397 results about "Static data" patented technology

The invention discloses an urban low-altitude-oriented grid dynamic evaluation and intelligent route planning system, and belongs to the field of urban low-altitude airspace and routes. And the data processing module is used for inputting static data including the task number, the task time, the unmanned aerial vehicle number, the take-off and landing field number and the total airspace, the continuous airspace is divided into grid units by a project, and a quantitative evaluation model with seven factors including the underlying surface, the communication quality, the navigation quality, the meteorological environment, the electromagnetic environment, the noise and the countering as the core is constructed. The airworthiness score of each grid is calculated through normalization processing and weighted summation, a real-time and scientific decision basis is provided for path planning of the unmanned aerial vehicle, crossing from static planning to dynamic evaluation is achieved, and a set of airspace gridding dynamic evaluation and task planning system is provided and implemented. The system aims to cope with flight risks caused by multiple factors such as complex underlying surfaces, dynamic weather and communication navigation interference in urban low-altitude flight, and the safety and reliability of unmanned aerial vehicle logistics are improved.

The invention relates to the technical field of urban traffic management and carbon emission reduction accounting, and provides a bus travel carbon emission reduction accounting method fused with Beidou spatio-temporal data, which comprises the following steps: acquiring spatio-temporal trajectory data based on a Beidou satellite navigation system, and constructing a passenger-trajectory-station three-dimensional correlation model for passenger flow analysis; constructing a passenger transfer analysis system based on the passenger flow analysis result; constructing a dynamic'perception-prediction-optimization 'dynamic scheduling optimization system for passenger flow detail analysis on the basis of space-time trajectory data and a reinforcement learning algorithm; and constructing a mileage-frequency distribution model and a station efficiency evaluation model based on the spatio-temporal trajectory data, respectively generating a trajectory matching optimization strategy and a differentiated station optimization scheme, and realizing adaptive optimization of the public transportation system under dynamic demand change. Compared with a traditional static data dependence method, the method achieves the dynamic and fine-grained monitoring of the operation process of the public transportation system, and effectively improves the timeliness and integrity of data.

The invention relates to a reservoir dispatching management method and system based on digital twinning, and the method comprises the following steps: carrying out the real-time parameter collection of a target reservoir, and obtaining a reservoir characteristic parameter matrix; performing three-dimensional space modeling based on the reservoir characteristic parameter matrix and the reservoir hydrogeological environment in the target reservoir to obtain a reservoir digital twinborn model; performing drainage basin rainfall-runoff dynamic response analysis on the reservoir digital twin model to obtain a drainage basin hydrological response characteristic curve group; performing prospective water balance calculation on the watershed hydrological response characteristic curve group to obtain a multi-period reservoir flow prediction sequence; and formulating an optimal scheduling instruction set of the target reservoir based on the multi-period reservoir inflow prediction sequence, and regulating and controlling a reservoir gate control system in the target reservoir based on the optimal scheduling instruction set. And the method is difficult to adapt to hydro meteorological conditions and multi-target scheduling requirements which change in real time.

The invention discloses an open source software supply chain security intelligent monitoring and risk assessment method, relates to the technical field of software supply chain security, and solves the problem that only existence of indirect dependence can be identified generally in the prior art, but whether a vulnerability code can be called on an actual execution path cannot be accurately judged. According to the method, through dependence locking and Hash fingerprint embedding during construction, cryptographic-level accurate traceability of a component version is realized, and version ambiguity is thoroughly eliminated; in combination with deep interception of a JVM probe for method calling and class loading events during operation, a loading source and a call stack context of each class in a real execution path can be captured, bidirectional association mapping is performed on dynamic behavior data and a static dependency tree, and whether a vulnerability component is located on an executable call chain or not is accurately identified. Compared with the defect that coarse-grained warning is carried out only through version matching in a traditional tool, the method has the advantage that by means of dynamic-static data fusion, it is avoided that a large number of unavailable indirect dependency vulnerabilities are misjudged to be high in risk.

