418 results about "Information quantity" patented technology

The invention relates to the field of multi-modal large models, and discloses an optimization method and system for a self-adaptive multi-stage fine-tuning multi-modal large model. The method comprises the following steps: constructing a multi-modal data set; coding modal data in a single group of samples based on a pre-trained multi-modal basic large model to generate feature representation; calculating a task correlation score, a feature information amount score and a gradient scale score of each modal data feature to generate a single-modal score; calculating a modal alignment degree score, an information complementation degree score and a collaborative gain degree score among different modals to generate a cross-modal interaction score; constructing a depth adjustment demand index and an interaction strength demand index according to the two scores, selecting a combination strategy of depth fine adjustment / shallow fine adjustment and strong interaction / weak interaction according to index values, and performing adaptive optimization on the multi-modal basic large model; and verifying whether the model performance meets the requirements or not so as to iteratively optimize to meet the requirements. According to the method, computing resources are saved, and the representation and generalization ability of the model is improved.

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

To increase the amount of information to be utilized.SOLUTION: An information processing system includes an information processing apparatus, a first terminal device that holds first user characteristic information, and a second terminal device that holds second user characteristic information. The information processing apparatus includes: an information collection unit which collects, from the first terminal device, first anonymized information generated by anonymizing the first user characteristic information, and collects, from the second terminal device, second anonymized information generated by anonymizing the second user characteristic information; a learning unit which generates a model configured to learn, by machine learning, a relationship between the first user characteristic information and the second user characteristic information, using the first anonymized information and the second anonymized information, as learning data, and output, on receipt of the first user characteristic information, estimated user characteristic information estimated from the relationship between the first user characteristic information and the second user characteristic information; and a model output unit which outputs the model to the first terminal device.SELECTED DRAWING: Figure 3

The invention relates to the field of knowledge base construction, and discloses a knowledge base construction method for assisting digital employee question answering, and the method comprises the following steps: collecting internal multi-modal data of an enterprise, including document data, voice data and metadata; carrying out information amount analysis on the collected data, and screening high-value data through an information entropy theory; performing interactive verification on the screened multi-modal data to analyze data consistency; dynamically correcting the verified data, and reducing data noise in combination with a prediction model and an observation model; and performing semantic extraction and clustering optimization on the corrected data to generate knowledge entries. Through the closed-loop process of multi-modal data collection, dynamic correction, semantic extraction, distributed updating and intelligent question and answer, the data processing efficiency, the knowledge base quality and the question and answer accuracy are remarkably improved, and the problems that in the prior art, unstructured data processing efficiency is low, updating is lagged, and dynamic optimization capacity is lacked are solved.

The invention discloses a data security risk assessment method based on a big data model, and relates to the technical field of data security, and the method comprises the steps: collecting and preprocessing multi-source data, collecting security-related data from network equipment, a server and an application system, carrying out the preprocessing, carrying out the adaptive feature extraction, and carrying out the data security risk assessment. The feature importance is evaluated by calculating the mutual information amount of features and risk tags, a standardized feature vector set is constructed, multi-model collaborative analysis is performed, feature vectors are input into a cascade collaborative network composed of an anomaly detection model, a threat recognition model, a correlation analysis model and a prediction model, and a risk risk is obtained. Through cross-model feature transmission and a bidirectional information feedback mechanism, deep collaborative analysis and multi-model deep fusion decision making are carried out, a weight is calculated according to historical accuracy of each model, a comprehensive risk score is calculated by adopting dynamic gating deep fusion, and a dynamic threshold value is calculated based on a sliding time window. And the risk is divided into three levels of high risk, medium risk and low risk.

The invention discloses an image region analysis method based on entropy driving feature enhancement, and relates to the technical field of image analysis and feature enhancement. According to the method, the local channel information entropy is used as a core feature statistical magnitude, and adaptive weighting and strengthening of different importance region features are realized through explicit quantification of image feature information amount; weight distribution is dynamically adjusted according to the characteristic values, the response of a high-information dense area is remarkably enhanced, and meanwhile low-information and noise interference areas are effectively restrained. On the basis, a cross-layer attention mechanism based on entropy prior is designed, feature statistical information is embedded into a gating and weight generation process, and attention distribution with feature significance as guidance is achieved. According to the method, through an entropy-driven adaptive feature enhancement mechanism, the perception capability, the feature discrimination capability and the analysis precision of the model on the salient region of the image are effectively improved.

