286 results about "Service load" patented technology

The invention provides a low-power-consumption communication method and system based on a 5GRedCap portable WiFi network, and relates to the technical field of low-power-consumption communication, and the method comprises the steps: carrying out the adaptive control of a DRX period predicted according to WiFi access flow, setting the wake-up control of a RedCap communication terminal module, setting the adaptive switching of a bandwidth part, and setting the switching operation of a cellular communication system. By introducing a DRX cycle adaptive control mechanism based on WiFi access flow prediction and an intelligent wake-up strategy of uplink and downlink linkage into a 5G RedCap communication terminal module, the DRX sleep and wake-up cycle can be dynamically adjusted according to the actual service load state, the power consumption during the idle period of the terminal is effectively reduced, the energy waste caused by frequent wake-up is avoided, and the user experience is improved. And precise energy-saving management of the honeycomb side is realized.

The invention discloses a wireless spectrum intelligent allocation and edge computing cooperation method, which relates to the technical field of mobile edge computing and wireless communication, and comprises the following steps: acquiring spectrum occupation data and service load data; constructing a spectrum resource allocation matrix according to the spectrum occupation data, and constructing an edge computing resource allocation matrix based on the service load data; and inputting the spectrum resource allocation matrix and the edge computing resource allocation matrix into a trained deep reinforcement learning model, outputting a joint optimization strategy, and performing collaborative scheduling of spectrum resources and computing tasks through a bidirectional matching algorithm. According to the invention, a multi-dimensional resource data acquisition mechanism is established, so that real-time monitoring and evaluation of a network resource state are realized; quantitative expression and association mapping of heterogeneous resource characteristics are realized by constructing a mathematical model of a spectrum resource allocation matrix and an edge computing resource allocation matrix; a deep reinforcement learning model is adopted for strategy optimization, and the limitation of a traditional fixed rule algorithm is overcome.

The invention discloses a micro-service dynamic weight load balancing method and related equipment. The method comprises the following steps: asynchronously acquiring a multi-dimensional real-time load index of a micro-service instance, obtaining a load prediction value by virtue of a model, fusing the real-time index, the prediction value and a service health degree, calculating a dynamic weight by depending on service load data, distributing a request according to the weight, and feeding back an adjustment coefficient. According to the method, by asynchronously collecting the multi-dimensional real-time indexes, an accurate basis is provided for weight calculation, so that the request allocates the actual load of the matched instance, and the situation that the response of a high-load instance is slow due to too many requests is avoided; by introducing a load prediction value, load change can be pre-judged in advance, a weight strategy is adjusted in advance, overload when an instance load suddenly increases is prevented, and the fluctuation resistance of the system is improved; the weight is calculated by fusing multi-dimensional data, so that the comprehensive load of the instance can be comprehensively reflected, and evaluation deviation caused by a single index is avoided; a feedback mechanism can enable weight calculation to continuously adapt to a system state, and load unevenness caused by weight fixing or adjustment lag is avoided.

The invention relates to the technical field of network load prediction, and particularly discloses a power supply energy saving system and method based on a load prediction intelligent dormancy strategy, and the method comprises the steps: collecting base station service load and power supply power data, and employing a neural network to accurately fit the nonlinear relation between the two for adjusting the power; constructing a multi-mode service load prediction model, generating a power supply power adjustment instruction in advance, and adjusting the power in time when the load prediction deviation exceeds a threshold value; frequent power fluctuation is avoided through a dynamic threshold mechanism based on hysteresis comparison; a sleep and wake-up strategy and a buffer mechanism are established to ensure that the state of the power supply is switched only when the load changes continuously, so that the switching accuracy and stability are improved; self-adaptive sleep duration and progressive wake-up adjustment are introduced, so that smooth transition and intelligent energy conservation of power supply operation are realized; the method can improve the energy efficiency of the power supply and reduce the energy consumption.

The invention discloses a server temperature control method and system based on deep learning, and belongs to the technical field of server temperature control, and the method comprises the steps: firstly, synchronously collecting server hardware temperature, business load and environment parameters, capturing a heat conduction relation through combining a thermal imaging technology, and constructing a dynamic relation network through time-space fusion processing; a heat flow prediction model is constructed based on the network, model parameters are dynamically corrected in combination with thermal response characteristic attenuation coefficients generated by service load characteristic analysis, and temperature distribution changes are predicted; generating a candidate control strategy set, screening an optimal strategy through composite scene simulation, and generating a control instruction; and comparing the actual temperature after the control instruction is executed with the model prediction temperature in real time, switching to a safety control mode when the deviation exceeds a threshold value, and extracting an incremental sample to update model parameters after the deviation is recovered, thereby realizing accurate temperature control.

