45 results about "Maturity assessment" patented technology

The present disclosure provides a method of facilitating security technology rationalization. Further, the method may include receiving, using a communication device, a security data from a user device. Further, the security data may include a list of security tools, security risks, and capability maturity assessment results. Further, the method may include analyzing, using the processing device, the security data in relation to a security reference data. Further, the analyzing may be based on an AI. Further, the method may include generating, using the processing device, a security result data based on the analyzing. Further, the generating may be based on the AI. Further, the method may include transmitting, using the communication device, the security result data to the user device.

The invention discloses a crown instance segmentation and maturity evaluation method based on dynamic expansion convolution. The method comprises the following steps: firstly, carrying out radiation correction, geometric correction and histogram equalization preprocessing on a forest RGB image acquired by an unmanned aerial vehicle; then extracting global contour features through an adaptive dynamic expansion convolution module, enhancing local detail capture capability in combination with an edge perception feature pyramid network, and realizing multi-level feature fusion by using a double attention mechanism; then, a density adaptive contour cross suppression algorithm is adopted to optimize the mask, and the distinguishing precision of the dense area is improved through dynamic adjustment of a suppression threshold value and ray method contour cross calculation; and finally, calculating canopy density based on a segmentation result, and constructing a maturity index model by fusing the shape, color and geometric features of the crown breadth. According to the method, the extraction capability of the sub-pixel-level details of the crown edge is remarkably improved, the problems of fuzzy irregular contour segmentation and high-density region misjudgment are effectively solved, and an efficient and accurate technical scheme is provided for forestry resource monitoring.

The invention relates to a network security management and control maturity evaluation method and device, computer equipment, a computer readable storage medium and a computer program product. Comprising the following steps: constructing a network security control maturity content model according to a preset index library and a preset domain structure; determining a key element corresponding to each three-level index; obtaining a binary evaluation result of the to-be-evaluated object in each key element; sequentially determining the score of each third-level index, the score of each second-level index and the score of each first-level index according to the binary evaluation result of each key element; determining a comprehensive score of the to-be-evaluated object according to preset weight information and the score of each first-level index; and according to the comprehensive score, mapping to obtain the maturity level of the to-be-evaluated object. The network security management and control maturity content model is clear in hierarchy, rigorous in logic and meticulous in particle; standardizing scoring standards, and realizing element quantification; differences of different domains are quantified, and the accuracy of network security management and control maturity evaluation is improved.

The invention discloses a multi-modal-oriented intelligent computing network management and control operation and maintenance intelligent agent system capability maturity evaluation method, and relates to the technical field of intelligent computing networks, and the method comprises the following steps: S1, determining an evaluation dimension; s2, setting subordinate test items for each evaluation dimension, and respectively endowing the subordinate test items with weights; s3, building a test environment; s4, executing a preset test step for each test item to obtain a test result; s5, scoring is carried out according to a preset scoring rule; s6, obtaining a comprehensive score of the system; and S7, determining a system capability maturity level based on the comprehensive score. According to the multi-mode-oriented capability maturity evaluation method for the intelligent computing network management and control operation and maintenance agent system, maturity testing is carried out from all dimensions, a testing method for all indexes is provided, maturity evaluation of the intelligent computing network management and control operation and maintenance agent system can be standardized in an omnibearing and multi-angle mode, and the evaluation accuracy of the intelligent computing network management and control operation and maintenance agent system is improved. And the maturity evaluation of the intelligent calculation network management and control operation and maintenance intelligent agent system can really approach the actual demand.

