977 results about "Benchmark data" patented technology

The invention discloses an intelligent financial risk early warning method and system based on a management decision, and relates to the technical field of data intelligence, and the method comprises a multi-source heterogeneous data collection module which obtains enterprise financial data, supply chain data, market public opinion data and industry reference data in real time through an API interface, risk keywords are extracted from news, social media and policy documents through a natural language processing technology according to the market public opinion data; the streaming data processing engine is constructed based on an Apache Flink framework, performs windowing processing on the real-time data stream, calculates the dynamic fluctuation ratio of financial indexes by sliding a time window, and compares the dynamic fluctuation ratio with a preset industry risk threshold value; and a risk decision fusion model, a dynamic threshold adaptive module and a man-machine collaborative early warning terminal. According to the method, millisecond-level financial index fluctuation monitoring is realized through a streaming computing framework, a knowledge graph and a natural language processing technology are fused, a risk entity and a causal chain are extracted from unstructured data, and a multi-dimensional risk portrait is constructed.

The invention relates to a cable online operation fault positioning, monitoring and early warning method and system based on a neural network algorithm, and the method comprises the steps: collecting the operation environment and state data of a cable through a multi-source sensor, and generating an original monitoring data set through noise reduction and time sequence alignment; aiming at the problem of periodic distortion caused by seasonal fluctuation of environment temperature and humidity, performing dynamic reference compensation to generate a reference data set without seasonal influence; aiming at a coupling effect of a dynamic load and temperature drift, calculating insulation performance evaluation data of temperature compensation through load-insulation correlation mapping; aiming at the problem of disconnection of waterproof sealing monitoring and fault positioning, a sealing degradation grade is generated based on regional high-humidity detection; aiming at the defect of a fixed threshold strategy, generating a fault coordinate and a risk probability in combination with spatial positioning and probability analysis; and finally, self-adaptive updating of the parameter library is realized through credibility verification.

The invention provides a self-adaptive adjustment Internet operation and maintenance strategy generation method and system, and relates to the technical field of Internet, and the method comprises the steps: 1, dynamically collecting the operation state data of a target operation and maintenance environment through a distributed sensor and an edge node, and generating a multi-dimensional time series data set; 2, performing space-time correlation analysis on the multi-dimensional time sequence data set, determining a reference data node, constructing a two-dimensional correlation structure, and generating a dynamic judgment interval; and step 3, respectively selecting monitoring sample sets in the inner domain and the outer domain of the dynamic judgment interval, generating a trajectory feature sequence according to the time evolution relationship of the sample sets, and calculating a dynamic correction coefficient based on the trajectory feature sequence. According to the method, the self-adaptive circulation control is formed by dynamically adjusting the threshold baseline, the judgment interval and the strategy generation rule, the accuracy and effectiveness of the internet operation and maintenance strategy are improved, and the stability of internet operation is enhanced.

The invention relates to the technical field of agricultural product storage management, in particular to an agricultural product storage intelligent management system based on big data, which comprises a multi-source data acquisition layer, an edge processing layer, a cloud processing layer and an execution control layer, compared with the prior art which only depends on manual recording of the picking date or simple visual sampling inspection for judging the freshness, the method has the advantages that the freshness key indexes such as the skin color degree and the texture structure of the agricultural product are synchronously and quantitatively extracted through the deep learning model in the warehousing link of the agricultural product; performing dual cross validation with the registration time to eliminate abnormal samples; the verified freshness reference data and parameters such as temperature, humidity, gas concentration and the like monitored in real time in a corresponding storage microenvironment are fused and input, and a self-adaptive shelf life prediction model based on a dynamic environmental stress factor is constructed; therefore, continuous and accurate quantification and early warning of decay processes of different types of agricultural products under various storage conditions are realized.

