A fault-tolerance mechanism is introduced in the deviation calculation process; when some data is missing, interpolation is used to complete the time series. Work time consumption statistics distinguish between active working time and waiting time, improving statistical accuracy. The calculation results of the sensitivity analysis model are displayed through visualization components to help administrators understand the impact of parameters. The update mechanism of the task and environment deviation table supports real-time incremental updates, reducing system overhead. The adaptive control algorithm adopts a proportional-integral-derivative controller structure; the proportional term processes the current deviation, the integral term accumulates historical deviations, and the derivative term predicts the trend. The historical pattern library of the environmental pattern recognition engine supports online learning and updates, continuously optimizing pattern matching accuracy. Optionally, the data collection frequency of the monitoring agents can be dynamically adjusted according to system load, and the API calls of the resource management platform can implement a circuit breaker mechanism to prevent avalanche effects.

[0047] In practical implementation, the rule base for the data cleaning step includes various data anomaly patterns, such as rules for detecting data breakpoints, sudden jumps, and constant values. The time synchronization protocol for deviation calculation adopts either a network time protocol or a precision time protocol, selecting the appropriate scheme based on accuracy requirements. The work time consumption statistics system can integrate an identity authentication module to accurately link work time with operators. The visualization component of the sensitivity analysis model supports multi-dimensional data drill-down, facilitating in-depth analysis of parameter relationships. The composite index design of the task and environment deviation table considers the balance between query frequency and data update frequency. The parameter tuning of the adaptive control algorithm uses the Ziegler-Nichols method or a self-tuning algorithm to ensure control stability. The online learning of the environmental pattern recognition engine uses an incremental learning algorithm to progressively optimize the quality of the pattern library. The implementation of the heuristic search algorithm can choose between simulated annealing or a genetic algorithm, selecting an appropriate search strategy based on the problem scale. In some embodiments, the monitoring agent network can adopt a hierarchical management architecture to improve scalability, and the application programming interface of the resource management platform can be designed with a version control mechanism to ensure compatibility. It is understood that data anomaly detection rules can be dynamically updated according to changes in business needs, and the identity authentication module can support multi-factor authentication to enhance security.

[0048] In practical implementation, the environmental pattern recognition engine's matching process employs multi-feature weighted similarity calculation, assigning different weight coefficients to different environmental features. The solution space representation of the heuristic search algorithm uses an encoding scheme to map resource allocation and time nodes to points in the search space. The generation of deviation correction output includes a verification step, which checks the feasibility and consistency of the correction scheme to prevent new conflicts. In some embodiments, the deviation calculation can incorporate confidence interval estimation to assess the reliability of the measurement results. A smoothing filter can be added to the output of the adaptive control algorithm to eliminate jitter caused by random fluctuations. It is understood that the weights for multi-feature weighted similarity calculation can be trained using machine learning methods, and the design of the solution space encoding scheme affects search efficiency and result quality. Optionally, the verification step can use formal verification methods to prove the correctness of the correction scheme, and the confidence interval estimation can be implemented using bootstrapping or parameter estimation methods.

[0049] In a specific implementation, the Dynamic Time Warping (VTW) algorithm is the core algorithm for calculating the deviation between task time and environmental changes. The implementation process begins by converting the planned task timeline and the actual execution timeline into two time series data. The planned task timeline extracts a standardized sequence of time points from the report processing schedule, while the actual execution timeline is reconstructed from timestamp logs collected by a distributed monitoring system. The algorithm constructs a cumulative cost matrix to find the optimal alignment path between the two time series. Each element of this matrix represents a distance metric between a point in the planned time series and a point in the actual execution time series, typically calculated using the absolute time difference. The VTW fills this cumulative cost matrix using dynamic programming, which calculates from the top left corner to the bottom right corner. The cumulative cost of each cell is determined by the direct distance to that point plus the minimum cumulative cost among adjacent cells. The optimal alignment path is obtained by backtracking from the bottom right corner to the top left corner of the matrix to find the minimum cost path. The pairs of points on this path represent the most matching time point correspondence between the planned and actual execution timelines. The deviation between task time and environmental changes is obtained by calculating the weighted average of the distance metrics of all point pairs on the alignment path. The deviation is recorded in the task-environment deviation table for subsequent analysis. The window constraint parameters of the dynamic time warping algorithm are configurable to limit the maximum scaling range during time series alignment, avoiding unrealistic time correspondences.

