192results about How to "Ensure fairness" patented technology

The invention provides a source-network-storage integrated power distribution network operation optimization method, which relates to the technical field of power distribution networks and comprises the steps of setting an intelligent sensor, collecting multi-dimensional power distribution operation parameters, performing semantic extraction and automatic labeling, determining logic association after conflict correction, obtaining a power distribution knowledge graph, constructing a dynamic prediction model and performing power distribution index prediction. Obtaining a prediction result and generating a key index prediction map; constructing a multi-temporal-spatial-scale collaborative optimization model, setting an objective function, constructing a physical optimization sub-model and a market game optimization sub-model, carrying out interactive coupling, and carrying out collaboration, interactive feedback and iterative convergence to obtain a multi-scale collaborative optimization scheme; and carrying out distributed evaluation, generating an evaluation result, if the evaluation result does not reach the standard, packaging the multi-temporal-spatial-scale collaborative optimization model into an optimization contract, triggering the optimization contract, generating a candidate optimization scheme, carrying out repeated evaluation until the standard is reached, and taking the candidate optimization scheme as a consensus scheme and executing the candidate optimization scheme.

The invention discloses a charging pile power regulation and control method and system, and particularly relates to the technical field of charging pile management, data is collected from a multi-dimensional data source and summarized to form a preliminary information set, and a standard information set is obtained through format standardization processing; feature data of the charging pile are extracted based on the standard information set, and charging task analysis and resource occupation analysis are carried out respectively; inputting the analysis result into a pre-trained machine learning model to generate a charging priority coefficient; constructing a priority matrix, and dividing the charging piles into high priorities and low priorities according to the charging priority coefficients; and finally, dynamically adjusting a power distribution strategy according to the priority type, optimizing low-priority task power distribution while meeting high-priority task requirements, and improving the operation efficiency and the resource utilization rate of the charging station. According to the method, the real-time operation state and the charging task characteristics of the charging piles are combined, the power distribution strategy is dynamically adjusted, the priority division and power distribution proportion of the charging piles are adjusted in real time, and the self-adaptive capacity of operation of the charging station is improved.

The invention provides a performance management assessment system based on big data and a mathematical model, and the system comprises a data collection module which is used for collecting the work performance data of employees from a plurality of data sources in real time; the data preprocessing module is used for carrying out cleaning, denoising, missing value complementing and normalization processing on the collected original data; the index weight calculation module is used for calculating weights of indexes of different dimensions based on an analytic hierarchy process; the performance scoring module is used for generating comprehensive performance scores of the employees through a fuzzy comprehensive evaluation model; the time sequence prediction module is used for predicting future performance of the employees in combination with historical performance data and a machine learning algorithm; and the dynamic feedback and adjustment module provides personalized feedback and improvement suggestions for the employees based on the real-time performance data. According to the invention, multi-dimensional quantitative evaluation can be carried out on the work performance of the employee, personalized improvement suggestions are provided, and future performance performance of the employee is analyzed through prediction.

The invention discloses an LLM-based graduation design thesis intelligent evaluation system. The system comprises a data input module, a preprocessing module, an LLM evaluation module and an evaluation result output module. Text data of graduation papers are input into the data input module, the data input module is connected with the preprocessing module, the text data, processed by the preprocessing module, of the graduation papers are input into the LLM evaluation module, and the evaluation result output module is connected with the LLM evaluation module. According to the system, a large language model is utilized to perform multi-dimensional evaluation on graduation theses submitted by students, including but not limited to content quality, logic structure, language expression, innovation, academic specification and the like. According to the LLM-based undergraduate thesis evaluation system, the LLM can adapt to thesis evaluation requirements in different subject fields through a fine adjustment mode.

The invention discloses a distributed cluster resource autonomous scheduling method based on DSACO, and the method mainly comprises the steps: monitoring the state of each node and network topology information in a cluster system in real time, and carrying out the dynamic tracking of the operation condition of a distributed cluster; secondly, constructing a distributed resource autonomous scheduling model based on a multi-objective planning model and a swarm intelligence optimization thought; building a multi-stage resource allocation algorithm (DSACO) based on an autonomous resource allocation model, fairly selecting a task set, carrying out dispersed search on different tasks, carrying out further optimization processing on a preliminary allocation result, and determining a better resource allocation scheme; and finally, performing judgment and processing according to the QoS evaluation index, and enhancing the cluster reliability under a high-load condition. According to the method, resource allocation is optimized from multiple dimensions of resource allocation fairness, resource utilization rate and comprehensive load, the rapid batch allocation capability of distributed cluster resources in a large-scale system can be improved, and the service quality under the high-load condition is improved.

