464results about How to "Improve comparability" patented technology

The invention relates to the technical field of geological exploration, and discloses an advanced geological forecasting method and system based on tunnel geological exploration. The method comprises the following steps: performing data denoising on a multi-source data set pre-collected in a tunnel construction area to obtain an original data set; performing preprocessing and multi-stage data fusion on the original data set to obtain a fused data set; performing three-dimensional space modeling and feature extraction on the fused data set to obtain an intelligent geological prediction result; performing multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of the tunnel; performing construction scheme optimization on the geological risk distribution map to obtain a construction guidance scheme; and carrying out comprehensive analysis and dynamic updating on the real-time monitoring data, the construction guidance scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. According to the method and the system, the defects of insufficient prediction precision, reliability and accuracy of a traditional geological prediction method are overcome, and the safety risk is reduced.

The invention relates to the technical field of model construction, in particular to an obstetric delivery decision model construction method based on a knowledge graph. The method comprises the following steps that multi-dimensional information collection is conducted on a pregnant woman to be delivered, individualized data are obtained, and the multi-dimensional information comprises a medical record text, a laboratory examination result, an ultrasonic image and fetal heart rate monitoring data; performing standardization and structural processing on the individualized data to generate a standardized multi-modal data set; performing entity identification and relation extraction processing on the standardized multi-modal data set according to a preset obstetrical domain ontology model to obtain an initial knowledge graph; and carrying out priori knowledge-based mapping expansion on the initial mapping knowledge domain to obtain a multi-modal mapping knowledge domain. Through an attention mechanism and a decision support interface, the interpretability of the model is enhanced, and doctors can understand and trust decision suggestions.

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

The invention discloses an explosion-proof junction box fault diagnosis method and system based on a sensor, which are used in the field of fault diagnosis, and the method comprises the following steps: obtaining historical multi-source sensor data reflecting the state of an explosion-proof junction box; extracting a time sequence sample of the preprocessed historical multi-source sensor data by using a sliding time window method; multi-domain features reflecting fault types are extracted from the time sequence samples, and a fault diagnosis model is constructed based on the feature combination with the optimal diagnosis performance; performing feature extraction on the multi-source sensor data acquired in real time, and inputting the extracted features into a fault diagnosis model for fault diagnosis; and the operation state and the diagnosis result of the explosion-proof junction box are displayed in real time. According to the method, the subtle change of the fault signal is captured from multiple angles, the Pearson correlation coefficient is used for screening the features significantly related to the fault type, and the dimension reduction processing is carried out through principal component analysis, so that the operation efficiency and accuracy of the model are improved.

The invention provides a dynamic supervision method and system for a marine ranch, and relates to the technical field of marine ranch supervision, and the method specifically comprises the steps: collecting biological parameters and environmental parameters of the marine ranch, calculating to obtain a biological activity index and an ecological health index, integrating the indexes, and carrying out the correlation analysis, generating a comprehensive detection index for reflecting the overall state of the marine ranch, comparing the comprehensive detection index with a preset evaluation threshold value, and performing comprehensive evaluation on the marine ranch. According to the method, the biological activity index and the ecological health index are formed by integrating the biological parameters and the environmental parameters, correlation analysis is carried out, and a systematized method is provided for dynamic monitoring of the marine ranching. Through data analysis of dimensionless processing, the comparability of each index is improved, and managers can master the ecological condition of the marine ranch in real time and take corresponding measures in time.

The invention relates to the technical field of biological information, in particular to a method and system for online integration of multi-source single cell data. The method comprises the following steps: acquiring multi-source single cell transcriptome data; performing data quality evaluation based on gene expression quantity, mitochondrial gene proportion and cell data depth on the single cell transcriptome data to obtain initial quality control data; setting standardized filtering parameters according to the initial quality control data, and performing quality control parameter filtering on the single cell transcriptome data to obtain filtered cell data; performing gene expression characteristic analysis on the filtered cell data to generate high-variation gene expression characteristic data; and performing gene expression spatio-temporal distribution statistics according to the high-variation gene expression feature data to generate gene expression statistical feature data. According to the method, through refined quality control and batch effect correction, data from different sources can be seamlessly integrated on the premise of ensuring data quality, so that noise caused by different batches and technical platforms is eliminated.

