970 results about "Data identification" patented technology

The invention discloses a bridge construction abnormity monitoring data identification method and system based on big data, and relates to the technical field of bridge construction monitoring, the system comprises a collection module, an analysis module, a process matching module and an execution module, the collection module collects construction data, transmits the construction data to the analysis module, carries out linkage verification on the construction data through an analysis unit, and carries out process matching on the construction data; identifying and outputting abnormal data, transmitting the abnormal data to a process matching module, constructing a judgment standard for dynamic matching through a construction process, comparing the abnormal data with the judgment standard, outputting a preliminary judgment result, transmitting the preliminary judgment result to an execution module, executing multi-stage cross validation on the preliminary judgment result, and outputting a final abnormal judgment result. By responding to multi-source data, missed judgment is prevented, a space-time linkage verification mechanism is constructed to improve anomaly recognition accuracy, a dynamic judgment standard adapts to risks of all stages, false anomalies are filtered through three-stage verification, the efficiency is improved through full-process automation, hidden danger early warning is assisted, accidents are avoided, and bridge construction quality and safety are guaranteed.

The invention relates to a mechanical equipment fault data identification method based on artificial intelligence, and belongs to the technical field of data processing and artificial intelligence. The problems of insensitive signal processing, insufficient feature extraction, significant noise interference, low model training efficiency and the like in fault diagnosis in the prior art are solved. According to the method, a self-adaptive normalization method based on energy density is provided, the influence of non-stationary signals is effectively inhibited, and key features are reserved; through mixed energy entropy feature extraction and inter-band energy jump penalty terms, the sensitivity to a complex fault mode is significantly enhanced; constructing a fault feature enhancement strategy based on a Gaussian potential well, and reinforcing the response of a fault feature accumulation area; a weighted loss function and a gradient directional correction mechanism are adopted, so that the robustness and accuracy of the model are improved; and in combination with an entropy weighted learning rate and a covariance scaling strategy, adaptive training is realized, and the convergence speed and adaptability are improved. According to the method, the precision and efficiency of mechanical fault diagnosis are improved, and support is provided for industrial intelligent development.

The invention relates to the technical field of inventory management, in particular to an intelligent warehouse management system for main shaft parts based on digital twinning, which comprises a main shaft mapping module for acquiring a main shaft shipping space and an allocation path, comparing shipping space nodes with a moving timestamp difference to generate an identification result, the fluctuation identification module extracts channel nodes to analyze fluctuation threshold deviation and generate a report, the allocation control module generates a scheduling list based on queuing duration and shipping space data distribution weight, and the abnormity positioning module extracts unallocated parts to analyze interface difference and mark abnormal links. And the state early warning module integrates four indexes of abnormal data identification risk paths to generate a management scheme. According to the method, bin position nodes and main shaft movement timestamps are synchronously compared to identify difference nodes, a dynamic weight distribution mechanism is established in combination with channel node fluctuation analysis, an interruption link difference trend and interface delay data are integrated to construct a risk assessment model, and abnormal conduction path positioning and early warning are realized. And scheduling conflicts and resource waste caused by data deviation are reduced.

The invention discloses a memory data migration method and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: determining the access heat of a storage device accepting the access of a computing node based on a memory access task executed by a switching controller and an address mapping table, so as to determine a source device and a candidate data unit, according to the access parameter of the candidate data unit and the state parameter of the storage device, calculating a performance score after the candidate data unit is stored in the storage device so as to determine the data unit to be migrated and the target device, so that cold and hot data identification from the perspective of a computer fast interconnection memory heterogeneous system is realized; the rationality of data migration is improved on the basis of quantitative performance scores; according to the method, the to-be-migrated data unit is controlled to be migrated from the source device to the target device, and the address mapping table is updated, so that the data migration task is prevented from influencing the host work of the computing node, and a cold and hot data migration scheme without being perceived by the host in the computer fast interconnection memory heterogeneous system is realized.

The invention discloses a horizontal wind field inversion method and device based on the radial velocity of a wind profile radar, and relates to the technical field of wind profile radars, and the method comprises the steps: reading the radial velocity data of a wind profile radar in each direction wave beam, carrying out the rearrangement and splicing arrangement of the radial velocity data of each direction wave beam according to a preset sequence, and carrying out the reconstruction of the radial velocity data of each direction wave beam; forming a beam radial velocity profile; performing data identification, one-dimensional median filtering and one-dimensional moving average filtering on the beam radial speed profile in sequence so as to perform consistency processing on the data of the beam radial speed profile; carrying out time-space continuity processing, and then carrying out time consistency average processing; and obtaining an inverted horizontal wind field according to the processed radial speed data of the beams in all directions based on a space geometrical relationship between the wind field and the radial speed data of the beams in all directions. According to the invention, the problem of low accuracy during horizontal wind field inversion in the prior art is solved.