The invention relates to the technical field of intelligent retrieval of papers, and discloses a paper review expert intelligent recommendation method and system.According to the method, word paraphrasing is carried out on review expert text information and to-be-reviewed paper text information, semantic connotation supplementation and extension can be carried out on text information with limited information amount, and therefore the content of paper review experts is improved. Richer and more useful information is provided; multi-source information fusion is carried out on the expanded text information, the expanded rich multi-source text information can be summarized, extracted and summarized, and comprehensive text information with sufficient information content and high distinction degree is provided; and performing overall vectorization on the comprehensive text information, performing similarity calculation and sorting on the review expert text vector and the to-be-reviewed paper text vector, and screening out review expert information with relatively high similarity as output. The method can effectively improve the matching precision of the paper'small peer 'review experts, reduces the rejection rate caused by inconsistent research directions, and provides technical support for improving the review quality and shortening the review period.

The invention provides a multi-modal content generation AI model construction method and device based on a diffusion model, and relates to the technical field of content generation. The method comprises a multi-source data processing module, a modal priority analysis module, a serialized modal processing module, a diffusion parameter adaptive adjustment module, an interactive feedback integration module and a collaborative output adjustment module. According to the method, multi-modal data input is integrated, model parameters are dynamically adjusted, information entropy is used for evaluating the information amount of the data, the processing priority is set according to the information amount, the recognition capacity of the model for data importance is enhanced, the model parameters are adjusted in real time to adapt to the characteristics of different data modalities, and the accuracy of data processing is improved. And the generation parameters are dynamically adjusted through user feedback, so that the generated content better fits the actual demand and preference of the user, and the consistency and integration of the content are ensured, the attraction of the content is improved and the propagation effect of the content is enhanced by coordinating the style, theme and emotion of the output content.

The invention relates to an event camera target detection method based on sparse Mama, and the method comprises the steps: adaptively discarding no-event and noise marks based on time-space continuity evaluation, capturing a global dependency relationship in space and channel dimensions, and showing the optimal balance between accuracy and efficiency; an information-first local scanning strategy (IPL-Scan) is provided and is used for guiding the model to pay attention to marks with high information content in the scanning process, so that the space context modeling capability is improved; and a global channel interaction module (GCI) is designed to aggregate channel information from the perspective of global space, and global interaction is expanded to a 3D feature space, so that the global modeling capability is further improved.

The invention discloses a method and device for deducing data frequency based on SQL query prices, and the method comprises the steps: constructing queries Q1 and Q2 for data v, obtaining prices p1 and p2, and directly purchasing and returning the frequency of the data v if any price is 0; otherwise, when the pricing algorithm is based on the usage amount, constructing a group of LIMIT queries Qk and querying prices pk corresponding to queries under different k values, and positioning the first element k meeting the condition that pk is equal to p1 as the frequency; and when the pricing algorithm is based on the information amount, constructing a group of HAVING queries Qi and inquiring the price pi of the corresponding query, determining the first element i of which pi is 0, constructing a group of new HAVING queries Qj, inquiring the price pj of the corresponding query, calculating the mean value of all the elements j meeting the condition that pj is equal to p2, and rounding down to obtain the frequency. The method can deduce the occurrence frequency of the original data from the SQL query price, the precision loss is less than or equal to 1, and the method is efficient.

The invention discloses an RAG information retrieval method based on tree-shaped mixed retrieval, and relates to the technical field of intelligent questions.The method includes the steps that after a to-be-traversed structure tree is initialized according to a hierarchical structure tree of document content in an external knowledge base, the priority of all nodes in the to-be-traversed structure tree is maintained; according to the RAG information retrieval method, traversal is carried out by combining the priority of each node on the basis of a traditional DFS method, and a to-be-traversed structure tree and the priority of the nodes are dynamically pruned and updated in the traversal process, so that the RAG information retrieval method can preferentially retrieve a better traversal path and dynamically prune the nodes with relatively low association degree with user query Query; according to the method, the related information of the Query queried by the user can be quickly read and effectively positioned in the document content with a large amount of information, the advantages of the recall rate and the response speed are integrated, the RAG information retrieval efficiency can be improved, and particularly, the outstanding effect is achieved in a large-scale document quick retrieval scene.

