576 results about "Historical record" patented technology

The invention relates to the technical field of vacuum electric furnace monitoring, and discloses a state monitoring system suitable for a vacuum electric furnace. A multi-source sensor array of the system collects multi-dimensional physical signals such as temperature distribution, pressure change and vacuum degree fluctuation in a furnace in real time; a furnace cavity feature reconstruction module extracts sampling point feature parameters and correlates coordinates to construct a three-dimensional dynamic feature field; the process anomaly analysis module calculates a process deviation degree in combination with a preset reference parameter, and marks an anomaly coordinate area; the state transition evaluation module analyzes an abnormal trend according to historical records and predicts a state transition path and rate; the collaborative regulation and control decision-making module generates a multi-stage vacuum maintenance compensation strategy and a heating power regulation gradient scheme according to the multi-stage vacuum maintenance compensation strategy; and the running log feedback module records a strategy execution process, and associates the three-dimensional feature field data to generate a state tracing log. The system can realize comprehensive monitoring of the state of the vacuum electric furnace, accurate abnormity identification, trend prediction, cooperative regulation and control and state tracing, and helps to improve the operation management level of the vacuum electric furnace.

The invention discloses an electrical equipment defect identification and alarm method and system based on deep learning. The method comprises the following steps: synchronously collecting and registering visible light and infrared thermal imaging images on the surface of power equipment, and constructing an instance segmentation network comprising a lightweight feature extraction network, a multi-scale feature fusion network and a frequency domain mask prediction branch; enhancing the diversity of training samples by adopting a generative adversarial strategy; based on the graph neural network, analyzing the incidence relation between the defects and the equipment topology and historical records, and deducing the defect causal relation and the risk level; generating interpretable alarm information including the thermodynamic diagram, the natural language report and the maintenance suggestion; real-time detection and deep analysis are realized by adopting an end-side cloud collaborative architecture; and the system performance is continuously improved through a closed-loop optimization mechanism. According to the method, high-precision defect detection under multi-modal data fusion is realized, the robustness and interpretability are high, and the operation and maintenance intelligence level of power equipment is remarkably improved.

The invention discloses a virtual power plant intelligent regulation and control method and system based on artificial intelligence, and belongs to the technical field of electric power system intelligent regulation and control, and the virtual power plant intelligent regulation and control method based on artificial intelligence comprises the following steps: S1, aggregating equipment side data, desensitizing to generate topological codes, and constructing time scale matrix synchronization; s2, constructing a dynamic model by equipment parameters, and mapping real-time data to output a difference map; s3, adding equipment constraints, building a multi-objective function, optimizing a strategy and performing correlation analysis; s4, a wind and light fluctuation overrun trigger RL strategy and an abnormal switching base line generate a mixed instruction; s5, locally verifying the instruction, and correcting and feeding back parameters if the prediction is out of limit; s6, generating a three-dimensional thermodynamic diagram, and displaying an association report and a historical record by AR; s7, aggregating the data to reconstruct the training set, locally fine-tuning the strategy network and performing incremental updating; the method has the beneficial effects that the regulation and control pain point of the virtual power plant is systematically solved, the operation and maintenance cost is reduced, the new energy consumption capability is improved, and the equipment out-of-limit risk is reduced.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a task processing method, device and equipment based on multi-agent cooperation and a medium. And constructing a sub-task dependency graph, sequentially scheduling and executing the sub-tasks, and performing parameter completion to obtain the completed sub-tasks. And tracking the execution progress and the historical record of the completion subtask through a preset progress agent, and generating an executable operation decision by using a preset decision agent. If the task execution does not reach the expected effect, feeding back difference information and a correction suggestion, adjusting an operation decision and generating an updating operation; if the task achieves the expected effect, task completion information is sent to the progress agent, and the state is updated to be task completion. According to the method, the perception accuracy is improved, a task dependence tracking and feedback correction mechanism is provided, and a cross-application automatic task is successfully realized.

