378 results about "Process analysis" patented technology

The invention provides an enterprise process intelligent analysis system based on a large language model. The enterprise process intelligent analysis system comprises a master control scheduling module, a data preprocessing module, a hierarchical analysis module, an insight extraction module, a report generation module and a knowledge retrieval module. The master control scheduling module generates a scheduling plan based on chain thinking reasoning, and dynamically calls each module; the data preprocessing module carries out cleaning and structured conversion on the enterprise event logs and outputs standardized JSON (JavaScript Object Notation) data; the knowledge retrieval module is combined with an RAG technology and a vector database to provide context support for a large language model; the hierarchical analysis module drives a model to execute process discovery and bottleneck identification through a structured cue word template; the insight extraction module converts an analysis result into a commercial insight text containing reasons, influences and suggestions, and has a self-repairing mechanism to guarantee consistency; and the report generation module automatically generates an image-text report. The system can improve the efficiency and accuracy of process analysis.

The invention relates to the technical field of ring piece rolling, and discloses a large special-shaped ring piece rolling forming process analysis method which comprises the following steps: (1) forge piece and blank design: designing different types of blanks; (2) material characteristic modeling: establishing a material model meeting rolling conditions; (3) finite element model establishment: establishing a geometric model of a rolling process by utilizing finite element analysis software; (4) rolling process scheme design: designing a plurality of rolling process schemes; (5) process simulation and defect prediction: analyzing strain, stress, temperature field and metal flow conditions in the simulated rolling process, and analyzing defect generation conditions; and (6) real-time monitoring and feedback control: monitoring changes of stress, strain and temperature in real time, and automatically adjusting process parameters. And through dynamic material characteristic modeling, a self-adaptive grid division technology and multi-physical field coupling analysis, the analysis accuracy of the rolling process is improved, so that defect prediction is more accurate, and the quality of finished products is improved.

The invention provides a dynamic construction carbon emission visualization method and system based on numerical simulation, and belongs to the field of carbon emission calculation. The method aims at solving the problems that an existing method cannot conduct visual display of construction carbon emission and cannot reflect the internal relation between dynamic carbon emission and the construction process. Firstly, a process analysis method and an input-output analysis method are used for accurately metering dynamic construction carbon emission, then carbon emission positioning is carried out according to a carbon footprint theory, then space-time correlation between construction carbon emission and a building model is achieved through a numerical simulation method, and finally the dynamic visualization target of construction carbon emission is achieved. According to the method, the construction process is regarded as the product production process, dynamic construction carbon emission metering is achieved by counting all resource investment of a project and final building composition, the relation between spatial and temporal distribution and internal composition of carbon emission can be simply, meticulously and visually displayed, and the construction carbon emission generation process is reduced to the maximum extent.

The invention provides a testing method and device, testing equipment, network equipment, a medium and a program product, and is applied to the technical field of testing. The method comprises the following steps: obtaining test requirement information of a to-be-tested scheme, and obtaining test process information obtained by executing the to-be-tested scheme by test execution equipment; analyzing the test process information according to the test requirement information by adopting a multi-modal large model to obtain a test intermediate execution instruction; and sending the test intermediate execution instruction to the test execution equipment for further test execution of the to-be-tested scheme. By adopting the method, the test process analysis in the multi-modal test process is realized by utilizing the analysis and reasoning capability of the multi-modal large model on the multi-modal information, and the test intermediate execution instruction for guiding the test execution is generated according to the test requirement information and is used for executing the subsequent test steps, so that the multi-modal test efficiency is improved under the multi-modal test scene. Automation of the interaction process is tested.

The invention provides an atmospheric pollution process analysis method, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the following steps: receiving an atmospheric pollution problem sent by a user, and respectively carrying out vector database retrieval and knowledge graph retrieval based on the atmospheric pollution problem to obtain a first retrieval result and a second retrieval result; wherein the vector database retrieval comprises the following steps: calculating cosine similarity between background information vectors in a vector database and atmospheric pollution problem vectors, and determining multiple pieces of background information of which the cosine similarity accords with a preset threshold value as a first retrieval result; the knowledge graph retrieval comprises the following steps: performing entity recognition and standardization on the atmospheric pollution problem, and generating a target knowledge triple as a second retrieval result; and fusing and inputting the atmospheric pollution problem, the first retrieval result and the second retrieval result into a large language model, and outputting an analysis result of the atmospheric pollution problem. Through the method provided by the invention, the illusion problem of a general large language model in the atmosphere field is relieved.