[0050] In a specific implementation, the environmental pattern recognition engine is a key component for matching actual environmental changes. The engine operates based on a historical environmental pattern library and a real-time pattern matching mechanism. The historical environmental pattern library is built through long-term collection and classification of environmental change data. Each environmental pattern is represented as a feature vector, which includes quantitative indicators across multiple dimensions, such as average network latency, system service anomaly frequency, and external interface timeout rate. The real-time pattern matching mechanism also converts the currently monitored environmental data into feature vectors and calculates the similarity between this feature vector and the feature vectors of each pattern in the historical environmental pattern library. The similarity calculation uses an improved cosine similarity algorithm, which introduces feature weight coefficients based on traditional cosine similarity. Different environmental features are assigned different weight values ​​according to their business importance. When a historical environmental pattern with a similarity exceeding a preset threshold is found, the environmental pattern recognition engine considers the current environmental change to have successfully matched that historical pattern. The matching result includes a pattern identifier and a similarity score. The environmental pattern recognition engine's online learning capability enables continuous updates to the pattern library. This online learning is achieved by analyzing the differences between newly emerging environmental change data and existing patterns. When a new combination of environmental change features is discovered, the engine creates a new environmental pattern record and adds it to the historical environmental pattern library. Successfully matched environmental patterns trigger predefined response strategies, which are stored in a pattern-strategy mapping table, providing a basis for subsequent resource allocation and timeline adjustments. The matching efficiency of the environmental pattern recognition engine is optimized by establishing a pattern feature index, ensuring that similarity calculations can still be completed quickly even with a large-scale pattern library.

[0051] Example 4: In specific implementation, the research report processing method based on deep semantic understanding involves the generation process of processing deviation correction output. The acquisition of processing deviation correction output begins with extracting monitoring data from the report processing schedule. The monitoring data includes two types of information: changes in the processing environment and resource status. Changes in the processing environment cover dynamic factors such as network latency fluctuations, abnormal interruptions of system services, and timeouts in external data interface responses. Resource status includes real-time indicators such as computing node online rate, storage space utilization ratio, and remaining memory capacity. Combining task time node data, data analysis methods are used to determine the impact of environmental and resource status on the processing progress. The data analysis method adopts multivariate correlation analysis technology. Multivariate correlation analysis technology quantifies the degree of impact by establishing a correlation coefficient matrix between environmental indicators, resource indicators, and task progress delays, generating environmental and resource status analysis results. The environmental and resource status analysis results are presented in the form of an impact report, which clearly lists the key influencing factors, the direction of influence (positive / negative), and the influence weight coefficient.

[0052] Using the results of environmental and resource status analysis, the deviation between task time and environmental changes is calculated. The deviation calculation employs a dynamic time warping algorithm to align the planned timeline with the actual execution timeline and measures the absolute time difference at corresponding points. The labor consumption and material surplus for each resource are statistically analyzed. Labor consumption is obtained by accumulating the actual working time of each resource instance, and material surplus is obtained by querying the real-time database of the resource inventory management system. Quantitative analysis methods are used to identify resource allocations and time nodes that need adjustment. These methods employ a sensitivity analysis model, which determines adjustment priorities by calculating the partial derivatives of the impact of changes in resource allocation parameters and time nodes on the overall progress. A task-environment deviation table is created, using a two-dimensional relational table structure to record the deviation type, deviation value, associated task number, resource identifier, deviation generation timestamp, and suggested adjustment direction.

[0053] Based on the task-environment deviation table, resource allocation and time nodes are adjusted. The adjustment process employs an adaptive control algorithm, which dynamically calculates the resource redistribution ratio and time offset based on the values ​​in the deviation table. Matching is performed to actual processing environment changes, an environmental pattern recognition engine that performs similarity matching between real-time environmental data and a historical pattern library. Task allocation is optimized using a heuristic search algorithm to find near-optimal solutions in the solution space, generating a processing deviation correction output. This output includes a corrected resource allocation map, revised time node sequence, resource operation status adjustment parameters, and a material surplus balance scheme. In practical implementation, the correlation coefficient matrix of multivariate correlation analysis can incorporate a time decay factor to enhance the influence of recent data. The window size parameter of the dynamic time warping algorithm can be dynamically configured according to the task cycle to improve alignment accuracy.