The invention discloses a server non-perception resource scheduling method and system based on cluster telescopic self-adaptive reinforcement learning. The method comprises the following steps: step 1, using a monitoring assembly based on a time sequence database to collect various operation load indexes of a cluster server; and step 2, constructing a function resource scheduler by using a Kubernetes scheduling framework. And step 3, constructing a scheduling decision agent by using a cluster telescopic adaptive reinforcement learning algorithm, inputting a variable-length observation sequence, and outputting a node deployment action. And step 4, using a historical cloud service load data set to construct a load generator to simulate a production environment to train and strengthen an intelligent agent deep neural network. And 5, using the trained scheduling decision agent to make a production environment scheduling decision. According to the method, the existing reinforcement learning algorithm is subjected to elastic cluster-oriented algorithm optimization, the expansibility and robustness of the scheduling algorithm when the cluster executes node scaling are improved, and the method has practical application value.

The invention relates to a cross-domain exchange engine equipment load balancing method based on dynamic consistency hash, and belongs to the technical field of load balancing. According to the method, a dynamic concept is introduced to enable a consistency Hash algorithm based on virtual nodes to adapt to a software and hardware load environment of cross-domain switching engine equipment, and key parameters such as a CPU utilization rate, a memory utilization rate, a disk IO rate, a network bandwidth occupancy rate and network time delay of the cross-domain switching engine equipment are comprehensively considered to reflect a current service load condition of the cross-domain switching engine equipment; therefore, requests are dynamically allocated to cross-domain exchange engine equipment on a hash ring, load balancing is achieved, request delay of a shared exchange system is effectively reduced, the system has higher availability, and the service capacity and the resource utilization rate of the system are effectively improved.

The invention discloses a method and a device for determining creep damage of a high-temperature part coupled with fatigue damage, a medium and a product, and relates to the field of high-temperature part safety assessment, the method comprises the following steps: constructing a cyclic elastic-plastic constitutive model; building a high-temperature part finite element model; the cyclic stress-strain and fatigue process of the high-temperature part under the service cyclic load is simulated; calculating the fatigue damage of the high-temperature part under the cyclic load spectrum; through fatigue damage and creep damage, the initial ratchet deformation serves as initial deformation of the creep damage stage of the high-temperature component, a service temperature field and a service load of the creep damage stage are applied to the high-temperature component, and elasticoplastic deformation and creep deformation of the component in the creep damage stage are obtained; and constructing a failure criterion and judging whether a failure occurs or not. According to the method, the creep-related structural integrity of the high-temperature part subjected to fatigue damage can be analyzed, and creep failure of the high-temperature part is prevented.

The invention discloses a communication network bandwidth resource visualized dynamic allocation method and system based on an SDN, and relates to the technical field of communication networks, and the method comprises the steps: obtaining the channel interference intensity, the spectrum occupancy rate and the service flow priority data of each node in a wireless network based on a southbound interface of an SDN controller, and obtaining the channel interference intensity, the spectrum occupancy rate and the service flow priority data of each node through a channel resource dynamic allocation table; generating a multi-dimensional resource view; according to the multi-dimensional resource view, multi-level coverage domains of the wireless network are divided, the service load level of each coverage domain is identified, and a pre-allocation scheme of cross-domain bandwidth resources is generated; and executing a pre-allocation scheme of cross-domain bandwidth resources by using a northbound interface of the SDN controller, and adjusting bandwidth allocation parameters of each coverage domain. According to the invention, visual monitoring, accurate division and flexible scheduling of the network resource state are realized, and the utilization efficiency of network bandwidth resources is remarkably improved.

The invention discloses a multi-modal data driven dynamic pressure test scene generation method and system, and the method comprises the steps: carrying out the event extraction and matching of a user operation log and a full-link API call sequence, and carrying out the feature enhancement of each alignment event pair, and obtaining a local feature vector; statistical features are extracted from each user session, a graph attention network is constructed, and a group feature matrix is obtained; obtaining a global incidence matrix according to the mapping between the service load and the resource consumption; and splicing the user session feature matrix and the group feature matrix to generate group enhancement features, performing matrix multiplication on the group enhancement features and the global incidence matrix to obtain system-level risk features, splicing local feature vectors and the encoded user behavior logs, splicing the system-level risk features and the encoded performance indexes, and obtaining the user behavior log. And finally, carrying out multi-source fusion to generate a pressure measurement scene. The simulation precision, the dynamic adaptive capacity, the abnormal reproduction capacity, the resource utilization rate and the like are remarkably improved.

