2014 results about "Management unit" patented technology

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

The invention relates to the technical field of safety monitoring, in particular to a bridge safety monitoring system based on sensor data. The method comprises the steps that a sensor measuring unit monitors key physical parameters of a bridge in real time; the data acquisition and transmission unit reads sensor data and transmits the read sensor data; the data processing and analysis unit extracts key features reflecting the health state of the bridge in the sensor data based on a multi-modal physical perception feature fusion method, identifies an abnormal mode in the sensor data through a support vector machine model, and evaluates the remaining life of the bridge based on a carbonization-corrosion-crack closed-loop feedback control rule; and the information management unit stores the analysis result of the data processing and analysis unit and displays the monitored bridge physical parameters. According to the design of the invention, a multi-modal physical perception feature fusion method is introduced, and adversarial learning, graph wavelet transformation and a three-dimensional convolutional neural network are combined, so that high-precision feature extraction of bridge structure response is realized.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses a self-adaptive management method and system for intelligent terminal equipment of a distributed power distribution network. According to the method, a self-organizing peer-to-peer communication network covering the whole network is constructed, and neighbor discovery and information sharing among intelligent terminal devices are achieved; the terminal independently completes control instruction calculation based on the local electrical parameters and a preset multi-objective function and optimizes an area control effect through a cooperation mechanism; introducing a self-adaptive adjustment strategy of a control algorithm; when abnormal operation is detected, the terminal can autonomously complete fault positioning and isolation, and power supply recovery is realized based on standby path judgment or micro-grid switching; the central management unit periodically carries out acceptance capacity evaluation, and terminal model parameters are aggregated in a federated learning mode; the terminal has an edge autonomous capability, and can maintain local control and data retention when communication is interrupted. The system has the characteristics of quick response, flexible structure, intelligent cooperation, high adaptability and the like, and is suitable for intelligent operation and management and control scenes of various types of power distribution systems.

The invention provides a high-density RFID tag group scanning system based on a multi-antenna array and beam forming, and relates to the technical field of the Internet of Things. The system comprises a multi-antenna array, an RFID reader-writer, a beam forming unit, a signal processing unit, a feedback unit and a data management unit. Through a digital beam forming technology, the system dynamically adjusts the direction and width of a beam, and achieves the precise scanning of a target area. And in combination with real-time signal feedback, a density peak clustering algorithm and a gradient descent algorithm are adopted to optimize beam parameters, so that the recognition efficiency is improved. The system further utilizes an anti-collision algorithm to dynamically adjust frame parameters, and performs logic grouping based on spatial positions and type characteristics of tags, so that signal collision and missed reading rates are remarkably reduced. Meanwhile, the data management unit stores and outputs the label data in a classified mode, and intelligent management capacity is provided for multi-scene application.

The invention discloses a mass concrete intelligent temperature monitoring system and method, and the system comprises a high-precision temperature sensor, a data collection node which is connected with the high-precision temperature sensor and is responsible for carrying out the primary processing and integration of temperature data collected by the sensor, and a data collection terminal which serves as a data convergence center of the whole monitoring system and is used for collecting the temperature data. The intelligent temperature monitoring system comprises a plurality of data acquisition nodes which are connected in a wired or wireless communication mode, a data monitoring platform which operates at a server end and is a core control and management unit of the whole intelligent temperature monitoring system, and a circulating water cooling control system which is composed of a cooling water pump, a cooling tower, a cooling water pipe network, a temperature control main station and the like. The cooling water pipe network is embedded in the concrete in advance to form a closed water circulation loop; the temperature control master station receives a cooling control instruction sent by the data monitoring platform, and adjusts the rotating speed of a cooling water pump and the working state of a cooling tower according to the instruction, so that the flow and temperature of cooling water in a cooling water pipe network are accurately controlled; through flowing of circulating water in the concrete, redundant heat in the concrete is taken away, effective regulation and control over the temperature in the concrete are achieved, key indexes such as the highest temperature and the temperature gradient of the concrete are controlled within a reasonable range all the time, and concrete cracks caused by temperature stress are avoided.

The invention provides a distributed task resource scheduling system and method, and the system comprises a resource management unit which is used for dividing resource nodes into conventional nodes and special nodes according to the important emergency degree; the task management unit is used for adding attribute identifiers for the tasks according to the relationship among the distributed tasks; determining the priority of the task according to the attribute identifier, and putting the task into a waiting queue for queuing based on the priority; the attribute identifier comprises a character string and a label, the character string comprises a task type, a priority and a system task number, and the label comprises an affiliation relationship and a dependency relationship; and the scheduling unit is used for carrying out node resource configuration according to the priority of the task and creating a contract. According to the method, through fine resource node division and task attribute identification, emergency task priority scheduling and accurate management of complex relations between tasks are realized, and by combining an advanced allocation algorithm and a dynamic scheduling strategy, the resource utilization rate and the task execution efficiency are improved.

