2718 results about "Electronic data processing" patented technology

The invention provides an environment monitoring data anomaly detection method, a medium and a system, and belongs to the technical field of electrical digital data process.The method comprises the steps that firstly, an environment monitoring data original matrix is constructed, time sequence segmentation is conducted, and a rule matrix and an anomaly matrix are obtained through matrix decomposition; calculating a historical mean value, a historical standard deviation, a spatial feature and a time feature of the environmental parameters, designing an anomaly detection optimization equation set based on the features, including an environmental parameter weight equation, an anomaly threshold equation and a comprehensive anomaly degree equation, and calculating an anomaly detection result by calculating an environmental parameter weight vector, a dynamic anomaly threshold matrix and a comprehensive anomaly degree index. Finally, dynamic optimization of anomaly detection is achieved through the gain vector and the feedback vector, anomaly detection of real-time environment monitoring data is completed, and the technical problem that in the prior art, an environment monitoring data anomaly detection method is difficult to give consideration to dynamic relevance of multi-dimensional environment parameters and the accuracy of anomaly detection at the same time is solved.

The invention relates to the technical field of electric digital data processing, in particular to an agricultural planting optimization system and method based on big data analysis. The method comprises the following steps: acquiring agricultural planting environment parameter data, and performing preprocessing and standardization to obtain a standardized environment feature vector; acquiring image, sound and smell data of crops; performing deep feature extraction on the image, sound and smell data to generate a multi-modal feature representation matrix; and performing multi-modal data fusion based on the standardized environment feature vector and the multi-modal feature representation matrix to obtain a comprehensive feature space. According to the invention, through multi-modal feature fusion and digital twinborn modeling, the precision and real-time performance of crop growth risk and pest and disease prediction are improved, and optimal regulation and control and efficient resource utilization of precision agriculture are realized.

The invention discloses an intelligent server management method and system in an edge computing environment, and relates to the field of electrical digital data process.The method comprises the steps that a dynamic load buffer database is established; calculating a load stability coefficient of each server node; when the load stability coefficient of the server node is smaller than a first preset threshold value, marking the server node as a fluctuation node; calculating a node cooperation efficiency value; determining a target server candidate set, and calculating to obtain a task allocation weight; distributing tasks on the fluctuation nodes to server nodes in the target server candidate set; when the continuous preset duration of the real-time load value of the fluctuation node is lower than a third preset threshold value, calculating a processing efficiency improvement proportion; for the task of which the processing efficiency improvement proportion is greater than a fourth preset threshold value, keeping continuous processing at the current server node; and for the tasks of which the processing efficiency improvement proportion is smaller than or equal to a fourth preset threshold value, returning to the original fluctuation node. The method and the device are used for reducing communication overhead and service processing delay between nodes.

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

The invention relates to the related technical field of digital data processing, in particular to a personalized federated learning method and system for a heterogeneous multi-source industrial internet, and the method comprises the steps: connecting a client, evaluating a load, time delay and modal similarity to generate a dynamic association table, deploying a hierarchical encryption protocol, and constructing a priority queue; a cache mechanism is set to coordinate distributed iterative optimization, so that the technical problem that network oscillation and computing resource waste are aggravated due to overhigh load of part of nodes caused by frequent access and exit of equipment and data volume difference in the industrial internet and repeated migration of clients and nodes is caused is solved, cross-equipment shared knowledge base vectors are extracted, and the computing efficiency is improved. The technical effects of reducing the influence of model isomerism on aggregation, dynamically scheduling high-frequency parameter local aggregation and low-frequency parameter cloud synchronization, optimizing the association weight of a client and a fog node in real time, realizing privacy protection and efficient personalized federated learning, and ensuring the privacy and security of user data in the training process are achieved.

The invention discloses an equipment state sensing system based on peak shifting and danger situation sensing, which relates to the technical field of electric digital data processing and comprises a data acquisition unit, a model training and optimizing unit, a situation sensing unit, a peak shifting regulation and control unit and a system interface and user interaction unit. According to the method, the device state data feature similarity is dynamically calculated by using the Gaussian kernel function, the similarity matrix is adjusted in combination with the gradient update item, dynamic weighting is performed in feature extraction, and redundant features are reduced by adopting the adaptive discarding probability, so that the feature extraction efficiency of the model in a high-dimensional feature space is improved; the problem that the dimension reduction effect is affected by too many redundant features of the equipment state data is solved. Equipment state features are extracted layer by layer through a deep forest algorithm of a multi-layer random forest structure, a decision tree is optimized in combination with a dynamic adjustment weight mechanism and a reinforcement learning strategy, and the classification capability of the model for equipment state changes and abnormal states is enhanced.