The invention provides a WSI long-tail data identification method and device based on multi-modal distillation guidance and a readable storage medium thereof, and aims to solve the problem of low tail category identification accuracy caused by long-tail distribution in weak supervision multi-instance learning of a digital pathological total image (WSI). According to the method, performance improvement is realized through double-branch integrated multi-instance learning, text feature extraction and prompt generation and multi-modal distillation optimization. The double branches are trained by adopting balanced distribution and original long-tail distribution data respectively, an aggregator is shared, and prediction consistency is constrained; text features containing semantic prompts are generated to serve as supervision signals, and semantic alignment is achieved through image-text comparison loss; soft and hard label collaborative distillation loss is designed, and a total loss function is constructed in combination with consistency loss. Experiments show that the algorithm can significantly improve the tail category and overall recognition performance, is high in universality, can adapt to various multi-instance learning methods, and provides reliable technical support for intelligent analysis of pathological images.

The invention discloses a data management method and device, electronic equipment and a medium, and the method comprises the steps: responding to obtained transmission data of first equipment, extracting the transmission data, and obtaining a check code and equipment information; when it is confirmed that the transmission data is qualified according to the check code, desensitizing the transmission data, and determining an encryption confidentiality level mode corresponding to the transmission data according to the equipment type indicated by the equipment information; according to an encryption method corresponding to the confidentiality level, encrypting the desensitized transmission data by adopting an encryption mode to obtain encrypted data; performing desensitization processing and identification processing on the encrypted data to obtain encrypted identification data after desensitization processing; and storing the encrypted data identification data after the desensitization processing to a target storage area. The method can be applied to the business fields of financial science and technology, medical health, old-age care and the like, the overall data safety is improved, privacy protection of sensitive information is increased, meanwhile, the data management and tracing capacity is improved, and balance between safety and performance is achieved.

The invention discloses an AI-based regulation and control abnormal data identification and correction method, system and device and a medium, and belongs to the technical field of power system regulation and control, and the method comprises the steps: obtaining multi-source data of a main distribution network, carrying out the preprocessing, and constructing a power grid topology knowledge graph; the multi-source data comprises full voltage class model data, real-time measurement data and equipment state data. According to the method, a three-layer detection system of a statistics-density mixed detection layer, a graph neural network association detection layer and a time sequence Transform detection layer based on an LOF algorithm and a 3 sigma principle is constructed, deep association analysis of multi-source data is realized in combination with a power grid topology knowledge graph, and an intelligent correction module is used for matching a differentiation correction algorithm for different types of anomalies; the correction weight is dynamically adjusted based on reinforcement learning, and the problems that a traditional method depends on artificial experience to set a threshold value, the recognition capacity of a single algorithm is limited, a correction strategy lacks adaptability, and multi-source data association analysis cannot be achieved are solved.

The invention provides a power cable test data identification method and system, and the method comprises the steps: obtaining an electrical signal sequence of a power cable under a plurality of preset test condition combinations, carrying out the space-frequency joint feature decomposition of the electrical signal sequence, and constructing a three-dimensional time-frequency data body comprising a space position, a time sequence and a frequency feature; constructing a dynamic phase space on the basis, reconstructing a state trajectory of each physical segment, calculating a fractal dimension spectrum and a nonlinear divergence coefficient set of the trajectory, and generating a defect evolution characteristic tensor; inputting the defect into a spatio-temporal chaotic mapping network, aligning characteristic evolution paths of different physical segments, and constructing a defect propagation spatio-temporal atlas; and analyzing the fault development trend of each physical segment in the time dimension and the defect diffusion path of each physical segment in the space dimension, and outputting an identification result containing a defect type evolution probability sequence and a three-dimensional position offset field. According to the invention, a more accurate and comprehensive basis is provided for state evaluation and maintenance of the power cable.

This disclosure describes methods, devices and systems for abnormal network data identification and policy enforcement. An example method includes obtaining incoming network data from a network device, the networking data including operating information for the network device and packet metadata. The method also includes classifying the incoming network data using one or more machine learning models, including identifying abnormal network data from the incoming network data. The method further includes causing a policy rule to be generated based on the abnormal network data.