The invention relates to the technical field of quantum communication security, and discloses a quantum key distribution secure communication system based on a quantum mechanics principle. The system comprises a quantum state preparation unit which controls a quantum emission device to output a quantum state sequence containing a preset polarization angle and a phase modulation photon group; a quantum channel feature extraction unit captures channel environment interference parameters and constructs a photon polarization distribution matrix; a quantum communication security evaluation unit receives the matrix, and calculates a channel security coefficient threshold through a quantum bit error rate analyzer; the quantum key dynamic optimization unit generates an optimized key segmentation strategy in combination with a security coefficient threshold and a historical key negotiation record; the quantum key rotation control unit switches quantum state modulation parameters according to a segmentation strategy and a time window; and the quantum node synchronization management unit monitors node clock offset and injects a calibration signal to realize key distribution time slot alignment. The system can dynamically cope with environmental interference, and improves the security and adaptability of quantum key distribution.

The embodiment of the invention provides a fault alarm self-healing system and method, and the system comprises a micro-service target unit, a management unit, a monitoring collection unit, a data calculation unit, and a plan processing unit, and the management unit is used for determining a to-be-added monitoring collection task and a to-be-deleted monitoring collection task according to the monitoring information carried by each metadata; the monitoring acquisition unit is used for acquiring monitoring index data according to a to-be-added monitoring acquisition task and sending the monitoring index data to the data calculation unit; the data calculation unit is used for detecting the monitoring index data in real time according to a preset index detection rule and generating time sequence index data according to a first detection result; the monitoring index data and the time sequence index data are detected in real time according to a preset anomaly detection rule, and an anomaly event is generated according to a second detection result; and the plan processing unit is used for retrieving a fault self-healing scheme matched with each abnormal event and carrying out fault self-healing processing on the abnormal events according to the fault self-healing scheme.

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention discloses a self-correction structured bid evaluation guide method for public resource transaction, and relates to the field of resource transaction processing, the method is based on a bid evaluation system, and the method comprises a data preprocessing unit, a structured analysis unit, a rule unit, an intelligent evaluation unit, a workflow management unit, a report generation unit and a data filing unit. Historical similar project scoring data is analyzed based on a large language model through an intelligent review unit, a real-time scoring reference interval is generated, expert experience and data driving are combined, professional judgment is reserved, scoring randomness is reduced through data quantification, a workflow management unit adopts an LSTM neural network to construct a review behavior prediction model, and the review behavior prediction model is used for predicting the review behavior. According to the method, the degree of deviation between expert scores and predicted values is compared in real time, meanwhile, multi-party review cases are integrated based on federal learning to form a dynamic knowledge base, review after the event is changed into real-time monitoring and intelligent intervention, the objectivity of bid evaluation is enhanced, and subjective item scores tend to be standardized.

The invention discloses a heat dissipation regulation and control method and device, computer equipment, a storage medium and a program product, and relates to the technical field of computers. According to the heat dissipation regulation and control method, firstly, state changes of all hardware partitions in a server can be detected to obtain state data, and the hardware partitions comprise host systems running independently; then, on the basis of the state data, a control weight of a heat dissipation device can be distributed to the hardware partition, and on the basis of the control weight, the heat dissipation device is controlled to dissipate heat of the hardware partition, so that the state of the hardware partition reaches a target state; the technical problem that a heat dissipation system is poor when global heat dissipation regulation and control are carried out through a management unit in the server is solved, and the technical effects that the control weight of the heat dissipation device is dynamically distributed, so that the heat dissipation power of the global heat dissipation device is dynamically optimized, and local overheating or power consumption waste of the server is avoided are achieved.

The invention relates to the technical field of space ranging, in particular to a smart agriculture big data cloud service supervision platform fusing aerial remote sensing and space ranging information. The method comprises the steps that a data acquisition unit acquires agricultural data based on a multi-source acquisition module; the data processing unit performs cleaning, correction and matching processing based on the agricultural data acquired by the data acquisition unit; the data analysis unit performs multi-aspect evaluation analysis through a multi-party analysis technology based on the agricultural data processed by the data processing unit; and the storage management unit manages and protects agricultural data through a cloud computing distributed storage technology based on an analysis result of the data analysis unit. According to the design of the invention, multi-dimensional evaluation and prediction are carried out on agricultural data by adopting a multi-party analysis technology, and a scientific basis and intelligent decision support are provided for agricultural production; through a cloud computing distributed storage technology, rapid access, backup and sharing of large-scale agricultural data are realized, and meanwhile, the security and privacy of the data are ensured.

