1225 results about "Resource consumption" patented technology

The invention discloses a communication engineering construction dynamic optimization method based on multi-dimensional perception, and the method comprises the steps: collecting the multi-dimensional perception data of a construction site in real time through a multi-source heterogeneous sensor network, including environment parameters, equipment operation states, construction progress and personnel behavior data; performing space-time alignment processing on the multi-dimensional sensing data, and constructing a space-time associated dynamic construction digital twinborn body; establishing a dynamic optimization model based on a reinforcement learning algorithm, taking construction efficiency maximization, resource loss minimization and safety risk minimization as target optimization functions, and embedding risk constraint conditions; inputting the dynamic construction digital twin into the dynamic optimization model, and outputting a multi-dimensional parameter optimization instruction set comprising equipment scheduling parameters, construction path parameters and resource configuration parameters; and performing real-time performance evaluation on the multi-dimensional parameter optimization instruction set through edge computing nodes, and dynamically adjusting weight parameters of the model to form closed-loop feedback control. The purpose of cooperatively improving the construction efficiency, the safety and the economical efficiency is achieved.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a self-adaptive data processing optimization method, device and equipment and a medium, and the method comprises the steps: collecting operation data, host performance data, network state data and historical task data of a target data source, and constructing an analysis model in combination with recovery parameters and strategy preference, predicting a task load state, resource consumption and execution duration, generating a task execution strategy, completing task scheduling and execution monitoring, and collecting execution feedback data for iterative optimization of the analysis model. According to the method, an analysis model is constructed by fusing multi-source system data and historical task information, a task execution strategy is generated in combination with a dynamic prediction result and a strategy weight, intelligent task scheduling and process monitoring are realized, and feedback data is used for model iterative optimization. The task execution efficiency is improved, the resource use rationalization is realized, and the model adaptive capability is enhanced.

The invention relates to the technical field of data processing, and discloses an automobile manufacturing industrial data distributed processing method and system based on digital twinning. The method comprises the following steps: monitoring and collecting production abnormal events, decision emergency degree parameters and simulation task types based on service states to generate a service priority evaluation data set, and performing priority ranking on a plurality of simulation tasks to form a dynamic priority queue and a resource demand matrix, the queue is input into a distributed simulation engine for CPU, memory and GPU load balancing processing to generate a node resource allocation scheme, and preemptive scheduling is executed to migrate low-priority tasks to idle nodes to form a task execution mapping table; and monitoring simulation progress and resource consumption through an adaptive scheduling mechanism to obtain scheduling performance feedback data, and updating the dynamic priority queue. According to the method and the device, the problem of lack of an intelligent scheduling mechanism during concurrent execution of multiple simulation tasks in the prior art is solved, and the response speed of the key simulation task and the utilization efficiency of computing resources are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an intelligent optimization method, device and equipment for query statements and a medium. The method comprises the steps of recording execution plan information and resource consumption information of an original query statement, generating original execution information, preprocessing the original query statement and the original execution information to generate preprocessing data, extracting feature information from the preprocessing data, generating optimization information through a pre-trained optimization model based on the feature information, and sending the optimization information to a server. And according to the optimization information, adjusting the content or execution mode of the original query statement, and generating an optimization query instruction. According to the method, the feature information is extracted by monitoring the actual query behavior and combining the execution performance, and the optimization strategy is generated by utilizing the training model, so that the automatic optimization of the original query statement is realized, the manual intervention cost is reduced, and the intelligent adaptation capability of the query performance is improved.

The invention relates to the technical field of project management, in particular to a BIM-based building project resource scheduling optimization method, which comprises the following steps: based on the current progress state of a construction task, collecting the consumption of constructors, mechanical equipment and materials, comparing the task plan progress with the real-time progress, screening out construction links with the backward task progress, and establishing a construction project resource scheduling optimization model; and judging a resource type, and establishing a task progress offset. According to the method, the construction progress is accurately monitored, real-time evaluation of resource consumption is achieved, waste and scheduling lag are avoided, the construction path is optimized by means of the BIM model, the key path monitoring precision is improved, progress management and control effectiveness is ensured, key path progress deviation is analyzed, the resource shortage risk is recognized, stable supply of construction resources is guaranteed, and on the basis of the influence of resource shortage, the construction efficiency is improved. The resource distribution sequence is optimized, the resource utilization efficiency is improved, the supply chain delivery cycle is matched, the resource scheduling is optimized, the influence of resource supply and demand imbalance on the construction progress is reduced, and the continuous and stable construction progress is ensured.