The invention provides a logistics resource sharing and dynamic optimization configuration method based on the industrial internet in order to solve the technical problems that an existing distribution task distribution mode is mostly based on static data and is not flexible and efficient enough, and most distribution systems do not fully utilize real-time state information of vehicles, so that dynamic adjustment and optimization cannot be achieved in the distribution process. On the basis of perception of logistics resource real-time state information and customer demand dynamic information, a vehicle path model suitable for mixed taking and delivering is constructed, and the model is a multi-target model considering vehicle cost, time window deviation punishment cost and the like. In combination with the state information of the current logistics resources and the dynamic change of the customer demand, the hybrid delivery vehicle path model is solved through an improved adaptive large-scale neighborhood search algorithm to obtain an optimized delivery path scheme, so that the logistics vehicles can dynamically respond to the customer demand in the delivery process, and the delivery efficiency is improved. The method is more flexibly suitable for dynamically changing distribution requirements and complex urban traffic environments, and efficient logistics services are realized.

The invention discloses a large-scale language model fine tuning method, device and equipment and a storage medium, and relates to the technical field of large-scale language model fine tuning. According to the method, a preset check point is firstly subjected to large-scale language model fine tuning based on training loss performance, sample embedding space distribution and sample reply score performance of a current model; and performing multi-dimensional self-reference diagnosis on the training data set, identifying a suboptimal sample which is not matched with the capability of the current model, and then processing the suboptimal sample through an adaptive optimization engine to complete dynamic evolution of the training data set. Therefore, dynamic evolution of the data set adaptation model can be realized, the problem of redundancy or insufficient adaptation of static data is avoided, and the training efficiency is improved; meanwhile, data waste is reduced, data diversity is reserved, the model generalization ability is promoted, manual intervention is not needed, and the data optimization cost is reduced.

The invention discloses a high-computing-power data center operation and maintenance automatic execution system, and relates to the technical field of data center operation and maintenance control. The method comprises the following steps: acquiring human-machine-ring-task full-dimensional data, and integrating historical and static data; forming an ability state level Cs through multi-dimensional index weighted analysis; calculating a task risk level Rt from four dimensions; constructing a matrix and a rule base, and mapping a control strategy; the Cs and the Rt are dynamically updated, and the operation and maintenance scheme is adjusted in a linkage mode; and writing back abnormal data, triggering training and reevaluation, and optimizing the model and rules. According to the method, traditional operation and maintenance data islands are broken, pain points such as risk perception lag and regulation and control discretization are solved, accurate matching of personnel capabilities and task risks is realized through full-process data linkage, operation and maintenance safety, accuracy and efficiency are improved, continuous optimization of the personnel capabilities and a system model is promoted, and reliable support is provided for stable operation of a data center.

The invention provides an anti-backflow system, and the system comprises a storage and charging integrated cabin which is used for storing electric energy; the storage and charging integrated cabin comprises a charging pile, an energy storage module, an intermediate relay and a switch which are electrically connected; the control module is configured to obtain static data and dynamic data; determining the output power of the energy storage module and the charging pile based on the static data and the dynamic data; acquiring power supply data; and based on the power supply data, adjusting the power supply condition among the energy storage module, the charging pile and the commercial power, and determining the opening or closing of a first switch node, a second switch node, a third switch node, a fourth switch node, a fifth switch node and an anti-countercurrent contactor, so as to solve the problems that the utilization rate of distributed energy is greatly reduced due to a current anti-countercurrent control scheme; and equipment loss is accelerated, the service life of equipment is shortened, and the operation and maintenance cost is increased.

