40 results about "Column generation" patented technology

The invention is applicable to the technical field of computing power distribution, and provides a power equipment edge gateway computing power dynamic distribution method, which comprises the following steps of: determining a system state representation vector based on multi-source data of power equipment; the system state representation vector comprises a real-time computing power occupancy rate, a task queue length, a network bandwidth utilization rate and an equipment energy consumption index; constructing target parameters and constraint conditions of the calculation power dynamic distribution model; taking the system state representation vector as input, solving the computing power dynamic allocation model through a reinforcement learning algorithm, and generating a task scheduling path and a resource allocation proportion; and optimizing a preset hierarchical buffer queue based on the task scheduling path and the resource allocation proportion, and generating a target dynamic allocation strategy. According to the method, the high-real-time task response speed and the resource utilization rate are improved, energy consumption is reduced, meanwhile, backlog tasks are efficiently processed in the idle period through an event-driven mechanism, and system load balance is guaranteed.

The invention discloses a multi-unmanned aerial vehicle distribution and bus charging combined path optimization method, which aims at minimizing task total time, constructs a mixed integer programming model based on a space-time network, and comprehensively considers unmanned aerial vehicle electric quantity constraint, demand point full coverage, bus time window and charging pile number limitation. An original model is decoupled to limit a main problem and a sub-problem by adopting branch pricing and a Dantzigzag-Wolfe decomposition theory, the main problem deals with demand coverage and charging resource allocation, and modeling is a set coverage problem; for single-machine path generation, the latter is modeled as a resource-constrained and replenishable shortest path problem with a time window dependent feature. A multi-unmanned aerial vehicle initial solution is constructed through a random generation method, a dual variable is iteratively solved after a main problem is initialized, a sub-problem is solved based on a multi-label algorithm, and global optimal solution search is realized in combination with a column generation mechanism and a branch strategy. According to the invention, through joint optimization of the departure time and path planning of the unmanned aerial vehicle, the collaborative optimization problem of distribution and charging is accurately solved.

A compiling method and apparatus, a device, a storage medium, and a program product. The compiling method comprises: unrolling an instruction sequence in a loop BB to generate multiple BBs and multiple DAGs corresponding to the multiple BBs, wherein the multiple BBs comprise an instruction sequence unrolled at least three times, and the multiple DAGs are used for indicating a data dependency relationship between instructions in the multiple BBs (810); performing window scheduling on the basis of the multiple BBs, and determining a target scheduling window and a scheduling result corresponding to the target scheduling window, wherein the window scheduling is used for dividing the instruction sequence in the loop BB into two parts on the basis of the scheduling window, such that the instruction sequence of the second part in a current loop and the instruction sequence of the first part in the next loop are synchronously executed, the target scheduling window is a scheduling window of which the execution time satisfies a time condition, and the execution time is the time required for executing instructions in the window and calculated on the basis of the data dependency relationship in the multiple DAGs (820); and on the basis of the scheduling result corresponding to the target scheduling window, performing compiling to obtain a target code of the loop BB in a target program (830).

The invention provides an autonomous controllable database intelligent adaptation method and system oriented to localization ecology, and belongs to the technical field of database middleware and cloud native resource management. When the request label is missing or wrong, JDBC metadata, a system function return value and system table structure features are collected through detection connection to generate database fingerprints, and the database type and version are recognized in the localized feature library. For an SQL execution request, parsing the SQL into an abstract syntax tree based on a unified syntax parser, rewriting nodes according to a dialect rule base to generate a target dialect SQL, and performing paging, identification column generation, identifier reference, transaction-related semantic verification, execution plan optimization and dynamic routing execution; and thermally loading from the plug-in warehouse when the driver is missing. And for the resource opening request, calling a Kubernetes Operator to automatically create a domestic database instance, and returning connection information. The system integrates a national cryptographic algorithm and behavior auditing, and realizes connection information protection and abnormal SQL detection.