The invention discloses an autonomous controllable environment-oriented power business system tuning method and device, and a medium. The method comprises the steps of obtaining a business load fluctuation rate and a resource health index of a power business system; according to the business load fluctuation rate and the resource health index, a target strategy model is determined, and the target strategy model comprises at least one of an offline strategy model, an online strategy model and a preset rule model; and obtaining target feature data of the power business system, and inputting the target feature data into the target strategy model to obtain a target resource scheduling strategy of the power business system. The problems that an existing power business system management mode is insufficient in static performance and flexibility and poor in uncertainty processing capacity are solved, and the scheduling strategy for optimizing power business system resources is achieved. When the power business system oriented to the autonomous controllable technology is in the initial stage of construction, an artificial intelligence method is applied to finally realize interpretable performance adjustment and optimization, and the performance index of the business system after credential adaptation transformation is improved.

The invention provides a gradient metal additive manufacturing method based on cross-scale collaborative interface strengthening. The implementation object of the additive manufacturing method is a heterogeneous interlayer interface of a gradient metal structure, corresponding cross-scale cooperative strengthening is jointly achieved based on bimetal macroscopic interface tooth-shaped meshing strengthening and microcosmic interface atom interaction diffusion strengthening, and the additive manufacturing method has the advantages of being high in reliability, wide in application system and convenient and fast to regulate and control. According to the specific means, a child-mother meshing structure is arranged on the two sides of the bimetallic interface for macroscopic strengthening, an atomic interaction diffusion layer with the specific thickness is formed on the bimetallic interface for microcosmic strengthening in combination with an additive process design, and therefore the macro-micro cross-scale cooperative strengthening effect of the heterogeneous interface is achieved. Based on the method, the heterogeneous metal structure interface can be effectively strengthened, the weak mechanical property area of the interface is eliminated, the method is particularly suitable for parts with severe and complex service loads, and the service life is prolonged.

The invention provides a Kubernetes elastic capacity expansion and contraction method and device based on traffic topology. The method comprises the following steps: acquiring traffic topology elastic rule information and interface request quantity data configured by each micro-service; querying and calculating a service proportion of the interface request quantity data in link tracking by utilizing the link tracking according to the traffic topology elastic rule information and the interface request quantity data; and according to the service proportion, calculating a capacity expansion and contraction proportion, and carrying out capacity expansion and contraction processing on the corresponding micro-service. In the scheme provided by the embodiment of the invention, the flow distribution and the service load condition on the whole link are analyzed through the flow topology, the hotspot service in the link is identified, and the hotspot service is preferentially expanded, so that the performance of the whole system can be effectively improved, the bottleneck transfer can be avoided, the overall availability of the micro service can be ensured, and the user experience can be improved. The optimal allocation of resources is ensured, a more comprehensive and accurate capacity expansion and contraction decision is made accordingly, the business fluctuation can be flexibly dealt with, the resource allocation is optimized, and the operation and maintenance management is simplified.

The invention relates to a communication method, a communication device, communication equipment, a storage medium and a program product. The method comprises the following steps: receiving a routing query request sent by a source terminal; the routing query request comprises a target terminal identifier and a target service identifier, then performing service verification on the source terminal according to the target terminal identifier and the target service identifier to obtain a first response result, and finally sending the first response result to the source terminal; the first response result is used for indicating the source terminal and the target terminal to perform service data transmission. According to the method, communication between the source terminal and the target terminal is carried out based on the integrated user service unit, communication between the terminals can be realized without a core network, the utilization efficiency of network resources can be improved, and the communication efficiency can be further improved, so that the user experience is improved. The method can adapt to the development requirements of multiple types of smart terminals, rich personalized applications, large communication service load between user equipment and the like in the 6G network technology, and can promote the development of the 6G network technology.