The invention is suitable for the technical field of computers, and provides an SATD identification method based on a query strategy combining representativeness and uncertainty, and the method comprises the following steps: data preprocessing and representation; constructing an initial training set; data selection based on a query strategy; stopping training; and analyzing a result. According to the method, the SATD recognition model trained on the basis of the BERT model is superior to an original method in F1 score index; a representative and uncertainty combined query strategy and an active learning method are adopted, a model with excellent performance can be obtained only by training part of data rich in information amount, and the manpower labeling cost is greatly reduced; the specific value of the number of all marked SATD annotations to the number of SATDs in all training sets serves as a stop threshold value, and flexible strategies are provided for different application scenes so as to obtain different SATD recognition models. According to the method, the SATD in the code annotation can be accurately and efficiently identified under the condition of using less data and label cost.

The invention relates to a dynamic desensitization processing method and device for biological medicine classification variable data. The method comprises the following steps of S1, performing data characteristic analysis on classification variables in original biological medicine information; s2, performing random recoding on the original classification variable data to obtain encrypted classification variable data; s3, increasing the number of types of encryption classification variables to exceed the number of types of original codes; an additional category is introduced into the encryption classification variable; s4, establishing a mapping file; and S5, verifying the data. According to the method, characteristic analysis and dynamic desensitization processing are carried out on the data of the classification variables, the risk that sensitive data information is leaked and recognized by a third party is greatly reduced while the real data information amount and statistical characteristics are reserved, and the mapping file is established to record the data corresponding relation before and after desensitization processing; and subsequent result interpretation is facilitated, and the data traceability is enhanced.

The invention discloses a power system multi-scene adaptive transient stability evaluation method based on active transfer learning, and the method comprises the following steps: constructing a transient stability evaluation model based on a Transform encoder, building a mapping relation between data and a stable state, and obtaining the source domain data of a power system; constructing a pre-training model based on the source domain data, and when it is detected that the pre-training model enters a new electrical system scene, performing short-term simulation to obtain an unlabeled target domain sample; calculating the information amount of each target domain sample through an active learning strategy; and carrying out fine adjustment on parameters of the model by utilizing transfer learning, selecting a transfer scheme by considering a difference degree between data distribution, enabling the transient stability evaluation model to quickly finish final updating training, evaluating the transient stability of the power system by using the updated model, and outputting a transient stability evaluation result. According to the method, the transient stable state of the power system can be accurately evaluated, and the model updating efficiency is higher.

The invention relates to the technical field of machine learning models, in particular to a geological disaster risk dynamic assessment method based on multi-source data fusion, which comprises the following steps: constructing a basic geographic information database; dividing geological disaster risk areas by adopting a machine learning algorithm; calculating the contribution degree of each environment factor to the geological disaster through an information amount model; fusing the dynamic rainfall data, carrying out weighted fusion on the dynamic rainfall data and the static geological disaster factors, and calculating a comprehensive risk value; dividing risk levels according to the risk values, and generating a geological disaster risk zoning map; compared with the prior art which mainly depends on single static geological data for analysis and has the problems of incomplete evaluation dimensions and poor timeliness, the scheme realizes multi-dimensional fusion analysis of geological conditions and real-time meteorological factors by constructing the geographic information database integrating the multi-source environmental factors and the dynamic rainfall data; and the comprehensiveness and the momentality of risk assessment are obviously improved.

The invention relates to the technical field of computers, discloses an astronomical sensitive data security processing method, device and system based on privacy calculation, and a storage medium, and aims to solve the problems that in astronomical data cross-mechanism joint analysis, original data is easy to leak, the cooperation efficiency is low, and a traditional desensitization or encryption method is difficult to consider security and availability at the same time. The method specifically comprises the following steps: each participant deploys a private computing agent node locally, and original data is not out of a domain; the task coordination center issues an analysis task; the proxy node extracts and preprocesses local data, and loads a corresponding secure multi-party computing protocol template; according to the method, share segmentation is carried out on multi-modal data such as images, spectrums and star catalogues by adopting addition homomorphic secret sharing, and distribution is carried out through a national secret SM4 encryption channel; and multiple parties cooperatively execute task-oriented security calculation in an encrypted state, and the result is aggregated and returned with the minimum information amount. The method has the effect of realizing high-precision safe collaborative analysis which is available and invisible.

