542 results about "Computation process" patented technology

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The invention relates to the technical field of code development, and provides an AI code effective proportion statistical method and device, a medium and equipment. The method comprises the steps of obtaining related information of codes submitted by a user; according to a user name in the related information, searching log information of an AI code generated by a corresponding user through adoption of a code generation tool; under the condition that the file name of the code submitted by the user is matched with the file name in the log information, searching a corresponding submitted code segment from the code submitted by the user according to the mark information in the log information; and calculating the similarity between the AI code and the submitted code segment, and counting the effective proportion corresponding to the AI code according to the similarity. Therefore, the calculation of the effective proportion considers the quality of the AI code, so that the effective proportion can accurately reflect the real contribution of the AI code, and the calculation process is automatically realized, thereby avoiding the tedious, time-consuming and labor-consuming conditions of manual labeling.

The invention relates to the technical field of industrial automation control and dynamic simulation, and discloses a centralized control console and 3D model linkage virtual-real simulation system, which comprises a control signal sampling interface, a load response modulation unit, a dynamic model evolution unit and a virtual-real closed-loop feedback unit, the load response modulation unit performs energy consumption weight accumulation on all the virtual controlled objects in transient action to generate a global time scaling factor so as to correct an inertia time constant of the system in real time; the dynamic model evolution unit introduces a hard limiting boundary by using an amplitude limiting link and calculates a process variable; the virtual-real closed-loop feedback unit extracts a control deviation signal and drives a physical instrument to generate a nonlinear damping action, and by establishing a global load modulation model based on energy constraint, a discrete control system spontaneously emerges a nonlinear hysteresis characteristic conforming to physical energy conservation under the low calculation power condition without complex fluid network solution.

The invention discloses a private data security sharing method based on federal learning, particularly relates to the field of private data security protection and sharing, and is used for solving the problems of insufficient model credibility and lack of precision control of data exchange in the existing cross-mechanism data collaboration process. According to the method, homomorphic encryption processing is carried out on local data of all participants, a ciphertext verifiable calculation task is constructed in combination with federated learning, forward propagation and back propagation calculation of a ciphertext are carried out by outsourcing calculation nodes, credible verification is carried out on a calculation process by utilizing zero-knowledge proof, and gradient aggregation and model updating are completed in a ciphertext domain; after model training is completed, based on federal learning model output, intelligent judgment is conducted on the sharing value of local data samples, ciphertext re-encryption and data exchange control are driven, multi-party safe and controllable data circulation is achieved, and therefore on the premise that privacy safety and compliance requirements are guaranteed, cross-mechanism data collaboration efficiency and decision-making precision are improved.

The invention provides a self-adaptive regulation and control method for a hydrogen production system based on real-time parameter feedback, and relates to the technical field of process control, the method comprises the following steps: collecting an input instruction signal and a controlled state variable signal of the hydrogen production system, and comparing a fluctuation energy index with a preset identification dead zone threshold; if the fluctuation energy index is lower than the identification dead zone threshold value, the current controller parameter is kept unchanged; if the fluctuation energy index is higher than the identification dead zone threshold value, activating an online parameter identification step; calculating a dynamic confidence interval of process model parameters; judging whether the process model parameters estimated in real time fall into the dynamic confidence interval or not; if the process model parameters fall into the dynamic confidence interval, converting the process model parameters estimated in real time into control parameters of a PID (Proportion Integration Differentiation) controller, and updating the control parameters into a control loop of the hydrogen production system; and if the process model parameter exceeds the dynamic confidence interval, resetting an internal state vector of the recursive algorithm in the online parameter identification step by using a boundary value.

The invention provides an industrial sensor intermittent fault detection method and device and a medium. The method comprises the following steps: acquiring a process input vector of a target industrial process at a current moment and a process output measurement value measured by a sensor to be measured; determining a process output predicted value according to the process input vector and the virtual sensor model; calculating a current residual between the process output measured value and the process output predicted value; if the current residual error exceeds a target early warning threshold value, marking an early warning signal, and generating a first fault mark under the condition that the continuous triggering times of the early warning signal on a time sequence reach a first preset times; based on the residual sequence from the historical starting moment to the current moment, the posterior probability that the sensor to be detected is in a fault state at the current moment is calculated through a hidden Markov model, and a second fault mark is generated under the condition that the posterior probability is larger than or equal to a preset probability threshold value; and if at least one of the first fault mark and the second fault mark is established, determining that the to-be-detected sensor has an intermittent fault.