The embodiment of the invention provides a hull united workshop digital maturity evaluation method and system. The method is applied to the technical field of industrial big data, and comprises the steps of training a hull workshop maturity scoring model through historical data, and performing scoring and monitoring based on real-time data. Historical hardware facility, software system, personnel skill and management process data are collected, a first data set is generated, and a workshop maturity scoring model is trained in combination with a corresponding historical score and a preset total score calculation formula. And acquiring real-time hardware, software, personnel and management data, inputting the real-time hardware, software, personnel and management data into the model, and generating a real-time dimension score and an overall workshop maturity score. And comparing the real-time score with a preset standard, and if a substandard item exists, sending alarm information to a control center. According to the scheme, real-time monitoring and early warning can be realized, and problems can be timely found and quickly responded. The scoring model automatically calculates the maturity score, human intervention is reduced, and the evaluation efficiency and accuracy are improved.

The invention discloses a digital transformation maturity evaluation system and method based on a multi-dimensional quantitative index in the technical field of building industry digitization. The method comprises the following steps: acquiring various system data of an enterprise; the method comprises the following steps of: normalizing original data of sub-indexes of each dimension in data of each system of an enterprise through an evaluation engine deployed in a cloud server, weighting and aggregating, and calculating a comprehensive maturity index; calculating a dynamic evolution rate by using an evaluation engine in combination with historical comprehensive maturity index data, an enterprise resource investment ledger and an industry reference library; calculating bottleneck factors by using an evaluation engine in combination with enterprise self-evaluation data and an industry benchmark database; the enterprise bottleneck dimension is evaluated based on the bottleneck factor and then an improved scheme is fed back, the enterprise efficiency level is evaluated based on the dynamic evolution rate and then resource investment is adjusted, and the enterprise maturity is evaluated based on the comprehensive maturity index and then a stage route map is generated. According to the invention, the digital level of the building enterprise can be scientifically diagnosed, and transformation path planning is guided.

The invention discloses a manufacturing enterprise supply chain digital transformation maturity evaluation method driven by multi-dimensional indexes. The method comprises the following steps: widely collecting internal and external multi-source data of an enterprise through a data acquisition module, storing preprocessed data in a graph structure through a knowledge graph construction module, and optimizing query efficiency; potential association and abnormal modes among multi-modal data mining indexes are fused through a semantic understanding and relation reasoning module, field adaptability parameter adjustment and optimization are performed on each key model through a model training optimization module, and a hierarchical evaluation system is established and a quantitative improvement scheme is generated through an intelligent evaluation and improvement suggestion module. And finally, a complete closed loop from data acquisition to decision optimization is formed, and accurate evaluation and continuous improvement of the digital transformation maturity of the supply chain of the manufacturing enterprise are realized. The method is characterized in that a manufacturing enterprise supply chain digital transformation maturity evaluation system driven by a multi-dimensional index is used for evaluation.

The invention discloses an artificial intelligence-based professional technology maturity evaluation method, which comprises the following steps of: constructing a dynamic knowledge base by crawling technical literatures, patents and industry reports, and performing cross-technical field semantic embedding in combination with an input technical text to generate semantic representation; extracting multi-scale features based on the dynamic knowledge base and semantic representation, and generating a comprehensive feature set; constructing a technical dependency graph by using the comprehensive feature set and a dynamic knowledge base, planning a reasoning path through reinforcement learning, evaluating the innovativeness, feasibility and commercial value of a professional technology, and generating an evaluation score; calculating a comprehensive score by adopting a cross-technical field weight of self-adaptive threshold adjustment, and mapping the comprehensive score into a maturity level; the evaluation result is optimized by integrating user feedback through online learning; according to the method, the problems of static knowledge base lagging, cross-domain term ambiguity and inference path insufficiency are solved, and the real-time performance, precision and specialty of evaluation are improved.

The invention relates to the technical field of artificial intelligence and machine learning, and discloses an enterprise marketing maturity evaluation method based on machine learning, and the method comprises the steps: obtaining enterprise multi-dimensional marketing data; separating a general marketing capability representation vector and an industry exclusive situation vector through a feature decoupling neural network; and generating a preliminary score by using a pre-trained general model, generating a modulation parameter through a lightweight industry adapter to dynamically calibrate the score, and outputting a final evaluation result. According to the method, through a feature decoupling and modular adaptation mechanism, the cross-industry generalization ability is remarkably improved while the evaluation precision is guaranteed, and the model maintenance and expansion cost is reduced.