The invention discloses a geological disaster early warning method based on information amount. The geological disaster early warning method comprises the following specific steps: determining a reference rainfall threshold value of a research area; dividing the research area based on ArcGIS to obtain a plurality of drainage basin units; selecting a plurality of index factors, and calculating the information amount of each index factor based on an information amount model to obtain the information amount of each drainage basin unit; constructing a relationship between the information amount and a threshold adjustment coefficient, and obtaining the threshold adjustment coefficient of each drainage basin unit based on the information amount of each drainage basin unit; obtaining the rainfall threshold value of each drainage basin unit based on the reference rainfall threshold value of the research area and the threshold value adjustment coefficient of each drainage basin unit; obtaining forecast rainfall data, and dividing rainfall grades based on the rainfall threshold value of each drainage basin unit; and determining a geological disaster early warning result based on the rainfall level. According to the method, the rainfall threshold value of each drainage basin unit is obtained based on the reference rainfall threshold value, the information amount of each drainage basin unit and the threshold value adjustment coefficient, so that one unit has one threshold value, and the geological disaster precision and the coverage rate are improved.

The basis of calculating memory capacity of modern computing is bit. Thus, the number of bits is the unit of information quantity of modern communication. The number of neurons (node number) in the neural networks in human brain is not the unit of the memory capacity of the human being. The complexity of neural network is much greater than the bit capacity. Hence, the current AI, which tries to imitate the human brain using computing with the basis on bits, performs inherently different processing of information from the human brain. In addition, computing based on bit number is always facing the limitation of integration. The present disclosure provides a system of information memory without relying on bits using three-dimensional neural networks. By replacing the electrical connection of non-volatile memory cells, which are distributed in a three-dimensional array, the mechanism of the information processing of the human brain can be imitated.

The invention belongs to the technical field of artificial intelligence and power system crossing, and discloses a marketing and distribution fusion scene-oriented multi-modal large model efficient fine tuning method and system, and the method comprises the steps: firstly constructing a small amount of high-quality multi-modal seed data with a thinking chain by field experts; performing domain knowledge injection and multi-modal alignment on the base model in stages by adopting a parameter efficient fine tuning technology; the method comprises the following steps of: selecting an unlabeled sample, further introducing an active learning iterative loop based on hybrid uncertainty perception, automatically screening the unlabeled sample with the most rich information amount by quantifying cognitive uncertainty and accidental uncertainty of a model, and labeling the unlabeled sample by an expert, so as to expand a data continuous optimization model and form a'fine tuning-evaluation-labeling 'closed loop. According to the method, rapid and accurate adaptation of the multi-modal large model in a marketing and distribution fusion complex scene is realized with extremely low expert labeling cost, the service reliability and safety of model output are ensured, and a long-acting mechanism of sustainable evolution of the model is established.

Example differential privacy techniques include receiving a request to perform a query on a set of data stored by a database. The request identifies a target accuracy and a maximum privacy spend. The target accuracy includes a maximum relative error. The maximum privacy spend includes a value of a zero-concentrated privacy parameter ρ associated with a degree of information released about the set of data due to the query. A differentially private count operation is performed on the set of data to produce a differentially private result. The differentially private count operation includes performing a count operation on data to produce a result and perturbing the result to produce a differentially private result using a noise value sampled from a Gaussian distribution and based on a fractional privacy spend comprising a fraction of the maximum privacy spend. The differentially private result is encoded for transmission to the client device.

The invention relates to the technical field of model data processing, and discloses a data processing method based on artificial intelligence modeling, and the method comprises the steps: data preprocessing: removing low-information-content features, and standardizing input data; feature screening and optimization: constructing a feature association network by using mutual information, removing redundant features through a spectrum segmentation algorithm, and optimizing feature distribution; modeling and evaluation: completing model construction, training and performance evaluation based on a sparse neural network, and generating feature contribution rate analysis; performing task scheduling and feedback, realizing automatic scheduling of data processing tasks through a time expression, and performing closed-loop management on processing results; and performing result analysis and optimization decision, performing deep analysis on the evaluation result and the feature contribution rate, and generating an optimization suggestion. According to the method, full-process automation of data processing is realized, the efficiency, the performance and the interpretability are improved, the problems of insufficient feature optimization, high modeling complexity, shallow result analysis and the like are solved, and the method is suitable for various data analysis scenes.