The invention discloses a project progress and resource early warning method and system based on dynamic regulation and control, and the method comprises the steps: 1) collecting project progress and resource data in real time, and constructing a multi-source data fusion model; 2) based on the constructed multi-source data fusion model, judging whether the construction progress and the resource consumption are abnormal or not, and if so, executing the step 3); otherwise, continuing conventional construction progress tracking and resource management; 3) triggering a grading early warning mechanism, generating and executing an optimization regulation and control strategy, and adjusting the construction progress and resource configuration; and 4) evaluating the regulation and control effect, and returning to the step 2) after collecting feedback data. According to the method, the actual construction progress deviation and the resource supply and demand imbalance condition are comprehensively considered, the current overall operation condition of the construction project is analyzed, the construction management strategies are flexibly switched, the management parameters are flexibly adjusted, the construction progress can be rapidly and effectively guaranteed, resource utilization is optimized, and the construction efficiency is improved. And the intelligent level and the response efficiency of construction management are obviously improved.

Systems for explainable large language model routing with immutable audit trails are disclosed. The system receives a query and determines its characteristics including complexity, domain, regulatory constraints, and performance requirements. It retrieves profiles for multiple LLMs from a model matrix containing performance attributes, resource consumption, and compliance parameters. The system selects a particular LLM by balancing resource consumption with performance requirements, evaluating regulatory compliance, ranking LLMs based on these factors, and prioritizing models with successful processing history. The system generates a human-readable explanation of the selection including decision factors, rationale, and alternatives considered. Finally, it records the selection and explanation in a tamper-evident, immutable audit trail data structure.

The invention relates to the technical field of decoration construction management, and discloses a BIM-based decoration construction scene resource consumption simulation analysis method. The method comprises the following steps: firstly, creating a multi-dimensional BIM resource model, and integrating building information model data, real-time construction data flow and historical resource consumption records; identifying resource consumption characteristics through the model, and generating an initial resource allocation strategy set comprising material allocation, equipment scheduling and process priority schemes; receiving a user interaction instruction, and dynamically adjusting the initial strategy set; and finally, executing the adjusted strategy, and updating the multi-dimensional BIM resource model based on a result. Wherein the resource consumption feature comprises construction stage division, resource node dynamic weight calculation and space correlation correction operation. According to the method, by integrating multi-dimensional data and dynamically analyzing a resource consumption rule, real-time optimization of a resource allocation strategy is realized, and the accuracy and flexibility of decoration construction resource management can be improved.

Reducing resource consumption spikes in an anomaly detection framework, including: gathering information describing historical job execution associated with a plurality of customers; assigning, based on the information describing historical job execution, each of the plurality of customers to a particular grouping of a plurality of groupings, wherein each of the plurality of groupings corresponds to a particular time offset within a time interval; and scheduling, for each grouping of the plurality of groupings, at a time offset of a corresponding grouping, execution of one or more jobs associated with one or more customers of the corresponding grouping.

The invention relates to the technical field of digital twinning, and particularly discloses a historical building structure evaluation system based on digital twinning, which comprises a multi-source data acquisition module, a dynamic digital twinning modeling module, a structure health evaluation module, an intelligent maintenance decision module and a collaborative optimization analysis module. According to the scheme, geometric, material and environment information of the historical building is captured, omnibearing monitoring of the historical building is realized through a digital twin modeling technology, an intelligent evaluation model is constructed in combination with finite element analysis and an SVM algorithm, a high-risk area is rapidly identified, and authenticity and accuracy of an evaluation result are ensured; a reinforcement learning algorithm is used to adapt to complex and changeable environmental requirements of historical buildings, a maintenance strategy is automatically generated, cooperation efficiency between modules is continuously optimized through collaborative optimization analysis, the overall operation performance is improved, evaluation precision is guaranteed, and meanwhile system resource consumption is effectively controlled.