The invention provides a distributed energy access-oriented intelligent electric meter multi-device collaborative optimization method and device, equipment and a medium, and the method comprises the steps: carrying out the interpolation of a unified electric meter, an inverter and energy storage data granularity, building a joint time error model, obtaining calibration reference data, and completing the multi-device time synchronization calibration; based on the reference data, acquiring operation data of three devices, and using a joint energy consumption model to distinguish electric meter hardware faults and multi-device collaborative anomalies to complete fault diagnosis; calculating local line loss by combining an inverter-to-ammeter line and electric quantity data, verifying residual electricity metering deviation, and tracing and correcting settlement data to complete line loss-bidirectional metering linkage verification; associating the fault result with the corresponding relation between the long-term statistical fault type of the metering data and the deviation rate and the line loss to establish a model to predict deviation trend early warning; and determining a multi-device cooperative abnormal source and transformer area load interference relation, calculating an interference coefficient, feeding back a scheduling system, and marking a line reconstruction priority. By adopting the method, the distributed energy consumption efficiency and the operation reliability of the power distribution network are improved.

The invention relates to the technical field of food processing, in particular to a system for predicting flavor formation and optimization decision in food processing based on machine learning, which comprises a sensing unit, a multi-source data acquisition and fusion module, a dynamic modeling module, an optimization decision module and an execution module which are in signal connection with one another, and the optimization decision module is used for receiving the flavor perception probability distribution data and the updated scoring reference data, solving a Pareto optimal solution set through a multi-target particle swarm optimization algorithm in combination with equipment physical constraint conditions, generating a candidate processing parameter scheme, inverting equipment control parameters for the candidate processing parameter scheme through a physical constraint neural network, and obtaining the flavor perception probability distribution data and the updated scoring reference data. And a final machining parameter adjusting instruction is generated and transmitted to the execution module. According to the method, through dynamic threshold modeling, a semantic-chemical attention mechanism and a time-space preference map dynamic correction technology, multi-source data and a multi-target optimization algorithm are fused, so that closed-loop accurate regulation and control of processing parameters are realized, and the flavor quality is improved.

The invention discloses a metal grinding machine tool grinding wheel wear compensation type intelligent control system, and relates to the technical field of mechanical manufacturing automation, and the system comprises a contact type reference detection module which is used for obtaining the initial contour and reference wear loss of a grinding wheel through discrete point measurement in a non-machining state; the multi-parameter non-contact real-time detection module is used for collecting acoustic emission, vibration and spindle current signals in the grinding process through three types of sensors; the grinding environment sensing module collects the temperature of a grinding area and the cutting fluid concentration; the intelligent control unit receives reference data and an environment signal, performs deep learning on a wear loss prediction model after multi-source data fusion, performs periodic reference calibration, analyzes the dynamic wear loss, and synchronously transmits the data to the data storage and tracing module; the executing mechanism adjusts grinding wheel feed amount compensation according to the dynamic abrasion loss; and the data storage and tracing module stores full-process data in real time. According to the system, high-precision and real-time wear compensation is achieved, the machining precision and consistency are improved, and high-end manufacturing requirements are met.

The invention provides an open vocabulary industrial defect detection method based on multi-modal prior prompt, which comprises the following steps: S1, collecting and sorting defect images of industrial products to be detected, and constructing a large industrial reference data set; s2, building a visual priori prompt pool, and injecting refined and fine-tuned priori into multi-scale industrial features; s3, designing a significance Gaussian distribution modeling mechanism, and capturing a complex spatial mode of position prior, so that fine-grained prior has better expression ability, and the generalization of prior is improved; s4, establishing a decoupling LoRA text encoder, and extracting an industrial semantic basis in the industrial text template through a hierarchical prompt template and a hierarchical decoupling LoRA mechanism; and S5, constructing a visual text fusion unit, and performing multi-modal fusion on text prompt embedding and visual features. According to the method, high-precision and strong-generalization defect detection is realized by fusing visual and text prior prompts, and the robustness and adaptability in a complex open environment are remarkably improved.

The embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.

The invention discloses a PCB laser drilling deviation detection method and system, and the method comprises the following steps: obtaining original image data corresponding to a PCB laser drilling region, standard hole site coordinate data in a drilling design file, and real-time operation parameter data corresponding to laser drilling equipment; performing preprocessing to generate preprocessed image data, a standard coordinate data set and an equipment operation reference data set; extracting edge contour feature data of a drilling area based on the preprocessed image data, and generating an initial deviation vector of an actual drilling contour and a design contour in combination with the standard coordinate data set; constructing a drilling deviation influence factor matrix based on the equipment operation reference data set in combination with the initial deviation vector, and generating a weight coefficient of each influence factor for drilling deviation; and correcting the initial deviation vector based on the weight coefficient, judging whether deviation exists in the drill hole or not, and generating a drill hole deviation detection result. According to the invention, the drilling deviation detection precision of the PCB can be improved.