The present invention belongs to the technical field of incremental-data synchronization. Provided is an incremental-data identification, positioning and updating method. The incremental-data identification, positioning and updating method based on an Ethereum MPT in the present invention uses a storage structure of an Ethereum MPT in the blockchain field and performs adaptive fine-tuning on the storage structure, and applies the hash pointer and prefix sharing characteristics of the Ethereum MPT to incremental-data identification, positioning and updating in the field of data synchronization.

The present invention relates to the technical field of intelligent out-of-distribution fault detection for construction machinery, and discloses an out-of-distribution fault detection method and system based on energy propagation and graph learning, and the method includes: acquiring vibration acceleration signals in typical fault states, carrying out similarity calculation to obtain an adjacency matrix composed of the maximum mutual information coefficients, and taking the adjacency matrix as input in a graph neural network; carrying out feature extraction on the adjacency matrix through adopting a GraphSage graph convolution method, and generating each node representation; calculating an energy score of each node, and distinguishing between in-distribution data and out-of-distribution data; and enhancing out-of-distribution data confidence estimation for each node, and carrying out out-of-distribution data identification and out-of-distribution data detection under different working conditions of a rolling bearing.

The invention discloses a transformer insulating oil chromatographic data abnormity identification method, and relates to the technical field of data identification, and the method comprises the following steps: carrying out dissolved gas analysis on an insulating oil sample through a gas chromatograph to obtain a chromatographic original response curve; performing baseline correction and de-noising processing on the chromatographic original response curve, positioning each gas peak based on a first-order derivative method and a second-order derivative method, and extracting chromatographic peak height and chromatographic peak area parameters; according to the extracted chromatographic peak height and chromatographic peak area, three Duval fault ratios are calculated, and the ratio of the total soluble gas concentration to each gas concentration threshold value is calculated; combining into a multi-dimensional diagnosis feature vector; according to the method, the chromatographic peak shape of the insulating oil sample is subjected to multi-stage baseline correction and de-noising treatment, and the gas peak position is accurately positioned based on the first-order and second-order derivative methods, so that the high precision of chromatographic peak height and chromatographic peak area extraction is ensured, the accuracy of dissolved gas concentration calculation is improved, and the misjudgment risk caused by noise or baseline drift is reduced.

The invention discloses a data filtering method and system for a digital twin industrial control safety target range. The method comprises the following steps: firstly, marking an IP address of an industrial control system network in a segmented manner; feature extraction is carried out on the network behavior data of each IP segment, and splicing is carried out on the IP segment label to serve as a feature sample for constructing an industrial control network security identification model; a plurality of inverted residual modules with the same structure are adopted as core construction units, network behavior features in the feature samples are extracted and learned, and classification training is carried out; deploying the trained industrial control network security identification model to a pre-filtering module of a digital twin industrial control security target range, and carrying out classification, identification and filtering on network behavior data flowing in each IP segment in real time; and the misjudged sample is subjected to backtracking analysis, and the IP marking rule is updated. According to the technical scheme, the accuracy of network behavior data identification can be improved, so that the effectiveness of industrial control system simulation is improved.

The invention relates to a disaster monitoring data identification method and device, a storage medium and electronic equipment, and relates to the technical field of disaster monitoring, and the method comprises the steps: carrying out the fitting of a monitoring data fluctuation curve and a corresponding normal fluctuation curve, and obtaining a fitting rate; if the fitting rate is smaller than a preset fitting rate threshold value, determining the corresponding monitoring dimension as a suspected abnormal fluctuation dimension, and determining a first risk coefficient of monitoring data abnormal fluctuation of the suspected abnormal fluctuation dimension influenced by an actual interference event; when the first risk coefficient does not exceed a preset first threshold value, determining that the monitoring data fluctuation of the suspected abnormal fluctuation dimension is abnormal fluctuation; and when the first risk coefficient exceeds a first threshold value, determining whether the monitoring data fluctuation of the suspected abnormal fluctuation dimension is abnormal fluctuation or not based on other suspected abnormal fluctuation dimensions. The method has the effect of improving the accuracy of identifying the disaster monitoring data.