The invention relates to the technical field of artificial intelligence, in particular to a scene application operation maintenance platform based on an artificial intelligence model, which comprises a reasoning state sensing module, a load balancing scheduling module, an intelligent resource allocation module, a deployment configuration adjustment module and a scene feedback integration module. According to the method, by sensing and reasoning process resource consumption and response time migration trend, abnormal tasks caused by resource fluctuation can be accurately recognized, the state monitoring sensitivity is improved, task allocation records are analyzed to construct conflict structures, the resource conflict recognition and visualization capability is enhanced, the model operation level and the GPU vacancy rate are combined, a task migration path is optimized, and the task migration efficiency is improved. Node resource balance is realized, storage frequency, memory margin and network delay are fused during deployment, migration stability is guaranteed, drift tasks are identified through response logs and call records, an operation and maintenance list is generated, operation and maintenance accuracy and multi-scene matching capability are improved, and reasoning task stability and resource scheduling intelligent level are enhanced.

The invention provides an AI model training acceleration method and system based on computing power service scheduling, and relates to the technical field of computing power service.Firstly, historical training process data of an AI model and structural features of a current training task model are collected, a computing power demand prediction model is constructed, the structural features of the current training task model are input to generate a computing power resource demand distribution sequence, and a computing power demand prediction model is constructed; the method comprises the following steps of: training a demand change curve of each stage of calculation core quantity, memory bandwidth and data transmission rate, screening a matched computing power resource combination scheme from a computing power service cluster according to a sequence, scheduling computing nodes to execute a training task, and collecting actual computing power resource consumption data in real time; and finally, comparing actual data with the prediction sequence, generating a deviation value, and dynamically adjusting the number of enabled computational nodes, thereby realizing accurate prediction and dynamic optimization scheduling of the computing power demand, improving the utilization rate of computing power resources, and accelerating AI model training.

The invention provides an intelligent maintenance method and system for the wall of a squat silo, and the method comprises the steps: applying controllable environment parameters to a concrete test block group with the same proportion through an environment simulation device, and obtaining test block deformation data, moisture content data and stress data; performing time sequence correlation mapping on the test block deformation data, the moisture content data and the stress data and environmental parameters to obtain a maintenance decision data set; acquiring a real-time environment vector through a sensor network deployed on the site of the squat silo; inputting the real-time environment vector into a maintenance decision model, and generating a spraying control instruction by matching the maintenance decision data set; and driving a spraying system to execute operation according to the spraying control instruction, and dynamically correcting spraying parameters based on moisture monitoring data. According to the method, through data-driven intelligent maintenance decision and dynamic closed-loop control, the crack risk of the wall of the squat silo is remarkably reduced, meanwhile, water resource consumption is reduced, and double breakthrough of maintenance quality and resource efficiency is achieved.

The invention relates to the technical field of computing power sharing, in particular to a computing power sharing system and method in a multi-tenant environment, and the system comprises a computing power monitoring module, a load analysis and prediction module, a task priority judgment module, a resource optimization configuration module and a fault management module. According to the invention, by implementing real-time resource monitoring and data analysis and accurately capturing the speed and mode of resource consumption, the resource allocation is more accurate, the waste of computing power resources is avoided, the response capability is improved, the load prediction is generated by using historical and real-time data, the computing power resources can be ensured to be fully utilized in the peak period of demand, overload is avoided, and the service life of the system is prolonged. Through intelligent analysis of the resource utilization rate and the predicted completion time and optimization of priority ranking of the tasks, the emergency tasks can be rapidly responded, implementation of a real-time computing power distribution strategy is achieved, high-efficiency computing power resource use is guaranteed, the stability and reliability of the system are improved, and the cost problem caused by fault response delay is reduced.

The invention provides a power industry-oriented algorithm model operation monitoring method and system, and relates to the field of model monitoring. Comprising the steps of collecting physical quantity data of power equipment through a dynamic causal chain construction module, generating a causal chain after noise suppression and preprocessing, binding the causal chain with algorithm model parameters, and recording a sudden change event and an associated parameter update log; a four-dimensional space-time lock synchronization module is utilized to add geographic coordinates and timestamps, and data space-time consistency is ensured; the multi-target game strategy engine module constructs an optimization strategy based on historical misinformation events and resource consumption records in combination with SLA time constraints of real-time detection tasks; the DEED-Trigger anti-intuition triggering module triggers an emergency response according to the deviation between the real-time data and the optimization strategy; and the verification platform injects a composite fault through a digital twin technology to test the causal chain robustness, and dynamically adjusts the causal chain structure and strategy.