The invention belongs to the technical field of atmospheric environment monitoring, and particularly relates to an atmospheric pollution channel transmission data analysis system which comprises an acquisition and analysis end and a visual display end which are in communication connection. The acquisition and analysis end comprises a multi-source heterogeneous data acquisition and integration unit, a data preprocessing unit, a pollution channel transmission identification unit, a transmission process analysis unit, a pollution source tracking unit and a dynamic prediction unit; according to the invention, a full-chain analysis framework from multi-source data acquisition and processing, pollution channel dynamic identification to source analysis and prediction and early warning is constructed, the identification precision and timeliness of a pollution transmission path are significantly improved, and a key supervision park in a monitoring area is determined through pollution management and control decision analysis. The key supervision parks are continuously monitored, and the atmospheric pollution emission management performance of the corresponding key supervision parks is judged, so that the targeted industrial park management is realized, and the pollution of each industrial park to the atmospheric environment is reduced.

The invention discloses a large-model-based government affair process intelligent optimization method, system and device and a medium, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of low efficiency, resource waste, difficulty in adapting to complex and changeable requirements and difficulty in solving the technical problems in the traditional government affair process. According to the technical scheme, the method comprises the steps of government affair information data collection: extracting related data from government affair information systems, databases and manual input interfaces, collecting manual operation records and feedback information, and integrating multi-source government affair process data; data preprocessing: performing data cleaning, data standardization, data classification and labeling and data feature extraction and selection on the collected government affair information data to obtain preprocessed government affair information data; training and adapting a large model; intelligent process analysis and optimization; evaluating and implementing a scheme; and effect monitoring and feedback.

The invention provides a numerical control machine tool milling machining multi-process signal intelligent marking method based on Transform. Firstly, multi-mode time sequence signals (such as spindle vibration, cutting force and acoustic emission signals) are collected, denoising is performed through wavelet transform, time-frequency domain features are extracted, signal samples are classified according to process categories, and a high-quality training set is constructed by combining specialists to label the samples. Secondly, designing a multi-modal feature extraction framework based on a Transform model, introducing a multi-head attention mechanism, dynamically adjusting modal weights to generate joint feature representation, determining key modals and features through improved multi-modal feature sensitivity analysis, and accurately marking signals in combination with a self-supervised learning and physical constraint optimization model; finally, a sequential mark verification strategy is adopted, a high-quality mark data set is generated, and the accuracy and consistency of analysis in the machining process are improved. The method can be widely applied to machining quality analysis and intelligent optimization of the numerical control machine tool for complex precise parts, and has high efficiency and reliability.

The invention relates to the technical field of process management, in particular to a sealing integrated collaborative process optimization method and system. The method comprises the following steps: obtaining sealing process data; performing sensor data acquisition according to the sealing process data to obtain a multi-modal sensor acquisition data set; performing data preprocessing on the multi-modal sensor acquisition data set to generate a standard multi-modal sensor acquisition data set; performing process flow analysis on the sealing process data to generate sealing process flow data, the sealing process flow data including a sealing flow, a cover closing flow and a cover pressing flow; and performing intelligent process modeling on the sealing process, the cover closing process and the cover pressing process to generate a process procedure mathematical model. According to the invention, through multi-modal data acquisition, detailed process flow analysis, real-time monitoring and dynamic adjustment strategies, the flexibility and adaptability of sealing process production process management are improved.

The invention discloses a reliability parameter assisted low-complexity polarization code rate matching method, and belongs to the technical field of wireless communication. Aiming at the problem that a proper punching site is difficult to select during rate matching due to a recursive structure of a polar code, the invention provides a polar code punching site selection algorithm based on a reliability parameter. An information bit reliability parameter is defined to calculate the reliability of candidate punching bits, and the optimal punching position is selected by traversing and solving the minimum value of the maximum reliability parameter. In order to solve the problem of high complexity of the existing polar code rate matching method, an interleaving algorithm and a pruning serial offset list decoding algorithm based on rate matching parameter mapping are provided, secondary sorting calculation of an interleaver is avoided, and a calculation unit updating strategy of a decoder is simplified. The polarization code rate matching method provided by the invention not only can improve the rate matching coding gain, but also can effectively reduce the calculation complexity.