The invention discloses an enterprise safety management maturity evaluation method and system and related equipment. The method comprises the following steps: constructing a multi-dimensional quantitative evaluation model of enterprise safety management maturity; the method comprises the following steps: collecting multi-dimensional index data of enterprise safety management, wherein the multi-dimensional index data comprises strategic index data, system index data, cultural index data and performance index data; carrying out weight-based comprehensive index score operation on the multi-dimensional index data through a multi-dimensional quantitative evaluation model to obtain a maturity evaluation score; and obtaining the level grade of the enterprise safety management maturity based on the numerical value interval of the maturity evaluation score in the level grade division table. According to the evaluation method, starting from multiple index dimensions such as enterprise strategy, system, culture and performance, comprehensive scoring is performed based on weights by using the constructed multi-dimensional quantitative evaluation model, the output evaluation result is more representative, and a reference basis can be better provided for subsequent improvement of enterprises.

The invention relates to the technical field of digital evaluation, and particularly discloses an enterprise digital management maturity evaluation method and system based on artificial intelligence, and the method comprises the steps: receiving a working scene fed back by a digital device, determining the working content containing a time label according to the working scene, and carrying out the evaluation of the maturity of the enterprise digital management. Synchronously determining the digitization difficulty containing the time label; querying a theoretical data volume of the work content, and calculating a standard data volume containing a time label according to the digitization difficulty and the theoretical data volume; the actual data volume is evaluated according to the standard data volume containing the time label, and the maturity is determined; according to the method, the environment condition is determined by the camera on each piece of digital equipment, then one data standard is determined, the locally stored data of the digital equipment is evaluated according to the data standard, the maturity of the digital equipment is determined, the maturity of all the digital equipment is counted, and the final enterprise maturity is obtained. The process does not need manual parameters, and the objectivity is extremely high.

The invention belongs to the technical field of intelligent manufacturing capability maturity evaluation, and discloses an intelligent manufacturing capability maturity evaluation method and system based on industry characteristics and semantic understanding. The evaluation method comprises the steps of constructing an industry feature knowledge base, generating a personalized evaluation information packet, performing semantic processing on evidence materials, calculating matching degree and similarity, calculating the maturity of the capability subdomains, performing comprehensive rating and the like, and finally integrating maturity scores of all the capability subdomains to form an intelligent manufacturing capability maturity grade result of an evaluated enterprise. Through industry feature matching, semantic vector analysis and dynamic weight calculation, objective, efficient and accurate evaluation of the intelligent manufacturing capability maturity is realized, and the method can be widely applied to manufacturing enterprises and has remarkable economic benefits and popularization values.

The invention relates to a ship pipeline passive film maturity evaluation method. The maturity of the passive film on the inner surface of the pipeline can be evaluated according to the detected film forming rate by determining the bad film forming pipeline and the bad position and arranging the passive film forming rate on-line measuring device at the bad film forming position. Then, when the passive film on the inner surface of the pipeline is not mature, the film forming period of the poor film forming position of each pipeline is evaluated, the pipeline with the too high or too low film forming rate is determined, and the flow speed of the pipeline with the too high or too low film forming rate is adjusted in real time, so that the film forming rates of the poor film forming positions of the pipelines are basically the same; therefore, all seawater pipelines can form a compact passivation film before a newly-built ship sails, and the inner wall surface of the ship pipeline is better in corrosion resistance.