A system for generating alternative shifts for contact center agent scheduling according to an embodiment includes a system that receives a staffing requirement forecast indicative of a number of agents required to handle a workload forecast. Agent data is received for a plurality of agents. The system receives a service level override value and performs column generation to identify shifts for the agents based on the staffing requirement forecast, agent working rules, and work plan constraints. A subset of the shifts is selected to generate an optimized agent shift schedule. A request to replace an individual shift of the optimized shift schedule with an alternative shift is received. In response, the system identifies the alternative shift from the plurality of shifts based on the service level override value. The individual shift is replaced with the identified alternative shift on the optimized shift schedule.

The invention discloses a dynamic column report generation method and system based on multi-source data fusion, a terminal and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data, and preprocessing the multi-source heterogeneous data to obtain preprocessed multi-source heterogeneous data; constructing a multi-source data association knowledge graph and a work order process execution track according to the preprocessed multi-source heterogeneous data, and constructing a distributed data association calculation engine according to the multi-source data association knowledge graph; and obtaining a target query condition, and according to the target query condition, performing report column number generation processing and report column number expansion processing by adopting the distributed data association calculation engine and the work order process execution track to obtain a target demand dynamic list. By constructing the distributed data association calculation engine, report column number generation processing and report column number expansion processing can be performed according to the target query condition, so that the target demand dynamic list is obtained, and the generation efficiency of the gas report is effectively improved.

The invention relates to the technical field of network security intelligence, in particular to an information acquisition scheduling system based on dynamic weight optimization, which comprises a data monitoring and acquisition module, a scheduling queue generation module, a content analysis and feedback module, a crawler execution module and a content analysis and feedback module. Collecting user interaction data, content change rate and the like of a target intelligence source through a data monitoring and collecting module; the dynamic weight calculation module calculates an activeness score, a historical hit rate and a low-frequency protection gain according to the data and fuses the data to obtain a comprehensive final weight; a scheduling queue generation module builds a main queue and a low-frequency guarantee pool according to weight sorting, and distributes differential grabbing parameters; the crawler execution module performs distributed parallel grabbing; the content analysis and feedback module judges validity and feeds back adjustment parameters to form a closed loop; according to the method, the capture coverage rate and timeliness of the low-frequency high-value intelligence source can be improved, the utilization rate of system resources is optimized, and the requirements of network security intelligence for information comprehensiveness, timeliness and accuracy are met.

The application discloses a kind of management systems and implementation methods of full life cycle operation and maintenance, it is related to full life cycle technical field.Constructs full cycle initial dynamic operation and maintenance hypergraph;Generation represents the dynamic hypergraph time sequence evolution sequence of system topology evolution along with life cycle;Generation full cycle space-time fusion operation and maintenance representation;Output the multidimensional abnormal risk prediction distribution atlas of the future operation trend of representation system;Based on the full life cycle management library, the corresponding resource degradation, isolation or expansion strategy is matched, and the adaptive operation and maintenance scheduling execution instruction set for directly intervening target system is generated.The application makes that when processing the full cycle long sequence data of mass node scale, the time axis resolution of operation and maintenance characteristics is significantly improved, especially in the correlation tracing of long-range latent fault.

A method for optimizing the cutting of pipe profiles with bevel cutting in mind includes the following steps: S1. Generates map data for the row spacing of all beveled parts to facilitate rapid data acquisition for subsequent layout; S2. Generates all feasible cutting columns for the maximum-length pipe profile material to ensure a universally feasible solution; S3. Screens feasible cutting columns that compress length in the case of bevel cutting, and simultaneously determines feasibility after extending the material length for bevel cutting; S4. Uses a Cplex-based integer programming model to solve the one-dimensional stock cutting problem for the obtained feasible columns; S5. Further optimizes large-scale problems in actual production processes using a solution space search scheme based on grouped iterative optimization. Different strategies for invoking the integer programming model are used to solve problems of different scales. This solution uses a specific optimization strategy to group the pipe profiles to obtain base solutions and combines them, narrowing the solution space of a single integer programming, thereby significantly reducing the overall problem-solving time.