The invention relates to the technical field of communication, and discloses a sea area wireless network coverage method and device and a computer readable storage medium, and the method comprises the steps: obtaining the historical link performance, real-time marine environment data and business load information of a target sea area; constructing a sea surface electromagnetic wave propagation attenuation dynamic prediction model based on a gated circulation unit; and in combination with the prediction result and the service demand, jointly optimizing the base station transmitting power, the antenna beam pointing angle and the subcarrier resource allocation, and issuing a regulation and control instruction. The system comprises a data acquisition module, a propagation modeling module, a resource scheduling module and an instruction execution module. According to the invention, through deep fusion of marine environment physical field perception and wireless communication system control, a dynamic coverage regulation and control mechanism driven by a sea condition is established; the provided sea surface electromagnetic wave propagation attenuation dynamic prediction model can pre-judge the signal attenuation trend under the complex sea condition in advance, so that network resource scheduling has perspectiveness.

The invention relates to a DataX distributed data synchronization method and a DataX distributed data synchronization device based on Kubernetes. The method comprises the steps of splitting a data synchronization task into a plurality of subtasks according to a data range, starting a plurality of Pods for parallel execution through Kubernetes Job, and starting a plurality of Task tasks in each Pod through channel parameters of DataX for concurrent processing of subtask data; splitting or merging of the fragments is dynamically triggered based on business indexes such as a line processing rate and system indexes such as a CPU (Central Processing Unit) and a memory utilization rate, fragment state information is recorded through ConfigMap, and the number of the fragments and resource allocation are dynamically adjusted; and combining the DataX execution result indexes of each Pod to ensure the data consistency. Through a two-stage fragmentation parallelization architecture, multi-level parallel processing is realized, the resource utilization rate is maximized, and the data synchronization efficiency is remarkably improved; the dynamic elastic capacity expansion and contraction mechanism can dynamically adjust resource allocation according to service load, optimize resource utilization and avoid waste or performance bottleneck; automatic fragmentation management does not need manual intervention, and the operation efficiency and reliability of the system are improved.

The invention discloses a dynamic base station dormancy / wake-up method based on a flow load and a channel state, which improves the accuracy and reliability of dormancy and wake-up judgment by comprehensively considering multi-dimensional factors such as a service load, a wireless channel state, user distribution, adjacent region bearing capacity and the like. By designing a multi-level dormancy and wake-up mechanism and combining flexible management of different functional modules, reasonable balance between energy consumption reduction and service continuity is realized; by introducing a neighbor cell cooperation mechanism and a pre-wake-up strategy, the wake-up time delay is shortened while the coverage and switching stability are maintained. Meanwhile, key indexes such as energy consumption, access time delay, offline rate and switching success rate are continuously monitored in the operation process, and the feedback information is used for dynamically optimizing parameters such as criterion weight, threshold value and residence time. Through a closed-loop adjustment mechanism, the system can keep stable and effective operation in different scenes, and long-term balance between energy-saving control and service performance is realized.

The invention relates to the technical field of data management, in particular to a real-scene three-dimensional data management system based on cloud computing, which comprises a model verification module, a hierarchy generation module, a label collection module, an authority configuration module and a request sorting module. According to the method, through connectivity calculation and direction cosine similarity analysis based on boundary line segments, problem points with closed defects or direction mutation in component boundaries can be accurately recognized, a consistency index set is constructed on the basis, the accuracy of model integrity judgment is improved, and the accuracy of model integrity judgment is improved in combination with patch structure parameters and a weighted hierarchical evaluation method. According to the method, the regional detail hierarchy level can be accurately delimited, spatial pertinence differential expression of detail hierarchy configuration is realized, priority calculation is performed according to the number of access request times and a function task matching result, the component calling efficiency and the rendering response performance are improved, and the reasonability of service load optimization and cloud resource scheduling under the multi-terminal access condition is ensured.

The invention relates to the technical field of intelligent power grid network security, in particular to an adaptive scanning concurrency number adjusting method under micro-grid isolation, which comprises the following steps: S1, respectively deploying load monitoring nodes in a control area, a non-control area and a management information area, collecting service load and network state data of each area in real time through the load monitoring node; and S2, based on the collected service load and network state data, constructing a concurrent number calculation formula, and through dynamic concurrent adjustment driven by the real-time load, the vulnerability scanning efficiency is ensured, and the service operation of each region of the micro-grid is prevented from being interfered.