The invention relates to a peripheral adaptation system based on a dynamic plug-in loading mechanism, and belongs to the technical field of plug-in management. The system comprises a dynamic plug-in loading management unit, a peripheral adaptation management unit and a front-end management unit, the dynamic plug-in loading management unit is used for managing the loading and hot plug functions of the plug-in and realizing the loading, isolation and unloading of the plug-in when the system runs; the peripheral adaptation management unit is used for communicating with a peripheral, and analyzing and identifying a special protocol and an interface of the peripheral through the intelligent adapter; and the front-end management unit is used for designing an API (Application Program Interface) for a front-end application to call, and performing authority control on sensitive operation through the security access module. Dynamic loading and flexible support of various peripherals are realized, compatibility of the peripherals is optimized, and maintainability and expansibility of the system are improved.

The invention discloses a soft network system capable of expanding a transmission mode and an implementation method, and the system comprises an equipment resource mapping management unit which automatically generates a unique channel code based on a native hardware interface and establishes a multi-dimensional mapping relation; the heterogeneous interface data acquisition unit is used for monitoring various hardware interfaces in a kernel space in parallel and realizing zero-copy data transmission through a three-buffer lockless annular buffer area; the transmission abstraction and dynamic expansion unit realizes transmission mode modularization in a plug-in form and supports atomic hot plug during operation; the protocol analysis and service processing unit is used for realizing unified scheduling and fault isolation in a single process through a dynamic link mechanism; and the cross-layer monitoring and self-healing unit realizes automatic switching based on a session state machine and a redundant channel. According to the method, the code complexity is reduced from O (N * M) to O (N + M), and the time lt is extended; fault switching is carried out at 1s; and the hardware cost is reduced by more than 40%, so that the method is particularly suitable for power and environment monitoring scenes with more than 1000 devices and more than 15 protocols, and has high creativity.

The invention relates to the technical field of power transformation equipment state monitoring, in particular to a power transformation equipment multi-parameter intelligent sensing and state monitoring system which comprises a sensing unit, a data processing unit, a state analysis and diagnosis unit and an upper computer monitoring and management unit. Through data collection preprocessing, lightweight AI model anomaly preliminary screening and hierarchical edge cloud cooperative transmission strategies, efficient cleaning of original data, rapid edge end anomaly identification and optimal utilization of network resources are realized, data transmission bandwidth occupation is greatly reduced, monitoring real-time performance is improved, and the method is suitable for large-scale popularization and application. The method integrates technologies such as digital twinning, federated learning and a time-space attention network, realizes equipment cross-time-space fault accurate positioning, fault type reliable identification and residual life dynamic prediction in combination with a quantification algorithm, triggers hierarchical early warning through hierarchical health assessment, and provides scientific and accurate decision support for refined operation and maintenance of power transformation equipment.

The invention discloses a BIM-based whole house intelligent control system. The system is mainly formed by mutual cooperation of a BIM model data integration unit, an Internet of Things access unit, a control and management unit, an energy consumption analysis and optimization unit and a user interaction and platform integration unit. The invention provides a BIM-based whole house intelligent control system, and the system achieves the deep fusion of a BIM model (a building information model) and intelligent household equipment (i.e., Internet of Things equipment), thereby achieving the bidirectional data interaction between the BIM model and the household Internet of Things equipment, achieving the equipment management, energy consumption optimization, automatic control and remote operation and maintenance, and achieving the intelligent control of the whole house. And the living experience of the user and the equipment management efficiency are improved.

The invention provides an intrusion prevention method, a management unit, a system and a storage medium, and relates to the field of network security, and the method comprises the steps: collecting context operation data corresponding to a to-be-tracked asset object in a kernel, and carrying out the security management of the context operation data, and obtaining a first target security control strategy corresponding to the to-be-tracked asset object; and mounting a first target BPF byte code corresponding to the first target security control strategy in a Linux security module so as to monitor and defend the target asset object through the Linux security module. Or in response to the security policy configuration request, extracting a second target security control policy from the security policy configuration request; and when the second target security control strategy is activated, mounting a second target BPF byte code corresponding to the second target security control strategy in the Linux security module. Therefore, through the above intrusion prevention method, the embodiment of the invention can immediately block attacks and improve the convenience of maintenance and deployment.