The invention provides a security event association analysis and question answering method and system and a medium, and the method comprises the following steps: context-aware query completion: receiving an original query input by a user, obtaining a historical record of a current dialogue, and forming a context enhanced query according to the original query and the historical record of the current dialogue; dynamic task identification: outputting a prediction task model according to the context enhancement query in combination with a dynamic task identification strategy; multi-dimensional retrieval: performing multi-dimensional retrieval on the prediction task model to obtain a final knowledge context packet; and knowledge-driven response generation: generating a knowledge-driven response based on the knowledge context packet, and outputting a final security analysis report after the generated content passes verification. According to the method, efficient, accurate and explainable intelligent association analysis of the multi-source heterogeneous security data is realized by constructing a multi-dimensional knowledge base, designing a dynamic task recognition mechanism and realizing context-aware query completion.

The invention discloses a digital twin autonomous optimization system based on building structure quality monitoring. According to the system, structure state data, environmental influence data, material performance data and historical record data are comprehensively collected through a multi-source data collection module; feature data such as a structure degradation rate and a material aging coefficient are extracted through an edge calculation preprocessing module; a digital twinborn body is constructed through a multi-layer fusion module, geometric layer fusion achieves crack grading labeling, physical layer fusion corrects parameters such as material elasticity modulus, and behavior layer fusion updates the structural damping ratio to reproduce dynamic behaviors. Outputting a structure health index, residual life prediction and maintenance suggestion through an autonomous optimization decision module; and in combination with a dynamic iteration updating module, the system is ensured to continuously adapt to the structure change through validity evaluation and parameter adjustment. The accuracy of building structure quality evaluation and the scientificity of maintenance decision making are improved, and the method is suitable for full-life-cycle management of various buildings.

The invention discloses an equipment diagnosis and maintenance method and system based on a large language model, a terminal and a storage medium. The method comprises the steps that multi-source heterogeneous data of target equipment is collected and preprocessed; constructing and continuously updating a historical case vector database containing equipment knowledge; state data of target equipment are monitored in real time, after fault diagnosis is triggered, the most relevant knowledge fragment is retrieved from the historical case vector database according to real-time abnormal data, and a diagnosis context is constructed; inputting the diagnosis context into a large language model for reasoning, and outputting a fault diagnosis report and a maintenance scheme; the fault diagnosis report and the maintenance scheme are presented to a user, and user feedback is collected to optimize the system. According to the method, real-time data, historical records and structured knowledge of equipment can be automatically integrated, a dynamic knowledge base is constructed, accurate and efficient diagnosis of equipment faults is realized by utilizing the powerful reasoning capability of a large language model, an executable maintenance scheme is automatically generated, and the maintenance efficiency is improved.

The invention relates to the field of intelligent operation and maintenance, and discloses a multi-source network data operation and maintenance system based on micro-service architecture and AI collaboration, comprising the steps of collecting multi-source data of a micro-service system, and performing cleaning, format unification and time alignment on the collected data; based on a data result of the data acquisition module, constructing a micro-service call chain and a dependency graph, embedding a real-time performance index and a log feature in each node, and dynamically updating a service relation graph; carrying out real-time anomaly detection on the multi-source data, and judging the alarm effectiveness in combination with a dynamic threshold and an AI alarm confidence self-learning mechanism; the AI conducts reasoning along the call chain anomaly map, causal relationship reasoning is added, and the anomaly propagation influence range is predicted; and feeding back a root cause positioning result and the optimized alarm information to an operation and maintenance system, optimizing an alarm threshold and decision parameters in combination with historical records, and outputting an updated operation and maintenance decision scheme. The method has the advantage of improving the operation stability of the system.

The application relates to the technical field of supply chain management, in particular to a supply chain management system based on big data, which comprises a data integration module, a demand prediction module, an inventory optimization module, a collaborative scheduling module and a feedback optimization module. The system realizes supply chain global state analysis and demand prediction through multi-source heterogeneous data collection and a deep learning model, combines dynamic inventory adjustment and cross-regional resource scheduling optimization, and improves the supply chain efficiency. The feedback optimization module further generates optimized control instructions by analyzing and intervening in the time delay of historical records. The application can improve the response speed and resource utilization efficiency of the supply chain and reduce the operation cost.