The invention discloses a cellular module power control method based on a network state and a service load, and aims to solve the problems of low energy utilization rate, unstable communication quality and the like of a traditional power control scheme. According to the method, power control parameters including a dynamic power protection threshold, a hardware protection delay and a hardware protection release threshold are defined and issued through a standardized interface. The network state and the service load are monitored in real time, and multi-dimensional data are collected to support power calculation. A module physical layer is combined with monitoring data, and through logic of basic power reference calculation, service load correction, parameter boundary verification and the like, target power giving consideration to communication quality and energy consumption is obtained. Dynamic power adjustment is executed according to the target power, when the target power reaches the upper limit, the standby power supply and the main power supply are triggered to supply power cooperatively, and power smooth transition and hardware protection are achieved in combination with hardware protection delay. And if the closed-loop monitoring adjustment effect does not reach the standard, iterative optimization is carried out again. The energy utilization rate is improved, and the communication transmission success rate is improved.

The invention discloses a resource management method and device, electronic equipment and a storage medium. The method is applied to a cloud platform and comprises the following steps: acquiring mean value sequence data of historical resource use data of the cloud platform; the mean value sequence data comprises a mean value of all virtual machine resource use data in the cloud platform at each moment; extracting trend features and periodic features of the mean value sequence data; performing data reconstruction based on the trend features and the periodic features to obtain time sequence data; loading a time sequence prediction model in the cloud platform, inputting the time sequence data into the time sequence prediction model for prediction, and obtaining a prediction result in a future preset time period; and generating early warning information under the condition that the prediction result meets the early warning condition. According to the resource management method and device, the future trend of resource use data is predicted, the resources are managed in time, the problem that the system cannot respond in time when the service load is suddenly increased is solved, and the flexibility of resource management is improved.

The invention relates to the technical field of wireless communication, and particularly discloses a satellite link-based network outage emergency communication guarantee system, which is characterized in that each distributed communication node periodically acquires local state information such as channel quality, service load and self energy of adjacent nodes, and constructs a dynamically updated local network state matrix; the nodes generate connection decision parameters updated in real time through depth feature extraction and multi-target parallel evaluation calculation based on the matrix; each node autonomously executes the connection relationship adjustment with the adjacent node according to the parameters, and realizes the dynamic reconstruction of the network topology through distributed state synchronization; a reconfigured network forms a multi-hop forwarding path through distributed routing discovery taking a satellite gateway as a root, nodes perform aggregation scheduling and forwarding according to data priorities, and trigger local path re-optimization under continuous link quality monitoring, so that an emergency communication link leading to a satellite link is autonomously and reliably formed and maintained.

The invention discloses a cloud resource management method, system and device and a storage medium, and relates to the technical field of cloud computing, and the method comprises the steps: predicting the cloud resource demand of a cloud platform in a future preset time window according to the current business index data and current resource use data of the cloud platform, the service index data and the resource use data are used for determining a coupling relationship between the service load and the resource consumption, so that prediction deviation generated by data isolation analysis is avoided, the prediction precision of the cloud resource demand with strong time-varying characteristics is improved, and finally, a dynamic adjustment strategy is generated according to the predicted cloud resource demand and a resource adjustment request is sent to a cloud platform. It is ensured that the adjusted cloud resources accurately adapt to requirements in a future preset time window, and cost waste and service performance reduction are avoided. The technical problem that the cloud resource demand at the future moment cannot be effectively and accurately predicted is solved, and the technical effect of dual optimization of the resource utilization rate and the service quality in the dynamic load scene is achieved.

The invention provides a server control method and device, equipment and a medium, and the method comprises the steps: obtaining a plurality of key index monitoring data of a server at different moments; predicting a service load comprehensive index of the server based on the plurality of key index monitoring data in combination with a preset prediction model; and determining an energy consumption adjustment strategy of the server based on the service load comprehensive index, and sending the energy consumption adjustment strategy to the server, so that the server performs energy consumption adjustment according to the energy consumption adjustment strategy. Through the prediction model, the monitoring data of a plurality of key indexes are predicted in real time, the server energy consumption is actively adjusted according to the predicted service load comprehensive indexes on the premise of guaranteeing the normal operation of the service, the passive response is upgraded to active optimization, and the effects of energy conservation, consumption reduction and efficient resource scheduling are achieved.