The invention is suitable for the technical field of data processing, and particularly relates to a financial data processing and classification method, system and equipment based on verifiable computing, and the method comprises the steps: obtaining on-chain transaction data verified by a block chain consensus node storage and risk-related index data verified by an under-chain oracle machine; the problems that the authenticity of traditional financial data is difficult to verify and the data is split are solved; calling a risk assessment model through an intelligent contract deployed on a block chain, and placing an assessment process in a decentralized block chain network; performing verifiable calculation through an intelligent contract to determine a total performance score and a risk index, and realizing whole-course tracing and verification of a calculation process by utilizing the characteristics of verifiable calculation of a block chain; the total performance score and the risk index are stored in the block chain as non-tampering records, and the risk rating groups are automatically divided according to the fixed rating rule on the chain through the smart contract, so that the authenticity, objectivity, verifiability and fairness of financial data processing are guaranteed.

The present invention relates to a processor designed to optimize memory bandwidth utilization for tensor transpositions in machine learning. An example processor includes an input tensor shift buffer, a staging buffer, and an output tensor shift buffer. The input tensor shift buffer reads an input tensor from input memory and performs multiple cycles of input tensor shifting. The shifted tensor data is then written into the staging buffer. The output tensor shift buffer reads the shifted tensor data from the staging buffer and performs multiple cycles of output tensor shifting. Finally, the result is written to the output memory. This configuration facilitates efficient handling and transformation of tensor data, optimizing the computational processes required in machine learning tasks.

The embodiment of the invention provides a light field space coherence length measurement method and system, and relates to the technical field of optical measurement. The method may first obtain a training set and train a measurement model using the training set. And obtaining an interference fringe image through numerical simulation generation or experimental acquisition, and inputting the interference fringe image as an input image into the measurement model so as to output a spatial coherence length prediction value of the input image through the measurement model. Wherein the measurement model is a convolutional neural network model for performing feature extraction by adopting a multi-layer cascaded convolutional pooling structure, and the features can be mapped into a spatial coherence length predicted value through a full connection layer. According to the method, one-step measurement from the original interference image to the coherence length can be realized, a tedious fringe contrast calculation process is avoided, the influence of experimental noise on measurement precision is reduced through depth feature extraction, the whole prediction process can be completed within millisecond-level time, and the measurement efficiency of the light source space coherence length is improved.

The invention provides a computer implementation method and device for determining space occupation information, equipment and a medium, multiple groups of binocular images collected by binocular cameras arranged in different directions of a moving body can be acquired, then image features and depth features of each group of binocular images are determined based on the multiple groups of binocular images, and then the space occupation information is determined. And projecting the image features and the depth features of each group of binocular images to an overlook coordinate system, determining target aerial view features in the overlook coordinate system, and finally, determining space occupation information in the look-around space of the moving body according to the target aerial view features. The target aerial view feature reflects the 3D space information around the moving body only through the 2D format, the storage space occupied by the 3D space information in the calculation process and the calculation complexity are reduced, and the calculation precision and the calculation efficiency of determining the space occupation information based on the target aerial view are further improved.

The invention discloses an awareness perception quantum gravitational nerve acceleration unified computing platform and method based on formalized verification, and belongs to the field of high-performance computing, medical AI and quantum information processing. A quantum gravitational coupling neural processing unit (QGCNPU); a consciousness information integral calculation core (phi ICC); formalizing a receipt chain verifier (FRCV); and the mirror reflection symmetry correction module (MRSCM) is used for eliminating the continuous projection mistake and the discrete Boolean mistake through topological equivalence verification of a local observer and a global GodCam visual angle. The system adopts an FPGA + ASIC + quantum processor three-layer heterogeneous architecture, and supports real-time pathological modeling (precision gt; gt) of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and ALS; 99.7%), drug target discovery (acceleration by 50 times) and consciousness state monitoring (time resolution lt; and meanwhile, the mathematical proving performance and the clinical auditing performance of the calculation process are ensured, and the requirements of the global 152 billion dollar neural science and technology market and the 89 billion dollar quantum calculation industry are met.