The invention relates to the technical field of intelligent electronic data processing in an underground comprehensive pipe gallery, and discloses an underground comprehensive pipe gallery intelligent data fusion and dynamic early warning system and method, and the system comprises a data collection module which is used for receiving monitoring data containing timestamps from a plurality of sensors in the underground comprehensive pipe gallery in real time; the time sequence causal chain synchronization module dynamically constructs a causal time sequence chain between the data according to a predefined physical topological structure; the lightweight causal pruning device module predefines a causal trunk path based on physical topology and activates complete causal chain construction when the causal trunk path is abnormal; and the dynamic early warning module is used for sending out early warning information based on a causal time sequence chain analysis result. According to the method, through micro timestamp deviation calibration and dynamic causal chain construction based on physical topology, real causes of abnormal events can be accurately identified, early symptoms omissed due to neglecting of time sequence relevance of a traditional system are captured, and precious time is won for timely disposal.

The invention discloses an operation and maintenance mode analysis model generation method and system, a medium and a program product, and relates to the field of electric digital data processing. Establishing an equipment basic life model based on the equipment fault time data and the non-fault working time data; calculating an environment correction coefficient according to the equipment operation environment parameter data and a preset environment influence factor, and combining the environment correction coefficient with the basic life model to obtain a corrected life prediction model; calculating the correlation degree of each equipment node based on the equipment topological structure, and constructing an equipment importance evaluation model; calculating equipment maintenance cost, replacement cost and shutdown loss according to a prediction result of the corrected life prediction model to obtain a cost loss model; and carrying out weighted fusion on an output result of the equipment importance evaluation model and a calculation result of the cost loss model to generate an operation and maintenance mode analysis model. The scientificity and accuracy of operation and maintenance mode analysis are improved.

This invention pertains to an intelligent electronic device designed for sophisticated monitoring and analysis of electrical power distribution. The device integrates at least one sensor for detecting electrical parameters from a distribution system to a load, coupled with an analog-to-digital converter to transform sensed analog signals into digital data. A processing module, linked to the converter, employs customized moving average filters to calculate the frequency of electrical power distribution, enhancing measurement accuracy by adjusting for transient fluctuations. It retrieves frequency threshold settings from memory to define operational bounds, logging frequency data around identified deviation events when measurements surpass these thresholds. This system facilitates precise frequency analysis and robust event logging, providing a comprehensive solution for maintaining electrical power distribution within defined standards.

The invention discloses a method and a system for realizing automatic code generation by using multiple AI Agents, and relates to the field of electric digital data processing, a test driving agent receives a task transmitted by a previous node, and generates a task number, a task description, test data, a unit test script and a code statement; the code generation agent generates or updates codes according to the received task description and code declaration; the test driving agent returns an integrated test script and test data according to the task number; the continuous integration agent performs integration verification and regression test and feeds back information; the test driving agent returns a function test script, a performance test script and test data according to the task number; and the continuous deployment agent carries out continuous delivery and deployment and feeds back information. By means of mutual conversation and cooperative work of multiple AI Agents and in combination with a feedback mechanism, the problem that a single large model cannot complete complex software development tasks is solved, full-process operation automation can be achieved, and the code generation quality and efficiency can be improved.

The invention discloses an uninsulated superconducting coil electromagnetic characteristic prediction method, system and device and a medium, and relates to the field of electrical digital data processing. Constructing a loss function based on the data fitting loss and physical constraint loss including a Kirchhoff's current law loss item, a T-A equation loss item and an E-J power law loss item; training, testing and verifying the network model by adopting a sample data set, balancing each loss item in a loss function by adopting an adaptive weight distribution strategy in the training process, and minimizing the loss function through an Adam optimization algorithm to obtain an electromagnetic characteristic prediction model; and inputting the obtained current working condition parameters into the electromagnetic characteristic prediction model to obtain an electromagnetic characteristic prediction result. According to the method, the problems of low calculation efficiency, poor physical consistency and generalization ability and the like in the existing method are solved, and a feasible tool is provided for rapid design, optimization and operation of the uninsulated superconducting coil.