The invention relates to a geomechanical data visualization method, device and equipment and a medium thereof. The method comprises the following steps: acquiring original data containing rock mass structural characteristics and temperature and humidity environmental parameters, and completing grid division and load and displacement constraint boundary condition setting by adopting finite element analysis to obtain a rock mass stress distribution model; core mechanical parameter evolution of rock mass at different time nodes is simulated based on the model, and a dynamic stress distribution sequence is generated; identifying and marking stress overrun potential event association points through a threshold value, extracting a key time-space change trend, and inputting the key time-space change trend into the time-space dynamic model to obtain a risk prediction distribution diagram containing a risk level, a coverage range and an occurrence probability; and constructing a three-dimensional dynamic view by adopting visual rendering, analyzing a mechanical parameter full-cycle evolution path and geological event association mode, and outputting a three-dimensional dynamic result integrating multi-dimensional information. According to the method, the problems of static data presentation and event association deficiency in the traditional technology can be solved, and the scientificity and the high efficiency of geological engineering analysis are improved.

The invention discloses a chronic obstructive pulmonary disease dynamic risk prediction method based on a time sequence convolutional network, and the method comprises the steps: obtaining static baseline data and dynamic time sequence data of a patient through multi-source data collection, and achieving the data synchronization through timestamp alignment; carrying out one-hot coding on the static data, constructing a feature matrix for the dynamic data, and carrying out standardization processing; a personalized context vector is constructed based on the static features, and multi-source dynamic time sequence feature adaptive fusion is realized by using an attention mechanism; performing time sequence dependency feature extraction by adopting a causal expansion convolutional network, and capturing a long-term dependency relationship through residual block stacking and exponential expansion rate design; and inputting the extracted time sequence features into a classifier, and outputting the dynamic risk probability of acute exacerbation of the chronic obstructive pulmonary disease patient. According to the method, self-adaptive feature fusion is realized in combination with personalized context vectors and an attention mechanism, a long-term time sequence dependency relationship is captured by adopting a causal expansion convolutional network, and a high-precision chronic obstructive pulmonary disease dynamic risk prediction model is constructed.

The invention relates to an intelligent scheduling decision support method and system for a community bus special line, and solves the problems of unreasonable transport capacity distribution and insufficient key travel demand guarantee caused by incapability of distinguishing travel purposes in an existing scheduling method, and the method comprises the steps: firstly, integrating card swiping, reservation and static data to construct a travel demand data set; secondly, through a clustering algorithm, identifying travel destination modes and extracting multi-modal features, generating a prediction report containing a passenger flow thermodynamic diagram and a space-time probability matrix through prediction of an adaptive multi-head attention model, and further calculating service priorities of the modes; then, a decision framework coordinated by route planning and station jump decision intelligent agents is constructed, a global reward function is endowed with differentiated weights according to priorities, and a dynamic scheduling scheme is jointly generated; the method has the following effects: community bus accurate scheduling based on travel purpose identification and priority guarantee is realized, and transport capacity sharing is upgraded to on-demand intelligent distribution.

The invention discloses a multi-modal frequency domain enhanced critical prediction method and device. The method comprises the following steps: acquiring first data and second data; extracting a frequency domain amplitude spectrum, a phase spectrum and a multi-scale wavelet coefficient corresponding to each modal time sequence signal; extracting static features from the medical record data; fusing the static characteristics and the frequency domain amplitude spectrum, the phase spectrum and the multi-scale wavelet coefficient corresponding to each modal time sequence signal to obtain a multi-modal characteristic matrix; constructing a frequency enhancement prediction model; and inputting the multi-modal characteristic matrix into a frequency enhancement prediction model to obtain a prediction result output by the frequency enhancement prediction model, and fully capturing global frequency domain characteristics and local time domain details of the signal by integrating the multi-modal time sequence signal and medical record static data and combining time-frequency analysis. The accuracy and the real-time performance of illness state prediction of the critical patient are effectively improved, and then reliable data support is provided for clinical diagnosis and treatment decisions.