[0054] In practical implementation, the generation process of environmental and resource status analysis results includes a data cleaning step. This step filters out abnormal sensor readings and incomplete monitoring records to ensure the reliability of the analyzed data. The absolute time difference measurement in deviation calculation employs a high-precision time synchronization protocol to guarantee consistency of time data across nodes. Work time consumption statistics are integrated with the work log analysis system to automatically identify effective working time periods. The sensitivity analysis model of the quantitative analysis method determines adjustment priorities by calculating the partial derivatives of the impact of resource allocation parameters and time node changes on the overall progress. A composite index is established in the two-dimensional relationship table of the task and environment deviation table to support fast multi-condition queries. The parameter adjustment of the adaptive control algorithm follows an incremental adjustment strategy to avoid large oscillations. The similarity matching of the environmental pattern recognition engine uses a dynamic time warping distance algorithm to identify the most similar historical environmental patterns. It can be understood that the data cleaning step can be configured with automatic repair rules to handle common data anomalies, and the convergence condition of the heuristic search algorithm can be set with a maximum number of iterations to prevent infinite loops. Refer to Table 1 for the specific structure of the task and environment deviation table.

[0055] Table 1: Structure Definition of Task-Environment Deviation Table

[0056] In practical implementation, monitoring of environmental changes is achieved through a distributed monitoring agent network. Monitoring agents are deployed on each processing node to collect environmental indicators at a fixed frequency. Resource status data is acquired periodically through the application programming interface (API) of the resource management platform to ensure real-time status. Multivariate correlation analysis technology employs a parallel computing framework to accelerate large-scale matrix operations. A fault-tolerance mechanism is introduced in the deviation calculation process; when some data is missing, interpolation is used to complete the time series. Work time consumption statistics distinguish between active working time and waiting time, improving statistical accuracy. The calculation results of the sensitivity analysis model are displayed through visualization components to help administrators understand the impact of parameters. The update mechanism of the task and environment deviation table supports real-time incremental updates, reducing system overhead. The adaptive control algorithm adopts a proportional-integral-derivative controller structure; the proportional term processes the current deviation, the integral term accumulates historical deviations, and the derivative term predicts the trend. The historical pattern library of the environmental pattern recognition engine supports online learning and updates, continuously optimizing pattern matching accuracy. Optionally, the data collection frequency of the monitoring agents can be dynamically adjusted according to system load, and the API calls of the resource management platform can implement a circuit breaker mechanism to prevent avalanche effects.

[0057] The resource operation status adjustment parameters included in the processing deviation correction output are generated by the resource health assessment module, which periodically checks the operation indicators of various resources and generates health scores. The processing time deviation correction value is calculated using a time compensation algorithm, which comprehensively considers historical deviation patterns and the current system load. The material surplus balance parameter is calculated using an inventory optimization model, which determines the safety stock level based on material consumption rate and procurement cycle. It can be understood that the resource operation status adjustment parameters can be further subdivided into subcategories such as CPU adjustment parameters, memory adjustment parameters, and storage adjustment parameters. The processing time deviation correction value includes both absolute and relative time correction values. The material surplus balance parameter is equipped with a dual protection mechanism of minimum safety threshold and maximum warning threshold.

[0058] In practical implementation, the rule base for the data cleaning step includes various data anomaly patterns, such as rules for detecting data breakpoints, sudden jumps, and constant values. The time synchronization protocol for deviation calculation adopts either a network time protocol or a precision time protocol, selecting the appropriate scheme based on accuracy requirements. The work time consumption statistics system can integrate an identity authentication module to accurately link work time with operators. The visualization component of the sensitivity analysis model supports multi-dimensional data drill-down, facilitating in-depth analysis of parameter relationships. The composite index design of the task and environment deviation table considers a balance between query frequency and data update frequency. The parameter tuning of the adaptive control algorithm uses the Ziegler-Nichols method or a self-tuning algorithm to ensure control stability. The online learning of the environmental pattern recognition engine uses an incremental learning algorithm to progressively optimize the quality of the pattern library. The implementation of the heuristic search algorithm can choose between simulated annealing or a genetic algorithm, selecting an appropriate search strategy based on the problem scale. In some embodiments, the monitoring agent network can adopt a hierarchical management architecture to improve scalability, and the application programming interface of the resource management platform can be designed with a version control mechanism to ensure compatibility.