The invention discloses a maturity evaluation system and method for digital transformation of the building industry in the technical field of digital transformation, and the method comprises the steps: obtaining evaluation data covering the full life cycle of digital transformation of a building enterprise through a multi-source heterogeneous data integration technology; calculating the evaluation data through a dynamic quantization formula to obtain an index evaluation result for evaluating the digital transformation maturity of the construction industry; and based on the index evaluation result, combining industry reference data and an enterprise strategic target, and through a priority determination algorithm, path staged logic and a dynamic deduction engine, generating a digital transformation optimization path. According to the method, the digital transformation evaluation of the building industry is realized from a static state to a dynamic system, from subjective experience to objective quantification and from result description to strategy generation, and a complex cooperative relationship is simulated through nonlinear correction, so that the method is closer to an actual business scene.

The invention relates to the technical field of aerospace COTS device evaluation systems, and discloses a satellite task-oriented aerospace COTS device total factor reliability quantitative evaluation system. The evaluation system mainly comprises a product maturity evaluation module, a manufacturer quality control evaluation module, a third-party evaluation result evaluation module, a product identification / identification evaluation module, a structure analysis evaluation module, a device anti-radiation capability evaluation module and a poor quality record evaluation module. The aerospace applicability and reliability of the COTS device are quantitatively evaluated through the seven modules. Through seven-module total factor evaluation, the limitation of traditional single test data is broken through, multi-dimensional information such as production quality, in-orbit recording and radiation resistance is integrated, the full life cycle performance of the device is comprehensively covered, the accuracy and comprehensiveness of an evaluation result are greatly improved, and more scientific guarantee is provided for the reliability of a satellite task.

The present invention discloses an artificial intelligence-based professional technology maturity assessment method, which includes the following steps: building a dynamic knowledge base by crawling technical literature, patents and industry reports, performing cross-technical field semantic embedding in combination with input technical text, and generating semantic representation; extracting multi-scale features based on the dynamic knowledge base and the semantic representation, and generating a comprehensive feature set; building a technology dependency graph using the comprehensive feature set and the dynamic knowledge base, and planning reasoning paths through reinforcement learning to assess the innovation, feasibility and commercial value of professional technologies and generate assessment scores; calculating comprehensive scores using cross-technical field weights with adaptive threshold adjustment, and mapping them to maturity levels; and optimizing assessment results by integrating user feedback through online learning. The method of the present invention overcomes the problems of lag in static knowledge bases, ambiguity in cross-technical fields and insufficient reasoning paths, and improves the real-time, precision and professionalism of the assessment.

The invention discloses a multi-dimensional risk management maturity model construction method and system, and the method comprises the steps: collecting monitoring data and risk management data of a preset power system, and carrying out the preprocessing of the monitoring data and the risk management data; performing risk identification on the monitoring data to obtain risk data, acquiring a risk type through the risk data by adopting a prior algorithm, and performing power prediction through the risk management data to obtain management and control data; performing dynamic causal matching on the management and control data and the risk type to obtain a matching degree, management data and fuzzy data, performing longitudinal comparison evaluation according to the historical data and the fuzzy data to obtain an optimization degree, and adding the fuzzy data of which the optimization degree is greater than an optimization threshold value into the management data; and constructing a maturity evaluation function according to comprehensive comparison of transverse and longitudinal maturity of the management data, and constructing a power risk management maturity model according to the maturity evaluation function.

The present invention relates to the field of artificial intelligence technology, and specifically relates to a method for evaluating the maturity of enterprise digital management based on artificial intelligence. The method uses a generative adversarial network algorithm based on a variant of momentum optimization to generate samples and achieve data augmentation. The augmented enterprise management data is input into a feature extraction model for training the feature extraction model. The enterprise management data after feature extraction is input into a feature dimensionality reduction model for training the feature dimensionality reduction model. The enterprise management data after dimensionality reduction is input into a classifier model for training the classifier. The trained model is used to process new sample data to evaluate the digital management maturity level it is in.