The invention discloses an underground space mass water inrush accident process simulation method and an escape safety level evaluation method, and the method comprises the steps: building a three-dimensional model according to the building structure and equipment arrangement of an underground space; grid division is carried out on the three-dimensional model, and unit and node data needed by fluid dynamics simulation are generated and calculated; determining initial conditions and boundary conditions; establishing a multiphase flow model according to the initial condition and the boundary condition; inputting the unit and node data, the initial condition and the boundary condition into a multiphase flow model, and simulating the dynamic process of a large number of water inrush accidents in the underground space to obtain a simulation result; and determining emergency disposal measures according to the simulation result. By means of the scheme, the dynamic process analysis precision of a large number of sudden water inrush accidents in the underground space can be improved, and therefore the escape safety level of each area in the underground space can be scientifically and reasonably evaluated.

The invention relates to the technical field of carbon resource management, in particular to a carbon emission management system for a building energy carbon supervision platform, which comprises a carbon emission statistics module, a carbon emission calculation and evaluation module and a carbon planning module, the carbon emission statistics module comprises a carbon emission factor management sub-module, a real-time data sub-module, a historical data sub-module and a system data sub-module, the carbon emission calculation and evaluation module comprises a carbon emission calculation sub-module and a carbon emission reduction benefit analysis sub-module, and the carbon planning module comprises a carbon neutralization calculation sub-module and a dual-carbon process analysis sub-module. The invention provides a carbon emission management system suitable for a building energy and carbon supervision platform, which can connect the computational statistics, analysis and evaluation and planning and expectation processes of building carbon emission to form a complete building carbon emission management system, and on the basis of the basic data and functions of the energy and carbon supervision platform, according to the actual project requirements, the energy and carbon supervision platform can be used for energy and carbon supervision. And a comprehensive and clear carbon emission management service is provided for the user.

The invention discloses a device and method for transporting powdery raw materials for hydrogen production, the device comprises a spiral conveying rod, a feeding / discharging assembly and a feeding flow optimization system, and the system works cooperatively through four modules: a material state analysis module for outputting a material state coefficient based on particle breakage rate and moisture content change; the equipment operation state analysis module outputs an equipment state coefficient according to the raw material temperature, the current fluctuation and the vibration amplitude; the transmission dynamic process analysis module is used for generating a rotating speed-density matching coefficient in combination with the material state coefficient, the equipment state coefficient, the temperature rise speed and the filling compactness; and the feeding flow optimization module is used for dynamically adjusting the target feeding flow through a feeding flow optimization model on the basis of the current feeding / discharging flow of the rotating speed-density matching coefficient. Material integrity protection, equipment operation stability improvement and flow self-adaptive control in the powder conveying process are achieved, and the hydrogen production raw material conveying efficiency and reliability are remarkably improved.

The invention discloses a watershed multi-scale water quality target overall regulation and control method, which is characterized in that a regulation and control unit is delimited based on DEM data, a multi-scale spatial analysis framework is constructed through a hydrological topology and watershed nesting relationship, and the coupling process analysis of pollutant discharge, absorption and transportation is realized; deducing a water quality control target of each catchment area step by step through a process coupling reverse algorithm by taking a watershed outlet section water quality assessment target as a reference; dynamically quantifying a balance relation among pollutant discharge, land area system interception and river network system absorption based on a source-sink profit and loss intensity index, and realizing source-sink state diagnosis of different regulation and control units; and finally, generating a basin overall regulation and control scheme by delimiting a pollution emission hot spot area and a weak absorption capacity area and analyzing a regulation and control scene. The method can be widely applied to water environment refined treatment scenes such as river and lake water quality target management and pollution control zoning, and provides scientific basis and technical support for watershed water environment protection and overall restoration.

The invention relates to the technical field of data processing, in particular to a mine slope intelligent early warning method and system based on dynamic threshold optimization and a medium. Comprising the following steps: acquiring a multi-modal heterogeneous data stream from a sensor network, generating a time sequence data matrix, and sending the time sequence data matrix into a parallel data processing model; the first data processing model executes statistical process analysis to generate a first data set; the second data processing model compares the data with the prediction data set to generate a second data set; fusing the data sets, verifying and judging, and generating a slope data record; when the conditions are met, an optimization process is started; and outputting a state list, storing the state list in a graph database, and performing parameter adjustment on the prediction model. According to the system, heterogeneous data streams are analyzed through a parallel data processing and data fusion method, comprehensive state evaluation is generated, data attribution and model closed-loop optimization are started when conditions are met, high-precision recognition of sudden change and gradual change modes in the data streams is achieved, and the diagnosis depth and the self-adaptive capacity of the analysis system are improved.