The invention provides a revenue distribution method for an internal alliance cooperative game of a virtual power plant, and the method comprises the steps: forming an alliance by distributed energy resources, and constructing a multi-agent optimization scheduling model in the virtual power plant with the maximized electricity selling revenue; constructing constraint conditions of a multi-body optimization scheduling model in the virtual power plant; determining a multi-agent cooperative game model of the game participants, the strategy set and the feature function; determining the overall electricity selling income of the virtual power plant, and designing a two-stage refined income distribution scheme of a multi-factor improved shapley value method and a kernel method based on odd-even circulation; and calling a CLPEX solver of the YALMIP on a Matlab platform to solve the cooperative game scheduling model and the income distribution strategy. According to the method, cooperative complementary operation among the alliances in the virtual power plant is realized, and fairness and rationality of income distribution among multiple subjects are ensured.

The invention discloses a self-adaptive resource scheduling method for improving cluster reconstruction efficiency, and relates to the field of resource scheduling distribution, and the method comprises the steps: monitoring the resource use data of each node in a cluster in real time through a monitoring agent, and obtaining the health state of the node through a heartbeat mechanism; collecting input task requests, predicting the resource demand quantity of each task, and performing preliminary queue processing on the tasks to obtain a task queue group; setting a resource competition constraint according to the task queue group and the resource use data and the health state of each node, obtaining dynamic resource scheduling corresponding to each task by using an optimization algorithm, and allocating the tasks to each node; a resource scheduling dynamic graph is constructed, a visual interface is set, and the resource scheduling dynamic graph is arranged on the visual interface and used for displaying resource scheduling, providing a user-friendly interface and realizing high-efficiency scheduling of adaptive resources in a cluster reconstruction process.

The invention relates to a class increment image classification method and system based on knowledge distillation. The method comprises the following steps: generating output representations of old class training samples among old classes through a teacher model, and respectively carrying out Z score standardization to obtain first standard output, generating old class training samples, inter-class output expressions of the new class training samples between new classes and old classes and intra-class output expressions of the old class training samples between the old classes through a student model, and respectively performing Z score standardization to obtain second standard output and third standard output; calculating inter-class distillation loss according to the first standard output and the second standard output, calculating intra-class distillation loss according to the first standard output and the third standard output, and obtaining total loss according to the inter-class distillation loss, the intra-class distillation loss and the classification loss; and optimizing the total loss of the image classification model to obtain a trained image classification model, and carrying out image classification by adopting the trained image classification model. By adopting the method, the accuracy of image classification can be improved.

The invention belongs to the technical field of federated learning, and particularly relates to a decentralized federated learning method and system oriented to a computing power network. The method comprises the steps that a model demand side in a computing power network uploads a federated learning task to a block chain network, screening computing power nodes according to a reputation score and a data score, and taking the screened computing power nodes as computing nodes to participate in training; the trained local model is verified through the block chain network and then is sent to a model demand side; and the model demander uses a weighted aggregation algorithm based on data fairness to aggregate the local models received in the current round and updates the global model by using an aggregation result. According to the method, the block chain is combined with federated learning under the background of the computing power network, and the universality of the model and the fairness of data are improved while the availability of the federated learning model and the node training enthusiasm are ensured.

The invention provides a post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency, and relates to the technical field of emergency logistics multi-target distribution optimization under the background of urban earthquake disasters, and the method comprises the following specific steps: S1, determining a basic hypothesis: considering the actual scene of disaster rescue operation, and making the basic hypothesis for the emergency material distribution problem; s2, establishing an emergency material distribution fairness model; s3, establishing a disaster point emergency material demand emergency degree evaluation index system; s4, establishing a multi-target path optimization model; and S5, solving the established multi-target model by using an improved mixed multivariate universe optimization algorithm. The mixed multivariate universe algorithm based on differential evolution is provided, higher convergence precision and better robustness and uniformity are achieved, the earthquake disaster emergency logistics distribution path optimization accuracy is improved, material demand urgency meeting and distribution fairness are guaranteed, the distribution cost is reduced, and the distribution efficiency is improved. And the disaster victim satisfaction degree and the demand emergency adaptation degree are improved.