The invention discloses a demand side capacity two-stage robust planning method considering cost and flexible resources, and belongs to the technical field of power system operation and maintenance. According to the method, uncertainty adjustment parameters are introduced, a box type uncertainty set capable of flexibly adjusting conservative property is constructed, the inherent defect that a traditional robust optimization method is too conservative is effectively relieved, the model can dynamically adjust the boundary of the uncertainty set according to different bearing capacities of an actual system to risks, and the robustness of the system is improved. Therefore, refined balance of economy and reliability can be realized in the planning stage. According to the constructed two-stage robust optimization model considering the electricity-carbon coupling mechanism, the carbon transaction cost is taken as an important component of the operation cost to be incorporated into the target function, and the requirements of the national green low-carbon energy strategy are effectively responded. According to the improved column generation and interior point hybrid algorithm designed by the invention, a self-adaptive scene screening mechanism and a gradient guide initialization strategy are provided, the solving time is remarkably shortened, and meanwhile, the global optimality of understanding is ensured.

The present invention provides a column-generated multi-vehicle path planning method, system, device, and medium, relating to the field of vehicle path planning technology. The method comprises: step S1: constructing data annotations and converting the data annotation problem into a mixed integer programming problem; step S2: solving the mixed integer programming problem through each iteration, annotating the data, and performing neural network training; and step S3: calculating multi-vehicle path planning based on the trained neural network. The present invention constructs a path planning algorithm applicable to a variable number of vehicles while maintaining speed and accuracy.

The invention discloses an efficient and parallel autoregressive diffusion model hardware acceleration system based on an FPGA (Field Programmable Gate Array), and relates to the technical field of FPGA and machine learning. The system comprises a host end and an FPGA hardware end, the host end is responsible for preprocessing and post-processing of data, and the FPGA hardware end comprises a system control scheduling layer, a three-level storage structure layer and an autoregressive diffusion parallel architecture. According to the method, a collaborative pipeline design is adopted, deep parallelism of an autoregression Token generation process and a diffusion picture generation process is realized through a Token grouping adding mechanism, and a diffusion denoising process can be started without waiting for the completion of complete sequence generation. Besides, a universal attention processing hardware module is designed in the system, QKV projection and matrix multiplication are realized by using a systolic array, and calculation resources are optimized in cooperation with a pipelined Softmax calculation and delay normalization strategy. Through collaborative design of software and hardware, the problems of high reasoning delay and large resource overhead of the autoregression diffusion model in an edge computing scene are effectively solved, and the throughput and the energy efficiency ratio are remarkably improved while the generation quality is ensured.

The invention provides an automatic testing method and device based on test sequence generation, equipment and a medium, and relates to the technical field of server testing. The method comprises the following steps: acquiring natural language test requirement data, and generating a structured target test step sequence based on a test requirement; then resource state information of the environment to be tested is collected, the resource state information is combined with the target test step sequence, and a test task scheduling instruction used for indicating a step execution strategy is generated; and distributing the test task scheduling instruction to a plurality of distributed execution nodes in a to-be-tested environment, and executing test steps in the scheduling instruction by the distributed execution nodes to obtain a corresponding test execution result. According to the scheme, automatic generation from natural language requirements to executable test steps can be achieved, reasonable scheduling is carried out based on the environment resource condition, and the execution efficiency and accuracy of the test task in the distributed environment are improved.

A secure aggregation computation device 10 comprises: a sorting unit 1 that, by sorting rows of a table which includes a share {→g} of a group key column →g, a share {→o} of an order key column →o, and a share {→v} of a data column →v on the basis of the order key column →o and the group key column →g, generates a sorted table using the share {→g}, the share {→o}, and the share {→v}; a group boundary flag column generation unit (2) that uses a share {→g'} of a sorted group key column →g' included in the sorted table to generate a share [→b] of a boundary flag column →b which indicates a boundary between groups in the sorted table; and an aggregation computation unit 3 that uses the share [→b] and a share {→v'} of a sorted data column →v' included in the sorted table to perform, for each group, a prescribed aggregation computation using a prescribed binary operator.