The invention relates to the technical field of operation and maintenance safety compliance, and discloses an operation and maintenance service automation safety compliance detection system and method. The system comprises an operation and maintenance event acquisition unit, a strategy execution unit, a compliance analysis unit, a strategy optimization unit and a violation tracing unit. The operation and maintenance event acquisition unit captures operation and maintenance operation events and execution environment parameters in real time, packages the operation and maintenance operation events and the execution environment parameters into event data packets and transmits the event data packets to the strategy execution unit; the strategy execution unit extracts a reference strategy rule from the security strategy library, performs matching verification on the event and generates a result; the compliance analysis unit analyzes compliance fluctuation characteristics through a multi-dimensional compliance deviation model in combination with historical records; the strategy optimization unit dynamically adjusts the event capture frequency and corrects a strategy matching rule according to the fluctuation characteristics; the violation tracing unit counts the risk cumulant and generates a violation tracing instruction when the risk cumulant exceeds a preset capacity. According to the system, automation and dynamic adjustment of operation and maintenance safety compliance detection are realized, and a risk accumulation process can be effectively tracked.

The invention discloses an acute pancreatitis complication assessment method based on artificial intelligence, and belongs to the technical field of medical information, and the method specifically comprises the steps: firstly, obtaining a clinical data set containing real-time clinical indexes and basic disease historical records; performing time sequence analysis on the historical records of the basic diseases, extracting long-term influence characteristics of the historical records of the basic diseases and generating basic disease influence factors; carrying out standardization processing on the real-time clinical indexes and the basic disease influence factors, and generating a comprehensive feature vector after calculating association weights among features; inputting the vector into a trained artificial intelligence evaluation model, and outputting a complication risk probability through layer-by-layer nonlinear transformation; and finally, mapping the risk probability value into a risk level, and integrating patient information to generate a structured risk assessment report. And an acute pancreatitis complication evaluation scheme integrating the acute stage index and the chronic basic disease influence is established.

The invention provides an intelligent infusion system and an intelligent liquid control method based on database dynamic feedback regulation and control, and relates to the technical field of medical equipment intelligent control. The system comprises an infusion terminal execution module, a multi-parameter monitoring module, a data transmission module, a central control server module and a dynamic database module; patient information, an infusion scheme, a threshold range and multi-dimensional data of historical records are stored through the dynamic database module, the central control server module calls the data and compares the data with real-time physiological parameters and infusion state parameters collected by the multi-parameter monitoring module for analysis, and the target infusion speed is calculated. Early warning is carried out according to the target infusion speed and the real-time monitoring parameters, accurate monitoring of the regulation and control parameters and the individual condition of the patient is ensured, the infusion speed is always in the optimal range adaptive to the physiological state of the patient, and the infusion adverse reaction caused by parameter errors and exceeding the physiological range is avoided.

The invention provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, and the method comprises the steps: constructing an accident chain knowledge graph and a physical trigger graph, and building a causal relationship and physical constraints among equipment, states, events and consequences; gathering and mapping historical records, expert rules and online observation data into map entities and relationships, forming a baseline accident scene and calculating a baseline index; generating candidate paths by utilizing graph reasoning and coupled multi-physics field simulation, and realizing a closed loop of graph reasoning and physical solution through consistency check; performing scoring and disturbance simulation analysis on the candidate paths, and screening robust target paths; and carrying out high-fidelity simulation on the target path, identifying key nodes in combination with sensitivity analysis and a minimum cut-off set, and generating disposal suggestions and action priorities. According to the method, high-credibility prediction of accident evolution and emergency response closed-loop linkage are realized, and the method has high precision, high robustness and engineering implementability.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

The invention relates to the technical field of forestry intelligent management and control, and discloses a forestry optimization management and control method and system based on artificial intelligence. The method comprises the following steps: collecting multi-source environment monitoring data such as soil humidity, illumination intensity and vegetation growth indexes in a forestry area, aligning a time sequence through a dynamic time warping algorithm, and separating steady-state and transient components; detecting anomalies through an isolated forest algorithm and generating a thermodynamic diagram, and obtaining a potential disease and pest outbreak area through causal reasoning in combination with historical records; constructing a dynamic growth reference curve, and identifying a disturbance sensitive area by using a generative adversarial network and a convolutional neural network; and integrating and generating a management and control priority partition map, optimizing resource allocation through deep reinforcement learning, outputting a task sequence, and driving equipment to execute irrigation, fertilization or pest control operation. According to the method, multiple artificial intelligence algorithms are fused, accurate control over the whole forestry process is achieved, and the method adapts to the dynamically-changing forestry environment.