The present invention discloses a method, device, medium and equipment for multi-department duty scheduling. The method first sorts the duty personnel by department to obtain a duty personnel sequence, and then arranges them into segmented time according to the cyclic order of the duty personnel sequence to form a duty scheduling table. When sorting by department, first sort according to the number of duty personnel in the department, and then calculate the insertion sequence number. The ranking position of the duty personnel in the duty personnel sequence is obtained by calculating the insertion sequence number, and finally the duty personnel sequence is formed. The method disperses the positions of the duty personnel of each department in the duty personnel sequence, so that the duty personnel of each department are averaged in the duty personnel sequence, thereby making the duty scheduling more scientific and fair.

The invention discloses an online car-hailing fee scheduling method based on supply and demand prediction and a storage medium. The invention relates to the technical field of online car hailing. Passenger request data including driver positioning information 1, weather forecast API information w, traffic flow data f and event calendar e are integrated to form a data set D; useful numeric features are screened out from the data set D, and comprise the thermodynamic levels of a departure place and a destination, weather classification codes and time sequence features; and the local space extractor processes geographical distribution data of a departure place and a destination by using a CNN, extracts a feature map in combination with GIS data, and carries out training to obtain local space correlation A of the online car-hailing demand. A model integrating the CNN, a dynamic selection regression model (gradient boosting tree) and global correlation analysis is adopted, and the online car-hailing demand is obtained. Therefore, the supply and demand conditions of online car-hailing can be predicted precisely. According to the method, the influence of local space, a time sequence and global factors on supply and demand can be captured, and the prediction accuracy and adaptability are improved.

The invention discloses a system and a method for intelligently identifying a bid invitation on the basis of artificial intelligence, relates to the technical field of intelligent bid invitation identification, and solves the technical problem that the execution time of a bid invitation project is easily delayed as a bid invitation behavior is not identified in time in a bid invitation process in the prior art. The method comprises the following steps: analyzing a price floating rate between a quoted price of each associated enterprise in an associated enterprise group and a bid invitation quoted price; respectively counting the number of associated enterprises whose price floating rates are higher than or lower than a floating threshold value; extracting a plurality of bid winning information from the historical data, and analyzing the historical bid winning rate of the associated enterprise group; analyzing the similarity between the bidding files finally participating in bidding, and analyzing the similarity ratio of the bidding files; the number of the associated enterprises with the price floating rate higher than or lower than the floating threshold value, the historical bid winning rate of the associated enterprise group and the similar proportion of the bidding files are synthesized, so that bid enclosing or bid confusion behaviors in the bidding process can be timely and effectively found, and a fair and impartial bidding and tendering competition environment is promoted.

The invention belongs to the technical field of computing power scheduling, and discloses a bare metal server computing power scheduling platform which comprises the following steps: when an rth server receives task data sent by a user side, setting a network delay condition according to the task data; the rth server records network delay during mutual communication of the R servers, the servers in the network delay condition are divided into a processing partition, and the servers in the same processing partition are connected through a block chain to form a partition block chain; the first analysis module marks a server receiving the task data of the user side as a receiving server, the receiving server extracts feature data of the task data, inputs the feature data into a pre-trained splitting evaluation model to obtain a splitting evaluation result, generates task description data according to the splitting evaluation result, and sends the task description data to the server; issuing the task description data to a partition block chain; computing resources are fully utilized, and efficient computing power resource scheduling is achieved.

The present invention belongs to the technical field of autonomous driving vehicles, and specifically, is a method for real-time evaluation of the complexity of continuous test scenarios of autonomous driving vehicles. The method comprises the following steps: step 1, establishing a scenario complexity evaluation framework; step 2, analyzing the feature weights of scenario elements; step 3, calculating the element-system complexity mapping relationship; step 4, synthesizing the scene complexity features obtained in steps 2 and 3 to obtain the real-time complexity evaluation results of the continuous test scenario. The present invention can evaluate the complexity of the surrounding environment in real time according to the vehicle driving process, and solves the non-real-time problem existing in the existing test scenario complexity evaluation method.