The invention discloses a traffic video question and answer method based on inter-frame difference, and the method comprises the steps: innovatively constructing an inter-frame difference extraction module on the basis of an image pre-training model based on the local abrupt change characteristic of a traffic video: effectively capturing the abrupt change characteristic of a moving target through the pixel-level difference calculation of adjacent frames; and designing a gating feature fusion mechanism, dynamically aligning spatial distribution of differential features and visual features on the basis of fusing problem features, and realizing collaborative characterization of static scenes and dynamic changes. In order to enhance cross-modal feature alignment, a hierarchical comparison learning strategy is provided: differential-visual feature comparison is implemented at a bottom layer to enhance local dynamic perception, and text-visual comparison is performed at a high layer to establish global semantic association. Experimental results show that the classification performance is improved on the basis of the SUTD-TrafficQA reference data set.

The invention discloses a machine vision-based numerical control machine tool cutter wear identification method and system, and relates to the technical field of machine vision. A numerical control machine tool cutter two-dimensional image sequence is collected, high-precision point clouds are obtained through three-dimensional reconstruction and calibration, and high-precision cutter reference three-dimensional point clouds are obtained; constructing a functional region division rule, segmenting the high-precision tool reference three-dimensional point cloud to obtain a tool functional region, and generating tool region structured reference data through geometric shape quantitative description algorithm analysis; collecting regional structured data of the cutter in different machining periods, and obtaining a shape evolution track of the cutter through space-time registration; constructing a tool wear recognition model based on the time sequence convolutional network, inputting a tool morphology evolution trajectory, and outputting a tool wear type; and collecting tool historical case data to construct a wear mechanism knowledge base, performing matching mapping on a tool wear type and the knowledge base, and generating a tool wear identification result. And based on the wear identification result, realizing the identification of the wear of the numerical control machine tool cutter.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

The invention discloses a knowledge graph completion method based on combined prediction of large and small models, which comprises the following steps: 1, constructing and preprocessing a knowledge graph completion reference data set, and training by adopting a RotatE model to obtain candidate entities generated by the model and confidence scores; 2, constructing a related triad, an adjacent triad and entity long text description based on the query to form a context prompt, inputting the context prompt and the query into a large language model, and performing semantic reordering and scoring by the large language model; and 3, constructing a fine tuning data set, and performing fine tuning on the large language model to obtain the KGC task optimization-oriented large language model. And based on the KGC score, the LLM score and the dynamic weight, outputting a complementation result through joint prediction of a fusion result. According to the method, the structured reasoning ability of the small model and the deep semantic understanding of the large language are effectively combined, the prediction accuracy, robustness and specialty are remarkably improved, and the defects that a single model is weak in generalization ability and insufficient in semantic utilization are overcome.

The invention relates to the technical field of large-size part three-dimensional measurement, and discloses a large-size part three-dimensional size measurement method and device based on machine vision, and the method comprises the steps: obtaining a material reflection characteristic reference data set and original point cloud data; according to the reference data set, laser power and gain are adjusted in a self-adaptive mode, and optimized scanning parameters are obtained; and performing denoising, feature extraction, region segmentation and plane fitting according to the optimized parameters and the original point cloud to obtain a high-precision geometric dimensioning result. According to the invention, high-precision, high-stability and high-efficiency three-dimensional size measurement can be realized on a large-size part made of a complex material and having a curved surface, and the three-dimensional size measurement method is obviously superior to a traditional scheme.