The invention relates to the technical field of production management, and discloses an MES system intelligent production scheduling and tracing method and system, and the method comprises the steps: generating a batch index according to a loading list and a UDI column, executing parallel batch matching on the basis of the batch index to obtain a loading sequence, and forming a loading matrix through cavity filling under the constraint of carrier capacity. Then, time mapping is obtained through environment sequence sliding window alignment, fluctuation suppression is executed according to the time mapping to obtain smooth mapping, stable input is obtained in a corresponding section of the loading matrix, binding check is executed on the smooth mapping through a cyclic curve to generate a binding matrix, threshold comparison is executed on the release record according to the binding matrix to obtain a consistent vector, and the consistent vector is obtained; the continuous processing enables adjacent batches to form continuous loading at the same carrier number and level position, reduces parallel loading failure and temporary distribution change, reduces sterilization switching loss, inhibits short-time fluctuation in a time mapping limited section, and further inhibits capacity fluctuation of the clean room.

The invention discloses an over-path traffic solving method for a completely-crowded large-scale public transportation network, and the method comprises the steps: obtaining a getting-on arc and a corresponding effective departure frequency through a first algorithm; the second algorithm calculates the use probability of passing through all the upper turning arcs under the super path, so that the complete upper turning arc flow distribution of the super path, the use probability vector of the upper turning arcs and the use probability vector upper turning arcs of the nodes are obtained; then, adopting column generation to construct an OD pair sub-problem, and determining the optimal transfer amount from a non-reference super path to a reference super path in the OD pair sub-problem through binary search; and the fourth algorithm obtains an over-path traffic equilibrium solution under the public transportation network equilibrium condition. Therefore, by setting the first algorithm, the second algorithm and the like, a complete algorithm capable of processing large-scale public transportation network distribution is constructed, so that the super path is constructed based on the effective departure frequency, and the method is suitable for practical application of the city-level large-scale public transportation network.

A secure aggregation computational device 10 comprises: a sorting unit 1 that generates a sorted table in which rows of a table are sorted on the basis of an order key column →o and a group key column →g; a group boundary flag column generation unit 2 that generates a share [→b1] of a boundary flag column →b1 representing boundaries of groups in the sorted table; an intermediate aggregation result column generation unit 4 that generates a share {→p} of an intermediate aggregation result column →p, an i-th element of which is the value obtained by calculation using some or all of the elements included in a group to which the i-th element belongs; a subgroup boundary flag column generation unit 5 that generates a share [→b2] of a boundary flag column →b2 representing boundaries of subgroups within each group in the sorted table, a subgroup being a portion within each group where the value of an order key is the same value consecutively; and a final aggregation result column generation unit 6 that generates a share {→s} of a final aggregation result column →s in which values aggregated from elements belonging to respective subgroups within the intermediate aggregation result column →p are the respective elements belonging to each subgroup.

This invention relates to a two-stage robust planning method for demand-side capacity that considers both cost and flexibility resources, belonging to the field of power system operation and maintenance technology. By introducing an uncertainty adjustment parameter, this invention constructs a box-shaped uncertainty set with flexibly adjustable conservatism, effectively mitigating the inherent drawbacks of overly conservative traditional robust optimization methods. This allows the model to dynamically adjust the boundary of the uncertainty set according to the different risk tolerances of the actual system, thus achieving a refined trade-off between economy and reliability during the planning stage. The two-stage robust optimization model constructed in this invention, considering the electricity-carbon coupling mechanism, incorporates carbon trading costs as an important component of operating costs into the objective function, effectively responding to the needs of the national green and low-carbon energy strategy. The improved column generation and interior point hybrid algorithm designed in this invention proposes an adaptive scenario selection mechanism and a gradient-guided initialization strategy, significantly shortening the solution time while ensuring the global optimality of the solution.