The invention provides a similarity retrieval system and method based on vector modulus length sorting. The system comprises a vectorization module, an index construction module, a self-adaptive candidate set generation module and a precise sorting module. Relates to the technical field, and the method comprises the following specific steps: S1, calculating a vector modulus length value of a database vector, generating a globally sorted ordered index according to the vector modulus length value, and constructing a density distribution diagram; s2, calculating the query modulus length of the query vector; s3, extracting a candidate vector set from the ordered index; s4, performing accurate similarity calculation on each candidate vector and the query vector, and outputting a retrieval result; according to the method, rapid coarse positioning is carried out by constructing a one-dimensional modular length ordered index, and the size of a candidate set is dynamically adjusted according to the local density of data by utilizing an adaptive modular length adjacent window calculation process, so that intelligent and adaptive regulation and control of a retrieval process are realized.

The invention discloses a federal edge communication and calculation optimization method, system and device based on task prediction and a medium, and belongs to the technical field of edge intelligent collaborative optimization, and the method comprises the steps: collecting historical task data, carrying out the modeling of a communication and calculation process, building a multi-dimensional task feature modeling mechanism, extracting the heterogeneous features of the communication and calculation process, and carrying out the calculation of the communication and calculation process. Performing task demand prediction and load perception to obtain a prediction result; and establishing an integer programming model, performing approximate solution through a heuristic algorithm to complete service cache optimization, performing calculation unloading and resource joint allocation, calculating key performance indexes, performing periodic acquisition, and performing dynamic adjustment on a prediction result. According to the method, an efficient cache strategy is generated by adopting a heuristic algorithm, the defects of single resource allocation, decision lag and lack of global coordination in a traditional method are overcome through joint optimization of resource allocation and a real-time performance monitoring feedback mechanism, and the system response efficiency and the resource utilization rate are improved while the task success rate and reliability are guaranteed.

Disclosed in the present invention are a method and apparatus for optimizing a two-phase flow gas-kinetic scheme algorithm, and a device and a storage medium. The method comprises: constructing a gas-kinetic scheme algorithm for phase-field solving; on the basis of Chapman-Enskog analysis, constructing a phase-field interface flux of the phase-field gas-kinetic scheme algorithm; constructing a gas-kinetic scheme algorithm for flow-field solving; on the basis of the Chapman-Enskog analysis, constructing a flow-field interface flux of the flow-field gas-kinetic scheme algorithm; and on the basis of a Cahn-Hilliard equation and a Navier-Stokes equation, implementing a two-phase flow numerical algorithm for coupling a phase field and a flow field. In the present invention, starting from a Boltzmann-BGK equation, the gas-kinetic scheme algorithm for coupling the phase field and the flow field is constructed within a finite volume framework, and by means of Chapman-Enskog expansion, numerical fluxes can be accurately recovered to macroscopic equations, thereby simplifying the computation process, and significantly improving the computation efficiency and accuracy. The method can effectively simulate interface dynamics and flow-field evolution in a complex two-phase flow system.

For generating a bill of materials and a corresponding bill of processes for a product, a natural language processing module maps at least one document specifying a product into a latent representation, which is an embedding in latent space. A bill of materials generator receives the latent representation as input and generates a bill of materials for the product. A user interface detects user interactions with the generated bill of materials and creates a curated bill of materials depending on the user interactions. A bill of processes generator receives the latent representation and the curated bill of materials as input and constructs a bill of processes for the product. The method and system, or at least some of their embodiments, provide semi-automatic generation of the bill of materials and bill of processes for a product, using an AI engine. Compared to manually setting up a bill of materials and / or bill of processes within a software-system, the approach of using AI for generating this information can significantly speed up the process of calculating costs and sustainability values of a product. In this way the time from request for quotation until submitting the actual offer can be lowered significantly.

The invention provides a water conservancy model parallel scheduling calculation method, system and equipment, which are applied to a water conservancy model parallel computing scheduling system, the water conservancy model parallel computing scheduling system comprises a computing engine, a visual interaction module, a GIS map and a user interaction interface, and the water conservancy model parallel computing scheduling method comprises the following steps: obtaining a control instruction, the control instruction is sent to the calculation engine; controlling the calculation engine to interact with the visual interaction module based on the control instruction, and obtaining an interaction result; and rendering the interaction result into the GIS map. According to the method, the whole process of the computing power resources is visually displayed, the integration of an algorithm is concerned in a water conservancy model platform, construction in the computing power resources is concerned at the same time, and the computing nodes, the computing unit states, the computing process grouping information and the like are visually displayed in the computing process, so that the computing power resource construction application is assisted to be optimized and improved.