The invention provides a reinforcement learning dynamic pricing method based on game feedback, and relates to the technical field of online data market dynamic pricing. Through combination of leader-follower strategy interaction of the Stackelberg game and the exploration-utilization balance principle of the dobby machine, the problems of price adjustment lag, large income fluctuation and insufficient strategy stability in a non-stable environment are solved. By introducing a data freshness evaluation mechanism, data weight is dynamically adjusted by integrating data timeliness attenuation and market correlation analysis, it is ensured that data input into a model always has high real-time performance and strong correlation, and the problem of decision lag caused by static data input in a comparison scheme is avoided; the exploration-utilization balance principle and the reinforcement learning model of the dobby machine are fused, the exploration rate is dynamically adjusted, a historical optimal strategy can be fully utilized in a non-stationary environment, a potential better scheme can be explored, and the static optimization bottleneck that a game model is limited to a preset objective function in a comparison scheme is broken through.

The invention discloses an emergency operation visual management method and device based on digital twinning, and relates to the technical field of digital twinning. The method comprises the following steps: constructing a three-dimensional twinborn scene corresponding to a physical emergency treatment space, and establishing a virtual mapping model; collecting multi-source heterogeneous data in real time; identifying a patient flow thermodynamic diagram and a resource usage rate based on a data-driven model update; identifying a homogeneous index according to the thermodynamic diagram and predicting a patient flow; a limit scheduling scheme and a reception flow are formed by combining the resource utilization rate and prediction sequence simulation scheduling; and visualizing the result and synchronizing a manager to remind. The technical problem that existing emergency treatment management depends on static data and artificial experience and cannot perform real-time perception, prediction and global optimization on dynamically changing patient flow and resource bottlenecks is solved, real-time dynamic visual management of emergency treatment operation is realized through the digital twinning technology, a patient flow thermodynamic diagram and the emergency treatment resource utilization rate are accurately identified, and the emergency treatment efficiency is improved. And the accuracy and timeliness of emergency operation management are improved.

The invention relates to the technical field of data processing, and particularly provides a hospital logistics equipment fault early warning method, system and equipment and a storage medium, and the method comprises the steps: accessing various hospital logistics equipment through an Internet of Things platform, carrying out the standardized processing and classification of operation data based on an equipment model, and forming a time sequence, an event and a static data flow; the time sequence data flow generates preliminary early warning through dynamic threshold calculation and multivariable time sequence prediction; the event data flow generates event early warning through rule matching and mode recognition; fusing the two types of early warning signals, and performing associated query on an equipment knowledge graph constructed by static data to generate a diagnosis hypothesis; and through comprehensive analysis of the decision engine, graded early warning is output, and operation and maintenance work orders and notices are triggered. According to the invention, through a multi-channel early warning architecture of multi-source data collaborative analysis, the dynamic threshold value and multivariable prediction are combined, the limitation of fixed threshold value and univariate analysis is effectively overcome, and the early warning accuracy and the operation and maintenance efficiency are significantly improved.

The invention relates to the technical field of comprehensive energy transformation, in particular to a digital twin-body-based comprehensive energy system transformation optimization method, which comprises the following steps of: constructing a digital twin body comprising a building thermal model and an energy equipment system model on the basis of static data, dynamic operation data and external environment data of a target area; global optimization with a preset objective function as a guide is carried out, an optimal system capacity configuration and architecture scheme is determined, and then local equipment type selection and control strategy optimization are carried out; issuing the optimized control strategy to a physical energy system for execution, and continuously collecting feedback data to calibrate the digital twin to form closed-loop optimization; a sensing-analysis-decision-execution-learning closed-loop system is constructed, an energy system of a physical world is deeply coupled with a digital model of a virtual world, global and local layered collaborative optimization is realized through a digital twin, and dynamic adaptability is improved through closed-loop operation.