The invention belongs to the field of remote virtual rehabilitation training, and particularly relates to a remote rehabilitation management method and system based on virtual reality. The method comprises the steps that firstly, the current health condition, the expected rehabilitation target and the needed period of a rehabilitation patient are predicted through a comprehensive evaluation algorithm and machine learning, secondly, a personalized training plan is intelligently recommended from a virtual rehabilitation training strategy library according to an evaluation result, and in the training implementation process, a sensor continuously monitors physiological indexes and micro-expression response of the rehabilitation patient; the rehabilitation progress is analyzed and evaluated through real-time data and fed back to a training strategy library in time, the training intensity and period are dynamically adjusted, and it is ensured that training is closely matched with the actual health state of a rehabilitative person; by fusing electronic medical record processing, virtual reality technology, machine learning prediction and visual emotion recognition, precise and intelligent management of rehabilitation training is realized, and rehabilitation efficiency and experience are remarkably improved.

The invention discloses a chronic disease medical and anti-medical fusion collaborative management and control system, belongs to the technical field of health data processing, and solves the problem that when an existing system extracts an electronic case file of a user, the health condition change of a patient cannot be reflected in real time, and consequently key feature extraction is inaccurate. The system comprises a user side, a fusion cloud platform, a medical defense judgment module and a follow-up visit customization module, and the fusion cloud platform is used for executing a risk assessment model and outputting a risk assessment result by taking a normalized data set associated with a user as input; a risk assessment model based on deep learning and an attention mechanism is constructed by fusing a cloud platform, so that the risk assessment model can identify and analyze a normalized data set collected by a user side, and the user side can collect a multi-source heterogeneous normalized data set which is good in timeliness and reflects the health condition of a patient in real time; therefore, it is ensured that the risk assessment model provides an accurate assessment path for chronic disease medical prevention and control.

The invention provides a traditional Chinese medicine abnormal state recognition method, system and device and a storage medium, and relates to the field of abnormal recognition. The method comprises the following steps: acquiring product information of a to-be-detected traditional Chinese medicine, and determining a data acquisition standard of the to-be-detected traditional Chinese medicine based on the product information of the to-be-detected traditional Chinese medicine and a preset detection rule; based on a data acquisition standard of the traditional Chinese medicine to be detected, inputting the image data into a preset image anomaly detection model to obtain a preliminary image anomaly region; performing feature fusion on the visual feature data and the spectral feature data to obtain an abnormal feature vector; and inputting the abnormal feature vector into a preset abnormal state judgment model, outputting abnormal state data, and performing abnormal state judgment based on the abnormal state data and a preset abnormal state judgment threshold to obtain an abnormal state detection result of the to-be-detected traditional Chinese medicine. Through the scheme, the abnormal state of the traditional Chinese medicine can be identified timely and accurately.

The invention relates to the technical field of energy storage power station management, and discloses an energy storage power station management system based on big data analysis, which comprises a data acquisition module, a preprocessing module, a performance analysis module, a comprehensive evaluation module and an early warning display unit. A foundation is laid for analysis; on the aspect of performance monitoring of the energy storage unit, the performance can be accurately evaluated, faults can be early warned in advance, replacement is reasonably guided, and stable operation of the energy storage unit is guaranteed; the overall performance of the energy storage power station is evaluated, the state can be comprehensively mastered, potential problems can be found in time, and operation and maintenance strategies can be formulated; in the aspect of operation optimization, the charging and discharging process can be optimized, the service life of equipment is prolonged, and the energy utilization efficiency is improved; in the aspects of information display and decision support, information is transmitted in real time, scientific decision is assisted, operation and maintenance personnel can conveniently and reasonably operate in time, and efficient, stable and safe operation of the energy storage power station is comprehensively guaranteed.