[0059] In practical implementation, the environmental pattern recognition engine's matching process employs multi-feature weighted similarity calculation, assigning different weight coefficients to different environmental features. The solution space representation of the heuristic search algorithm uses an encoding scheme to map resource allocation and time nodes to points in the search space. The generation of deviation correction output includes a verification phase, which checks the feasibility and consistency of the correction scheme to prevent new conflicts. Optionally, confidence interval estimation can be introduced into the deviation calculation to assess the reliability of the measurement results. A smoothing filter can be added to the output of the adaptive control algorithm to eliminate jitter caused by random fluctuations. It is understood that the weights for multi-feature weighted similarity calculation can be obtained through machine learning training methods, and the design of the solution space encoding scheme affects search efficiency and result quality. The verification phase can use formal verification methods to prove the correctness of the correction scheme, and confidence interval estimation can be implemented using bootstrapping or parameter estimation methods.

[0060] In a specific implementation, the resource health assessment module is the core component for generating resource operation status adjustment parameters. The module operates based on multi-dimensional resource indicator collection and a comprehensive health calculation model. Multi-dimensional resource indicator collection is achieved through monitoring agents deployed on various resource nodes. These agents periodically collect performance indicators such as CPU utilization curves, memory page fault rates, storage device read / write throughput, and network interface packet loss rates. This raw indicator data is standardized and preprocessed before being converted into a unified unit of measurement. The comprehensive health calculation model employs a weighted scoring card method. This method sets a set of health assessment dimensions for each type of resource; for example, for computing resources, it sets load, stability, and capacity dimensions. Each dimension contains multiple specific indicators and assigns corresponding weight coefficients. The health score calculation normalizes each indicator, mapping it to a standard score of 0-100. The scores are then weighted and summed according to the dimension and indicator weights to obtain the comprehensive health score of the resource. The resource health assessment module also includes an anomaly detection algorithm. This algorithm establishes a historical baseline model of resource indicators, and triggers a health degradation mechanism when real-time indicators deviate from the baseline model by more than a preset threshold. The generated resource operation status adjustment parameters include a resource health level identifier, suggested adjustment operations (such as restart, migration, or expansion), and an estimated recovery time. These parameters are encapsulated as structured messages for subsequent processing. The assessment cycle of the resource health assessment module can be dynamically configured according to resource type, with more frequent assessments for critical resources. Assessment results are stored in a historical resource health database for trend analysis.

[0061] In a specific implementation, the time compensation algorithm is the core algorithm for calculating the time deviation correction value. The calculation process of the time compensation algorithm combines historical deviation pattern analysis and current system load assessment. Historical deviation pattern analysis is achieved by analyzing the time deviation records stored in the task and environment deviation table. This analysis uses a sliding window technique to extract recent deviation data and applies time series analysis to identify the periodic patterns and trends of deviations, establishing a deviation prediction model. Current system load assessment is achieved by real-time monitoring of system resource utilization. This assessment calculates indicators such as the overall system load rate, resource queue length, and task waiting time to quantify the current system processing capacity. The time compensation algorithm integrates the historical deviation prediction value with the current load influencing factors. This integration uses an adaptive weighting method, increasing the weight of the load factor when the system load is high and increasing the weight of historical deviation predictions when the system is stable. The time deviation correction value output includes both absolute and relative time correction values. The absolute time correction value directly specifies the new time node, while the relative time correction value provides an offset suggestion based on the original planned time. The time compensation algorithm also includes a correction effect feedback mechanism. This mechanism dynamically adjusts the weight parameters and model coefficients in the algorithm by comparing the actual execution effect after correction with the expected effect, gradually optimizing the compensation accuracy. The execution cycle of the time compensation algorithm is synchronized with the task scheduling cycle to ensure the timeliness and effectiveness of the correction value. During the algorithm's calculation process, a transaction mechanism is used to ensure data consistency and avoid calculation errors caused by concurrent access.