The invention relates to the technical field of enterprise management optimization, in particular to an enterprise management optimization method and system based on big data matching, and the method comprises the following steps: S1, collecting enterprise operation data, industry reference data and supply chain dynamic data, and outputting standardized enterprise data; s2, matching the standardized enterprise data with a preset industry feature library to generate a multi-dimensional service label set; s3, constructing a dynamic priority evaluation model, and generating a priority ranking matrix; s4, establishing a resource characteristic spectrum database, and generating a strategy matching degree matrix; s5, generating a resource configuration scheme according to the strategy matching degree matrix; and S6, when the monitoring threshold changes, triggering a weight calibration mechanism. According to the invention, through big data matching and dynamic priority evaluation, enterprise resource configuration decisions are optimized, operation efficiency and market adaptability are improved, and risks and cost are reduced at the same time.

The invention relates to the technical field of large model algorithms, in particular to a large model dynamic compression optimization method and system based on sparse pruning, and the method comprises the steps: capturing original weight fluctuation data generated by resource fluctuation in reasoning, and obtaining sparse weight reference data through sparse processing; analyzing calculation complexity through model reasoning delay data, and separating reasoning delay amount caused by a model scale; dynamically controlling the model compression ratio within a preset performance range based on the delay amount and the sparse reference data, and collecting reasoning precision distribution data under different compression parameters; evaluating a model performance state under each parameter by means of a neural network simulation method, and generating a performance state simulation result; determining a model quality optimization compensation parameter based on a simulation result by combining resource fluctuation data acquired in real time in a compression process; and finally, the compression strategy is adaptively regulated and controlled through the compensation parameters, and collaborative optimization of model calculation complexity, reasoning precision and delay during dynamic change of hardware resources is realized.

A method, a system, and a computer program product for generating a benchmarking dataset. One or more queries for generation of one or more summaries of one or more electronic documents are received. The queries are modified using one or more parameters associated with the electronic documents to generate modified queries. The electronic documents are sent to a generative artificial intelligence (AI) model. The generative AI model generates summaries of the electronic documents based on at least one of: the initial queries and the modified queries. One or more labels for the electronic documents are generated using the summaries.

The invention discloses an aerospace equipment data-real fusion performance test method and device based on reinforcement learning, and the device comprises a digital twin model building module which is used for building an aerospace equipment high-fidelity mechanism model fusing multi-physical field features, a lightweight digital twinborn body adaptive to reinforcement learning training is generated through a model order reduction technology; the digital twinborn model correction module is used for carrying out consistency calibration of multi-dimensional features and key physical parameter correction on the reduced-order model based on object reference data; the performance test module based on reinforcement learning actively explores a coupling scene of an extreme working condition and multiple faults in an interaction environment by constructing an attacker agent, and autonomously explores a performance boundary and a limit envelope of equipment; and the number-real fusion verification module is used for carrying out a hardware-in-the-loop point compensation test by taking the performance limit obtained by the digital test as guidance so as to assist in decision making and optimization of a test scheme. According to the method, the coverage, the safety and the test efficiency of the limit performance test of the aerospace equipment are improved.

The invention relates to the technical field of power system relay protection, and discloses a relay protection device mutual inductor adjustment system and method, and the system comprises a system connection initialization module, a data collection reference data generation module, a power frequency background signal offset module, a pilot frequency current injection error calibration module, and a calibration result verification output module. The method comprises the following steps: initializing sampling frequency of a to-be-calibrated mutual inductor and calibration source output precision of a calibration system, collecting amplitude and phase data of real-time power frequency current of the mutual inductor, generating offset current with equal amplitude and opposite directions through power electronic compensation based on reference data, optimizing offset current parameters through a closed-loop adjustment algorithm, and adjusting the offset current parameters through a closed-loop adjustment algorithm. Power frequency background signal offset is completed, pilot frequency calibration parameters are set, a difference value between a mutual inductor pilot frequency output signal and a calibration source standard signal is extracted by adopting a zero pointing technology, an optimal calibration coefficient is automatically calculated, verification of a calibration result is completed, and accuracy and reliability of the calibration result are ensured.