The embodiment of the invention provides a data processing method and electronic equipment, and the method comprises the steps: obtaining the length information of target data and the target information amount corresponding to the length information of the target data, and when the target information amount is greater than the target residual capacity, obtaining the target residual capacity of the target data; according to the length information of the target data, the target information amount and the target residual capacity, the target data is packaged to obtain a first data packet, the first data packet is sent to a receiving device, and the length information of the target data is carried in a data header frame of the first data packet and at least one data frame of the first data packet. By means of the data transmission method and device, the problem of data packet loss or analysis errors caused by incomplete length information or incapability of indication when large-capacity data is transmitted due to the fact that the frame capacity of the data header is limited is solved, and then the effects of improving the efficiency of processing the large-capacity data and ensuring the accuracy of data transmission are achieved.

The invention discloses a stomach cancer postoperative survival prediction method and system based on machine learning, and belongs to the technical field of medical worker crossing and medical worker combination. According to the technical scheme, the method comprises the following steps: acquiring clinical data of a gastric cancer patient, wherein the clinical data comprises demographic characteristics, tumor pathology characteristics, operation related parameters and laboratory detection indexes; filling missing values in the clinical data by using an iterative random forest missing value filling method based on mutual information weighting; on the basis of the filled data, a feature subset with the most information content for postoperative three-year survival prediction is screened out through a dual feature selection strategy; training a machine learning model by using the feature subset so as to predict the survival risk of the gastric cancer patient in three years after operation; and outputting a prediction result. The method has the beneficial effects that a plurality of key challenges from data preprocessing, feature engineering and model construction to interpretability and clinical application are systematically solved, and an accurate, reliable, transparent and practical gastric cancer postoperative survival prediction solution is finally formed.

The invention relates to the technical field of curriculum construction, and particularly discloses a custom curriculum system construction method and system.The method comprises the steps that browsing data, containing browsing parameters, of a user are obtained based on preset permissions, the browsing data containing the browsing parameters are recognized, and the information amount of each piece of browsing data is determined; obtaining personal information of a user, and determining the integrating degree of each piece of browsing data according to the personal information; selecting browsing data as reference data according to the integrating degree and the information amount, selecting courses in a preset course library based on the reference data, and constructing the course library; using parameters pointing to all courses in the course library are obtained in real time, and the course selection process is adjusted according to the using parameters; according to the method, the browsing data containing the browsing parameters are acquired, the target course is quickly matched in the course library and the course library is constructed in combination with the personal information of the user, and then the course matching process is optimized according to the feedback condition of the user on the course, so that the efficiency and the accuracy are high.

The invention relates to image processing, in particular to a complex environment-oriented power transmission line inspection image processing method, which comprises the following steps of: screening key frames with high geometric information content by quantitatively evaluating inter-frame motion amplitude and feature tracking quality; extracting features of each key frame by using a pre-trained visual model; performing intra-frame feature aggregation on the features of each key frame by using an intra-frame self-attention mechanism to obtain corresponding intra-frame feature representation; performing global feature aggregation on the intra-frame feature representations of all the key frames by using a global self-attention layer to obtain inter-frame feature representations; constructing a multi-task learning network, and carrying out end-to-end collaborative optimization on depth estimation, image defogging and high-level semantic segmentation; inputting the inter-frame feature representation into a multi-task learning network, and obtaining a restored clear image through deep fusion of scene depth information and image degradation priori; according to the method, the defect that the dual requirements of quality improvement and feature retention of the power transmission line inspection image in a complex environment cannot be met can be overcome.