The embodiment of the invention relates to the field of data processing, and provides a financial institution business process intelligent optimization method and a related device, and the method comprises the steps: obtaining business process node data, business interaction support data and business result feedback data of a to-be-processed financial institution; based on a dynamic condition response mechanism, performing dynamic bottleneck process mining processing according to the business process node data and the business interaction support data to obtain bottleneck process analysis data; performing bottleneck process influence quantification processing according to the bottleneck process analysis data and the service result feedback data to obtain bottleneck process priority data; performing bottleneck process association rule mining processing according to the bottleneck process priority data, the business process node data and the business interaction support data to obtain bottleneck process association relationship data; according to the bottleneck process analysis data, the bottleneck process priority data and the bottleneck process association relationship data, business process optimization processing is carried out, and a more accurate business process optimization strategy can be obtained.

The invention discloses a multi-vortex system space energy evolution process analysis method, belongs to the field of fluid mechanics and aerodynamics, and aims to solve the problem of energy analysis errors caused by integral radius dependence and single-vortex hypothesis limitation in a traditional method. According to the method, a vortex core is positioned through a Liutex method, a vortex to be analyzed is translated to an original point, a pseudo time sequence is generated in combination with evolution direction slices, and a first modal energy distribution and evolution law is extracted through eigen-orthogonal modal decomposition. According to the method, the integral radius does not need to be manually set, the adaptability is high, the vortex global energy space change process can be comprehensively reflected, the method is particularly suitable for energy evolution analysis in complex vortex system scenes such as bird flight and complex aircraft wake flow, a brand new view angle is provided for vortex energy evolution, and the method has certain engineering practical value. The method can be used for quantitative analysis of complex vortex field scenes such as aircraft design, biological bionics and wind energy engineering, and has important engineering application value.

The invention provides a fusion method of ground station monitoring and satellite remote sensing inversion PM2.5 data, and relates to the technical field of PM2.5 pollution prevention and control. Ground station data and satellite remote sensing data are subjected to time matching and space matching and then are fused by using a universal Kriging method to obtain a high-precision and large-area coverage PM2.5 product. The PM2.5 data spatial correlation is considered, so that the fusion result is closer to reality, the calculation complexity is reduced, and meanwhile, the reliability of the fusion result can be judged by giving an error after data fusion. According to the method, the limitations of insufficient spatial consideration and high calculation complexity of a traditional method are made up, and the PM2.5 product can be used for pollution process analysis, climate change and other researches.

The invention discloses a writing process analysis method based on computer vision, which relates to the technical field of handwritten character recognition, and comprises the following steps: carrying out perspective transformation on real-time writing data, obtaining a writing image frame sequence, carrying out motion blur correction and pen point coordinate positioning on the writing image frame sequence by adopting an RAFT (Reversible Addition Fragmentation Transform) optical flow algorithm, and forming a writing track data flow; and performing curvature segmentation on the writing trace data stream to obtain discrete stroke segments, and performing spatial topological correlation and geometric structure mapping on the discrete stroke segments to generate a writing stroke topological graph. Through the RAFT optical flow algorithm and the stroke semantic analysis model, the precision of stroke recognition is improved, and efficient and accurate analysis from dynamic visual input to structured character output is achieved.

The invention discloses a robot-assisted operation error detection method based on cooperative agent reasoning. The method comprises the following steps: constructing a multi-classification operation error system and a clinical informed knowledge base; based on a multi-classification operation error system and a knowledge base, a chain type reasoning prompt base is constructed according to different professional levels and analysis perspectives; for an input operation video clip, by calculating a composite risk score of a technical complexity score and a clinical influence score, allocating error instances of different risk scores to corresponding professional level reasoning paths; and deploying a time analysis agent, a space analysis agent and a process analysis agent for each professional level reasoning path, calling a corresponding chain reasoning prompt by each agent to analyze the operation video clip, and outputting a final error detection result based on an optimization threshold value by weighting and fusing analysis results of each agent. According to the invention, accurate detection of various surgical errors can be realized, and detection efficiency and clinical suitability are considered at the same time.