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

The invention discloses a road protection multi-scene coupling simulation method and system based on digital twinning, and relates to the field of electric digital data processing, and the method comprises the steps: collecting dynamic response data (strain, acceleration and displacement) of a traffic protection facility through multiple sensors, and building a scene feature library containing vehicle collision, wind load and seismic waves; and projecting response data to a unified time axis, identifying a multi-scene overlapping interval, and determining an action sequence and duration. Primary and secondary scenes are judged according to scene action parameters, and mechanical correction values and standard response parameters are extracted. And establishing a digital twinborn model for coupling simulation to obtain a stress distribution condition, evaluating a structural damage level and generating a safety performance report. By implementing the method, the deviation between the mechanical property simulation result of the protection facility and the actual working condition can be reduced.

The invention belongs to the technical field of electric digital data processing, and discloses a dynamic remote sensing monitoring method and system based on land utilization. The method comprises the steps of obtaining a multi-source heterogeneous time sequence data set, and generating standardized spatio-temporal data through preprocessing; extracting space-time correlation features based on the dynamic graph convolutional network, and generating dynamic mode data through multi-dimensional feature fusion; performing classification decision and state transition analysis on the dynamic mode data, identifying a state change area and generating a dynamic analysis report; constructing an interactive visualization engine to map a space-time thermodynamic diagram and a trend graph; and in combination with the incremental learning optimization model, outputting a long-term evolution prediction result. Through the dynamic graph convolutional network and the adaptive cross-modal alignment technology, the problems that multi-source data space-time correlation modeling efficiency is low and the noise suppression capability is insufficient are solved, the dynamic mode recognition precision is remarkably improved, real-time interaction analysis and long-term prediction are supported, and the method is suitable for accurate decision making of industrial monitoring, traffic planning and other scenes.

The invention provides an industrial data online migration method, medium and system of a productivity middle platform, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, historical access records of an industrial data source are collected, and a deep neural network model comprising a multi-head attention layer, a time sequence feature extraction layer and a residual connection layer is constructed; and the prediction of the data change rate is realized. And based on a prediction result, dynamically calculating a data change rate demarcation point by utilizing an optimization equation set, and dividing a data table into different change frequency categories. And then constructing a data dependency relationship matrix, and determining a migration sequence by adopting an improved topological sorting algorithm. Batch migration, incremental migration and real-time synchronization mechanisms are adopted for different types of data, and model updating is triggered through real-time monitoring, so that dynamic optimization of a migration strategy is ensured. Finally, data consistency verification is carried out, migration accuracy is guaranteed, and the technical problem that the online migration efficiency of industrial data is low due to real-time dynamic change of the data change rate is solved.

The invention provides a robot control instruction analysis method and system fusing continuous instructions, and relates to the technical field of electric digital data processing, and the method comprises the steps: carrying out the quantitative evaluation of the instruction execution quality and resource consumption according to prediction data, and generating a quality score and a consumption estimation; constructing a multi-objective optimization framework based on the quality score and the consumption estimation, and determining a candidate path set; switching cost and execution time are calculated according to the candidate path set, and optimized path schemes meeting constraint conditions are screened; the instruction execution priority and the resource allocation proportion are adjusted in combination with the real-time environment data, and a scheduling strategy is generated. According to the method, environment changes and task quality are monitored in real time, instruction priorities and resource allocation are dynamically adjusted, when execution efficiency is lower than a threshold value, online updating of a prediction model is triggered, model parameters are finely adjusted to adapt to new conditions, and through continuous optimization and monitoring, the execution efficiency and task quality of an instruction sequence can be improved, and the execution efficiency is improved. And intelligent instruction scheduling and resource allocation are realized.