The invention relates to the technical field of ecological data analysis, in particular to a pumped storage power station slope ecological restoration effect evaluation method, which comprises the following steps: monitoring a pumped storage power station slope ecological restoration area; calculating a dry season restoration state characterization value; calculating a frozen season restoration state characterization value; calculating the representation value of the restoration state in the rainy season; outputting a corresponding repair effect evaluation prompt according to each repair level; and correcting the preset evaluation duration. Through real-time monitoring and dynamic weight adjustment, the accuracy and adaptability of an evaluation result are ensured, environmental changes can be responded in time, comprehensive ecological restoration state analysis is provided through comprehensive consideration of various environmental parameters, different restoration levels are clearly distinguished through the evaluation result, and the method has the advantages of being high in practicability and the like. Clear guidance is provided for taking targeted restoration measures, and the problem that the restoration effect assessment is inaccurate due to the fact that the adaptability to extreme climate conditions is insufficient and ecological restoration measures cannot be adjusted in time is effectively solved.

The invention relates to the technical field of biological information mining, in particular to a multi-omics data optimization method and system for intestinal flora matching and a medium. The method comprises the following steps: acquiring historical intestinal tract multi-omics data and integrating the historical intestinal tract multi-omics data to obtain host intestinal tract multi-omics data; performing multi-omics data fusion based on the host intestinal multi-omics data to obtain microbial flora fusion data; performing pattern classification according to the host intestinal tract multi-omics data to obtain host intestinal tract physiological pattern data; carrying out physiological pattern influence factor calculation on the microbial flora fusion data to obtain host intestinal tract physiological pattern influence factors; and performing host intestinal flora matching on the microbial flora fusion data and the host intestinal physiological data according to the host intestinal physiological pattern influence factor, thereby obtaining intestinal flora matching data. According to the method, the accuracy of flora matching prediction can be improved.

The invention relates to the technical field of bioinformatics, in particular to a gene pool data retrieval method and system and a storage medium. The method comprises the following steps: acquiring multi-source genome data, performing quality control on the multi-source genome data based on an FAIR principle, and performing efficient storage and indexing by utilizing a distributed storage system so as to obtain a distributed genome database; performing cross-species homology analysis on the distributed genome database, and constructing a multi-level evolutionary tree model, so as to obtain evolutionary relationship map data; mapping gene structures and functions among different species according to the evolutionary relationship map data so as to obtain cross-species gene mapping data; receiving retrieval query data input by a user; and performing query type identification on the retrieval query data to obtain optimized query data. Through the multilevel evolutionary tree model and cross-species gene mapping, gene function comparison and prediction among different species are realized.

The invention relates to the technical field of single cell data processing, in particular to a database visualization method based on single cells. The method comprises the following steps: acquiring a single cell data set; standardizing the single cell data set to obtain standardized single cell data; performing high-dimensional feature extraction on the standardized single-cell data to obtain single-cell high-dimensional feature data; and performing nonlinear dimension reduction processing on the single-cell high-dimensional feature data to obtain single-cell dimension reduction data. Performing data clustering on the single cell dimension reduction data to obtain single cell clustering data; performing gene differential expression extraction on the single cell clustering data to obtain differential expression gene data; multi-modal data fusion analysis is carried out on single cell data through a data processing technology, a feature mapping technology and a visualization technology; and dynamic weight division is carried out on the single cell database, so that the effect of visualization of the single cell database is enhanced.

The invention discloses a land cover change and thermal environment influence research method based on multi-temporal data, and the method comprises the following implementation steps: 1), obtaining a multi-temporal remote sensing image of a to-be-monitored region, carrying out the land cover classification mapping research of the remote sensing data of different time phases of the to-be-monitored region, and carrying out the statistics of the area of each land cover type; 2) performing surface temperature inversion on the remote sensing data of different time phases of the to-be-monitored area, grading the temperature, and analyzing the temporal and spatial change conditions of different temperature grades; 3) in the aspect of exploring the influence of the land coverage and the change thereof on the surface temperature, firstly, the temperature response of the surface temperature of different land coverage types in different seasons is analyzed; secondly, semi-quantitatively researching the relationship between the surface temperature and the surface temperature by introducing surface characteristic parameters; then quantitatively analyzing the contribution ratio of the land cover type to the surface temperature after the land cover type is changed; and finally, by introducing a distribution function, quantitatively analyzing heat source area information of different land coverage types in the research area.