The invention relates to the field of resource adaptive scheduling, and discloses an intelligent door lock control method and system based on deep learning, and the method comprises the steps: obtaining the system load, execution time, resource occupancy rate and other data, and obtaining the overall performance state description; according to the state description, a dynamic conflict quantized value is calculated, execution time deviation is evaluated, and a reasoning stage list needing to be adjusted is determined; extracting fluctuation trend characteristics based on historical records, and optimizing a resource allocation proportion to obtain an adjusted execution strategy scheme; judging whether the actual accuracy rate reaches the preset accuracy rate or not by applying the strategy scheme; if yes, the cycle interval duration of the self-adaptive control mechanism is determined; and scheduling the next monitoring process through the cycle interval duration, repeating the dynamic conflict evaluation, and dynamically regulating and controlling the execution strategy scheme. According to the method, real-time and self-adaptive allocation of computing resources of the door lock control system can be realized, dynamic conflicts are effectively solved, and millisecond response speed and recognition accuracy under high load are guaranteed.

The invention provides a medical data query optimization method and system based on semantic understanding, and relates to the technical field of medical data query, and the method comprises the steps: firstly obtaining a user medical data query request, a historical query record set and a current medical scene description; performing semantic intention scene deconstruction and historical association mapping on the query request to generate a deconstruction result; constructing a semantic path dynamic adaptation model based on the deconstruction result, and generating a model parameter set; calling the initial query path, performing path reconstruction according to the model output adaptation relation, and generating an optimized query path; and executing query operation to obtain an initial result, collecting user feedback for semantic calibration, generating an optimization result, feeding back the optimization result to the user terminal, and associating related information to a historical record set. According to the invention, the accuracy and efficiency of medical data query are improved.

The invention relates to a manufacturing system risk control knowledge matching method based on semantic embedding and clustering analysis, and the method comprises the following steps: collecting and preprocessing risk control text data: collecting unstructured text data of a manufacturing system history record, and obtaining preprocessed risk control text data, constructing a professional corpus for a discrete manufacturing scene; text semantic embedding generation; semantic clustering modeling: performing unsupervised clustering modeling on all semantic vectors, mining semantic association and potential structures between texts, obtaining semantic representations of risk control knowledge through a clustering algorithm, and assisting in generating clustering tags; and a risk knowledge matching mechanism.

The invention provides a laser cutting production line task automatic allocation control method based on multi-device state coupling analysis, and belongs to the technical field of intelligent manufacturing and industrial automation. The method comprises the following steps: constructing a dynamic closed-loop control process through a central control scheduling system: receiving a task information packet of an MES; constructing an equipment state vector based on a real-time station state, and introducing a dynamic weight factor set to generate a weighted state vector; performing task triggering judgment through a coupling triggering judgment function in combination with the task dependency graph and historical task records; when the conditions are met, a task instruction is issued to the target station; and feeding back the state and updating the historical record after the task is completed. The invention further relates to AGV intelligent scheduling, visual positioning compensation, process parameter dynamic adjustment, predictive conflict detection, weight self-optimization and the like. According to the method, the production line cooperation efficiency is remarkably improved, manual intervention and system delay are reduced, and the method is suitable for an intelligent laser processing scene in which multiple devices run in parallel.

The invention relates to the technical field of artificial intelligence, in particular to a multi-modal large model-based bidding and cross-bidding early warning and blocking method and system, and the method comprises the following steps: receiving and preprocessing various qualification files, including business licenses, industry licenses, auditing reports and bidding files, submitted by suppliers; applying a document layout analysis model based on joint training of a visual coding network and a text alignment network; the method has the beneficial effects that a scanned certificate, an audit report and a bidding file are automatically analyzed into structured semantic fragments; performing same semantic space alignment on the fragments, supplier basic information, historical records and real-time captured data of an authoritative authentication website; any contradiction or gap among different sources of key information such as registration capital, permission items, financial indexes and the like can be quickly found, and interpretable risk scores and processing suggestions can be given; on the premise of ensuring the private security of the data, the qualification auditing efficiency and accuracy are improved, and the workload of manual checking is reduced.