The invention provides a federated learning-based client selection method and system. The method comprises the following steps of: logically grouping clients participating in federated learning; based on the received locally optimized model parameters of the clients, calculating angular distance mixing similarities between the locally optimized model parameters and the clients in the group; wherein the calculation of the angular distance mixing similarity specifically comprises the following steps: carrying out principal component analysis on the locally optimized model parameters of the clients, and calculating the similarity of the two clients by adopting an angle and distance combined mode based on the component scores corresponding to the obtained principal components; determining a selected weight of the current client based on the obtained angular distance mixing similarity between the client and other clients in the group; and based on the weight of each client in the group, determining the clients participating in global model aggregation in the current iteration round.

The invention discloses an intelligent college entrance examination voluntary adaptation method and system, relates to the technical field of education examination assistance, and aims to solve the problems of information asymmetry, individual difference of examinees and voluntary filling efficiency and accuracy during voluntary filling of the examinees. According to the method, advanced technologies such as big data analysis and an artificial intelligence algorithm are integrated, a personalized and accurate volunteer filling scheme is provided for examinees, and the scientificity and rationality of volunteer selection are remarkably improved. The system automatically processes mass data, relieves the burden of examinees, updates information in real time, and ensures the accuracy of decision basis. And meanwhile, the system ensures fairness and transparency, the educational resource distribution is optimized, and the admission satisfaction is improved. Generally speaking, the patent not only renovates a traditional volunteer filling mode and improves efficiency and satisfaction, but also promotes educational fairness through a scientific method and realizes effective adaptation between examinees and colleges.

The invention belongs to the technical field of airport flight scheduling and information, and discloses a flight reduction and timing method and system based on an airport transportation and management committee mode. The method comprises the following steps: acquiring flight adjustment information, and establishing a flight operation adjustment information set; the bureau side confirms a next-day flight scheduling and reducing target set according to the flight operation adjusting information set; after the airport operation command center obtains the next-day flight adjustment and reduction target, consultation participated by the airport, the airline and the bureau is carried out, the influenced next-day flight is evaluated, and a flight adjustment scheme is negotiated; and for the flight adjustment scheme, each airline confirms and is consistent with an airport and a bureau, and a final flight adjustment and reduction scheme is formed. The airport flight cooperation capability can be effectively improved. Information sharing is carried out by establishing multi-party information channels, and flight adjustment information can be obtained in time; flight adjustment gives consideration to flight reduction and time adjustment, and airport flight throughput can be ensured as much as possible under the condition of maximum capacity.

The invention discloses a power transaction management and control system and method based on a block chain. The system comprises a data collection module, a transaction module, a transaction auction module, a block chain intelligent contract module and an enterprise evaluation module. The data collection module is used for receiving power purchase application information of a buyer and power sale information of a seller, and storing the information on a block chain; and the transaction auction module is used for solving to obtain the power purchase application information of the optimal combination and storing the power purchase application information on the block chain. The block chain intelligent contract module is used for generating a seller preset transaction order and a buyer preset transaction order so as to carry out power transaction; and the enterprise evaluation module is used for evaluating the transaction credit of the buyer and the seller. According to the method, the transaction information is reliable and cannot be tampered based on the block chain, an auction mode of dynamic fluctuation of the number and price of power transactions is realized, meanwhile, a selling scheme of optimal benefit of a seller is solved based on the target function and the constraint condition, and good operation of an order success rate and a platform is effectively promoted.

The invention relates to an edge computing task allocation method, device and system, a storage medium and a product. The method is applied to an edge cluster, the edge cluster comprises an auditing node, a leader node and a plurality of edge nodes, and the method comprises the following steps: the auditing node obtains registration information sent by each edge node and registers all the edge nodes added into the edge cluster according to the registration information; and the leader node splits the task into a plurality of sub-tasks, distributes the sub-tasks to the at least two edge nodes which are registered, performs correctness verification on execution results of the sub-tasks, integrates the sub-tasks with correct verification results, and sends the integrated task result to the user side. Therefore, a plurality of edge nodes serve as a collaborative whole and can adapt to dynamic node changes, consistency can be quickly achieved no matter the edge nodes exit or join, and fairness and consistency of task allocation in a dynamic and unreliable network environment are guaranteed.

