124 results about "Decision function" patented technology

The invention discloses a rope net ladder data production scheduling and traceability system based on big data storage, and particularly relates to the field of data scheduling and traceability, comprising the following steps: the system obtains total factor data of equipment, process, quality and the like through a data perception and digital acquisition module and standardizes the total factor data; the integrated storage and quantitative analysis module constructs functions such as efficiency evaluation and quality risk to quantify production indexes; the adaptive decision optimization and scheduling module generates an optimal scheduling scheme and a dynamic rescheduling instruction based on a multi-target comprehensive decision function; the full-link tracing and self-feedback optimization module realizes full-link bidirectional tracing and drives process parameter and model self-feedback optimization; the system realizes the intelligent control of the whole production process, balances the efficiency, quality, cost and delivery target, improves the resource utilization rate and production stability, and is suitable for the precise production scene of structural members such as rope net ladders.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and device fusing lane dynamic evaluation and safety indexes and a medium. Real-time collision risks are calculated by collecting dynamic data of a vehicle and surrounding vehicles, multi-lane traffic flow data are collected under triggering of specific requirements, and characteristics such as density, speed and flow are extracted; and a lane basic passage score is calculated by fusing the flow and the large vehicle influence, and correction is carried out by combining the lane changing intention of surrounding vehicles to obtain a dynamic lane evaluation result. And constructing a multi-objective decision function based on the safety index and the lane evaluation result, and finally generating and selecting a trajectory with the minimum comprehensive cost value in a decision period for execution. According to the invention, collaborative evaluation of the vehicle close-range safety risk and the lane passing potential is realized, and the lane evaluation can reflect the traffic flow interaction influence. The driving efficiency and the lane adaptability are optimized, and the decision-making ability of the automatic driving system in the traffic environment is improved.

The invention discloses an anti-unmanned ship unmanned aerial vehicle swarm dynamic clustering algorithm and a saturation attack path planning method, and belongs to the technical field of path planning. The invention discloses an anti-unmanned ship unmanned aerial vehicle swarm dynamic clustering algorithm and saturation attack path planning method. The method comprises the steps of constructing a multi-dimensional situation awareness index system; constructing a clustering decision function; and establishing a cluster structure dynamic adjustment mechanism, and carrying out collaborative scheduling optimization on the cluster structure. According to the invention, the problem of decision error caused by incapability of timely obtaining accurate information when communication is interrupted in the prior art is solved. According to the method, dynamic changes of the bee colony targets can be more flexibly coped with, it is ensured that each bee colony target is covered with the corresponding interception cluster, even if part of communication is interrupted, all clusters can still make decisions autonomously according to local information, certain interception capacity is maintained, basic interception tasks continue to be executed under the condition that communication is limited, and the communication efficiency is improved. It is ensured that interception preparation is completed before the bee colony reaches the defense target, and defense failure caused by response delay is effectively avoided.

The invention discloses a low-orbit satellite beam switching method and a communication terminal, and belongs to the technical field of satellite communication, and the method comprises the steps: synchronously collecting real-time multi-source data; preprocessing the real-time multi-source data to obtain a real-time multi-dimensional feature vector, and based on the real-time multi-dimensional feature vector, performing link quality prediction on links of all visible beams in a period of time in the future by adopting a prediction model to obtain an updated candidate beam set, and providing a time window for decision making; and constructing a comprehensive decision function fused with a multi-dimensional optimization target, calculating a comprehensive score of the updated candidate beam set, identifying a global optimal beam, and performing switching judgment and execution. According to the invention, the switching strategy conversion from passive response to active optimization is realized, the time delay and interruption probability of beam switching are obviously reduced, and the system resource utilization rate and the communication quality are improved.

The invention relates to the technical field of computers, and belongs to a BIOS (Basic Input / Output System) and BMC (Baseboard Management Controller)-based GPU (Graphics Processing Unit) resource intelligent dynamic optimization method and system. A four-layer collaborative architecture of a firmware layer (BIOS), a management controller layer (BMC), a system layer (operating system kernel) and a hardware layer (GPU equipment) is adopted, and the architecture is a cross-layer closed-loop optimization architecture. An optimization decision function is decoupled from an operating system kernel and is deployed in an independent hardware management unit, namely a baseboard management controller (BMC). The system collects GPU operation data in real time through an operation system kernel space and sends the GPU operation data to a BMC; an intelligent optimization engine deployed in the BMC analyzes and calculates the data to generate an optimal GPU resource scheduling strategy; the strategy is returned to an operating system kernel and finally executed by a hardware layer, and intelligent dynamic optimization of GPU resources is achieved through cross-level interaction. Therefore, a continuous intelligent closed-loop optimization link of'deployment-data acquisition-intelligent decision-making-strategy execution 'is formed.