[0062] See Figure 4 This diagram represents the core visualization outcome of the deviation correction output stage in a deep semantic understanding-based investment research report processing method. It corresponds to the technical implementation of the resource health assessment module generating resource operation status adjustment parameters. Technically, the diagram's generation relies on the resource health assessment logic: multi-dimensional indicators of each resource node are collected through a distributed monitoring agent, standardized, and then input into a comprehensive health calculation model based on a weighted scoring card. The final output is a visualized health score for each node. These data are crucial inputs for deviation correction output. When an abnormal resource node health is detected, the system triggers resource operation status adjustments, ensuring resource stability in the investment research report processing workflow. The diagram's value lies in its intuitive quantification of the health status of all types of resource nodes, providing resource-level decision-making support for the deviation correction stage. It verifies the practicality of the resource health assessment-deviation correction-resource optimization closed-loop mechanism, helping the investment research report processing system to adjust promptly when resources are abnormal, ensuring processing quality and efficiency.

[0063] Example 5: In specific implementation, the investment research report processing method based on deep semantic understanding involves determining the resource allocation ratio and generating the task interval optimization order. The resource allocation ratio is determined by dynamically adjusting the resource allocation weights based on the complexity of semantic feature elements and the historical performance of processing resources, ensuring a balance between the resource allocation scheme and task requirements. The task interval optimization order is generated based on task priority and resource availability, using time series analysis technology to optimize the intervals between tasks to maximize resource utilization efficiency. These methods achieve refined management of the investment research report processing workflow through intelligent algorithms, improving overall processing efficiency and resource utilization.

[0064] In practice, the determination of resource allocation ratios is achieved through the collaborative work of a semantic feature complexity assessment module and a historical performance analysis module for processing resources. The semantic feature complexity assessment module uses natural language processing technology to parse the research report text, extracting key semantic features such as entity density, syntactic structure complexity, and logical relationship depth. For example, when processing a company annual report, the semantic feature complexity assessment module performs word segmentation and part-of-speech tagging, calculates the frequency and evenness of noun entity occurrences, analyzes the coefficient of variation of sentence length and the nesting level of clauses, and generates a complexity score by combining these indicators. The historical performance analysis module for processing resources retrieves historical task execution records from the resource management database, collecting performance indicators such as response time, error rate, and throughput of processing resources. For example, for computing resource nodes, the historical performance analysis module queries the average execution time and success rate of the node in processing similar reports over the past thirty days, generating a performance evaluation report. The process of dynamically adjusting resource allocation weights employs an adaptive weighting algorithm. This algorithm calculates resource allocation ratios based on real-time feedback of complexity scores and performance data. For example, when the semantic feature complexity assessment module outputs a high complexity score and the historical performance analysis module shows that a certain resource node's performance is stable, the adaptive weighting algorithm increases the allocation weight for that resource node; conversely, it decreases the weight. The balance between the resource allocation scheme and task requirements is ensured through a balance verification mechanism. This mechanism compares the differences between the resource allocation ratios and the actual task requirements and fine-tunes the weight parameters accordingly.

[0065] In some embodiments, the semantic feature complexity assessment module can integrate multiple assessment models, such as a deep learning-based complexity prediction model, to improve assessment accuracy. The resource historical performance analysis module can employ a sliding window technique to update historical data, ensuring the timeliness of performance assessment. The adaptive weighting algorithm for dynamically adjusting resource allocation weights can configure maximum and minimum weight thresholds to prevent extreme allocation ratios. It is understood that the balance verification mechanism can introduce fuzzy logic to handle uncertainties and enhance robustness.

[0066] In practical implementation, the task interval optimization order is generated based on task priority ranking and resource availability status. Task priority ranking is obtained from the task priority sequence, including the urgency and resource requirements of each task. Resource availability status is collected in real-time by a resource monitoring system, including resource idle time windows and load conditions. Time series analysis is used to analyze historical task execution data, employing an autoregressive integral moving average model to predict future resource demand fluctuations. For example, for a set of periodic report processing tasks, time series analysis analyzes the task execution time series over the past few months, identifying peak and trough patterns to predict high-load periods in the next cycle. Optimizing the intervals between tasks is achieved through an interval adjustment algorithm, which calculates the optimal interval length to minimize resource idle time. For example, when a task has high priority and resource availability is tight, the interval adjustment algorithm shortens the task interval to speed up processing; conversely, it lengthens the interval to balance the load. Maximizing resource utilization efficiency is achieved through objective function optimization, with the objective function setting maximizing resource utilization as the primary objective while considering task deadline constraints. In some embodiments, the acquisition of task priority ranking results can be combined with a manual intervention mechanism, allowing administrators to adjust the automatic ranking results. Monitoring of resource availability can be extended to multi-cloud environments, enabling cross-platform resource integration. Model parameters for time-series analysis can be trained using machine learning methods to adapt to data changes. Interval adjustment algorithms support dynamic rescheduling to handle sudden task insertions. It can be understood that objective function optimization can introduce multi-objective trade-off strategies to handle conflicting objectives.