The invention relates to a target molecule identification and quantification method based on a solid nanopore detection technology, electronic equipment and a computer readable storage medium. The method comprises the following steps: (1) acquiring current signal data generated by a target molecule through a nanopore sensor within a period of time; (2) carrying out filtering processing on the current signal data; (3) identifying the filtered data to obtain a target via hole event set; (4) filtering the target via hole event set, and extracting signal data of the filtered events to obtain a target molecule recognition event; and (5) calculating the average interval time of occurrence of the target molecule recognition event, and obtaining the occurrence frequency of the target molecule based on the average interval time, namely the relative concentration of the target molecule. According to the method, automatic identification and quantitative analysis are realized aiming at mass signal data generated by a solid-state nanopore detection technology, and the analysis efficiency and accuracy are improved by optimizing a data identification method.

The invention provides an energy efficiency prediction and energy-saving scheduling optimization system and method for a food cold chain system, and relates to the technical field of cold chain system energy-saving scheduling, and the system comprises a data recognition module which is used for recognizing cold chain loading state data, and analyzing the space influence characteristics of the loading state data of a cold chain loading space; the strategy obtaining module is used for carrying out energy efficiency optimization on cold chain energy supply according to the space energy efficiency relation between the cold chain space and the link energy supply parameters and the space influence characteristics; the state chain establishment module is used for carrying out loading state dynamic analysis based on the cold chain vehicle path and predicting a path switching node; and the optimization strategy obtaining module is used for carrying out path state node scheduling optimization according to the cold chain path state chain by taking a space energy efficiency strategy as an initial parameter based on the vehicle energy supply coupling relationship and the space energy efficiency relationship of the cold chain system. The technical problem of energy consumption waste in cold chain transportation in the prior art can be solved, and the technical effect of reducing cold chain energy consumption is achieved.

The invention provides a matched tobacco appliance identification method and system based on a metal heating inductor, and belongs to the technical field of data identification. In the process of heating the metal heating inductor, a plurality of temperature points are set, real-time temperature parameters and electromagnetic parameters of the metal heating inductor are obtained, and a temperature-electromagnetic mapping table is established; constructing a three-dimensional parameter grid based on the electromagnetic parameters, performing mean division on the three-dimensional parameter grid to obtain a mean grid, and constructing a coupling change rate matrix according to the mean grid; performing eigenvalue decomposition on the parameter coupling change rate matrix to obtain a deformation eigenvector, and comparing the deformation eigenvector with a pre-stored appliance mode recognition vector to obtain a mode similarity score; acquiring an appliance judgment mode based on the mode similarity score, and executing a mode mechanism according to the appliance judgment mode; the interference of temperature drift on an identification result is reduced, the problem that a traditional identification mode depends on voltage, current and resistance value detection is solved, and the misjudgment probability is reduced.

The invention discloses an inspection report generation method and device, equipment and a storage medium. The method comprises the following steps: determining abnormal inspection data and a report object of data center equipment; determining structured inspection data according to the abnormal inspection data, a preset equipment knowledge graph and a historical case library; determining a report constraint condition according to the report object and a preset report generation rule; and inputting the structured inspection data and the report constraint condition into a pre-trained large language model, and generating an inspection report oriented to the report object. According to the technical scheme, the preset equipment knowledge graph and the historical case library are constructed in advance, the fault linkage effect and the historical similar cases across the equipment are identified based on the inspection data, the inspection reports facing different report objects are dynamically generated, and the generation efficiency, the flexibility and the comprehensiveness of the inspection reports are improved.

The invention relates to a sensitive data identification and desensitization method and system based on an AI large model, and belongs to the technical field of data security processing and artificial intelligence. The method comprises the following steps: receiving a multi-source data processing request, establishing a sensitive data identification framework, analyzing elements of sensitive data, and extracting a sensitive data feature vector through a natural language processing algorithm; classifying the multi-source data, dynamically adjusting an identification threshold value of the sensitive data identification framework through a sensitive data feature vector, and generating a self-adaptive sensitive data identification strategy; establishing a terminal desensitization verification model, performing desensitization verification on the identified sensitive data, performing mask replacement or generalization processing according to privacy requirements, and outputting a desensitization retry processing scheme; the sensitive data is subjected to block desensitization, a large data set is divided into small data sets for independent desensitization, replaceable entities in information are removed according to data attributes, and the desensitized data is further subjected to secondary desensitization.