The invention relates to the technical field of aluminum oxide production, and provides an evaporation station intelligent optimization system based on multivariable coupling regulation, which comprises a DCS (Distributed Control System) lifting module used for optimizing a control strategy and hardware support; the intelligent optimization control module is used for realizing multivariable coupling regulation and control; the data storage and analysis module is used for historical data recording and process analysis; the execution module is used for receiving the control instruction and driving field equipment; the DCS control system lifting module, the intelligent optimization control module and the data storage and analysis module are in bidirectional communication connection in sequence, and the intelligent optimization control module is in unidirectional communication connection with the execution module. Through multivariable coupling regulation and an intelligent algorithm, mother liquor discharging index fluctuation is stabilized, and the operation qualification rate and the alkali circulation efficiency are improved; steam consumption matching is optimized, and energy conservation and consumption reduction are achieved; variable fluctuation chain reaction is reduced, and abnormal response time is shortened; automation of key procedures is promoted, and manual errors are reduced.

The invention provides an engineering investigation surveying and mapping intelligent management method and system based on a geographic feature processing technology. The method comprises the following steps: acquiring building distribution density, ground gradient and underground rock stratum distribution data of a target area, deploying a ground deformation monitoring radar at a selected position based on the building density and the rock stratum data, generating ground deformation data through radar images, and then separating ground subsidence change and original terrain characteristics to form a subsidence map; meanwhile, image reflection characteristics are analyzed to mark dangerous areas. And then integrating the ground gradient, the settlement trend and the rock stratum structure complexity to construct a land bearing evaluation model. The influence weight is further adjusted in combination with engineering requirements, and a bearing force evaluation result is generated after the model is input. Therefore, a scientific three-dimensional development planning scheme is generated, and optimized utilization and risk management and control of land resources are realized. According to the invention, through multi-source data fusion and model calculation, full-process analysis from land deformation monitoring to load bearing evaluation is realized.

The invention discloses a financial analysis agent training method and device, equipment and a storage medium, and relates to the technical field of machine learning, and the method comprises the steps: carrying out the full-process analysis of a financial problem sample, so as to obtain the thinking process of an initial financial analysis agent; displaying the decision tree corresponding to the thinking process by using a target memory, and obtaining feedback information of an industry expert to an intermediate node of the decision tree and a target score of a processing result of the financial problem sample; converting the feedback information and the score into a multi-dimensional reward value, and performing reinforcement learning training on the initial financial analysis agent based on a target reinforcement learning framework and the multi-dimensional reward value; wherein the multi-dimensional reward values comprise reward values corresponding to the thinking logicality and the thinking path of the decision tree respectively. The thinking process is used as a clear component of reinforcement learning, so that the agent can optimize the thinking mode of the agent instead of only optimizing the final output, and the reasoning ability of the trained agent is improved.

The invention discloses a BMS (Battery Management System) fault prediction method and system in the technical field of battery management system testing, which are used for solving the technical problem that random crash is difficult to position and reproduce in BMS operation. According to the method, the electrical, thermodynamic and RTOS task scheduling operation states of a BMS are synchronized in real time by constructing a multi-physics field coupling digital twinborn model, and feature extraction and fault diagnosis are performed based on simulation process parameters. And carrying out accelerated reproduction on the fault in the multi-physics field coupling digital twinborn model, recording a key parameter combination causing the BMS crash, carrying out verification on an actual BMS hardware platform, and positioning a crash reason and a critical condition. According to the method, real-time data acquisition, digital twin simulation and test verification are combined, and full-process analysis from data acquisition to fault reproduction can be completely realized.

The invention provides a carrier rocket ground test system and method supporting visual process configuration. The system mainly comprises: (1) a visual process configuration module, which configures a test process file through a graphical interface; (2) a process analysis and scheduling module for analyzing a process file and converting the process file into a task sequence; the test execution engine module (3) is used for driving bottom hardware and executing a test instruction sequence; (4) the test resource library module is used for storing and managing various information of test elements and various information of test equipment; the execution monitoring and data management module (5) is used for displaying a test execution state in real time and recording whole-process data; according to the method, the test logic and the execution program are separated, and a user can quickly construct, modify and reuse an automatic test process in a graphical mode, so that the test flexibility is greatly improved, and the test adaptability and efficiency are remarkably improved.