The invention provides a logistics supply chain management system based on a block chain technology, and relates to the technical field of data processing, and the system comprises a correction module which is used for carrying out the standardization processing of the coal transportation time, cost and loss rate, calculating the correlation coefficient between the standard deviation and indexes, determining the information amount and weight of each index based on the standard deviation and conflict, and carrying out the calculation of the weight of each index; fusing the information amount and the weight to calculate a correction index; the fusion module is used for calculating a mean value and a standard deviation of initial comprehensive scores as a reference, standardizing a difference value between the score and the mean value of each candidate scheme into a relative deviation degree, generating a weight coefficient according to a preset distribution form mapping rule, normalizing the weight coefficient into a proportional weight, and outputting the proportional weight; and fusing the proportional weight, the correction index and the initial score to generate a corrected comprehensive score. The transportation time is shortened, and the vehicle utilization rate is increased.

The invention discloses a privacy enhanced CPPS anomaly detection method based on longitudinal federated learning, and relates to the field of CPPS anomaly detection. According to the method, through longitudinal federated learning, an SCINet model and a Transform model are deployed at clients of a physical side and an information side respectively and are used for local deep feature extraction; in the feature uploading stage, feature compression processing is performed on the physical side data deep features and the information side data deep features, so that effective compression of the uploaded features is realized, and meanwhile, the sensitive information amount possibly leaked in the middle features is greatly reduced. In addition, a bidirectional collaborative optimization mechanism between the client and the server is constructed, and a local feature extraction strategy can be optimized in real time. According to the method, the data privacy protection effect is remarkably improved while the anomaly detection accuracy is guaranteed, the defects of a traditional anomaly detection method in the aspects of privacy security, feature compression effectiveness, bilateral collaborative optimization and the like are overcome, and the practicability and security of CPPS anomaly detection are effectively improved.

The invention discloses a power corridor disaster risk assessment method and system based on an entropy weight method, and particularly relates to the technical field of power corridor disaster risk assessment, and the method comprises the steps: obtaining original multi-source point location data, related to disaster risk assessment, of a power corridor region in the southwest region, carrying out the coordinate unification and time alignment, and carrying out the coordinate unification and time alignment; an original multi-source point location data set is obtained and subjected to quality control processing, the consistency and stability of the original multi-source point location data set are checked, missing values and abnormal values are removed, and the multi-source point location data set is output; on the basis of electric power corridor axis segmentation, double-threshold amplitude limiting conditions of an information amount retention rate and an extreme value order are set, a segmentation optimal interpolation kernel and a time window parameter set are constructed, and an interpolation scale and time aggregation granularity are dynamically backtracked and adjusted, so that risk integral offset caused by terrain fracture and window averaging is eliminated, and the power corridor axis segmentation accuracy is improved. And the credibility of the information amount of entropy weight evaluation is ensured.

The invention relates to the technical field of privacy protection, in particular to a dynamic data privacy protection evaluation method and system based on big data analysis, and the method carries out the analysis of information importance based on the privacy type condition, the correlation derivation condition and the information amount condition of information in a corresponding scene. The corresponding information risk is evaluated through the analysis of the stealing condition of the corresponding information in the scene and the stealing means in the scene, and the information stealing hazard is evaluated through the information importance analysis result, the information risk evaluation result and the hazard condition generated by stealing. The encryption means are dynamically allocated according to the information stealing hazard assessment results of different levels, and the encryption means are dynamically allocated according to the information stealing hazard assessment results of different levels, so that waste of resources can be avoided.

The invention relates to the technical field of shale gas favorable area optimization and resource evaluation, in particular to a quantitative optimization method and device for a shale gas favorable area. The method comprises the following steps: dividing each acquired parameter into a predetermined number of level intervals, and determining a corresponding information amount weight; standardizing each parameter according to the weight of the information amount to obtain a corresponding standardized value; adding all standardized values of each sampling point to obtain a comprehensive evaluation information index of the sampling point, and grading the comprehensive evaluation information index; and drawing a work area comprehensive plane distribution diagram containing the evaluation comprehensive indexes, and optimizing the favorable area of the work area by using the plane graph. The objective of the invention is to solve the problems of unclear boundary, fuzziness, disorder, difficulty in defining and the like of an existing shale gas favorable area optimization method through a multi-index isoline plane superimposed graph, and low reliability of a final optimization result caused by poor rationality of parameter weight distribution and lack of systematic quantitative evaluation criteria of a standardization method.