The invention provides a diabetic nephropathy early warning method, system, equipment and medium, and relates to the technical field of medical data analysis and prediction.The method comprises the steps that multiple urine samples of a user are collected, target urine protein detection is conducted, and multiple urine protein concentrations are obtained; each urine protein concentration is combined with a normal urine protein concentration set to be input into an abnormal data recognition tree, the number of layers of multiple recognition nodes is obtained, and the detection confidence coefficient is obtained through calculation; configuring an abnormal proportion according to the detection confidence coefficient, inputting a plurality of urine protein concentration combination normal urine protein concentration sets into an abnormal data identification tree, deleting urine protein concentrations divided into abnormal data in the abnormal proportion before, and obtaining screened urine protein concentrations; according to the screened urine protein concentration, an auxiliary early warning coefficient is obtained through calculation, and early warning is carried out. The technical problem of low early warning accuracy of diabetic nephropathy in the prior art is solved.

The invention relates to the technical field of water conservancy project construction, and discloses a water conservancy project construction intelligent inspection method and system based on an unmanned aerial vehicle, and the method comprises a route planning step, an inspection preparation step, a data collection step, a data recognition step, a data uploading step and a closed-loop management step. The system corresponds to the method. According to the invention, through full-automatic inspection of the unmanned aerial vehicle, full-coverage imaging and special detection are carried out on construction key parts and risk areas, so that no-dead-corner inspection of the construction areas is realized; defects are automatically recognized based on an AI recognition system, positions are marked and graded, an inspection log is generated, and the defect recognition efficiency and accuracy are improved; secondly, the ground terminal uploads the data to a supervision platform, the supervision platform carries out filing according to construction nodes and generates a closed-loop tracking list, and an information island is broken; therefore, the supervision accuracy, timeliness and traceability are improved, the visual, quantifiable and traceable supervision requirements of the construction period of the water conservancy project are met, and the construction quality and safety are guaranteed.

The invention relates to an artificial intelligence technology, can be applied to the technical field of finance, and discloses an artificial intelligence-based abnormal data identification method, device, equipment and medium, and the method comprises the steps: collecting investment data in real time, and inputting the investment data into an investment data semantic large model; performing anomaly identification on the investment data based on the investment data semantic large model to generate an abnormal data identification result; obtaining abnormal investment data in the abnormal data identification result, analyzing an abnormal condition of the abnormal investment data based on a graph neural network algorithm, and generating an abnormal reason and abnormal positioning information; and obtaining a repair suggestion based on the exception reason, and pushing the exception reason, the exception positioning information and the repair suggestion to a visual interface. According to the invention, investment data is scanned in real time through the AI algorithm, abnormal values are rapidly identified, and human errors are reduced; abnormal reasons can be automatically positioned, repair suggestions can be provided, real-time monitoring is achieved, and abnormal data recognition efficiency is improved.

The invention discloses a carbon emission abnormity traceability method and device, a medium and equipment, and belongs to the field of carbon emission, and the method comprises the steps: receiving a traceability request containing a to-be-verified data identifier, positioning target link data from a block chain storage, and executing signature verification and Hash consistency verification, thereby guaranteeing the authenticity and consistency of the data. And if the verification of the target link data is passed, verifying the inclusion relation of Merkle tree root hash of each link upwards layer by layer to confirm the integrity of the full-chain data. And if verification failure of any level is found, the feature data of the level is combined with an anomaly detection model based on an isolated forest algorithm, and an abnormal data node is positioned. And then, according to the signature information of the abnormal data node and the preset tracking secret key, a group signature verification and private key derivation process is applied, the responsibility participant is traced, and the tracing process of the carbon emission abnormity is completed, so that the problem that the carbon emission abnormal point cannot be accurately and efficiently traced in the prior art is effectively solved.

The invention discloses a distributed block storage method and device, electronic equipment and a storage medium. The load index is calculated by collecting the multi-dimensional performance indexes of the storage nodes in real time, and the data fragment storage position is dynamically adjusted according to the load. The self-adaptive multi-dimensional load sensing model can accurately adapt to various service scenes, real-time balance of loads between nodes is achieved, hot nodes are effectively avoided, and the overall performance of the system is improved. And when the load index exceeds the preset threshold value, hot data identification, node affinity evaluation and migration income calculation modes are started to perform data fragmentation migration, so that the utilization rate of mixed medium resources is improved, a high-performance storage medium is reasonably utilized, and the data access efficiency is improved while the storage cost is reduced. Through cooperative work of load monitoring, fragmentation decision making, migration execution, metadata management and the like, a fragmentation strategy is automatically optimized according to input and output characteristics of different service types, and the overall performance under different service scenes is improved.