The invention relates to the technical field of intelligent gardens and precise irrigation, and particularly discloses an intelligent irrigation monitoring method and system. According to the method, a multi-source sensor array is deployed, Kalman filtering is adopted to fuse environmental data, and a three-dimensional state vector input reinforcement learning model is constructed to generate an irrigation decision; predicting a vegetation water demand by combining a gradient lifting decision tree, forming a graded irrigation strategy and converting the graded irrigation strategy into a water pump control instruction; soil humidity feedback data are collected in real time, decision model parameters and filtering rules are dynamically adjusted, and closed-loop optimization is achieved. Through multi-source data fusion and a self-adaptive decision-making mechanism, the irrigation precision and the water resource utilization rate are remarkably improved, meanwhile, the response capacity of the system to the vegetation growth dynamic state and the environment change is enhanced, and the beneficial effects of optimization process closed loop, decision dynamic adaptation and controllable resource consumption are achieved.

A power system human resource allocation method based on multi-agent cooperation comprises the steps that S1, a human resource task collection module is constructed, and newly-added human tasks of a power system are collected in real time; s2, quantitatively decomposing each human task into a plurality of sub-tasks, calculating the complexity of each sub-task, and constructing a human task balance optimization model; s3, constructing an intelligent agent role allocation model according to the sub-task complexity, the intelligent agent capability matrix and the real-time load of the intelligent agent; and S4, performing iterative operation through a multi-objective optimization algorithm, solving the human task balance optimization model and the agent role allocation model, allocating the subtasks to the agents to obtain a task allocation matrix and estimated completion time, and executing the subtasks by the agents according to the allocation matrix to realize allocation of the human tasks. The design not only considers the balance of resource consumption and processing time of human tasks, but also allocates based on allocation efficiency and task completion efficiency.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a model task processing acceleration method, device and equipment and a medium. The attention weight is obtained before a first preset processing layer, the importance score of the first type of data marks is calculated in the processing layer, a subset of the first type of data marks is screened out, then deep processing is executed on the subset and the second type of data marks, and an output sequence is generated. According to the method, the attention weight is extracted and the importance score of the data mark is calculated in the shallow layer stage, so that the redundant visual mark is effectively identified and eliminated, only necessary information is reserved for deep processing, and the invalid calculation overhead is avoided, and therefore, on the premise of not changing the structure and the performance of an original model, the accuracy of the model is improved. The model reasoning efficiency is effectively improved; and the resource consumption is reduced.

The invention provides a defective part reprocessing decision-making method based on multi-target balance, which comprises the following steps: acquiring real-time defect rate data and part distribution information from production, analyzing a defect rate change trend and a part quantity fluctuation condition, and setting a standard defect threshold according to an industrial standard and historical data; if the defect rate exceeds a standard defect threshold value, identifying and fitting part performance parameters to obtain an average performance curve, calculating slope change and abrupt change amplitude of the curve, and determining time and influence range of a variation point; acquiring a factory processing load, combining the priority distribution sequence with the target part grouping scheme and the factory processing load to obtain a reprocessing flow configuration scheme, and identifying a balance point of resource consumption and a reprocessing coverage rate through simulation; and acquiring a real-time ratio of the reprocessing coverage rate and the resource consumption corresponding to the balance point, comparing the real-time ratio with a historical optimal ratio, and if the real-time ratio is not smaller than the historical optimal value, determining that a performance bottleneck risk exists, and analyzing a risk cause.

The invention discloses a computing power scheduling management method and system based on an algorithm. The method comprises the steps that an initial resource consumption data set is obtained, and the data set comprises the processor occupancy rate and the memory usage amount of a plurality of calculation tasks in different calculation stages; according to the initial resource consumption data set, a fixed time window is adopted to segment historical data, and a smooth resource consumption sequence is generated through moving average calculation; for the smooth resource consumption sequence, applying a trend decomposition method to separate long-term trend and periodic fluctuation features, and constructing a resource dynamic feature matrix; and according to the resource dynamic characteristic matrix, constructing a multi-layer long-short-term memory network model, and optimizing parameters through a gradient descent method to generate a resource demand prediction curve and the like. According to the method disclosed by the invention, the resource consumption data of the computing task is deeply analyzed and processed, so that efficient computing power resource allocation and scheduling are realized to cope with the change of the demand of the computing task on the processor and memory resources in different stages.