The invention provides a medical network security assessment method and system and a storage medium, and the method comprises the steps: obtaining hospital information of a to-be-assessed hospital and an operation index of network security equipment, and determining a target maturity level according to the hospital information and the operation index; performing data acquisition on the network security equipment to obtain equipment acquisition data, and calculating a process domain score and a capability domain score according to the equipment acquisition data; calculating a comprehensive maturity score according to the process domain score and the ability domain score, and determining a medical network security assessment level according to the comprehensive maturity score; and performing hierarchical comparison on the medical network security evaluation hierarchy and the target maturity hierarchy, and outputting a maturity evaluation report according to a hierarchical comparison result. According to the embodiment of the invention, medical network security assessment is carried out from three dimensions of a process domain, a capability domain and a hierarchy domain, all aspects of hospital network security operation are covered, and the accuracy of medical network security assessment is improved.

The present invention relates to the field of digital assessment technology, and specifically discloses an enterprise digital management maturity assessment method and system based on artificial intelligence. The method comprises receiving work scenarios fed back by digital devices, determining work content with time tags based on the work scenarios, and synchronously determining the digitization difficulty with time tags; querying the theoretical data volume of the work content, and calculating the standard data volume with time tags based on the digitization difficulty and the theoretical data volume; evaluating the actual data volume based on the standard data volume with time tags to determine maturity; the present invention determines the environmental conditions by a camera on each digital device, and then determines a data standard, evaluates the data locally stored in the digital device based on the data standard, determines the maturity of the digital device, and calculates the maturity of all digital devices to obtain the final enterprise maturity. This process does not require manual parameters and is highly objective.

A computer-implemented method includes: generating, by a computing device, a set of customized guidelines using at least user data and a current set of known guidelines; improving, by the computing device, the set of customized guidelines using a refined customer profile including user data of a current client; providing, by the computing device, a maturity assessment of the current client using the improved set of customized guidelines and known maturity assessments; and providing a recommendation to the current client based on the improved set of customized guidelines and maturity assessment.

The invention discloses an intelligent manufacturing maturity evaluation and upgrading method and system for a manufacturing enterprise, and relates to the technical field of end-cloud collaboration, intelligent terminals and sensors are deployed in a workshop, an end-cloud collaboration data acquisition system is constructed, production, quality and equipment data are acquired in real time, and the data are preprocessed through an edge gateway and then uploaded to a cloud platform. According to the invention, by constructing the end-cloud collaborative data acquisition system, comprehensive data perception and integration from production equipment, production line stations to logistics nodes are realized, a unified digital image can be established based on real-time acquisition, edge preprocessing and cloud platform convergence of multi-source heterogeneous data, a multi-dimensional data basis is provided for the evaluation of the intelligent manufacturing maturity, and the evaluation of the intelligent manufacturing maturity is facilitated. And through fusion of intelligent algorithms such as an analytic hierarchy process, an entropy weight method and clustering analysis, the model can dynamically generate an evaluation result with strategic suitability and objectivity, and scientificity and reliability of enterprise intelligent transformation are remarkably improved.

The invention relates to the technical field of data processing, in particular to a smart campus service maturity assessment method based on dynamic weight and multi-modal data, and the method comprises the steps: collecting multi-modal data including educational activities, mental health, campus point cloud atlas and security events, and IT service capability, and carrying out the preprocessing to form a standardized assessment data set; constructing a six-dimension evaluation index system, determining an initial weight by combining a Delphi method with an analytic hierarchy process, and dynamically adjusting the weight based on a knowledge graph and an LSTM neural network; a feature level and decision level mixed fusion strategy is adopted for evaluation, dimension scores are calculated, and maturity levels are divided; and visually analyzing an evaluation result, generating a report containing resource configuration suggestions, and combining feedback to optimize a management strategy. According to the method, the problems of single evaluation data, fixed weight and insufficient practicability in the prior art are solved, accurate evaluation and dynamic optimization are realized, and support is provided for campus resource allocation and policy adjustment.