The invention relates to the technical field of artificial intelligence, and provides an attention mechanism calculation method, electronic equipment and a storage medium, and the method comprises the steps: configuring a plurality of thread beam groups which share the same group of thread local registers; the calculation process of the attention mechanism is divided into a plurality of calculation sub-steps, the calculation sub-steps are distributed to a plurality of thread beam groups respectively, and each thread beam group is configured to execute the corresponding distributed calculation sub-steps; and controlling the plurality of thread bundle groups to execute the plurality of calculation sub-steps in parallel in an assembly line mode through a read-write shared thread local register to obtain a calculation result of the attention mechanism. According to the method, the shared thread local register is utilized to construct a multi-thread bundle group cooperative pipeline calculation mode, efficient copy transmission of data and deep parallelism of different calculation units in the attention mechanism calculation process are achieved, and the method has the advantages of being low in communication overhead, high in hardware resource utilization rate, high in operator execution performance and the like.

An abnormal irregularity cause identifying device includes a process data acquisition unit that reads, from a storage device storing process data, the pieces of process data, an abnormality determination unit that calculates an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit, and a cause diagnosis unit that obtains accuracy of a cause of the irregularity based on a proportion of process data having the abnormality degree calculated by the abnormality determination unit satisfying a predetermined criterion to the plurality of types of pieces of process data, by using causal relation information defining a combination between the cause and the irregularity, which appears as an influence resulting from the cause, of each of the plurality of types of pieces of process data.

The invention relates to the technical field of electric power security, and discloses a regional building group electric power transaction security guarantee system, which comprises the following modules: a trusted computing environment authentication module, a transaction data encryption and commitment module, an under-chain privacy computing and verification module, and a computing process certification and settlement module. According to the method, the algorithm hash value recorded in the program registry contract is combined with the remote authentication mechanism of the trusted execution environment, so that the traceability and verifiability of the under-chain computing environment are constructed. The system digitally signs a report containing an algorithm code hash value by using a hardware built-in private key, and submits the report to an on-chain contract for comparison, and the mechanism ensures that any under-chain privacy computing task is executed in a specified algorithm environment which is approved by block chain consensus and is not tampered with codes, so that the privacy computing efficiency is improved. The security risk caused by the untrusted computing environment or the non-compliance of the algorithm logic is completely eradicated from the source, and the basic guarantee is provided for the correctness of the subsequent computing result.

The invention relates to a lightweight reasoning acceleration architecture based on an instruction set architecture, and the architecture comprises the steps: initializing hardware level configuration, building a software level environment, and constructing a basic operation environment of the lightweight reasoning acceleration architecture; a model analysis mode is adopted to reduce the memory demand and carry out allocation optimization on the memory, and a reasoning execution basis is constructed; completing calculation parameter configuration by adopting modes of accelerator parameter configuration, weight data preloading and output data loading; based on the calculation task and monitoring information of the calculation process, calculation resources are dynamically scheduled, and efficient scheduling and execution of the calculation task are achieved. The energy efficiency is improved based on a data reuse strategy, memory access optimization and power consumption management; a comprehensive anomaly detection system is constructed, and anomaly conditions are detected and reported; the collaborative optimization of reducing the power consumption and improving the calculation efficiency is achieved, the real-time performance and the stability are both considered, and the efficient and reliable lightweight reasoning effect is achieved in the edge calculation scene.

The invention discloses a soil density compaction degree compaction detection method and system, and relates to the related technical field of soil detection, and the method comprises the following steps: automatically identifying a unique identification code on a sample loader through a scanning module, and associating the unique identification code with a test task; weighing is automatically triggered after the sample is placed, and mass data and an identification code are bound and transmitted to a data processing terminal; the terminal calls a corresponding reference parameter from a pre-stored parameter library according to the identification code, and then utilizes a built-in multi-level calculation engine to parallelly calculate the soil density, the compaction degree and the compaction characteristic parameters in combination with the quality data, the reference parameter and a dynamically selected physical formula; and performing cross logic verification to output a detection result. The technical problems that in the prior art, detection data records are prone to dislocation, the parameter matching efficiency is low, and the calculation process lacks automatic verification are solved, and the technical effects that full-process automation of detection, accurate data association and intelligent parameter matching are achieved, and the soil detection efficiency and the result reliability and consistency are improved are achieved.