The invention provides a robot hand-eye online calibration method based on a self-supervised consistency optimization algorithm, and the method comprises the steps: employing the posture change of a mechanical arm collected in a production task, enabling a camera to observe the posture of a workpiece and an actual error after grabbing / assembling, and building an initial hand-eye relation through combination with one-time offline calibration; the eye pose adjustment amount of the player is calculated in a segmented mode under the incremental geometric framework; the self-supervised network outputs observation reliability and an offset type from time sequence features, carries out screening and reconstruction to generate a long-term hand-eye pose adjustment amount and a single-task pose compensation amount which are respectively used for updating a hand-eye relationship and correcting a control instruction, and carries out self-supervised updating on the network based on historical records; according to the method, separation treatment of slow deviation and short-time disturbance of the structure is achieved, manual re-calibration is reduced, the positioning precision and stability of visual guidance grabbing / assembling are improved, and the method is suitable for industrial robots running on line.

The invention discloses a cross-number user portrait association and intelligent dialogue optimization method based on voiceprint recognition, and the method comprises the steps: receiving and processing an incoming call voice stream of a user, carrying out the detection of an adversarial sample after the voice preprocessing, and extracting a voiceprint vector from compliant voice through an ECAPA-TDNN model; the user is retrieved and matched through FAISS / Milvus to obtain the user, the portrait, the work order and the historical record of the user are inquired, and intelligent customer service personalized service guidance is achieved. Wherein noise reduction adopts an RNNoise model of a GRU and a dynamic threshold strategy for user matching, a user portrait is based on a hierarchical label system, a block chain and a private cloud are combined for storage, and the cross-number user identification efficiency and the service personalization level are improved through micro-service architecture deployment.

The invention discloses a data real-time analysis and fault early warning method and system based on a digital electric port box transformer substation, and belongs to the technical field of power equipment monitoring and control systems. The method comprises the following steps: acquiring environmental parameters of each component of the box transformer substation in real time through a monitoring network, and performing data smooth denoising to obtain a parameter sequence; constructing a dynamic storage framework based on the sequence in combination with space-time attributes; extracting historical records of the target component to perform multi-dimensional sorting and change rate calculation, and identifying potential anomalies; when the change rate exceeds the limit and the distribution boundary breaks through, generating an alarm sequence and determining a preliminary abnormal mark; comparing the mark with the historical record, fusing the influence weight analysis verification accuracy, and obtaining a final early warning grading result; and parameter summary information is pushed to a control system according to a grading result, response feedback is obtained, and closed-loop control of the operation state is formed. According to the invention, the problem of control lag caused by data noise and delay is solved, and the timeliness, reliability and automation level of box transformer substation control are improved.

The invention provides a resource scheduling collaborative elevator rescue path optimization method and system, and relates to the field of elevator rescue, and the method comprises the steps: obtaining rescue knowledge documents and historical records, and constructing a structured knowledge base and a fault rule base; the fault information is received for matching analysis, a fault development path diagram is constructed, and early warning information is generated; and determining the optimal rescue opportunity based on the fault evolution rate, and generating a dynamic rescue scheme in combination with the aging coefficient. Accurate prediction and efficient rescue of elevator faults are achieved, and the rescue success rate and the resource utilization efficiency are improved.

The invention discloses a geological settlement monitoring method and system fusing deep learning and multi-source data, and relates to the technical field of geological settlement, and the method comprises the steps: collecting multi-source settlement observation and driving data, generating a settlement risk area mask based on historical records, and carrying out the self-adaptive grid division; a multi-source settlement field is generated through Kriging interpolation, and a driving factor grid field is mapped; performing spatial pyramid and time multi-scale decomposition on the settlement field and the driving factor to obtain a time-space sub-band; performing Bayesian fusion based on the sub-band confidence weight to obtain a fusion settlement field; inputting the fusion field and the driving factor into a deep learning model to train a multi-scale prediction sub-model, and reconstructing a global continuous settlement prediction field through cross-scale consistency; and dynamically optimizing the risk mask and the grid according to a prediction result to realize iterative monitoring. The problems of difficulty in multi-source data fusion, spatial scale heterogeneity and difficulty in accurate prediction of local high-risk area settlement in geological settlement monitoring are solved.