[0067] Optionally, the evaluation cycle of the semantic feature complexity assessment module can be customized according to the report type, such as using more frequent assessments for high-frequency trading reports. The data storage for the resource historical performance analysis module can utilize a columnar database to improve query efficiency. The calculation of the adaptive weight algorithm can be performed in a distributed parallel manner to shorten response time. The update of task priority ranking results can be set with an event-triggered mechanism to reflect changes promptly. Data compression technology can be implemented for resource availability status collection to reduce network overhead. The prediction results of time series analysis technology can be visualized to assist decision-making. Optionally, the interval adjustment algorithm can be implemented using genetic algorithms or simulated annealing algorithms to find the global optimum. Objective function optimization can be combined with constraint programming techniques to handle complex constraints. The process of determining resource allocation ratios can be logged for easy traceability and analysis. The generation of the task interval optimization order can be integrated into a real-time simulation environment to test the optimization effect. Optionally, the semantic feature complexity assessment module can add a calibration function to periodically compare and correct with manual assessment results. The resource historical performance analysis module can support a data export interface for third-party analysis. The process of dynamically adjusting resource allocation weights can be configured with a smooth transition mechanism to avoid drastic changes. The generation of the task interval optimization sequence can take energy consumption factors into account, thus achieving green computing.

[0068] In practical implementation, the method for determining resource allocation ratios is applied in the actual investment research report processing scenario as follows: Suppose a macroeconomic analysis report is being processed. The semantic feature element complexity assessment module identifies that the report contains a large amount of chart data and complex econometric models, and outputs a high complexity score; the historical performance analysis module of the processing resources shows that server cluster A has a shorter average response time and a lower error rate when processing similar reports in the past week; the adaptive weighting algorithm increases the resource allocation weight of server cluster A accordingly, allocating more computing resources to the report processing task, while reducing the weight of poorly performing nodes; after the balance verification mechanism verifies the allocation ratio, a resource allocation scheme is generated to ensure efficient task execution.

[0069] In practical implementation, an example of how the task interval optimization order generation method is applied in actual investment research report processing scenarios is as follows: Assuming multiple company quarterly reports need to be processed, the task priority ranking results indicate that the financial data verification task is of high priority; resource availability status shows that database resources are under low load during the morning session; time series analysis technology predicts resource competition will occur in the afternoon session; the interval adjustment algorithm schedules high-priority tasks for execution in the morning with shorter intervals, while delaying low-priority tasks to the afternoon with increased intervals; after objective function optimization, the task interval optimization order is generated, maximizing database resource utilization during peak periods and avoiding bottlenecks. It can be understood that the method for determining resource allocation ratios and the method for generating the task interval optimization order can operate independently or collaboratively, flexibly configured according to actual needs. These methods reduce manual intervention through automated processing, improving the intelligence and adaptability of the investment research report processing system.

[0070] In a specific implementation, the adaptive weighting algorithm is the core computing engine for dynamically adjusting resource allocation ratios. The execution of the adaptive weighting algorithm begins with receiving real-time complexity scores from the semantic feature element complexity evaluation module and performance data from the resource historical performance analysis module. Internally, the algorithm maintains a weight matrix, where rows correspond to different resource types and columns correspond to different task complexity levels. Each matrix element stores the initial weight value for a specific resource type handling a specific complexity task. When new complexity scores and performance data arrive, the adaptive weighting algorithm initiates a weight update cycle. This cycle normalizes the input data, mapping the complexity score to the range of 0-1 and converting the performance data into a relative performance index. The algorithm calculates the weight adjustment amount, which is based on the product of the normalized complexity score and the performance index, multiplied by the learning rate parameter. The learning rate parameter controls the magnitude of the weight adjustment and can be dynamically configured according to system stability requirements. The algorithm applies the weight adjustment amount to update the corresponding elements in the weight matrix and performs a weight balance check. This check ensures that the sum of weights for the same resource type on tasks of different complexity levels remains constant, and that the weight distribution for different resource types at the same complexity level is reasonable. The algorithm outputs updated resource allocation ratios, which are provided to the resource scheduler in the form of a mapping table. The adaptive weight algorithm also includes an anomaly handling mechanism that monitors for abnormal fluctuations in the input data. When an outlier is detected, it automatically switches to a conservative adjustment mode to prevent drastic weight oscillations. Throughout the algorithm's operation, all weight adjustment records are written to the audit log for subsequent analysis and optimization.