The invention relates to a sensitive information protection method and device, a storage medium and electronic equipment. The method comprises the following steps: executing structured field identification and general sensitive data identification processing on original input data to identify all sensitive data fragments; performing identification calculation and integrity signature processing on each sensitive data segment to generate a corresponding placeholder, and storing a mapping relationship between each sensitive data segment and the placeholder thereof in a mapping table; according to the mapping relation, replacing each sensitive data segment in the original input data with a corresponding placeholder to obtain replacement input data, and inputting the replacement input data and the output rule into the target large model to enable the target large model to output the original output data; and according to the mapping relationship, replacing each placeholder in the original output data with the corresponding sensitive data fragment to obtain final output data. The technical problem that sensitive data desensitization is prone to misoperation is solved.

The invention discloses a charging pile data transmission supervision method and system based on data analysis, and the method comprises the steps: segmenting multi-dimensional time series data collected by a charging pile into subsequences, constructing a feature density map and a feature association map, and forming the representation data of a heterogeneous density-association space; the representation data are input into an improved Transform model, a local manifold curvature is calculated to update a self-attention weight, and a heterogeneous space curvature anomaly score is obtained; estimating a generalized Pareto distribution parameter in real time based on the abnormal score, and dynamically calculating a dynamic threshold value of the abnormal score; and comparing the abnormal score with a dynamic threshold value in real time to determine an abnormal level, and automatically triggering a supervision response rule through a Kafka message queue to realize a real-time supervision closed loop of abnormal data. According to the method, the real-time performance and accuracy of abnormal data identification are remarkably improved.

The invention relates to a circuit board defect identification method and system based on multi-dimensional image data, and belongs to the technical field of data identification processing, and the method comprises the following steps: obtaining synchronous image data of a circuit board to be detected in a plurality of imaging modes; performing space-spectrum joint registration on each modal image to generate a multi-dimensional image cube with a unified coordinate system and pixel alignment; inputting the multi-dimensional image cube into a pre-trained multi-branch heterogeneous fusion neural network; generating a pixel-level defect probability graph by utilizing a defect sensing context decoder, and performing geometric constraint optimization on the probability graph by combining prior information of a circuit board design layout; outputting defect types, positions and confidence coefficients, and establishing an interpretable defect fingerprint database according to the multi-dimensional response characteristics of the defects; the method has the beneficial effects that false defect signals generated by image noise and circuit board surface texture interference can be effectively inhibited, the omission ratio and the false detection ratio are greatly reduced, and pixel-level accurate defect positioning is realized.

The invention relates to the field of wastewater treatment, and discloses an intelligent monitoring and regulation system for metal surface treatment wastewater based on Internet of Things, comprising: a wastewater data acquisition and purification unit for performing real-time acquisition of wastewater parameters through a sensor array to obtain multi-dimensional wastewater original data; performing abnormal data identification and noise filtration on the multi-dimensional wastewater original data to obtain a purified wastewater basic parameter set; according to the method, the real-time data of the wastewater are acquired through the sensor array of the Internet of Things, and the accuracy and reliability of the data are ensured by adopting an advanced anomaly detection and noise filtering algorithm; the system can adjust the wastewater treatment process in real time according to environmental factors and production line operation loads, multi-objective optimization is achieved through a deep reinforcement learning algorithm, and it is ensured that the wastewater treatment process can be efficiently operated under various complex environmental conditions.

The invention discloses an intelligent regulation and control method and system for temperature and humidity data of a logistics warehouse, and relates to the technical field of data identification, and the method comprises the steps: collecting the spatial and temporal distribution data of a temperature and humidity field in the warehouse through a multi-source sensor network; processing the data by adopting a multi-source time sequence data alignment algorithm and a feature extraction method; constructing and updating a spatio-temporal evolution prediction model; generating a coordinated regulation and control instruction sequence; a regulation and control instruction is issued, and closed-loop feedback is formed. The system comprises an environment sensing module, a data fusion and feature extraction module, a dynamic prediction modeling module, a multi-target optimization decision module and an instruction execution and feedback control module. According to the method, the spatial-temporal heterogeneity of the temperature and humidity field can be captured at high resolution, the temperature and humidity evolution trend in a future time window can be accurately predicted, the environmental safety and the energy consumption efficiency are comprehensively considered, the stability of the storage environment is remarkably improved, and energy consumption optimization is realized on the premise that the cargo storage safety is guaranteed.