The invention discloses an orthokeratology parameter optimization method and system, a medium and a product, and relates to the technical field of data processing. By implementing the method, the system is optimized to obtain the corneal topographic maps of the patient before and after the standardized mechanical stress is applied, and point-to-point comparison is carried out, so that the corneal dynamic response deformation map for quantifying actual corneal deformation is obtained. The individualized corneal shaping response coefficient calculated based on the corneal dynamic response deformation map objectively reflects the sensitivity of the cornea of the patient to the mechanical stress. Different from related technologies which only depend on static data and empirical formulas, the method has the advantages that the orthokeratology lens parameter set is subjected to targeted correction through the individualized orthokeratology response coefficient, on one hand, the shaping effect deviation caused by the difference of individual cornea biomechanical characteristics (such as elasticity and viscosity) can be corrected, and on the other hand, the orthokeratology lens parameter set can be corrected; and on the other hand, a finally generated personalized orthokeratology lens parameter set can be more predicated and matched with an actual orthokeratology result.

The invention relates to the technical field of security and protection monitoring video storage, in particular to a high-definition monitoring video storage method and system based on RGB color pixel coding, and the method comprises the steps: building coordinates for an initial frame pixel by pixel, collecting original RGB data, and building an initial database; the dynamic degree of the picture is analyzed in real time, and the acquisition frame rate and the pixel change judgment threshold value are automatically matched. Then, collecting subsequent frames according to the matching frame rate, calculating the color change degree relative to the previous frame pixel by pixel, and judging whether the pixels need to be updated or not according to a threshold value; storing coordinates, new RGB codes and timestamps of the pixels needing to be updated; and for the non-updated pixels, not storing the RGB data of the non-updated pixels, and caching the complete RGB data of the non-updated pixels when the whole frame is judged to be a static frame. During playback, an initial database is taken as a basis, and the cached static data and the stored dynamic data are combined to directly render an RGB picture, so that lossless, efficient and low-delay playback is realized.

This invention discloses an air-sea collaborative underwater topographic survey system and its deployment control method, belonging to the field of underwater topographic survey technology. It adjusts the flight attitude of the target UAV in the hoisted state through a center-of-gravity adaptive hoisting module, compensating for the center-of-gravity shift of the target unmanned vessel, improving transport speed, and ensuring flight stability. A sonar water-entry protection module decelerates the target unmanned vessel, reducing mechanical impact upon entry into the water and preventing sonar damage. An air-sea collaborative scheduling module enables synchronous scheduling of the target UAV and the target unmanned vessel, ensuring accurate and automatic recovery of the target unmanned vessel, greatly improving the efficiency and automation level of underwater topographic mapping. A multi-dimensional sonar fusion sensing module performs synchronous multi-sonar scanning, avoiding blind spots at land-water boundaries and misalignment of dynamic and static data, forming an accurate point cloud map, and greatly improving the geometric precision and spatial continuity of the detection results.

The invention discloses an anti-collision and talent data reconstruction method for resume editing in a recruitment system. The method comprises the following steps: receiving an editing request of a target resume and judging whether an identifier change event is triggered or not; when the detection result meets the preset sensitive state condition, generating a process interruption instruction and a manual judgment request, and suspending the process; and calling a unified data synchronization engine to carry out atomization synchronization and consistency verification, and recording a whole-process operation log to generate a traceable transaction execution chain. Resume editing operation is performed in the context of the dynamic recruitment process for conflict detection and decision, so that the problem that the dynamic business process is interfered by static data operation is fundamentally avoided, the business safety and integrity of the recruitment process are guaranteed, and process interruption and attribution errors are prevented; and through an automatic detection-strategic processing assembly line, the intelligence and automation level of resume management is improved.

The invention discloses an SV message dynamic channel real-time mapping device based on a modification instruction set, comprising a configuration computer which generates a data packet corresponding to the SV modification instruction set and a data packet corresponding to SV message static data, and the message modification module modifies SV message dynamic data in a to-be-matched SV message received by the message receiving module from the to-be-matched source device based on the SV modification instruction set, then generates a modified SV message in combination with the SV message static data, and sends the modified SV message to the target device. The invention further discloses an SV message dynamic channel real-time mapping method based on the modification instruction set, and the to-be-matched SV message from the to-be-matched source device is modified and converted into the message which can be received by the target device in real time. The problems that cross-interval equipment stops for multiple times and SV messages of new and old devices are not matched in the transformation process of the secondary equipment of the intelligent substation are solved, and new equipment debugging, synchronous operation of the old equipment and seamless transformation switching of the new and old equipment are achieved.