The invention discloses a solid state disk data processing method and device, a storage medium and a program product, and relates to the field of electrical digital data processing.The method comprises the steps that a network data transmission request is obtained, a network bandwidth state corresponding to the network data transmission request is detected, and bandwidth state parameters are generated according to the network bandwidth state; calculating a data transmission time consumption predicted value, and determining a buffer capacity threshold according to the data transmission time consumption predicted value; adjusting the capacity of the data buffer area according to the buffer area capacity threshold, and adjusting the data buffer area; monitoring the data occupancy rate of the adjusted data buffer area, and when the data occupancy rate is lower than a preset occupancy rate threshold value, calculating an idle time window of the target solid state disk according to the network bandwidth state; determining a target power consumption mode of the target solid state disk according to the duration of the idle time window; and controlling the target solid state disk to enter the target power consumption mode in the idle time window. By implementing the application, the energy consumption of the solid state disk can be reduced.

The invention discloses a fault diagnosis method and device for a server, and relates to the technical field of electric digital data processing, and the method comprises the steps: collecting the actual operation data of a target server, obtaining the configuration information and historical fault data, and detecting whether the target server breaks down or not; when a fault occurs, inputting the configuration information, the actual operation data and the historical fault data into a pre-constructed fault path generation model so as to output a corresponding diagnosis path; and determining a diagnosis rule, a diagnosis label and a fault type when the target server is diagnosed according to the diagnosis path to generate a current fault diagnosis result, thereby solving the technical problems that the flow is solidified and single, the dynamic adjustment cannot be realized, the flexibility and pertinence are lacked, the diagnosis efficiency is low and the diagnosis precision is poor, and achieving the purposes of shortening the diagnosis time and improving the diagnosis efficiency. The server energy consumption and maintenance cost are reduced, the fault positioning accuracy is improved, the fault solving time is shortened, and the technical effect of automatic fault diagnosis is achieved.

The invention discloses a solid state disk bad block wear balancing method and system, a medium and a program product, and relates to the field of electrical digital data process.The method comprises the steps that a storage area of a solid state disk is divided into a plurality of sub-areas, and a sub-area distribution table is generated; establishing a data block rotation link in each sub-region based on the sub-region distribution table and recording the data block rotation link as a rotation sequence table; executing a data block rotation operation according to a preset period, and recording a wear state of each sub-region; when a newly added bad block is detected, marking the sub-region containing the bad block as a to-be-migrated source region in the sub-region distribution table; determining a target region from other sub-regions according to the number of bad blocks and wear states recorded in the sub-region distribution table, and establishing a mapping relationship between the to-be-migrated source region and the target region; and migrating the data in the to-be-migrated sub-region to the target region according to the sequence in the rotation sequence table based on the mapping relationship and the rotation sequence table. The utilization efficiency of the standby block is improved, and the service life of the solid state disk is further prolonged.

The invention relates to the technical field of electric digital data processing, in particular to an artificial intelligence algorithm-driven digital human language understanding and corresponding reaction interaction method. The method comprises the following steps: collecting historical language data, text data, action instruction data and physical environment information of a user to obtain structured training data; vectorizing the text data to obtain a text semantic vector; environment feature vectors are extracted from the physical environment information; splicing the text semantic vector and the environment feature vector to obtain an environment enhanced text vector; and constructing a pre-training language model based on the environment enhanced text vector, and performing secondary pre-training to generate an optimized language model parameter. According to the invention, by fusing the user language and the physical environment information and combining with the multi-module intelligent component, context perception, efficient understanding and multi-modal natural interaction of the digital human in a complex scene are realized, and the intelligence, adaptability and user experience of the system are remarkably improved.

The invention discloses an intelligent user permission conflict detection method and system, and relates to the field of electrical digital data process.The method comprises the steps that a service flow diagram is constructed based on historical records; extracting a service flow direction sequence of the permission roles from the service flow direction graph, and calculating service association degrees among the permission roles according to the service flow direction sequence; grouping and clustering the authority roles according to the business association degree to obtain a plurality of authority role clusters; calculating a service boundary value of each permission role cluster; determining a target permission role cluster to which the target permission role belongs from a plurality of permission role clusters; calculating the business association strength between the permission role currently owned by the target user and the target permission role; when the business association strength exceeds a business boundary value, generating a first permission allocation control instruction; and when the business association strength does not exceed the business boundary value, generating a second permission allocation control instruction. The method and the device are used for solving the problem of authority conflict in a newly-added business scene, so that the security of the system is improved.