The invention relates to the technical field of equipment operation evaluation, and discloses a stable operation limit evaluation method and system for power electronic equipment under a weak power grid condition. Comprising the steps of multi-source heterogeneous parameter fusion acquisition, dynamic adaptive coupling modeling, full-scene disturbance sequence generation, real-time simulation scheduling operation, multi-dimensional time sequence feature mining and stability margin quantitative judgment. Multi-source heterogeneous parameter fusion acquisition synchronously covers a power grid side impedance spectrum, voltage transient characteristics and equipment side switch transient parameters, and an effective parameter set is constructed through heterogeneous data calibration and redundancy suppression processing; according to the method, the problems of single parameter acquisition and insufficient data validity in traditional evaluation are solved, evaluation one-sidedness caused by single parameter acquisition is avoided, invalid data are eliminated through redundancy suppression, and data precision is guaranteed through calibration processing; the technical problem that parameters in a weak power grid and power electronic equipment interaction scene are complex and diverse and are difficult to effectively collect is solved.

The embodiment of the invention provides a multi-DAG-based distributed heterogeneous software task scheduling sequence generation method. The method comprises the following steps: merging a plurality of sub-DAG task graph sequences into a total DAG task graph; performing key importance level division on tasks in the total DAG task graph, and expressing the key importance level of each task node in a quantitative assignment form; calculating the quadratic variance of the calculation cost of each task node in the total DAG task graph; calculating the communication cost of each task node in the total DAG task graph; and forming a task scheduling sequence according to the key importance level of each task node in the total DAG task graph, the quadratic variance of the calculation cost and the communication cost. According to the embodiment of the invention, the problem of multi-task reasonable scheduling in a heterogeneous environment is effectively solved, blocking can be avoided, and potential problems possibly existing when a simulation system processes different task nodes can be found in time, so that simulation node resource allocation is better optimized, and the system robustness is enhanced.

The invention provides a self-adaptive scheduling and resource optimization method and system for an autonomous mobile robot group in a hospital scene. The method comprises the steps that tasks issued by a hospital HIS / ERP are coded into multi-dimensional constraint vectors; performing constraint matching on the task demand and the AMR to generate a feasible set; constructing a candidate path set meeting conditions on the topological graph of the clean partitions; constructing a plurality of uncertain scenes to calculate scenarized risk indexes and CVaR robust constraints for each task to obtain a risk index set; obtaining task-robot-path initial allocation by adopting column generation / mixed integer programming; obtaining an executable queue by adopting a cloud-edge-end layering technology; through online learning, travel time-varying distribution, people flow density and risk parameters are updated, and self-adaptive convergence is carried out through feedback operation; the whole process is in bidirectional butt joint with HIS / ERP of a hospital, an auditable event tracing chain is formed, and an abnormal closed loop and compliance report is output.

This paper relates to an intelligent packing method, device, and system for heterogeneous items considering segmentation constraints. First, it obtains the item dimensions, box dimensions, and a segmentation constraint matrix defining item conflict relationships. Then, it constructs a hybrid integer programming model that simultaneously incorporates geometric and segmentation logic constraints. A hybrid branch-and-price algorithm is used to solve the model. This algorithm iteratively optimizes through a column generation framework, where the pricing subproblem integrates particle swarm optimization and spatial heuristic decoding to efficiently generate new single-box loading patterns. Finally, a branch-and-bound strategy is combined to obtain the integer optimal solution. The system includes modules for data acquisition, optimization calculation, and solution output, achieving a closed loop from perception and decision-making to execution. This invention solves the problem that existing technologies cannot collaboratively optimize space utilization and complex business rule constraints. It can minimize the number of packaging boxes used and improve the feasibility and execution efficiency of packing schemes while strictly meeting segmentation requirements such as the isolation of fragile items.