The invention discloses a 5G base station energy efficiency optimization method and system based on dynamic spectrum sharing. The method comprises the following steps: S1, sensing user load distribution, service types and channel state information in a base station coverage area in real time; s2, dynamically allocating a shared spectrum resource pool composed of an authorized frequency band and an unauthorized frequency band according to the user load distribution and the service type; and S3, generating a spectrum allocation proportion and transmitting power joint optimization instruction based on a reinforcement learning algorithm. By dynamically fusing authorized and unauthorized spectrum resources and combining real-time service load awareness and reinforcement learning decision mechanisms, joint improvement of spectrum efficiency and energy consumption efficiency is realized on the premise of ensuring service quality. The prediction model embedded in the method can pre-judge network flow fluctuation in advance, symbol-level fine-grained energy-saving control is matched, the conflict between communication performance and equipment power consumption is effectively balanced, and the method is particularly suitable for heterogeneous network scenes with uneven user distribution and complex service types.

The invention relates to the technical field of subkeys and communication, discloses a quantum key distribution and wavelength division multiplexing cooperative scheduling method for a metropolitan optical network, and aims to solve the problems of insufficient key rate and classic service quality reduction caused by mutual interference of quantum signals and classic services in a shared optical fiber. The method comprises the following steps: modeling Raman scattering and crosstalk noise between quantum and classical signals; constructing a joint optimization model which aims at minimizing classic service degradation and ensures that the quantum key rate is not lower than a threshold; solving wavelength, power and routing allocation by combining mixed integer programming and an intelligent algorithm; the key generation window and the reconfiguration wavelength are dynamically scheduled based on the service load prediction; according to the technical scheme, dual optimization of safety performance and network resource utilization efficiency can be realized.

The invention discloses a server resource allocation method and device, a storage medium and electronic equipment, and relates to the technical field of servers, the method is applied to a server cluster, the server cluster at least comprises a first server, and the method comprises the following steps: training a data processing amount prediction model through historical data processing amount of a first task, through the data processing amount prediction model, a second data processing amount generated by processing a first task by a first server in a second preset time period can be predicted according to a first data processing amount generated by processing the first task by the first server in a first preset time period; and adjusting server resources allocated to the first task by the first server according to the second data processing amount. The technical problem that server resource allocation cannot be matched with service load requirements is solved, and the technical effect of ensuring dynamic and reasonable allocation of server resources is achieved.

According to the QLC medium write amplification optimization method and device, by responding to a data write-in request, metadata of a RocksDB database is written into a metadata pool formed by TLC media, and user data is written into a data pool formed by QLC media. The service load monitoring module is used for collecting operation state data, and the intelligent load analysis module is used for judging whether the current state is in a non-service or low-service load state or not. In a low-load or non-business state, closing a garbage collection (GC) mechanism, and only executing Purge operation to recycle invalid data; and under a normal load, the GC trigger condition is controlled according to the storage space utilization rate and the data block life cycle set by the dynamic threshold configuration module. And obtaining the write-in data volume of the QLC medium through the SMART interface, and calculating the write magnification to evaluate the optimization effect. The method can be applied to electronic equipment such as a server, the write magnification of the QLC medium is effectively reduced, the service life of the QLC medium is prolonged, and the performance and stability of a storage system are improved.

The invention provides an intelligent regulation and control method and system for container service load, and the method comprises the steps: collecting and preprocessing the operation data of container service in real time, the operation data comprising container layer data, service layer data, node layer data and external events; based on the operation data, obtaining a load prediction result by using a load prediction model, the load prediction result being used for indicating a change trend of a load of the container service in a future time period; automatically generating a load regulation and control strategy according to a load prediction result; and deploying the load regulation and control strategy in the container service and executing a strategy action. According to the method, prospective prediction of load change can be realized, the adaptivity and accuracy of regulation and control are remarkably improved, resource waste can be effectively reduced, the service interruption risk is reduced, and the resource utilization rate is improved.

The invention relates to the technical field of server deployment and capacity expansion, in particular to a service dynamic capacity expansion method and device, equipment and a storage medium. Comprising the following steps: acquiring page view data of a plurality of target pages and call volume data of services in a preset time period, and calculating an average page view of each back-end service in the preset time period based on the call volume data; according to the page view data and the call volume data, calculating a current conversion rate from each target page to the corresponding back-end service, and calculating a current service load value of the corresponding back-end service after routing conversion; judging whether the current service load value is greater than a preset threshold, and if so, calling a historical data set corresponding to a preset time period in a historical database; and based on the historical data set, the comprehensive average access amount and the current conversion rate, judging whether a capacity expansion triggering condition is met, and if yes, triggering dynamic capacity expansion of the corresponding back-end service. The resource utilization rate and the better cost effectiveness can be improved, and the user experience is improved.