The invention discloses an adaptive multi-loop network InSAR phase unwrapping method and device, and relates to the technical field of interferometric synthetic aperture radar measurement. The method comprises the following steps: acquiring an interferometric phase diagram and a topographic map of a complex topographic region; calculating coherence, a topographic gradient map, a phase gradient standard deviation and residual density based on the interferometric phase map and the topographic map; based on the coherence, the terrain gradient map, the phase gradient standard deviation and the residual density, dynamically selecting the size of a ring through a decision function, and constructing a multi-ring network; determining the connectivity of the multi-ring network according to a bridging strategy and a connectivity detection method, and outputting a global connected network; on the basis of the global connected network, layered residual optimization is carried out by adopting a local absorption and global planning double-layer strategy, and an optimized residual is output; and based on the optimized residual error, unwrapping all rings in the global connected network through an in-ring local unwrapping and global optimization strategy, and outputting an unwrapping result. According to the invention, the problem of network connectivity in a complex area can be solved.

The invention discloses a realization method and system for a domestic environment-oriented MBSE collaborative modeling platform architecture. The method comprises the following steps: constructing a distributed operation environment adaptive to a domestic chip, an operation system and a database based on a micro-service architecture; deploying a modeling engine, cooperative control and data persistence micro-service; multi-user operation requests are received through cooperative control micro-service, conflict detection and coordination are carried out by using an operation transformation algorithm based on a vector clock and a priority decision function, and a global ordered operation sequence is generated; executing the sequence through a modeling engine to update a model state and perform consistency verification; storing the model state and the historical record to a domestic database by using a data persistence micro-service; a bidirectional traceability matrix is constructed through demand influence degree analysis based on a graph neural network. According to the method, the problems of MBSE tool missing, low collaboration efficiency and data isolated island in a localized environment are solved, and safe and controllable efficient collaboration design and full-life-cycle management are realized.

The application belongs to the technical field of data asset management, and particularly relates to a data asset operation decision method and system based on artificial intelligence. Firstly, multi-dimensional characteristic data of target data assets is collected, and the collected multi-dimensional characteristic data is vectorized and fused to form a feature representation F. Then, the feature representation F is quality evaluated. Next, the feature representation F, a comprehensive quality score Q and historical data are used to predict and evaluate a future expected value Vp, a predicted risk value Ri and a predicted cost value Zm of the target data assets. Secondly, a multi-objective decision function is constructed with the future expected value Vp maximization, the predicted risk value Ri minimization and the predicted cost value Zm optimization as targets. Strategy optimization is performed through an optimization algorithm, and a recommended operation action Aopt is generated. The prediction result can be automatically converted into an executable operation decision, and the dependence of the decision on artificial experience can be greatly reduced.

The invention discloses a hierarchical control system and method for a robot, and the system comprises a driving function domain, a control function domain and a decision function domain, the driving function domain is used for collecting the states of a sensor and an actuator, obtaining state data, and publishing the state data for the decision function domain and the control function domain to subscribe; the decision function domain is used for subscribing state data and receiving a user instruction, generating an action instruction based on the state data and the user instruction, and issuing the action instruction to the control function domain; the control function domain is used for subscribing state data and receiving an action instruction, converting the action instruction into a control instruction according to the state data and issuing the control instruction to the driving function domain; and the driving function domain is also used for receiving the control instruction and carrying out driving control on the actuator based on the control instruction. According to the technical scheme, hierarchical decoupling of a robot control system can be achieved, a closed-loop data stream is constructed, the system operation stability and instruction execution accuracy are improved, and meanwhile independent expansion and upgrading of system functions are facilitated.