The invention provides an HC-MOGA-based agent task collaborative scheduling method in an industrial internet, and aims to solve the key problems of low scheduling efficiency, poor system robustness and the like caused by heterogeneous types of equipment (subsequent modeling is an agent), complex task dependence and non-uniform resource distribution in an industrial site. According to the method, a heterogeneous agent scheduling model with task identification, resource matching and dependent modeling capabilities is constructed for a multi-stage operation task cooperatively completed by a data acquisition agent and an operation type agent in an industrial internet environment. An HC-MOGA (heterogeneous constraint multi-objective genetic algorithm) is provided, multi-dimensional chromosome coding, a layered mapping mechanism, a dual-stage variation strategy and a dynamic constraint repair mechanism are introduced, and a dual-objective optimization model fusing work completion income and comprehensive resource consumption and potential loss is established. The method is suitable for industrial internet typical application scenes such as intelligent manufacturing, automatic production lines and multi-agent task collaborative management.

The invention discloses a cross-platform job resource integrated management system and method based on the Internet, and belongs to the technical field of job resource management. A baseline model is constructed, and whether resource consumption data is abnormal or not is judged; extracting an abnormal resource type, obtaining a corresponding resource demand, identifying tasks having resource competition with the current job, and obtaining an abnormal competition task set; defining job attributes, determining a resource association relationship between tasks, and constructing an abnormal competition task relationship graph; determining the latest execution time of each competition task; analyzing the maximum bearing capacity of the current resource meeting all task execution under the conflict-free condition; calculating the quantity of resources needing to be added; subtracting the actually added resource quantity from the resource quantity needing to be added to obtain a vacant resource quantity; when personnel resources are vacant, the competition tasks are sorted, and the competition tasks are preferentially allocated to high-priority exclusive jobs in combination with the maximum bearing capacity; when the device resources are vacant, idle special devices are preferentially allocated for exclusive jobs.

The invention relates to the technical field of artificial intelligence, and discloses a large model continuous learning method and device based on low-rank adaptation, equipment and a medium, which are applied to a scene that a financial institution updates a credit scoring model of a user in real time, and the method comprises the following steps: when a basic large model continuous learning task is received, obtaining an initial task text, constructing training data based on the initial task text; performing singular value decomposition on the parameters of the original low-rank matrix to generate principal component parameters and residual parameters; freezing the principal component parameters, and updating the updated original low-rank matrix by adopting a singular value loss function and an orthogonal loss function based on the residual parameters of the training data to generate a basic low-rank matrix; performing iterative training on the basic large model based on the training data and the basic low-rank matrix to generate a target large language model; and predicting the to-be-predicted task data through the target large language model. According to the method, the problem of disastrous forgetting is relieved, and computing resource consumption is reduced.

The invention relates to the technical field of security auditing, in particular to a security auditing method and system for an industrial control system, and the method comprises the following steps: aiming at a key control task, a communication task and a security task of a real-time operating system, collecting a period, a starting timestamp, a finishing timestamp, a central processing unit occupied time slice and peak memory usage amount data. According to the method, the period, timestamp, central processing unit occupation and memory usage data of a key task of a real-time operating system are collected, a task execution time boundary and a resource consumption envelope are set, and then an expected relation rule set of a task time sequence and resource consumption is constructed by applying historical data statistics and logic rule deduction; and meanwhile, the key length of symmetric and asymmetric encryption, initialization vector generation, hash algorithm selection, key derivation parameters and encryption operation context are stipulated, so that a comprehensive and specific ICS behavior specification baseline is established.

The invention provides a cost-aware efficient tool planning method based on a large model. According to the method, efficiency and cost optimization of tool scheduling is realized by constructing a CATP-LLM framework. The method comprises the steps that a tool planning language TPL is designed, a non-linear multi-branch parallel scheme is generated through structured Token support, and the task execution efficiency is remarkably improved; in combination with a cost-aware offline reinforcement learning CAORL algorithm, a tool selection strategy is dynamically optimized based on historical data, and task performance and resource consumption are balanced; and the balance relationship between the performance and the cost is quantified through the QoP of the scheme quality index, and dynamic adjustment is realized to output an efficient and low-cost planning scheme. Compared with the prior art, parallel tool calling, dynamic cost optimization and complex task adaptability are supported, the execution cost is remarkably reduced while the task quality is ensured, and an efficient solution is provided for multi-scene task planning.