[0071] In a specific implementation, the interval adjustment algorithm is the core decision-making mechanism for optimizing the time interval between tasks. The algorithm operates based on two main input sources: a task priority queue and a resource availability timeline. The algorithm constructs a time slot map, dividing a future period into discrete time slots of equal length. Each time slot is marked with its resource availability status and information about tasks already occupied. The algorithm scans the task priority queue, starting with the highest priority task. For each task to be scheduled, the interval adjustment algorithm calculates its feasible time window, which is determined by task dependencies, resource constraints, and the interval requirements between preceding and following tasks. Within the feasible time window, the algorithm searches for the optimal insertion position. The evaluation of the optimal insertion position uses a cost function, which comprehensively considers multiple factors such as resource utilization, task waiting time, and interval uniformity. The algorithm tentatively inserts the task into different time slots, evaluating the total cost of each insertion scheme. After determining the optimal insertion position, the algorithm updates the occupancy status of the time slot map and calculates the actual interval length between the task and its preceding and following tasks. The interval length is compared with preset minimum and maximum interval thresholds; if the requirements are not met, the insertion position is readjusted. After all task scheduling is complete, the interval adjustment algorithm outputs a task timeline arrangement. This timeline details the start and end times of each task, as well as the time intervals with adjacent tasks. The interval adjustment algorithm employs an incremental scheduling strategy. This strategy allows for local rescheduling only of the affected time periods when inserting new tasks into an existing scheduling scheme, reducing computational overhead. The algorithm also includes a conflict resolution mechanism. When resource contention prevents all interval requirements from being met, this mechanism arbitrates the interval adjustment based on task priority.

[0072] See Figure 5 This diagram is a core visualization of the task interval optimization sequence generation stage in an investment research report processing method based on deep semantic understanding. It represents a technological achievement that optimizes task intervals based on task priority and resource availability using time series analysis to maximize resource utilization efficiency. Technically, the diagram's generation relies on first analyzing the semantic features of the investment research report using deep semantic understanding to determine the task priority sequence; then, combining real-time data from a resource monitoring system, using time series analysis to predict resource supply and demand fluctuations; and finally, optimizing task intervals through an interval adjustment algorithm to maximize resource utilization. The value of this diagram lies in its quantitative and visual verification of the optimization effect. By compressing task intervals, the resource idle cost in the investment research report processing workflow is significantly reduced, and the overall throughput is greatly improved. This provides direct evidence for the practicality of the task interval optimization sequence generation method and offers a practical visual reference for the intelligent upgrade of investment research report processing systems in task scheduling and resource allocation.