This invention discloses a macadamia nut maturity assessment system, belonging to the field of maturity assessment technology. The system includes a monitoring trigger module, an image processing module, a spectral analysis module, and a detection adjustment module. Its key technical points are: it achieves refined assessment and dynamic adjustment of macadamia nut maturity; it analyzes the hardness of fruits that do not meet the standards, and constructs a dynamic adjustment calculation model by combining hardness values, standard values, and adjustment datasets to generate corrected assessment thresholds, effectively solving the problem of inaccurate assessment results caused by fixed thresholds in traditional assessment methods; by introducing two adjustment terms—the proportion of abnormal hardness and the average hardness deviation—it comprehensively considers the hardness of fruits that do not meet the standards, achieving refined adjustment of the assessment thresholds and improving the accuracy and flexibility of the assessment; simultaneously, by dynamically adjusting the assessment thresholds, it can more accurately reflect the actual maturity of the fruits, providing a scientific basis for agricultural production.

The application discloses a kind of multidimensional risk management maturity model construction method and system, including the monitoring data and risk management data of the acquisition preset power system, the monitoring data and the risk management data are preprocessed;Risk identification is carried out to the monitoring data to obtain risk data, risk type is obtained by priori algorithm through the risk data, control data is obtained by power prediction through the risk management data;Dynamic causal matching is carried out to the control data and the risk type to obtain matching degree, management data and fuzzy data are obtained, according to the historical data and the fuzzy data, longitudinal comparison and evaluation are carried out to obtain optimization degree, the fuzzy data of the optimization degree greater than optimization threshold is added to the management data;According to the management data, horizontal longitudinal maturity comprehensive comparison is constructed to form maturity evaluation function, and the power risk management maturity model is constructed according to the maturity evaluation function.

The invention belongs to the technical field of enterprise transformation evaluation, and discloses an enterprise digital transformation maturity evaluation system, which comprises a standard formulation module, an investigation module, a data acquisition module, a calculation module, a grade evaluation module and a feedback module, the method mainly comprises a technical capability support index, a business process index, an organization capability support index and a data capability support index. According to the method, the enterprise digital transformation maturity is calculated according to the specific score conditions of the technical capability support index, the business process index, the organization capability support index, the data capability support index and the digital transformation index, so that the enterprise is helped to define the specific grade of the enterprise in the digital transformation process; and meanwhile, according to the score condition of each index, the enterprise can be helped to know the advantages and deficiencies of the enterprise in the aspect of digital transformation, so that the enterprise can conveniently adjust the transformation target and strategy in time.

Systems and methods are provided utilizing a consensus protocol that assesses a trust factor of one or more nodes, and which executes a block creation process by selecting and assigning trusted nodes, based on the trust factor assessment.

In one aspect, a method for assessing FinOps governance maturity in a multi-cloud environment is presented. The method begins with onboarding a cloud account to a multi-cloud governance platform. Following onboarding, the method involves pulling resources, resource utilizations, resource costs, budget data, and cloud provider recommendations from the cloud account. Next, a set of predefined policies is executed on the resources to identify optimization recommendations for each of the resources. Based on these optimization recommendations, a maturity score for the cloud account is calculated. The method then generates a FinOps governance maturity assessment report comprising the maturity score and the optimization recommendations. Finally, the FinOps governance maturity assessment report is provided to a user interface for display to the user.

The invention relates to the crossing field of artificial intelligence and cloud computing, and particularly discloses an artificial intelligence application development platform resource scheduling method based on a cloud native architecture, which comprises the following main steps: evaluating cloud native maturity and component reusability; carrying out multi-scale cloud native resource perception; performing artificial intelligence task demand measurement; and performing artificial intelligence task resource matching. According to the method, a multi-dimensional evaluation index system including the containerization rate, the pooling rate and the nano-management rate is constructed, and the single-dimensional limitation of traditional cloud native maturity evaluation is broken through; a joint optimization model of feature similarity matching and conflict detection is provided, and the resource fluctuation prediction precision is improved in combination with a sliding window algorithm. According to the method, through accurate resource pre-matching, the average completion time of a large-scale model training task is greatly shortened, and the operation and maintenance cost is reduced.