A method for authorizing a treatment may include receiving a treatment authorization request, creating an extracted text of a historical record using optical character recognition on the historical record, determining whether to analyze authorization performance of the treatment using a machine learning authorization process, in response to a determination to analyze authorization performance of the treatment using a machine learning authorization process: identifying authorization criteria for the treatment based on treatment authorization guidelines; identifying a natural language record processing model corresponding to expense authorization guidelines, and performing natural language processing on the extracted text of the record in accordance with the identified natural language record processing model to identify relevant record data in the record, determining whether the relevant record data meets the authorization criteria and in response to a determination that the relevant record data meets the authorization criteria, authorizing the performance of the treatment.

The invention provides an accurate control method and system for multi-axis linkage of welding equipment, and the method comprises the steps: S1, obtaining position feedback signals from each axis through a sensor, integrating the position feedback signals into a unified data set, and obtaining a real-time position information set; s2, applying a unified clock reference to each signal in the real-time position information set to obtain a feedback data set after time synchronization; s5, extracting coordinate instruction parameters of each axis from the optimized transport stream, distributing the coordinate instruction parameters to a controller, and determining unified motion track planning; s6, adjusting the speed and position of each axis in real time according to the unified motion track planning, and obtaining coordinated motion output; and S8, extracting efficiency index data from the final welding control sequence, comparing the efficiency index data with historical records, and determining system optimization and adjustment parameters. The system performance can be dynamically optimized, and the precision, stability and production efficiency of the welding process are remarkably improved.

The invention discloses an intelligent voice outbound system capable of realizing automatic adjustment, and relates to the technical field of intelligent voice interaction, the system comprises a voice acquisition and preprocessing module, a voiceprint analysis and emotion recognition module, a dynamic verbal skill generation module, a verbal skill real-time optimization module and a voice synthesis and outbound module; the communication efficiency and personalized service are improved through a'generation-optimization-feedback 'closed-loop design, the system generates scene-fitting verbal skills according to customer emotions, historical records and demands, adjusts strategies according to instant feedback of customers, reduces invalid conversations, reduces customer conflicts, shortens the communication period, and meanwhile, improves the user experience. Voiceprint features are decomposed into four quantifiable dimensions, emotion is accurately recognized in combination with a combination rule, limitation of a single index is avoided, the dynamic verbal skill generation module is based on a generative adversarial network, high-quality verbal skill can be learned, the dynamic verbal skill generation module adapts to multiple scenes, customization cost is reduced, and intelligent outbound large-scale application is promoted.

The invention relates to the technical field of pipeline deformation monitoring, and discloses a buried steel pipe deformation degree detection method and system based on multi-sensor fusion. The method comprises the steps that data of an accelerometer and a magnetometer are collected and processed to obtain an initial space orientation angle estimated value, the initial space orientation angle estimated value is compared with a preset steel pipe axis model, and if deviation exceeds a threshold value, dynamic deviation compensation is conducted through gyroscope data to obtain a real-time posture correction vector; extracting a multi-point detection sequence fitting deformation curve to obtain a continuity parameter set, and screening a low-consistency paragraph fusion neural network to obtain a multi-point deformation data set; and determining a final deformation evaluation value through underground interference simulation, noise modeling and particle adjustment, matching the final deformation evaluation value with historical records, and generating an alarm and risk early warning report when the matching degree is low. The method can improve the detection precision and data consistency, and guarantees the safe operation of the buried steel pipe.

The invention provides a project contract construction period management method and system based on causal relationship modeling, and the method comprises the steps: firstly obtaining a construction period element set which comprises a contract agreed process execution sequence, a resource configuration scheme and historical similar project construction period influence records; the method comprises the steps of obtaining a causal dependency relationship chain among process nodes, generating a dynamic construction period intervention scheme based on the causal dependency relationship chain and process execution state data collected in real time, updating a reference construction period plan of a project contract according to the dynamic construction period intervention scheme, and obtaining an adjusted construction period plan. And comparing and verifying the adjusted construction period plan with the contract construction period constraint condition, and generating a construction period management report, thereby improving scientificity, accuracy and flexibility of project contract construction period management, and reducing construction period delay risk.