The invention discloses a program behavior intervention method based on a quantum state, particularly relates to the technical field of computer security protection, and is used for solving the problems of resource conflict and stability reduction caused by systematic mismatching when quantum randomness is directly introduced into a classical computing system. A quantum random number sequence is obtained in real time, system state parameters of a target program are monitored synchronously, then a clustering mode in the random sequence is recognized, the system state is coupled to determine an intervening scheduling gap of an operating system, and then the matching degree of the clustering mode and the scheduling gap is evaluated to judge the compatibility relation. On the basis of the relation and the system state parameters, constructing a Pareto leading edge of intervention efficiency and system stability and determining an optimal solution set, selecting an adaptive operation point by evaluating the cumulative effect of optimal solutions and historical intervention, and finally modulating a quantum random number sequence according to the operation point to generate an intervention instruction and injecting the intervention instruction into a target program; and adaptive cooperation of quantum randomness and system certainty is realized.

The invention relates to the technical field of data processing, and discloses an Excel column value difference comparison method and device.The method comprises the steps that a first data column and a second data column to be compared are obtained, and a standardized data queue is generated; based on the standardized data queue, character string values are extracted line by line for character level difference comparison, and a primary difference sequence is generated; inputting the primary difference sequence into a dynamic context feature analysis model, and outputting a corrected difference result containing a difference position and a difference type; generating a visual annotation instruction set according to the corrected difference result, and executing annotation operation in the Excel interface to generate an annotation result; and integrating the labeling results of all rows and the difference statistical information, and outputting the labeling results and the difference statistical information to an independent result file. According to the method and the device, the problem that in the prior art, character-by-character fine comparison and difference character labeling cannot be carried out in Excel can be solved, accurate difference comparison and visual labeling of Excel column values are realized, and the accuracy and the efficiency of checking and correcting Excel data are improved.

The invention relates to the technical field of computers, in particular to an e-commerce data integration analysis method and system, and the method comprises the steps: obtaining and associating structured business data and unstructured text data of an e-commerce platform; performing cleaning, word segmentation and word form normalization on the unstructured text data to obtain a lexical item sequence; lexical item weight features and semantic embedding features are generated based on the lexical item sequence, the lexical item weight features are jointly determined by the occurrence degree of a single text and corpus distinction degree, and the semantic embedding features are obtained by predicting context word training through head words; inputting the lexical item weight features, the semantic embedding features and the structured business data into a deep learning semantic model, outputting probability distribution and confidence of emotion or intention, and constructing physiological signal features according to the probability distribution, based on the time sequence information, the message direction information and the interaction event sequence, constructing an interaction state feature to represent a change trend of the message quantity along with time, a dialogue dominant relationship and an upgrading moment; standardizing and fusing multi-source features into a feature matrix, and establishing a perception-context matrix to realize dimension mapping from key terms to service quality; training an isolation forest algorithm tree set for each service quality dimension, randomly selecting features and segmentation points of each isolation tree, performing recursive division until a sample is isolated or meets a preset termination condition, and obtaining an expected path length; and calculating an abnormal score according to an expected path length, generating an abnormal mark and an abnormal explanation in combination with a decision path, and outputting a disposal suggestion, and using an execution result to update a perception-context matrix and a deep learning semantic model parameter. According to the method, the problems that the structured business data and the unstructured text data in the e-commerce platform are difficult to effectively associate and fuse, the service quality abnormity in the dialogue process is difficult to identify in time, and the abnormity source and the key trigger factor are difficult to explain can be solved.