The invention discloses a clindamycin phosphate crystallization tracing method and system based on a block chain, and belongs to the technical field of medicine manufacturing process control. The method comprises the following steps: collecting multi-modal data of a crystallization process in real time through a Raman spectrum sensor, an FBRM sensor and other process analysis technology sensors; constructing a dynamic space-time atlas based on the data to characterize a process state; deeply analyzing the map by using a time-space diagram neural network model so as to identify key crystallization events such as nucleation and crystal transformation; the AI oracle machine automatically generates an event certificate containing event details, an original data hash value and a digital signature for the identified CCE; and finally, submitting the certificate as a transaction to a block chain network for uplink evidence storage, and forming a tamper-resistant tracing record. According to the method, the AI and block chain technologies are fused, so that the problem that key microscopic events are lack of credible and non-tampering records in the crystallization process is solved, and a high-fidelity tracing record with cryptographic guarantee is provided for the pharmaceutical production process.

The invention provides a coal mine production process management and control system, and relates to the technical field of process management, and the system comprises a data collection module which is used for obtaining the data of each production process in a mine field in real time, and outputting initial data; the preprocessing module is used for preprocessing the initial data to obtain to-be-analyzed data; the model training module is used for training a to-be-trained model selected from the model database in combination with a preset data resource, outputting an alternative model, and outputting the alternative model meeting a preset performance condition as an analysis model; and the data analysis module is used for analyzing the to-be-analyzed data based on the analysis model, outputting a process analysis result, and performing parameter updating and performance optimization on the analysis model in combination with preset data resources and cloud data. According to the invention, data of each production flow in the mine field can be monitored in real time, potential risks of each production flow can be found in time, parameter updating and performance optimization are continuously carried out on the model, and the real-time performance and accuracy of a flow analysis result are ensured.

The invention provides a process exception processing method, which comprises the following steps: after determining that an exceptional thread exists in a target process, executing a stack grabbing operation on the exceptional thread to obtain thread stack information corresponding to the exceptional thread; calling a first processing thread to perform address analysis on the thread stack information to obtain address information of the function stored in a memory; calling a second processing thread to perform cross-process analysis on address information, stored in the memory, of the function to obtain attribute information of the function and a function calling hierarchical relationship; and sending the attribute information of the function and the function calling hierarchical relationship to a server, so that the server locates the abnormal code according to the attribute information of the function and the function calling hierarchical relationship. Through the lightweight thread-level stack grabbing operation, the interference to other parts of the system is reduced, the stability and reliability of the system are improved, and the state of the abnormal thread is accurately captured under the condition that the system performance is not obviously influenced, so that information is provided for subsequent fault diagnosis and repair.

The invention relates to the technical field of business and financial management, in particular to a business and financial fusion analysis decision management system and method based on multi-modal AI, and the method comprises the steps: configuring deep learning and testing according to financial use and formation information, and obtaining a multi-source contact analysis record; according to the multi-source connection analysis record, feature line and non-feature line difference is configured, and a fusion decision result is obtained; according to the fusion decision result, configuring information-driven machine learning and clustering analysis to obtain a decision value decision record; configuring real-time information analysis according to the real-time decision plan to obtain a real-time financial analysis record; configuring flow analysis according to the real-time financial analysis record to obtain a flow decision result; and configuring real-time information flow analysis and personalized management according to a process decision result to obtain a real-time management result. According to the method, the capability of effectively integrating different information sources is achieved, and the real-time decision-making and long-term financial demand evaluation effects are good.

The invention belongs to the field of oil field ground process analysis and evaluation, particularly relates to an onshore oil field green low-carbon level index evaluation method and system and electronic equipment, and aims to solve the problem that an existing evaluation system is incomplete and inaccurate in consideration. The method comprises the following steps: based on production operation of an oil field ground process system, selecting a green and low-carbon level main body evaluation index; based on the subject evaluation indexes, dividing evaluation indexes to which the subject evaluation indexes belong; the subject evaluation index and the affiliated evaluation index are both endowed with weights; calculating a standardized score of each subject evaluation index; calculating a standardized score of each affiliated evaluation index; calculating a comprehensive score of the green low-carbon level of the to-be-evaluated oil field based on the standardized scores of the subject evaluation indexes and the affiliated evaluation indexes and the weights of the subject evaluation indexes and the affiliated evaluation indexes; and performing overall evaluation based on the comprehensive score. The method guides the improvement of the production construction level, promotes the refinement of production management, and provides a quantifiable scientific evaluation method.