The invention discloses a security constraint unit commitment method based on tensor completion approximate dynamic programming, and the method comprises the following steps: firstly, building a security constraint unit commitment model based on a Markov decision process; secondly, decoupling the multi-period security constraint unit commitment model into a single-period sub; then, obtaining a value function and a decision function by adopting an approximate dynamic programming algorithm based on tensor completion; according to the approximate dynamic programming algorithm based on tensor completion, the approximate dynamic programming algorithm is improved from the angle of tensor completion, and all value functions and decision functions are approximately obtained by sampling a small number of state variables and decision variables. Decision is made from the angle of tensor complementation, in the face of a discrete state space, the value function of the whole state space can be approximated only by sampling a small number of state points, the calculation burden of an approximate dynamic programming algorithm in the aspect of value function approximation is effectively relieved, and the calculation efficiency of the algorithm can be greatly improved.

This invention discloses a method for identifying dust components in electronic dismantling plants based on polarization scattering imaging. The method includes: acquiring multi-angle polarization scattering images of dust areas in the electronic dismantling plant using a UAV system equipped with a multi-angle polarization imaging module; calculating Stokes vector parameters based on polarization optics theory to extract the linear polarization degree matrix and polarization angle matrix of the dust scattered light; constructing a multi-dimensional polarization scattering fingerprint feature, fusing statistical features, distribution features, and contrast features to form a polarization scattering fingerprint feature vector; inputting the polarization scattering fingerprint feature vector into a pre-trained deep neural network for classification calculation, outputting the probability distribution of various dust components; and applying a decision function based on Bayesian posterior probability calculation to output the final identification result of the main dust components in the electronic dismantling plant. This invention can provide reliable technical support for environmental monitoring, resource recycling, and safety protection in electronic dismantling plants.

The application discloses an egg chicken feed raw material enzymolysis efficiency digital twin modeling optimization method, system, device and medium. In the enzymolysis process, production parameter data, product concentration data and real-time enzyme activity data are collected at a predetermined period, a batch-aware time series prediction model for predicting product concentration changes is trained based on all the collected data, a state vector of the digital twin model is constructed, and an operation variable corresponding to the controllable production parameter is constructed. Using the batch-aware time series prediction model, a state transition function from the current state vector and the operation variable to the next state vector is established in the digital twin environment, and a reward function associated with the unit cost product increment is defined; based on the state vector, the operation variable, the state transition function and the reward function, the control decision function is pre-trained in the digital twin simulation environment, the parameters of the control decision function and the digital twin model are updated through the virtual-real interaction iteration mechanism, and the application can realize accurate prediction and iterative optimization of the enzymolysis efficiency.

The present application relates to the technical field of scene rendering, in particular to a kind of adaptive hybrid rendering method, system and medium based on multidimensional feature, the method is generated by geometry channel G-Buffer containing scene information, comprehensively utilize depth, normal, material and motion and so on Multidimensional feature information;Using decision function, screen space pixel is adaptively divided into visual key area and non-visual key area;For non-visual key area, delay rendering technology is used, for visual key area, path tracking rendering technology is used;The rendering result is weighted and fused to obtain final rendering result.The present application can effectively reduce the invalid calculation overhead of non-visual key area, significantly improve the update efficiency of acceleration structure under dynamic scene, while guaranteeing the physical reality of large-scale dynamic three-dimensional scene, meet the performance requirements of real-time interaction.

The invention discloses a resource allocation method and device, electronic equipment and a computer readable medium, and relates to the technical field of resource allocation, and the method comprises the steps: responding to a user request, and obtaining an environment state corresponding to the user request and a global trade-off factor at the current moment, the global trade-off factor is obtained by solving a target function in a resource allocation scene at the current moment, and the target function is constructed based on a target load; solving a decision function based on the global trade-off factor to obtain a target action, the decision function being constructed based on an estimated request value, an estimated load and the global trade-off factor, and the estimated request value and the estimated load being estimated based on an environment state and an action in a given preset action space; and performing resource allocation according to the target action. The system overload rate is suppressed while the resource utilization rate is improved.

The invention is suitable for the technical field of power equipment detection, and provides a power cable damage detection device and method.The method comprises the steps that firstly, a self-adaptive multi-frequency excitation signal is adopted to scan a to-be-detected cable, and a capacitance-frequency characteristic array of the to-be-detected cable is obtained; secondly, carrying out preprocessing and weighted selection on the obtained feature data; and then, inputting the processed features into an improved support vector machine classification model for damage identification. According to the method, the decision function and the kernel function of the SVM are optimized in a targeted manner, so that the method is more suitable for a cable damage small sample and nonlinear detection scene, similarity measurement based on capacitance characteristics is introduced into the kernel function, and the training of the decision function preferentially selects the support vector with remarkable capacitance change. According to the method, the problems of low accuracy and weak generalization ability of a traditional method in waste cable damage detection are effectively solved, efficient and accurate identification of the cable damage state and type is realized, and the cable recycling rate is remarkably improved.