[0073] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for processing investment research reports based on deep semantic understanding, characterized in that, The method comprises: The method comprises: According to the resource allocation scheme, the starting time and task interval of the key processing task are determined, the calling order of the processing resource is arranged, the task resource utilization efficiency and interval influence factor are evaluated, and a task priority sequence is generated; Based on the task priority sequence, the semantic constraint condition and resource supply cycle are analyzed, the resource allocation conflict and resource shortage task are identified, the task execution time interval is adjusted, and a constraint parameter set is generated; According to the constraint parameter set, the time node and task distribution difference of report processing are summarized, the resource utilization rate and resource available time are matched, the task time adjustment range is calculated, the task timeline and resource allocation are reorganized, and a report processing progress arrangement is generated; The environment change and resource state in the report processing process are monitored, the deviation between the task time and the environment change is measured, the resource working hour consumption and material remaining amount are counted, the resource allocation and time plan are adjusted, and a processing deviation correction output is generated. 2.The deep semantic understanding-based research report processing method according to claim 1, characterized in that, The resource allocation scheme acquisition step specifically comprises: collecting the semantic feature elements and processing resource information of the research report, comprehensively counting the resource demand and available amount in the multi-task area, comparing the matching degree of resource capacity and task demand, calculating the deviation of total resource demand and available resource, and forming a resource demand deviation set; analyzing the resource demand deviation set, adjusting the allocation proportion of resources and artificial according to the availability of artificial processing capacity, and establishing a resource and artificial allocation adjustment set; according to the resource and artificial allocation adjustment set, the resource and artificial allocation are compared to obtain the resource allocation scheme. 3.The deep semantic understanding-based research report processing method according to claim 2, characterized in that, The task priority sequence acquisition step specifically comprises: according to the resource allocation scheme, analyzing the resource configuration of each key processing task, calculating the task start time, and using an optimization algorithm to predict the optimal task sequence to generate a start time table of each key task; through the start time table of each key task, a priority sorting algorithm is used to arrange the priority of the processing resource, the resource allocation is adjusted combined with the task urgency, and a task priority list is established; using the task priority list, combined with the interval requirement in actual task execution, the task resource utilization rate and task interval weight of the key task are calculated to generate a task priority sequence. 4.The deep semantic understanding-based research report processing method according to claim 3, characterized in that, The constraint parameter set acquisition step specifically comprises: extracting the start time and resource cycle of each task from the task priority sequence, analyzing the resource usage, applying a resource matching algorithm to determine the resource demand of each task, and generating a resource demand analysis result; according to the resource demand analysis result, marking the time conflict of all resource supply and demand and the conflict and resource shortage in resource allocation, and creating a conflict and shortage index table; using the conflict and shortage index table, the execution interval of each task is recalculated, the task execution plan is optimized, and a constraint parameter set is generated. 5.The deep semantic understanding-based research report processing method according to claim 4, characterized in that, The report processing schedule obtaining step specifically comprises: extracting the time node of report processing and the difference in distribution between tasks from the constraint parameter set, analyzing the task execution order and resource utilization efficiency based on the comparison between task time and resource distribution, and generating a time node and task difference distribution table; using the time node and task difference distribution table, pairing resource utilization rate and resource available time, and based on the matching analysis of resource allocation and task requirements, marking time conflict and resource insufficient tasks, and creating resource conflict and insufficient task results; based on the resource conflict and insufficient task results, calculating the adjustment range of task execution and the time node correction value, optimizing the task time distribution, and generating the report processing schedule. 6.The deep semantic understanding-based research report processing method according to claim 5, characterized in that, The processing deviation correction output obtaining step specifically comprises: extracting monitoring data from the report processing schedule, including processing environment changes and resource status, combining task time nodes, using data analysis methods to determine the influence of environment and resource status on processing schedule, and generating environment and resource status analysis results; using the environment and resource status analysis results, calculating the deviation between task time and environment changes, and statistically analyzing the working hour consumption of each resource and the remaining amount of materials, and through quantitative analysis methods, marking the resource allocation and time node that need to be adjusted, and creating a task and environment deviation table; based on the task and environment deviation table, adjusting resource allocation and time node, matching actual processing environment changes, optimizing task allocation, and generating processing deviation correction output. 7.The deep semantic understanding-based research report processing method according to claim 6, characterized in that, The resource allocation scheme further comprises resource allocation proportion, manual allocation matching result, and resource and task demand difference analysis, the task priority sequence further comprises key task priority, task resource utilization efficiency, and task interval optimization order, the constraint parameter set further comprises resource allocation time parameter, resource insufficient task identification, and task execution interval correction value, the report processing schedule further comprises processing time node arrangement, resource allocation scheme, and task execution time adjustment, and the processing deviation correction output further comprises resource running state adjustment, processing time deviation correction, and material remaining amount balance parameter. 8.The deep semantic understanding-based research report processing method according to claim 7, characterized in that, The resource allocation proportion is determined by dynamically adjusting resource allocation weight according to the complexity of semantic feature elements and the historical performance of processing resources, to ensure the balance between resource allocation scheme and task demand. 9.The deep semantic understanding-based research report processing method according to claim 8, characterized in that, The task interval optimization order is generated by using time series analysis technology to optimize the interval between tasks based on task priority and resource availability, to maximize resource utilization efficiency. 10.A deep semantic understanding based research report processing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor, when executing the computer program, implements the steps of the report processing method based on deep semantic understanding according to any one of claims 1 to 9.

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