The invention relates to the technical field of chaotic cryptography, and discloses a non-degeneration sequence generation method based on time-delay chaos and irrational rotation disturbance and application of the non-degeneration sequence generation method in a pseudo-random number generator. Selected seed mapping is adopted as a mapping kernel, a double-time-delay feedback structure is introduced, and double-time-delay discrete mapping is executed; generating a Beatty sequence in real time based on an irrational rotation principle; and performing disturbance on the generated state variable by combining a control function with a Beatty sequence, continuously iterating, updating the state sequence through a sliding window mechanism, and outputting a final non-degradation sequence after the iteration is finished. A pseudorandom sequence PRNG is generated based on the non-degeneration sequence. Compared with the prior art, the method has the advantages that low cost of one-dimensional iteration is kept, the problem of periodic degradation in a limited precision environment is effectively solved, and the problem of insufficient randomness of the existing PRNG in a resource limited environment is solved at the same time.

A secure aggregation computational device 10 comprises: a sorting unit 1 that uses a share {→g} and a share {→o} to generate a sorted table in which rows of a table are sorted on the basis of an order key column →o and a group key column →g; a group boundary flag column generation unit 2 that generates a share [→b1] of a boundary flag column →b1 representing boundaries of groups in the sorted table; an intermediate aggregation result column generation unit 4 that generates a share {→p} of an intermediate aggregation result column →p representing an ordinal row position within each group; a subgroup boundary flag column generation unit 5 that generates a share [→b2] of a boundary flag column →b2 representing boundaries of subgroups within each group in the sorted table, a subgroup being a portion within each group where the value of an order key is the same value consecutively; and a final aggregation result column generation unit 6 that generates a share {→s} of a final aggregation result column →s representing an ordinal position of the value of each order key within each group.

A flag sequence generator (12) generates {x0}, . . . , {xλ−1} indicating a msb of a. A bit sequence generator (13) calculates {yi}:={x2i} XOR {x2i+1} to generate {y0}, . . . , {yλ′−1}. A flag calculator (14) calculates an exclusive logical sum of all {xj} to calculate [r] for each odd j. A public value multiplier setting-unit (16) sets r′ that becomes √2 when λ is an odd and 1 when λ is an even. A normalization multiplier generator (17) bit-connects {y0}, . . . to generate [c′]. A normalization multiplier generator (18) bit-connects {xλ−1}, . . . to generate [c]. A normalizer (19) calculates [b]:=[a][c]. A square root calculator (20) calculates [w]:=[√b]*(r′ / √2) when r=1, and [w′]:=[√b]*r′ when r=0. An inverse normalizer (21) calculates [w][c′] and performs λ′ bits right-shift.

The invention relates to a resource multi-dimensional response potential dynamic assessment method and system, and the method comprises the steps: obtaining multi-source data of a target assessment resource, the multi-source data at least comprising resource inherent attribute data, external dynamic factor data and real-time operation state data; fractal-dimension adjustable capability boundary calculation is carried out, specifically, based on the multi-source data, adjustable capability boundaries of the resources under at least two different dimensions are calculated respectively, and the adjustable capability boundaries are power boundaries; on the basis of external dynamic factor data in a future time period, the fractal-dimension adjustable capability boundary calculation is executed in a rolling mode, and a dynamic potential boundary sequence under all dimensions in the future time period is generated; and according to the dynamic potential boundary sequence, generating a constraint condition for the resource scheduling power, and outputting the priority of each dimension according to a predefined scene rule. According to the method, the accuracy and the real-time performance of the evaluation result can be improved, and the maximization and the optimal utilization of the resource potential are realized.

The invention discloses an aviation airport ground service intelligent scheduling and optimization method based on deep learning, and the method comprises the following steps: S1, collecting task data, resource states and scheduling records, and constructing an input data set; s2, constructing a task and resource heterogeneous graph, and extracting a coupling representation; s3, constructing a priori knowledge constraint set, and embedding a state control module; s4, coding a service time window, fusing and inputting into a gating mechanism module; s5, key events are extracted, and sparse event enhancement representation is generated; s6, splicing various types of information to form a scheduling input sequence, and inputting the scheduling input sequence into the improved Hyena model to generate a scheduling sequence; and S7, generating a resource scheduling instruction according to the scheduling sequence, and completing task execution. According to the invention, intelligent scheduling and automatic optimization in a complex airport scene are realized.