The invention provides a method, a device and a computer program product for measuring sludge sedimentation parameters. The method comprises the following steps: obtaining a first image sequence of a sedimentation process of sludge at a first concentration; determining a sludge sample area and a background sample area not including the sludge sample area in the initial to-be-processed image in the first image sequence; executing the following operations on each of a plurality of to-be-processed images in the first image sequence: determining a first mean vector based on color vectors of all pixels in a sludge sample region of the to-be-processed image, and determining a second mean vector based on color vectors of all pixels in a background sample region of the to-be-processed image; training a binarization judgment function for the to-be-processed image; obtaining a binary image of the to-be-processed image; and obtaining the sludge area height of the to-be-processed image based on the binary image of the to-be-processed image.

The invention provides an artificial intelligence diagnosis and treatment analog simulation system and method, and the system comprises a knowledge graph construction module which is used for constructing the knowledge association relation among the medical information, the disease type and the treatment scheme of a patient, generating a medical knowledge graph, and carrying out the iteration; the medical model construction module is used for constructing a large medical model for disease recognition and treatment scheme generation; and the diagnosis and treatment aid decision-making module is used for performing personalized analysis on the medical information of the patient in combination with the knowledge graph construction module and the medical model construction module to generate diagnosis aid information and a treatment aid decision-making scheme. Therefore, by constructing the medical knowledge spectrogram and fusing the large-scale pre-training model ability, the medical information of the patient is automatically analyzed, the auxiliary information of the personalized diagnosis and treatment scheme is generated, and the auxiliary decision making function of the AI doctor role is achieved.

The invention discloses a transparent transmission and adaptive packaging method, system and device for sensing data of an icing monitoring terminal and a medium, and belongs to the technical field of power system data transmission, and the method comprises the steps: obtaining the sensing data of the icing monitoring terminal; constructing a data feature vector containing a data type, a data scale, a volatility index and a priority index; according to the data feature vector, the network state parameter and the terminal energy state, a packaging strategy is determined through a decision function; packaging the sensing data according to the packaging strategy to generate a data packet containing a metadata head and a data load; and calculating a scheduling weight of the data packet based on the data feature vector, and determining a transmission sequence for sending according to the scheduling weight and a priority queue mechanism. According to the method, the problem of long-term retention of low-change-rate data is solved by quantifying data feature differences and introducing time enhancement factors, network condition changes are adapted by dynamically selecting a packaging strategy, and transmission resource allocation is optimized by a weighted scheduling mechanism.

The application relates to a development language disorder early identification method and system fusing neural activity features and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring original neurophysiological signals and matched clinical information, and constructing a data set; power features and coherence features of the signals are normalized to generate enhanced feature vectors; attention weighted feature vectors are obtained through mutual information based attention mechanism weighting; the radial basis function is used to nonlinearly map the feature vectors to a high-dimensional space to obtain extended feature vectors; a development language disorder early identification model is constructed, brain region level feature aggregation and modulation, clinical factor fusion and gate modulation are adopted, a decision function fusing nonlinear interaction items is introduced, and a binary cross entropy loss function is used to optimize the model; after model training is completed by using a training set, signals of a subject to be evaluated are input into the model after the same preprocessing, and an identification result is output. The application can improve the accuracy of development language disorder early identification.

This invention relates to the field of power supply or distribution, specifically to an intelligent control box power distribution system supporting automatic reclosing. At the instant the fault current is interrupted, the transient recovery voltage waveform and the residual current waveform across the circuit breaker contacts are acquired, and the transient recovery voltage rise rate characteristic and dielectric recovery strength change characteristic are extracted. A dynamic impedance recovery model for the downstream load is established, and the expected recovery value of the load impedance at the moment of reclosing is calculated based on the load type and power outage time. Using the above characteristics and the expected recovery value as input, the target phase angle is calculated through a closing phase angle decision function, and the reclosing delay time is dynamically adjusted so that the circuit breaker performs closing when the dielectric recovery strength is greater than the transient recovery voltage and the load impedance is in a stable range. This invention suppresses secondary overcurrent and system oscillations caused by reclosing and reduces the probability of arc reignition at the circuit breaker contacts.