The invention discloses a Fluent-based numerical simulation and experimental design method for shaking of liquid in a tank of a tank truck, and belongs to the technical field of numerical simulation of fluid dynamics (CFD). The method includes the steps that the hole opening position of the wave-proof plate serves as an independent variable, multiple hole opening position schemes are designed, liquid shaking related indexes are determined to serve as dependent variables, a geometric model is established according to the actual structure of a tank truck tank body, a fluid mechanics model and boundary conditions are set, a solver is configured, output parameters and numerical simulation initial values are set, and an experiment is executed. The liquid shaking process of a certain swash plate trepanning scheme is simulated, acting force data, namely shaking index data, between liquid shaking and the inner wall of the tank body is recorded, the advantages and disadvantages of each trepanning position scheme are evaluated based on the shaking index data, and the optimal scheme is determined. According to the method, a real object experiment is replaced by CFD numerical simulation, parameter setting is carried out, the experiment cost and the experiment risk are remarkably reduced, the experiment efficiency is improved, and data support can be provided for industry design.

The invention relates to a task generation and robot scheduling method and system based on a cache region, and the method comprises the steps: a scheduling system generates a cache task of a cache region bound with each station point in a designated region according to the task demand of the designated region monitored in real time; the scheduling system schedules the robots among the plurality of cache regions in a balanced manner in a cache period according to the cache tasks of all the cache regions and all the to-be-scheduled robots, so that the robots are cached in each cache region; and the scheduling system issues a scheduling instruction to all the robots in the cache region bound to each station point after the robot at each station point completes the task, and the scheduling instruction is used for enabling each robot to move to the next node according to a one-way queuing mechanism so as to complete the task in order. According to the method, the problem of robot idling caused by mismatching of upstream and downstream tasks is effectively solved, and the overall operation efficiency of a factory automation system is improved.

The invention relates to a block chain and proxy re-encryption-based outbound data sampling inspection supervision method. The method comprises the steps of generating a first public and private key pair of a data outbound enterprise and a second public and private key pair of a supervisor according to public parameters; encrypting the plaintext data by using the first public and private key pair; carrying out sampling inspection on the first ciphertext obtained by encryption to obtain a second ciphertext, and sending sampling inspection information obtained according to the second ciphertext to the block chain node; after the re-encryption key passes verification, the block chain node re-encrypts the second ciphertext by using the generated re-encryption key; decrypting the obtained third ciphertext to obtain plaintext data; and checking the plaintext data by using the declaration record, and uploading an obtained spot check result to the block chain node. According to the method, the risk of large-scale privacy disclosure is avoided, the supervision efficiency and the supervision effectiveness and reliability are improved, spot check information can be provided as evidence, the transparency of a supervision process is improved, and the trust between an enterprise and a supervision department is enhanced.

The invention provides an error evaluation method, device and equipment of a current transformer and a medium, and belongs to the technical field of transformer evaluation. The error assessment method comprises the following steps: acquiring secondary current data of a current transformer to be assessed; decomposing the secondary current data into an intrinsic mode component and a residual component by using an ensemble empirical mode decomposition method; reconstructing the intrinsic mode component to obtain a high-frequency component and a low-frequency component; and inputting the secondary current data, the high-frequency component, the low-frequency component and the residual component into an error evaluation model, and evaluating the error state of the current transformer. In order to solve the problem of large fluctuation of the current data, the secondary current data of the transformer is decomposed and reconstructed, the reconstructed data is processed in a layered iteration fusion mode, and finally the evaluation result of the current transformer is obtained, so that the evaluation precision is high.