The invention provides a vehicle fault root cause diagnosis method, equipment, equipment and a medium, and the method comprises the steps: obtaining a vehicle target fault phenomenon, determining a to-be-detected fault event set based on a preset fault logic relation, constructing an initial scoring matrix, enabling a row vector to correspond to a fault event, enabling a column vector to correspond to the state feature parameters of a plurality of evaluation dimensions, and carrying out the detection of the fault event set; executing the detection task of the highest comprehensive score fault event, obtaining feedback data, updating the initial score matrix parameters based on the feedback data to obtain an updated score matrix, and calculating the comprehensive score of each event based on the updated score matrix again. Iteratively executing the detection task corresponding to the updated highest score event to identify whether the detection event is a fault root cause or not until the fault root cause of the target fault phenomenon is determined; according to the method, through a dynamic priority scheduling mechanism, real-time feedback data is fused in multi-dimensional evaluation, a high-value diagnosis task is executed preferentially, the resource consumption of traditional traversal diagnosis is remarkably reduced, and the fault positioning efficiency is effectively improved.

Sanitizing data can be a cumbersome task, particularly when the volume of data is large, the content is sensitive, and / or the type of sanitation requires contextual determinations. Sanitizing large amounts of data is tedious and may often require highly trained personnel with clearances and / or other qualifications. In the systems and methods of the present disclosure, language models (LMs) are used to solve these and other technical issues with tools that may allow sanitizing data easily, with high versatility, context awareness, and / or low demand for computational resources. In particular, some of the disclosed systems and methods use a first language model and a second language model (being less resource-intensive than the first language model) to generate sanitized output data with improved efficiency and accuracy. This dual-model approach ensures that sensitive information is handled appropriately while optimizing computer resource usage.

Systems and methods disclosed herein automatically detect, analyze, and mitigate anomalous resource distribution among artificial intelligence (AI)-based agents within a distributed computational network. The system receives a resource allocation request specifying computational resources, agent parameters, and performance objectives for a network of agents. A first AI model set monitors agent activity by tracking resource consumption and behavioral deviations from baseline profiles. The system compares resource usage of agents with historical norms and / or predetermined thresholds to generate an anomaly score for each agent. A second AI model set aggregates scores to construct a multi-dimensional data structure that indicates the comparison and anomaly score. The system ranks agents by anomaly severity to isolate agents with high scores (e.g., misaligned agents), and reallocates resources and updates access privileges for the misaligned agents.

The invention relates to the technical field of wind power, in particular to a dynamic response test method for a wind power variable pitch system. Comprising the steps of building a high-fidelity digital twinborn model, performing virtual testing and case optimization, performing physical self-adaptive testing execution, performing real-time analysis and decision-making driven by machine learning, dynamically adjusting testing stress, performing data feedback and model calibration and generating a comprehensive health report. Compared with the prior art which mainly depends on a fixed physical test process and artificial experience judgment and has the defects of low test efficiency, large resource consumption and difficulty in comprehensively covering extreme working conditions, the method innovatively introduces a high-fidelity digital twinning technology and a virtual test optimization process; according to the method, massive tests are executed in the virtual environment in a risk-free manner, and the enhanced test cases are intelligently generated, so that test forward movement and accurate optimization are realized, the pertinence and efficiency of physical tests are remarkably improved, and the test cost and period are greatly reduced.

The invention relates to the technical field of resource auditing, in particular to a natural resource auditing method and system. The method comprises the following steps: acquiring remote sensing data of a monitoring area and sensing data of the monitoring area, and carrying out resource data atlas combination to obtain a resource atlas of the monitoring area; carrying out resource consumption modeling based on the monitoring area resource map to obtain an area resource consumption prediction model; performing monitoring area resource consumption risk area division on the monitoring area resource map according to the area resource consumption prediction model to obtain resource consumption risk area data; performing low-efficiency development link identification on the resource consumption risk area data according to the monitoring area resource map to obtain risk area low-efficiency development link efficiency data; and performing regional natural resource auditing compliance analysis based on the risk area low-efficiency development link efficiency data and the regional resource consumption trend prediction data so as to obtain a natural resource management compliance report. According to the method, the comprehensiveness and efficiency of natural resource auditing can be improved.