The invention provides a fatigue driving early warning processing system and method applied to a commercial vehicle, and relates to the technical field of vehicle fatigue driving early warning. The system comprises a data acquisition device, a data processing device, a fatigue detection device and an early warning processing device, the devices are in data connection and are connected with a vehicle control unit of the commercial vehicle; the data acquisition device is used for acquiring vehicle driving behavior data and user data; the data processing device is used for preprocessing the behavior data and self data to obtain to-be-matched data; the fatigue detection device is used for performing fatigue detection processing according to the to-be-matched data, pre-stored reference data, a preset matching condition and a pre-training deep learning model, and determining a driving state; the early warning processing device is used for executing early warning processing and generating an early warning signal when the driving state is a fatigue state. The vehicle driving behavior data and the user data are fused to judge the driving state, the defect of a single data source is overcome, and the detection accuracy can be improved.

The invention provides a digital twinning-based scene information reverse transmission method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the space-time alignment and abnormal value processing of collected multi-source data, forming a standardized data sequence, selecting a reference data segment from the standardized data sequence, and carrying out the calculation of the reference data segment; generating two feature subsequences based on the reference data segment, and calculating the dynamic correlation of the two feature subsequences to form a feature interval; selecting a key data unit in the feature interval, and selecting two key data units outside the feature interval; constructing a nonlinear data track according to the time continuity of the three key data units; generating a correction factor according to the nonlinear data track; and jointly inputting the correction factor and the standardized data sequence into a generative adversarial network to generate extreme fault time sequence data under multi-source interference. According to the invention, the adaptability and security of reverse transmission are improved.

The invention provides a method for improving the association rate of bank transaction details and electronic receipts, and relates to the technical field of data processing, and the method comprises the steps: 1, automatically obtaining transaction detail data and electronic receipt data from a plurality of bank accounts opened by an enterprise through a bank-enterprise direct connection port; step 2, identifying and analyzing the electronic receipt data, extracting key accounting elements including transaction serial numbers, dates, amounts and transaction opponent information, and generating structured receipt data; and step 3, constructing a transaction data feature set based on the structured receipt data, determining a reference data point from the feature set, and generating two groups of data association features based on the reference data point. According to the invention, enterprises are assisted to efficiently complete electronic receipt reconciliation and archiving, efficient electronic certificate service is provided for financial auditing, and the electronic and digital process of judicial enterprises is promoted.

The invention provides a printing color consistency automatic calibration and management system and method, and the method comprises the steps: collecting a current environment temperature and humidity ink matching parameter density curve data dot increase compensation parameter in real time through a state detection device, and forming a current state data set; performing similarity analysis on the reference data set and the current state data set by adopting a matching degree evaluation algorithm, and judging a relationship between a matching degree numerical value and a preset threshold value; adjusting and correcting the calibration reference value and the color control parameter in the reference data set according to the offset compensation set to obtain a compensated and adjusted parameter set; on the basis of the compensated and adjusted parameter set, generating update code data by combining a current time identifier and an operator identifier, and forming new two-dimensional code information; and outputting the new two-dimensional code information to a subsequent work order carrier by adopting a code generation module, and establishing a parameter transmission tracing chain.

The invention provides an industrial air conditioner energy-saving optimization control method based on multi-source data fusion and related equipment, and is applied to the technical field of data processing. Industrial air conditioner energy-saving optimization is taken as a core, and multi-source data such as equipment parameters and environment variables and system-level reference data such as a fusion layer processing standard and an AI decision operation reference are acquired. And combining the multi-source data fusion parameters and the control strategy parameters of each level, matching data processing and instruction execution logic, and establishing a collaboration rule and generating an energy-saving constraint condition by means of hierarchical processing and multi-modal integration. And based on the data and constraint acquisition energy consumption, operation, fault early warning and other data, energy consumption prediction, supply and demand adjustment and other functional parameters are combined to generate energy-saving effect and operation state associated data. And finally, through visual analysis of an energy management platform and ISO50001 compliance auditing, an energy-saving optimization and system reliability evaluation result is output.

The invention relates to the technical field of deep counterfeit image detection, and discloses a deep counterfeit detection method and system, a storage medium and computer equipment. The method comprises the following steps: firstly, constructing a reference data set containing a forged image and an original real image; secondly, through an integrated model, generating antagonistic samples for the reference data set, and integrating the successfully attacked antagonistic samples into an antagonistic sample set; and finally, merging the reference data set and the adversarial sample set, and constructing a robustness enhanced data set containing four types of samples. In the model training stage, multi-classification cross entropy loss and comparative learning loss are combined, and expression of the model in a feature space is optimized through comparative learning constraint, so that the model learns discriminative features with more compact intra-class features and more dispersed inter-class features. The model trained by the method not only can effectively defend against attack and improve robustness, but also surpasses original detection performance on clean samples, and has remarkable technical advantages and application value.