The laboratory equipment informatization management system is provided with experimental equipment, a middle-layer monitoring unit and an upper-layer online management unit, the middle-layer monitoring unit accesses a serial port of the experimental equipment through a switch, collects state data of the experimental equipment, collects environment data of an environment where the experimental equipment is located, and sends the state data to the upper-layer online management unit; collecting image data of a space where the experimental equipment is located; according to a parameter setting instruction and a start-stop instruction of the upper-layer online management unit, working parameters and start or stop operation of the experimental equipment are controlled; the upper-layer online management unit communicates with the middle-layer monitoring unit through a Modbus TCP / IP communication protocol, then collects state data, environment data and image data collected by the middle-layer monitoring unit, and sends set parameters and start-stop instructions to the middle-layer monitoring unit. The system can be compatible with communication and management of experimental equipment of different types and brands, and can also automatically collect and record state data of the experimental equipment.

The invention discloses a multi-target snapshot method, device and equipment in an examination room environment and a storage medium, and relates to the technical field of computer vision, and the method comprises the steps: detecting the central point coordinate position of each target object in an examination room panoramic top view shot by a panoramic camera; for any target object, determining a to-be-rotated angle of the close-up camera and a field angle required for focusing the close-up camera to the target object based on the central point coordinate position of the target object; and adjusting the close-up camera according to the to-be-rotated angle and the field angle, and capturing the target object through the adjusted close-up camera. According to the method, the binocular camera is installed at the top of the examination room, so that the target object is quickly and accurately captured, the working efficiency of invigilators is improved, meanwhile, the fairness of examinees is ensured, and the effect of efficiently and reliably scanning the examination paper in the remote examination paper marking process is achieved.

According to the offline chess and card activity cheating detection method, system and equipment, analysis is carried out based on facial expressions and hand actions of players and card surface conditions on a card table during chess and card activities, whether the players cheat or not can be comprehensively judged, and compared with a single-factor judgment method, the judgment result is more accurate, and the detection efficiency is improved. And the fairness of offline chess and card activities is effectively improved.

The invention discloses an electricity consumption measurement fee settlement optimization method based on a causal conduction model. The method comprises the following steps: S1, collecting user electricity consumption behavior data and policy intervention data to form a data set; s2, key user power consumption behavior characteristics are extracted from the user data set through principal component analysis, a clustering algorithm and a random forest algorithm are used for analyzing response modes of users to different policies, and user data sets and policy intervention data sets of different user category matrixes are formed; s3, analyzing the causal relationship between the power consumption behavior of the user and the policy intervention change through Granger causal verification and a structural equation model; and S4, simulating user power consumption behavior responses in different policy intervention scenes by adopting a reinforcement learning algorithm, simulating influences of different policy combinations by adopting a Monte Carlo method, and determining an optimal policy intervention strategy. According to the invention, the problems of complex non-linear relationship and difficult policy intervention effect prediction in the power consumption side fee settlement process under the power marketization background are solved.

The invention discloses a fair federated learning method based on homomorphic encryption and capable of protecting user privacy, and relates to the technical field of federated learning, and the method comprises the steps: assuming that a local gradient for aggregation is defined as # imgabs0 # gradient delta wj based on local calculation, j represents the jth round of aggregation, and i represents the ith participant; and the core element is a sound coefficient and is represented by # imgabs1 #. According to the homomorphic encryption-based fair federated learning method capable of protecting the privacy of the user, a homomorphic encryption method is adopted, local gradient is used, the usability of training is improved, and meanwhile, the privacy of the user is protected; the federal learning method which can realize privacy protection and fair cooperation is provided, correctness and safety can be considered, and fairness can be kept; the method belongs to a fair federal learning scheme, that is to say, for participants with more contribution data, models with higher accuracy or higher quality can be obtained.

The present invention belongs to the field of data integrity auditing, and specifically relates to a continuous auditing scheme for big data integrity in decentralized storage. It includes a data owner and a storage provider, wherein the data owner is a verifier and the storage provider is a prover. The steps of the scheme are as follows: the data owner preprocesses the data and generates public parameters. The data owner uses the data and public parameters to generate a label set, and sends the public parameters to the storage provider. The storage provider uses a continuous verifiable delay function and a retrievability proof technology and a verifiable random function to calculate through the data and public parameters to obtain a result set, a proof set and a challenge set. The generated result set is then sent to the data owner. The data owner performs verification calculations on the received result set to obtain a verification result. The present invention not only ensures that accurate verification of data integrity can be performed continuously, but also reduces the transmission costs of the challenge stage and the proof stage.