The application discloses a novel compact gas reservoir gas well reasonable production allocation method and system based on stable production period, and the reasonable production allocation method comprises the following steps: S1, obtaining the production-time data of a target gas well, and arranging the production-time data according to the production size in a power reduction manner; S2, calculating the matter balance time-production data of the production data arranged in the power reduction manner; obtaining the maximum matter balance time according to the matter balance time-production data; S3, judging the relationship between the stable production time and the maximum matter balance time, and then substituting the stable production time into the matter balance time data according to the matter balance time-production data to obtain the reasonable working system of the target gas well at the stable production time. The method depends on the static data and dynamic data in the gas well production data, effectively reduces the cost of a large number of experiments and dynamic monitoring required by other methods, can quickly determine a reasonable production allocation scheme, and realizes efficient and stable exploitation of the gas well.

This invention proposes a geological identification method and system based on dynamic-static multi-source information fusion, comprising: acquiring dynamic and static data for geological identification during TBM construction; processing the dynamic and static data respectively to obtain dynamic and static key indicator data; inputting the dynamic and static key indicator data into a first geological identification model and a second geological identification model respectively to obtain static and dynamic identification results; and fusing the static and dynamic identification results using D-S evidence theory to obtain the final geological identification result. Based on the dynamic and static data obtained during TBM tunnel construction, geological identification is performed based on both dynamic and static data, and the identification results are fused using D-S evidence theory. This fully utilizes the multi-source data obtained during TBM construction, ensuring the comprehensiveness of the acquired features and improving the accuracy of identification.

The invention provides a tight gas reservoir gridding evaluation method, system and device and a storage medium, and the method comprises the steps: obtaining dynamic and static data of a target block, the dynamic and static data comprising geological data and production dynamic data; geological reserve gridding parameters are determined based on the geological data, and the geological reserves are gridded; determining the dynamic reserves of each well in the target block based on the production dynamic data, and meshing the dynamic reserves of each well; calculating the unused reserves and the ratio of the dynamic reserves to the static reserves of the target block grid by grid based on the geological reserve gridding and the dynamic reserve gridding of each well; and obtaining a gas reservoir evaluation result based on the unused reserves and the dynamic and static reserve ratio. According to the method, fine evaluation and infilled well arrangement of the tight gas reservoir are achieved, well pattern infilled evaluation refinement is high, calculation is convenient, and the method has great significance in development evaluation of the tight gas reservoir, development scheme compiling, recovery ratio improving countermeasure making and the like.

The application relates to the technical field of quantifiable test indexes of new energy vehicles, and particularly discloses a method for constructing a typical road spectrum working condition based on a genetic algorithm and new energy platform data, data acquisition, invalid data section elimination rules including static data section elimination and abnormal continuous duration elimination; data abnormal value identification rules: speed abnormal values are defined and a correction method is set; data smoothing and denoising rules: a median filter is used to preliminarily denoise a speed sequence; characteristic parameter calculation: including kinematic characteristic parameters and new energy light truck characteristic parameters; construction of an initial section library; multi-objective genetic algorithm optimization; road spectrum synthesis and post-processing: optimal sections filtered by the algorithm are spliced, and smoothing processing is carried out to ensure the continuity of a speed curve; multi-objective genetic algorithm selection: selecting an NSGA-III algorithm as a multi-genetic objective algorithm condition; the application realizes rapid construction and iteration of the road spectrum, and is high in efficiency.