The invention discloses a sliding bearing fault diagnosis model construction method and device, equipment and a medium. The method comprises the steps of generating a training set based on historical operation data of a sliding bearing and corresponding multi-dimensional target features; constructing a convex quadratic programming problem of a support vector machine by using the training set according to an interval maximization strategy, and converting the convex quadratic programming problem into a second-order cone programming problem; and solving the second-order cone programming problem by using a solver to obtain an optimal sparse weight vector and offset, and obtaining a sliding bearing fault decision function based on the optimal sparse weight vector and offset to obtain a sliding bearing fault diagnosis model so as to carry out sliding bearing fault diagnosis. By fusing the multi-source operation data and the multi-dimensional target characteristics of the sliding bearing, the defect of single-dimensional data is overcome, and the accuracy of fault state diagnosis is improved; and moreover, the convex quadratic programming problem of the support vector machine is converted into the second-order cone programming problem for efficient solving, so that the solving efficiency is improved.

A transformer on-load tap-changer defect identification method comprises the following steps: establishing a linear mathematical model of voltage, current, acceleration and temperature sensor output, and forming a multi-dimensional information fusion vector; carrying out low-pass filtering processing, constructing a multi-dimensional feature set comprising a mean value, a variance, a dominant frequency amplitude and a wavelet coefficient under an optimal scale, and carrying out feature extraction; based on defect identification and classification decision of multi-dimensional features, introducing a linear scoring function to evaluate an abnormal degree reflected by a current feature vector, performing nonlinear transformation on scores, calculating probabilities of various defects, calculating confidence, and setting a preset threshold to perform binary alarm judgment; and three levels of early warning states are divided through the defect probability, a comprehensive decision function fusing the early warning level and the recognition confidence is designed, and the reliability of the diagnosis result is evaluated. According to the invention, fusion perception and intelligent identification of electrical, mechanical and thermal multi-dimensional information are realized, and the defect identification accuracy and the early warning capability are improved.

The application relates to the technical field of video monitoring and discloses an image data processing method and system based on cloud edge cooperation, which are used for realizing optimized allocation of computing resources and efficient execution of video intelligent analysis tasks. The method comprises the following steps: an edge gateway firstly calls a student model to perform preliminary processing on a target image of a terminal; then a decision function is called to judge whether the preliminary processing result triggers an uploading mechanism facing a cloud server, if yes, the target image is uploaded, and after receiving a secondary processing result returned after the cloud server calls a teacher model, the secondary processing result is output to a user; if no, the preliminary processing result is directly output to the user; wherein the condition for triggering the uploading mechanism is that a predicted value of an importance factor in the preliminary processing result is greater than a set first threshold value, a predicted value of a performance index factor is less than a set second threshold value, and a predicted value of a context correlation factor is greater than a set third threshold value.

The invention provides a flexible detection method for metering equipment based on composite robot distribution, and the method comprises the following steps: S1, enabling a robot to collect the working state data of the metering equipment in real time based on a multi-modal sensor, evaluating the priority of a calculation task based on a task state, and carrying out the task priority sorting; s2, based on the path cost function, considering the cooperative effect between tasks, introducing a cooperative path cost function, and performing robot path planning; and S3, in multi-task execution of the robot, based on the task switching decision function, deciding whether to perform task switching, and performing robot path real-time planning. According to the method, the complex tasks can be efficiently executed, the execution sequence of the multiple tasks is optimized, meanwhile, the task execution process is more flexible, the detection efficiency of the metering equipment is effectively improved, and the method can be suitable for complex work requirements.

An autonomous driving system includes a plurality of sensor combinations and a plurality of edge nodes, each of the plurality of sensor combinations includes at least one sensor; the plurality of edge nodes are in a one-to-one correspondence with the plurality of sensor combinations; and a first edge node in the plurality of edge nodes is configured to determine first perception information of a vehicle based on data collected by a first sensor combination corresponding to the first edge node. A central node is connected to the plurality of edge nodes. The central node is configured to delegate a first state decision function to the first edge node so that the first edge node determines that the vehicle enters a first state or determines a first driving policy of the vehicle in the first state by executing the first state decision function.