The invention relates to the technical field of carbon footprint analysis, in particular to a carbon footprint data quality evaluation method, which comprises the following steps of listing all processes of each life cycle stage in a target product system boundary; judging the type of each process according to the decision tree, and matching a data quality evaluation strategy according to the process type; implementing a data quality evaluation process, and outputting all index scores; calculating the weighted average of the scores of the same index of the target product; calculating a data quality evaluation comprehensive score of the carbon footprint research of the target product according to the weighted average; and evaluating the data quality grade of the target product carbon footprint research according to the data quality evaluation comprehensive score. According to the method, the quality evaluation strategies of various types of data in the carbon footprint of the target product are determined, the decision tree is adopted to guide selection of the evaluation strategies, the data quality evaluation process is completed, the data quality grade is determined, and the comparability, quality and credibility of the research result of the carbon footprint of the target product can be improved.

The invention relates to the field of computer vision and artificial intelligence, in particular to a real-time multi-person motion analysis system based on motion perception and semantic understanding, which comprises a motion perception module, a motion tracking module, a motion semantic understanding module and a motion performance analysis module. The motion area segmentation unit adopts a background modeling or inter-frame difference method to identify a motion area and generate a motion mask; the target detection unit is connected with the central processing unit through a data bus and used for processing video frame data and detecting the position and the bounding box of a moving target. The motion area segmentation unit is connected with the target detection unit and is used for generating a motion mask. By integrating a plurality of modules, the motion data can be comprehensively captured and analyzed, and the accuracy and real-time performance of motion analysis are improved. Through combination of the target detection unit and the motion area segmentation unit, the system can accurately identify a motion target and a motion area, and a solid foundation is laid for subsequent tracking and analysis.

The invention discloses an outdoor environment monitoring method and system, a storage medium and a program product, and the method comprises the steps: obtaining a plurality of initial detection sub-regions; performing preliminary sampling at the central point of the initial detection sub-region to obtain initial environment data; constructing an environment parameter space distribution model; calculating a variation coefficient, and determining a high-variation sub-region; judging whether the area of the high-variation sub-region is greater than a preset minimum area or not; if yes, the minimum sub-region is divided, and the center point serves as a sampling point; if not, increasing a preset number of sampling points; controlling the detection unmanned aerial vehicle to perform secondary sampling at the sampling points to obtain initial environment data, and updating the environment parameter spatial distribution model; calculating an updated overall prediction error, and judging whether the updated overall prediction error is greater than a preset error; and if not, determining the initial environment data with the maximum similarity, and determining the region corresponding to the sampling point as a candidate experiment region. According to the invention, the accuracy of the environmental parameters detected in different outdoor environments is improved.

The invention discloses a digital construction data processing method, a server, a medium and a product, and relates to the technical field of data processing, and the method comprises the steps: obtaining construction project data; performing preprocessing operation on the construction project data to obtain digital processing data; generating a digital construction data processing system based on the digital processing data; inputting the real-time construction data into a digital construction data processing system to obtain progress deviation, resource deviation, quality deviation and safety risk assessment; determining a construction adjustment suggestion based on the progress deviation, the resource deviation, the quality deviation and the safety risk assessment; and the construction adjustment suggestion is sent to the construction site end, so that the construction site end adjusts the construction project. By implementing the method, the efficiency and accuracy of construction management are improved.

The invention relates to the technical field of sensing data fusion, in particular to a multi-sensor data fusion method for an unmanned pre-controller. The method comprises the following steps: acquiring environmental obstacle position data, environmental object distance data and environmental object motion state data through unmanned driving environment multiple sensors, performing time sequence synchronization and standardization processing, and performing feature space vector conversion at the same time; carrying out multi-sensor particle filtering fusion analysis and multi-sensor uncertainty inference adjustment analysis to obtain an environment multi-sensor uncertainty adjustment coefficient; and performing multi-sensor uncertainty fusion optimization and environment space modeling processing according to the environment multi-sensor uncertainty adjustment coefficient, performing environment state driving control prediction analysis, and generating an unmanned driving environment state change driving control strategy so as to execute a corresponding unmanned driving environment change pre-control function. According to the invention, rapid fusion analysis and utilization of unmanned multi-sensor data can be realized.