The invention discloses a ship target identification tracking method and system based on multi-source data fusion, and relates to the field of electric digital data processing, and the method comprises the steps: collecting ship monitoring data of a multi-source sensor, including position coordinates, area and color features; generating an initial ship trajectory according to the monitoring data, calculating the speed, area and color similarity of a detection point, and associating to obtain a trajectory pair; carrying out abnormal point identification and trajectory optimization based on a similarity energy function containing options such as dynamics and continuity; calculating the state difference between the optimized trajectories to perform similarity test, and judging the trajectories exceeding a threshold value as the same target to form a multi-source trajectory group; and calculating a dynamic fusion weight according to the error standard deviation of the trajectory relative to the average trajectory, and performing weighted fusion to obtain a target trajectory. By implementing the method, the fusion precision of the ship target trajectory can be improved.

The invention relates to the technical field of electric digital data processing, in particular to a display process temperature management optimization system which comprises a data acquisition module, a dynamic modeling module, a cooperative compensation module and an early warning module. The data acquisition module acquires temperature data of a heating area through a multi-type temperature sensor array, and generates a temperature field matrix with a confidence label through multi-source verification; the dynamic modeling module receives the matrix, operates a plurality of machine learning algorithms in parallel to generate a thermal coupling strength distribution diagram, and outputs a region coupling coefficient and a confidence coefficient thermodynamic diagram; the cooperative compensation module calls knowledge base strategies in a layered mode according to the thermodynamic diagram and feeds back early warning signals when abnormity is detected. According to the system, a cross-module closed-loop control mechanism is formed through confidence label driving algorithm weight distribution, confidence thermodynamic diagram control strategy priority, abnormal signal triggering model reconstruction and knowledge base updating, and millisecond-level cooperative suppression of the thermal crosstalk effect of multiple heating areas is achieved.

The invention discloses an intelligent water supply management system and method based on wind-light-water multi-source data fusion, and relates to the technical field of electric digital data processing. The system comprises a time alignment deviation quantification module, a time alignment regulation and control module, a data fusion timeliness response quantification module and a timeliness response regulation and control module. According to the method, the time alignment deviation quantization result is obtained by quantizing the multi-source data time parameters, whether time alignment regulation is carried out or not is judged, if yes, the timeliness response parameters are obtained after regulation, and if not, the timeliness response parameters are directly obtained and quantized to obtain the data fusion timeliness response quantization result; and whether PLC scanning interval regulation and monitoring verification are carried out or not is judged based on the data fusion timeliness response quantification result, so that the timeliness of water supply management demand response is improved, and the problem that in the prior art, the water supply management execution response flexibility is poor due to the fact that the time difference of multi-source data processing is not fully considered is solved.

The invention provides an intelligent fire-fighting fire prediction method and system, a medium and a program product, and relates to the technical field of electric digital data processing. The method comprises the following steps: firstly, acquiring real-time smoke monitoring data of a smoke alarm sensor in a target building, and once the data reaches a preset threshold value, immediately determining fire occurrence and locking a target fire source range; thirdly, calculating a fire risk value of the range by means of a fire risk assessment model trained based on multiple factors of a target building and a machine learning algorithm, and determining that a high-risk fire source point exists when the fire risk value is higher than a threshold value; then, combining meteorological data to obtain multi-dimensional environment characteristic data, and inputting the high-risk fire source point and the meteorological data into a fire spreading prediction model trained by a deep learning algorithm to obtain a fire spreading prediction condition; and finally, the situation is sent to a management end for alarming, and according to the method, rapid and accurate pre-judgment of the fire situation is achieved by means of multi-source data and an intelligent model.

The invention provides an intelligent road crack detection method, and belongs to the technical field of electrical digital data processing.The intelligent road crack detection method comprises the steps that firstly, a line structured light scanner is adopted to obtain road surface point cloud data, and attitude compensation processing is conducted in combination with an inertial navigation system; then noise is eliminated through bidirectional moving average filtering, and the depth matrix is converted into an image and segmented; gaussian background modeling is used to screen abnormal points, a connected graph is constructed through a minimum spanning tree algorithm, and pruning is carried out to obtain a crack skeleton; using a feature enhancement function to optimize the crack saliency; and finally, inputting a pre-trained multi-scale pavement damage identification network, and outputting a crack identification result and classification. According to the method, three-dimensional point cloud data, geometric feature analysis and a deep learning technology are fused, the technical problem of poor adaptability of a complex pavement environment is solved, and accurate identification of various crack types is realized.