The invention discloses a high-precision full-elevation sky-ground radiation signal correction method and device, and belongs to the technical field of radiation signal processing, and the method comprises the steps: carrying out the preprocessing of a to-be-processed sky-ground radiation signal, and obtaining a zero-offset sky-ground radiation signal; calculating a soft decision function transformation coefficient and a mixed function weight coefficient according to the skewness value of the zero-offset sky-earth radiation signal; substituting the soft decision function transformation coefficient and the mixed function weight coefficient into a pre-constructed mixed function transformation model, and using the mixed function transformation model to perform soft saturation transformation on the zero-offset sky-earth radiation signal to obtain a rectified sky-earth radiation signal; and fitting the rectified space-ground radiation signal to obtain a rectified space-ground radiation signal fitting curve. The method does not need to depend on a simplified mathematical model, does not need external reference information, can achieve the rectification of the space-ground radiation signal in a dynamic environment, and remarkably improves the reliability and robustness of a space-ground radiation measurement system.

The application provides a speech recognition technology based on spatiotemporal clustering information inference to solve the problem of high speech recognition misrecognition rate under special conditions. The speech recognition technology comprises the following processing stages: a data segmentation stage, in which signals are acquired by a sound acquisition sensor, transmitted and stored on a local or cloud server, speech recognition and segmentation are performed, and data are stored in a data structure of "signal (word level)-recognition result (word level)-position-time"; a data clustering stage, in which the saved structural data are clustered in two forms: first-class clustering, in which clustering is performed according to time intervals in the "time" dimension, and second-class clustering, in which clustering is performed according to spatial distances in the "space" dimension; and a speech recognition stage, in which two recognition strategies are adopted according to whether positioning information can be acquired, speech recognition is performed under the condition that positioning is not started by using a first-class clustering result to assist a decision function, and speech recognition is performed under the condition that positioning is started by using a second-class clustering result to assist the decision function.

The application discloses a device with obstacle avoidance decision function, and belongs to the field of non-electric variable control or regulation system. The application is used to solve the technical problems of high power consumption, large delay and being easily disturbed by self motion and environmental light of the existing obstacle avoidance scheme. The application comprises a modulated light source, an event-based sensor and a processing unit. The modulated light source generates light at a specific frequency and forms a three-dimensional space. The event-based sensor senses the change of light intensity in the three-dimensional space and outputs an event stream. The processing unit is configured to trigger an obstacle avoidance decision process if an object contour is detected according to the event stream. The application can be used for real-time obstacle avoidance of mobile robots, unmanned aerial vehicles or intelligent vehicles, and has the characteristics of low power consumption, small delay, high robustness and low cost.

The invention discloses a low-code component template implementation method, relates to the technical field of low-code component templates, and aims to solve the technical problems of poor component reusability and high technical threshold caused by scene abstraction and deficiency of a current low-code platform. S200, abstracting and constructing a layout template; s300, abstracting and constructing a page template; s400, abstract modeling of a component template scene is carried out; and S500, page production is carried out. Components are summarized into three core scenes including lists, forms and details, a three-level asset library of a layout template, a page template and a component template is combined with a template matching decision function, accurate retrieval, reuse and precipitation of the template are achieved, repeated development is greatly reduced, meanwhile, a front-end interaction logic function is preset, and the template matching decision function is optimized. Core processes such as data refreshing, real-time verification and state retention are covered, developers do not need to manually write interaction codes, the development period is shortened, and the technical threshold is reduced.

The application relates to the technical field of electronic communication, and discloses an electronic device network data detection optimization control method based on edge computing. The application performs real-time detection on data flow flowing through an edge node, dynamically calculates data value parameters which are fused with time decay, event characteristics and context correlation, synchronously monitors network transmission cost and cache pressure parameters, inputs the above parameters into a preset decision function, and generates optimization control instructions such as instant forwarding, cache waiting or aggregation / discard for each data unit. The application combines the dynamic evaluation of data intrinsic value with the deep perception of external resource state, realizes the adaptive global optimization of transmission strategy, effectively guarantees the transmission efficiency of high-value data in a complex edge environment, and significantly improves the overall utilization efficiency of network bandwidth and cache resources.