This invention discloses a method, apparatus, device, and storage medium for predicting transformer overload. By calculating the multi-dimensional dynamically coupled adjacency matrix of the transformer, this adjacency matrix can be used in conjunction with a spatiotemporal attention bidirectional feedback network to effectively improve the accuracy of overload prediction and reduce the false judgment rate under extreme conditions. Furthermore, both the multi-dimensional dynamically coupled adjacency matrix and the spatiotemporal attention bidirectional feedback network are lightweight parallel computing architectures, resulting in a short prediction time for the target transformer's load rate and low resource consumption, which can meet the real-time dispatching requirements of the power grid. The invention also obtains overload and overload benchmark thresholds based on the historical number of overloads in the target transformer's static equipment dataset. These thresholds are used to predict whether the target transformer is overloaded or overloaded, allowing for threshold settings based on the specific conditions of the target transformer to reflect individual differences and further improve prediction accuracy.

The invention relates to the technical field of ultrasonic knife equipment, in particular to a method for detecting and determining a rubber coating point of a waveguide rod, and aims to solve the problem that the method for detecting and determining the rubber coating point of the waveguide rod is poor in effect. According to the technical scheme, the method comprises the following steps: obtaining an initial simulation waveguide rod structure diagram, and extracting a displacement diagram of longitudinal vibration of the initial simulation waveguide rod to obtain an initial '0' point location diagram; carrying out processing heat treatment according to the initial simulation waveguide rod structure diagram to obtain a bare waveguide rod, and testing static data of the bare waveguide rod, including a bare waveguide rod impedance value R; and taking the impedance value R as a reference value, testing a dynamic real-time impedance value R0 when the bare waveguide rod vibrates under the working frequency, if R-R0 is less than or equal to a preset value, making the bare waveguide rod by using the initial simulation waveguide rod structure diagram to continue testing, and testing dynamic test data of the bare waveguide rod by using the movement of the interference assembly part instead of positioning encapsulation. The theoretical deviation of pure acoustic simulation is reduced, and the cost of repeated mold opening verification is reduced.

The invention discloses a telephone number authenticity judgment method based on a power grid artificial intelligence platform. According to the method, a unified analysis data set is constructed, so that deep fusion and efficient treatment of multi-source heterogeneous data are realized; through preset first static consistency comparison, the technical problem that a traditional method cannot automatically identify the number use state in batches is solved; by introducing the second dynamic time sequence comparison, the technical bottleneck that the authenticity of the number cannot be evaluated only depending on static data is broken through. According to the invention, the technical span of telephone number authenticity judgment from artificial experience decision making to data intelligent driving is finally realized, and the automation level of the judgment process and the accuracy of the result are remarkably improved.

The present application relates to the technical field of information security and data-driven permission management, in particular to a paperless interaction permission allocation method and system based on user group and role mapping, comprising: collecting multi-source heterogeneous data from a business system; classifying the multi-source heterogeneous data to obtain static data, semi-dynamic data and dynamic data; modeling user operation behavior based on the dynamic data to generate a de facto work cluster; integrating the static data, the semi-dynamic data and the dynamic data to construct a knowledge graph; calculating a dynamic permission health score in combination with the de facto work cluster and the knowledge graph; and constructing a permission optimization strategy based on the dynamic permission health score; the present application effectively solves the problem of permission mismatch caused by the disconnection between roles and actual work content.

This application relates to a standardized extraction method for mobile phone data involved in a case, belonging to the field of data forensics technology. The method includes: acquiring multi-source data streams from the mobile phone involved in the case; dividing the data into static and dynamic data sets based on the data update frequency and structural stability; constructing a standard feature vector library based on the static data set to provide a consistency benchmark for the data; constructing a behavioral pattern association network based on the dynamic data set to represent the complex relationships between data using a graph structure; generating a dynamic data extraction graph based on this network, and integrating the standard feature vector library and the dynamic data extraction graph to generate a unified data association topology; and finally outputting a standardized data extraction set based on this topology. This invention significantly improves the effectiveness, relevance, and controllability of forensic data by providing in-depth, standardized, and automated extraction and association analysis of mobile phone data involved in cases.