The invention discloses a key value cache management method and device and electronic equipment, and the method comprises the steps: determining a target micro-block corresponding to target key value cache data based on a target mapping relation between a target token and a target model layer when a reasoning calculation process reaches the target model layer; wherein the micro block is used for caching key value cache data generated by the plurality of continuous tokens in the calculation process of the plurality of continuous model layers; responding to the condition that the target micro-block is not loaded into the graphics processor video memory, and loading the target micro-block into the graphics processor video memory from the system memory; and in response to monitoring that the utilization rate of the graphics processor video memory meets a target condition, determining a to-be-unloaded micro-block from the micro-blocks loaded into the graphics processor video memory so as to unload the to-be-unloaded micro-block from the graphics processor video memory.

The invention discloses a bridge ship impact point positioning method and system based on shock wave time difference analysis, and the method comprises the steps: arranging a vibration sensor array on a plurality of piers in a non-navigable hole region of a bridge, and connecting the vibration sensor array with an edge calculation module; impact characteristics are extracted, and vibration acceleration is calculated; setting a dynamic threshold value, and calculating the coordinates of the impact point when the vibration acceleration peak value exceeds the dynamic threshold value; in the calculation process, double constraint conditions are constructed by using time-frequency joint similarity and time difference of impact characteristics, and correlation of other vibration sensors is carried out; constructing a positioning equation set special for the concrete structure bridge; and resolving a positioning equation set and outputting impact point coordinates. According to the system, a vibration sensor array is used for collecting vibration signals; the edge calculation module is used for extracting impact characteristics and calculating vibration acceleration and impact point coordinates of the vibration sensor; the central processing unit is used for outputting impact point coordinates and damage indexes. According to the method and the system, the impact point in the event that the ship impacts the bridge can be quickly and accurately positioned.

The invention discloses a process equipment state determination method and device and a computer program product. The method relates to the field of process equipment state monitoring, and comprises the following steps: acquiring data of N operation parameters of process equipment to obtain N measured values; the N measured values are input into a preset model set, predicted values of the N operation parameters are obtained, the preset model set comprises N different polynomial fitting models, and each polynomial fitting model takes one target operation parameter as a dependent variable and takes operation parameters except the target operation parameter in the N operation parameters as independent variables; for each operation parameter, calculating a difference value between the measured value and the predicted value to obtain a deviation value of the operation parameter, and calculating a state index value of the process equipment based on the deviation value of the N operation parameters; and under the condition that the state index value is smaller than the index value threshold value, determining that the process equipment has a fault. According to the invention, the problem that the operation state of the equipment in the monitoring process is inaccurate in the related technology is solved.

The invention discloses a machine room safety operation and maintenance method and system based on a large model operation and maintenance assistant, and belongs to the technical field of machine room operation and maintenance, and the method comprises the steps: collecting machine room data, and carrying out the safety processing, specifically, carrying out the privacy calculation of to-be-processed data through a constructed double-layer safety credible environment, and carrying out the privacy calculation of the to-be-processed data; virus discrimination and pollution data isolation are carried out in the privacy calculation process; optimizing the large model of the operation and maintenance assistant; using the optimized large model to generate operation and maintenance question and answer data, and performing security processing on interaction data in a question and answer process; in the operation process of the operation and maintenance assistant large model, an NPU storage encryption engine is adopted to carry out dynamic encryption storage on storage data of the large model, and the influence of the NPU storage encryption engine on memory access characteristics in the operation process of the large model is quantitatively analyzed through a simulation platform. The operation and maintenance automation level of the machine room can be guaranteed, data safety is guaranteed, the performance of an operation and maintenance assistant is improved, and the stability and reliability of an operation and maintenance system are improved.