The invention relates to an intelligent fish community stability evaluation method and system based on a network model, and the method comprises the steps: optimizing species distribution data based on the influence of a single species on community biodiversity and the influence of interspecific interaction on a community structure, obtaining species distribution optimization data, and constructing a community association network model; quantifying the functional diversity overlapping degree of the species by adopting a PCoA method and constructing a functional ecological niche network model; constructing a comprehensive network model according to the community association network model and the functional ecological niche network model; and constructing a radar map according to the community association network model, the functional ecological niche network model and the comprehensive network model, and evaluating the network stability based on a radar map area method. According to the method, the deficiency of the interaction relationship between the species caused by incomplete sampling can be better reflected, the network structure is more complex and closer to the real situation, the interaction relationship between the species can be deeply mined, and the accuracy of the evaluation result and the comparability between different biocenosis networks are improved.

The invention discloses a switch optical communication interface electric signal quality test method and device and a medium, and the method comprises the steps: constructing a standard electric signal generation link based on a bit error tester, a first variable ISI board and an oscilloscope, and constructing a standard electric signal conforming to a preset eye pattern specification through the standard electric signal generation link; constructing a worst receiving link by combining a second variable ISI board and based on the maximum insertion loss value corresponding to the first optical communication interface of the switch; accessing a second optical communication interface of the switch to the test link, and sending a signal of a standard test code pattern through the second optical communication interface; and counting the receiving bit error rate corresponding to the first optical communication interface through the worst receiving link, and determining the electric signal quality of the optical communication interface of the switch according to a counting result. Through the method, potential performance problems can be found and solved in time, and the stability and reliability of the switch in practical application are ensured.

The invention discloses a product full life cycle carbon emission tracking method, which is embedded in a product carbon footprint management service platform, and specifically comprises the following steps: S1, formulating a product definition boundary and a truncation standard based on a carbon emission calculation formula; s2, data acquisition and accounting are completed according to local conditions, and real-time monitoring and data acquisition are carried out on all links in the product production process. According to the method, the total emission amount of greenhouse gases directly or indirectly generated by a product in the whole life cycle of the product is calculated, including the emission in the extraction, production, transportation, use and waste processes of raw materials, so that the calculation and result traceability of the carbon footprint in the whole life cycle of the product are realized. Accurate carbon emission data are provided for enterprises, the green level of the production process and technical equipment is improved, and the transformation of the enterprises and the society to low-carbon economy is promoted.

The invention relates to the technical field of medical care, in particular to a tumor prognosis prediction method based on tumor database text recognition, which comprises the following steps: S1, data collection and preprocessing: collecting medical data related to tumors from a plurality of tumor databases; s2, text information extraction: performing information extraction on clinical text data; s3, predicting a tumor metastasis path: predicting the metastasis path by utilizing the graph neural network model, predicting a potential metastasis path of the tumor, and analyzing the influence of different metastasis paths on the prognosis of the patient; s4, prognosis evaluation and risk prediction: predicting the tumor prognosis risk in combination with the medical data of the patient, and comprehensively evaluating the five-year survival rate and the recurrence risk of the patient. According to the method, the medical institution can be assisted in optimizing resource allocation, a scientific personalized diagnosis and treatment scheme is formulated, and the life quality and the treatment effect of the patient are remarkably improved.

The invention discloses a fermentation strain yield prediction method based on artificial intelligence, particularly relates to the field of biological fermentation production, and is used for solving the problems that in the prior art, due to the fact that the yield is difficult to directly map in concentration prediction, monitoring indexes are normal, the final yield deviates from expectation, and reliable strain comparison and production management decision cannot be supported. A historical batch process observation sequence and a material account sequence are aligned to generate a conversion efficiency characterization sequence to form a yield learning sample training yield prediction model, and the conversion efficiency characterization sequence is generated on batches and input into the model to obtain a yield prediction value. And calculating an accounting volume trajectory consistency parameter and a conversion contribution steady-state proportion parameter, inputting the parameters into a coefficient analysis model to obtain a decision credible coefficient, generating a yield risk judgment result for a yield prediction value based on the decision credible coefficient, and realizing a new-batch online prediction record.