The invention discloses a distribution box room environment parameter integrated measurement method, and particularly relates to the field of multi-parameter measurement, and the method comprises the steps: firstly constructing a machine room three-dimensional model and a sensor correlation degree matrix, then building a heat balance, humidity diffusion and airflow motion model, and calibrating parameters; deploying a measurement system and calibrating through dual synchronization and cross check; establishing a sensor confidence evaluation system and an adaptive threshold based on 72-hour reference data; fusing calibration data by adopting a three-level correlation calibration mechanism; four types of measurement modes and conversion logics are designed, and measurement resources are dynamically allocated through environmental risk assessment; multi-dimensional state parameters are extracted, a comprehensive evaluation value is calculated through normalization, dynamic weighting and combinatorial algorithms, and five-level early warning response is achieved; according to the method, multiple models and an intelligent algorithm are integrated, the parameter measurement precision and the environment risk identification efficiency are improved, the response delay is reduced, the fault diagnosis accuracy is improved, and reliable technical support is provided for safe operation and maintenance of the distribution box room.

The invention relates to a multi-dimensional intelligent cooperative detection method, system and device based on a tunnel inspection robot and a medium. The method comprises the steps that initialization is carried out based on tunnel environment parameters and task requirements, and a standardized initialization reference data set is generated; based on the data set, starting an inspection robot to carry out data acquisition, preprocessing original data, and generating a synchronized multi-source data acquisition stream; then abnormal event monitoring, structure health detection and environment perception detection are executed in parallel, and a standardized detection result set is generated; and performing multi-source data fusion processing on the standardized detection result set to obtain a core detection result and a comprehensive confidence coefficient, and performing matching in combination with a preset decision rule base to generate a standardized decision instruction set. According to the method, through multi-module collaborative data acquisition and multi-source data fusion processing, the comprehensiveness, accuracy and efficiency of tunnel inspection can be improved, and then the safety and efficiency of tunnel operation and maintenance management are effectively improved.

The invention relates to the technical field of intelligent power monitoring, in particular to an electric energy meter data acquisition method and system based on edge computing, and the method comprises the following steps: obtaining a data reading signal and task time of an electric energy meter, collecting a continuous state sequence according to a reference data set, analyzing the variation amplitude, verifying that the task completion is consistent with the time range check amplitude, and judging whether the task completion is consistent with the time range check amplitude. And updating an execution parameter based on the state record adjustment reference value, filling a control configuration activation rhythm signal write-in interface, and obtaining an electric energy meter data acquisition automation control information set. According to the method, real-time verification and self-correction of the acquisition process are realized through benchmark and revision processing of the task triggering process and state fluctuation analysis, so that the execution time and the state change are coordinated and consistent, the acquisition timeliness and stability are effectively improved, and the continuous monitoring capability of the running state is enhanced; adaptive updating of acquisition and control signals is realized, continuous and accurate information acquisition is ensured, and operation efficiency and response flexibility are improved.

The invention discloses a concrete structure tall formwork supporting system stress deformation actual measurement method, and relates to the technical field of constructional engineering structure safety, the method comprises the following steps: determining initial stress distribution reference data through a pre-established supporting system geometric model database, and determining final construction adjustment guidance data; according to the final digital construction scheme, a construction safety assessment report is automatically generated, final conclusion data of safety assessment is determined, a result is fed back to the design optimization module through the data transmission module, and the unsolved uneven stress or deformation problem is extracted from the feedback data; obtaining input parameters of a new round of design optimization, and obtaining a continuously improved design iteration result; according to the actual measurement method for stress deformation of the high and large formwork supporting system of the concrete structure, the design rationality and construction safety of the high and large formwork supporting system are improved, and intelligent and digital whole-process management and control are achieved.