The invention provides a block floating point arithmetic device and a differential equation calculation system, and relates to the field of calculation devices, and the differential equation calculation system manages the flow of data organized in a differential format among storage hierarchies through a buffer controller. And a processing unit array synchronously executes Gaussian-Seidel iterative calculation for differential format optimization based on a wavefront sequence, then compresses a result through a block floating point quantizer, and outputs a solution matrix through a differential reduction unit after convergence, a plurality of parallel processing units in the processing unit array combine precision configuration with a block floating point input format to form a collaborative optimization calculation mode, the complexity of mantissa processing is further controlled in the calculation process by sharing index compression data and dynamic precision parameter permission, and the problems of resource occupation requirements, memory access times and high energy consumption are solved.

The invention discloses a variable length sequence-oriented model training method, which comprises the steps of determining a training normal form, a training sample and a micro-batch size corresponding to model fine tuning according to a fine tuning request for a target model; the training sample is a variable-length sequence; dividing the training samples into micro-batches according to the sizes of the micro-batches so as to obtain micro-batch samples, and inputting the micro-batch samples into an assembly line stage so as to convert the micro-batch samples into intermediate representation; performing shape adjustment on the intermediate representation according to the sequence length of the training sample, the micro-batch size and the role type of the assembly line stage when the intermediate representation is transmitted between the adjacent assembly line stages; in the attention calculation process in each assembly line stage, performing attention weight constraint on the intermediate representation input into the stage by using an attention mask; in the pipeline stage, shape adjustment and attention weight constraint are executed through forward propagation, back propagation is completed to calculate a gradient, and parameters of a target model are updated. According to the invention, the training efficiency is improved and the computing resource consumption is reduced.

The application relates to a failure prediction maintenance method and device with a preventable strategy, and relates to the field of equipment prediction and maintenance technology. In view of the problems in the prior art that a traditional two-state reliability model is difficult to accurately describe the degradation degree, failure condition and maintenance state of a system in a service life, the calculation process is relatively single, and the result error is relatively large, the technical scheme is as follows: the failure prediction maintenance method with the preventable strategy comprises the following steps: collecting a probability distribution obeyed by the residual life of a system; collecting maintenance time and maintenance cost; establishing an optimization model; iteratively processing the model to obtain an optimal failure prediction interval and a residual life threshold value; when the optimal failure prediction interval is reached, the residual life of the system is predicted, the residual life is compared with the threshold value, and if the residual life is lower than the threshold value, the system is subjected to corrective maintenance, and if the residual life is higher than the threshold value, the system is subjected to preventive maintenance. The application is suitable for application in preventive maintenance work, and establishes a new research basis for the research of preventive maintenance work.

The application discloses a semi-airborne full-time electromagnetic forward method based on an Octree grid, which performs spatial discretization on time-domain Maxwell equations, thereby effectively reducing unknowns of equations to be solved. For full-time electromagnetic field numerical simulation, the application combines the high calculation precision of the second-order backward Euler method with the high OFF-Time calculation efficiency of the model reduction algorithm, adopts the second-order BDF and the model reduction algorithm to respectively solve ON-Time and OFF-Time time-domain control equations, forms a joint algorithm to realize simulation of semi-airborne time-domain electromagnetic full-time response, and finally completes the forward calculation process. Through numerical simulation verification, in the full-time transient electromagnetic forward process, the application can keep small occupied memory and also has higher calculation efficiency, and the calculation precision is not lower than that of other methods.

The invention discloses a carbon emission factor calculation verification method based on a block chain, particularly relates to the technical field of carbon emission accounting and block chain credible measurement, and is used for solving the problems that an existing carbon emission factor calculation process depends on a dispersed system and manual summarization, the publication caliber is not uniform, a traceable evidence chain is lacked, and the calculation efficiency is low. And a supervision mechanism and a third party are difficult to carry out independent re-calculation and credible verification on the factor result submitted by the enterprise. A carbon emission factor calculation template is taken as a core at an enterprise node side, a device range, a statistical period and a data preprocessing rule are configured in a unified manner, abstract identifiers are generated step by step for dimensionalized data abstracts and calculation evidence chain fingerprints, calculation certificates are written into an alliance chain, and a calculation result is obtained. And the verification node obtains a verification fingerprint and registers a verification result based on the same template fingerprint and data slice recalculation, so that an integrated evidence closed loop of factor calculation, recalculation verification and factor selection is formed, and the problems that a factor source is non-transparent, cross-subject recalculation is difficult and a credible basis is lacked are solved.