The invention discloses an agricultural multi-source knowledge migration method based on a graph model, and belongs to the technical field of agricultural information, and the method comprises the steps: carrying out the reduction, feature selection and feature fusion of agricultural literature data; the data processing efficiency and accuracy are improved by utilizing agricultural data reduction based on self-organizing mapping and feature selection and fusion based on spatial mapping; constructing a knowledge migration network based on a graph model, and performing knowledge semantic extraction and knowledge migration relationship mining; agricultural multi-carrier meta-knowledge acquisition and information feedback are adjusted, and meta-knowledge acquisition and network weight adjustment are optimized. According to the method, large-scale and heterogeneous agricultural data can be effectively processed, effective migration of knowledge is realized, the performance of agricultural learning tasks is greatly improved, and the method is of great significance to improvement of agricultural production efficiency.

The invention discloses a photovoltaic array fault detection method based on IV characteristics and a data model, and relates to the technical field of fault detection.The method comprises the steps that firstly, the collection frequency Cp is determined by comprehensively considering the dynamic characteristics, historical faults and the occurrence speed, it is ensured that key fault information is not missed, and then by means of second derivative analysis and squareness calculation of an IV curve, the fault information is obtained; and a data model is combined to accurately identify a local shadow fault, and the situation that previous fault judgment depends on a single index is changed. In the aspect of energy conversion efficiency optimization, a local maximum power point is determined according to a judgment factor Pz and an IV curve, photovoltaic array output and inverter input are dynamically matched, and compared with a traditional fixed matching mode, the power generation efficiency is further improved, and energy waste is reduced. By starting a model verification mechanism, the accuracy and stability of the model can be verified in different scenes and time spans.

The invention relates to the technical field of petroleum product detection, and provides an infrared spectroscopic analysis detection method and a detection device for a petroleum product, aiming at the problem that the existing petroleum aging evaluation method is not distinguished according to the use of the petroleum and adopts a unified large model to carry out aging evaluation, so that the evaluation result is inaccurate. The method comprises the following steps: acquiring the purpose of to-be-tested petroleum, acquiring an infrared spectrogram of the to-be-tested petroleum and the to-be-tested petroleum before aging, determining a preprocessing scheme of an infrared spectrum according to the purpose, calculating a difference graph of the preprocessed infrared spectrogram, selecting a model for petroleum aging evaluation, and inputting the difference graph into a trained aging evaluation model. And outputting the aging result of the to-be-tested petroleum, so that the aging degree of the petroleum can be evaluated in a more targeted manner, and the accuracy of the evaluation result is improved.

The present invention relates to the technical field of sleep diagnosis, and in particular to a sleep staging method based on deep learning, the method comprising: collecting a number of target physiological signals, preprocessing each target physiological signal to obtain a number of standard physiological signals; synchronously aligning and integrating each standard physiological signal in turn to obtain a standard sleep data set; determining the actual correlation between each physiological signal to be integrated in the standard sleep data set, and determining an actual signal combination strategy according to the actual correlation; generating an actual sleep data set according to the actual signal combination strategy, and extracting features from the actual sleep data set to generate actual feature parameters; inputting the actual feature parameters into a deep learning model to determine the actual sleep state; analyzing the actual sleep state, and performing corresponding sleep intervention according to the analysis results. The present invention realizes low-cost non-intrusive sleep monitoring, thereby improving people's physical health.

A transparent visual power grid operation and maintenance system comprises a data acquisition and preprocessing module which is composed of a sensor network and SCADA system docking unit and a data preprocessing unit; the data processing and analysis module is composed of a fault prediction and diagnosis unit, an operation and maintenance strategy optimization unit and a three-dimensional modeling and dynamic simulation unit; the visual display and user interaction module is composed of an AR / VR interface development unit and a user-defined interface and report generation unit; and the system integration and communication module is composed of a standardized API interface and cloud platform unit and a remote access and cooperation unit, and the scheme of the invention solves the problem that the complexity of power grid operation and maintenance system data causes that detection information is difficult to provide visually, thereby affecting the operation and maintenance work quality and efficiency.