The invention provides a high-temporal-spatial-resolution surface temperature reconstruction system for urban thermal environment refined monitoring, and relates to the field of electric digital data processing. Comprising a multi-source heterogeneous data intelligent acquisition and preprocessing module, an urban thermal environment multi-dimensional feature knowledge modeling module, a physical constraint deep learning surface temperature reconstruction module and an intelligent monitoring early warning and decision support module. The multi-source heterogeneous data intelligent acquisition and preprocessing module is responsible for collecting and preprocessing various remote sensing and ground observation data, and the urban thermal environment multi-dimensional feature knowledge modeling module constructs a knowledge system of an urban underlying surface, a three-dimensional form and a thermal process. The physical constraint deep learning surface temperature reconstruction module is used for realizing accurate reconstruction of high temporal-spatial resolution surface temperature, and the intelligent monitoring early warning and decision support module converts a reconstruction result into visual display, risk early warning and regulation and control decision suggestions; according to the system, high-precision, physically consistent and interpretable urban surface temperature reconstruction can be realized.

The invention provides a method, a device and equipment for realizing matrix operation on TPU (thermoplastic polyurethane) and a medium, and belongs to the technical field of electric digital data processing. The method comprises the following steps: dividing an input matrix according to the number of tensor calculation cores in TPU to obtain to-be-processed sub-matrixes corresponding to the tensor calculation cores; obtaining the matrix dimension of the to-be-processed sub-matrix and the capacity of a vector memory; according to the matrix dimension and the vector memory capacity, matching a target data processing template from the multiple pre-compiled data processing templates; according to the target data processing template, loading corresponding matrix data in the to-be-processed sub-matrix into a vector memory, so that a tensor calculation core performs matrix operation on the matrix data to obtain a sub-matrix operation result; and splicing the sub-matrix operation results to obtain a final matrix operation result. According to the method, the performance advantages of the TPU can be exerted, and efficient matrix operation on the TPU is achieved.

A power plant full life cycle carbon footprint monitoring method based on an intelligent carbon tube platform relates to the field of electric digital data processing, and comprises the following steps: acquiring unit load data and fuel consumption data, and detecting desulfurization outlet gas according to a multi-layer infrared detection structure to obtain gas concentration data; integrating the gas concentration data, the unit load data and the fuel consumption data to obtain a synchronous data set; and inputting the synchronous data set into a carbon emission dynamic measurement and calculation model to obtain a carbon emission trend prediction result of a preset future time period, inputting the synchronous data set into a multi-target optimization model, and carrying out optimization calculation by taking carbon emission intensity minimization, carbon quota utilization rate optimization and power generation cost minimization as targets to obtain an optimization scheme. And generating a carbon emission monitoring result based on the carbon emission trend and the optimization scheme, and performing unit start-stop and system load adjustment control according to the carbon emission monitoring result. By implementing the method, the monitoring precision of power plant carbon footprint monitoring can be improved.

The invention belongs to the field of electric digital data processing, and particularly relates to a multi-source cross-modal threat intelligence processing method, device and equipment and a medium. Innovation of a threat intelligence processing normal form is realized through a technical route of'double-layer progressive fusion ': a cross-modal fusion layer focuses on heterogeneous feature deep coupling of natural language CTI and picture CTI, and mapping of natural language semantics and visual information is realized by adopting a three-stage processing mechanism of'intra-modal self-attention, cross-modal cross attention and LSTM time sequence fusion'; and the multi-source alignment layer is used for carrying out joint modeling on CTI feature vectors subjected to cross-modal fusion, malicious codes and log data, constructing a three-dimensional association network of a CTI knowledge graph, a malicious code graph and a log time sequence graph, and realizing cross-source semantic space unification through a heterogeneous graph attention network. The problems that when a traditional threat intelligence processing method faces multi-source cross-modal data, collection and integration are difficult, preprocessing is insufficient, cross-modal feature fusion is difficult, and knowledge graph construction is imperfect are solved.