The invention discloses an intelligent biological reaction monitoring system and method, and belongs to the field of intelligent monitoring, and the system comprises a reaction stage dividing unit which is used for dividing and determining multiple reaction stages; the variable relation mining unit is used for determining a dominant variable and an auxiliary variable, mining a variable linear relation and a collaborative insensitive loss function, and supervising and training the soft monitoring module; the continuous monitoring acquisition unit is used for configuring a monitoring equipment group, performing continuous monitoring and determining reaction monitoring data; the data analyzing and screening unit is used for preprocessing the data, performing linear analysis and abnormal reaction positioning and determining effective monitoring data; the game equilibrium decision-making unit is used for performing game equilibrium decision-making on the abnormal reaction identifier and determining a reaction adjustment strategy; and the reaction monitoring management unit is used for responding to the adjustment strategy, carrying out feedback monitoring analysis and reaction monitoring management, improving the biological reaction monitoring precision, realizing real-time online monitoring and carrying out intelligent regulation and control aiming at abnormal conditions.

The invention discloses a vertical retaining structure displacement automatic monitoring device and a monitoring method thereof, and relates to the technical field of displacement automatic monitoring, and the device comprises a data acquisition unit which is used for configuring monitoring nodes, obtaining sensor data monitored at the top of a retaining wall, and preprocessing the sensor data to obtain effective inclination angle data; the displacement conversion unit is used for recognizing and optimizing the attitude angle by using a dynamic Kalman filtering algorithm, and calculating a displacement value of the vertical soil retaining structure by combining the vertical distance from the bottom to the top of the soil retaining wall; the monitoring and early warning unit is used for uploading the displacement value of the vertical retaining structure to a remote data platform, constructing a monitoring network, analyzing and predicting the deformation trend of the retaining wall and triggering early warning according to an analysis result; and the battery management unit is used for providing battery state monitoring and management. The displacement of the top of the retaining wall can be accurately sensed and monitored through the inclination angle data, and comprehensive and accurate monitoring is ensured by adopting an attitude solver and a dynamic Kalman filtering algorithm.

To provide island equipment on which external devices can be easily mounted.SOLUTION: Island equipment comprises: a holding member 320 capable of holding one or more management units 200. The holding member 320 has a side wall 323 and a side wall 324. The side wall 323 and the side wall 324 are aligned in a width direction. Between the side wall 323 and the side wall 324, a space S4 is formed for storing one or more management units 200. The space S4 includes: a space S4a for storing a first management unit 210; and a space S4b for storing a second management unit 220 whose length along the width direction is longer than that of the first management unit 210. The spaces S4a, S4b are spaces where parts or whole parts of them do not overlap. At least one of the side walls 323, 324, has a protrusion part protruded to the space S4a side. A minimum separation distance between the side walls 323, 324 in the space S4a, is shorter than a minimum separation distance between the side walls 323, 324 in the space S4b.SELECTED DRAWING: Figure 20

The invention relates to the field of metering equipment maintenance and management, and in particular relates to a metering equipment maintenance and management system based on data analysis, which is used for solving the problem that periodic maintenance work can cause untimely maintenance or too frequent maintenance of metering equipment. Comprising an equipment management unit, a maintenance management unit, a maintenance period analysis unit, a use statistical analysis unit and an environment statistical analysis unit, in the invention, when the metering equipment is maintained and managed, the metering equipment is not maintained through a fixed-frequency maintenance interval any more, the usage amount of the metering equipment is analyzed according to the workload and the working duration of the metering equipment, and the usage amount is used as a basis for maintaining the metering equipment; and meanwhile, the external environment of the metering equipment is analyzed, and environmental interference factors caused by environmental factors are fully combined, so that the maintenance of the metering equipment is more in line with the actual situation, and the problem that the maintenance is not timely or too frequent due to use differences is solved.

The invention discloses a mine crushing intelligent safety management and control system based on a digital twinning technology, which relates to the technical field of intelligent safety management and control and comprises a data acquisition management unit, an ore processing unit, a signal monitoring unit, a data relay unit, a safety early warning unit and a safety control unit. The ore processing unit is used for detecting the image of the ore particles on the conveying belt, the size of the doped crushed particles and the position distribution of the ore in the transportation process are analyzed, data are provided for the ore crushing degree of the crusher and the safety degree of the ore in the transportation process, and early warning is facilitated; in addition, blasting vibration waveform signals are collected through the signal monitoring unit, data are provided for the blasting vibration state so that safety early warning can be carried out, the data are summarized and transmitted through the data relay unit, the vibration signals are visually displayed, flexible transmission of the data is achieved, and timeliness of data transmission is guaranteed.

To provide island equipment on which external devices can be mounted easily.SOLUTION: A holding member 320 has a side wall 323 and a side wall 324. The side wall 323 and the side wall 324 are aligned in a width direction. Between the side wall 323 and the side wall 324, a space S4 is formed for storing one or more management units 200. The space S4 includes: a space S4a for storing a first management unit 210; and a space S4b for storing a second management unit 220 whose length along the width direction is longer than that of the first management unit 210. The spaces S4a and S4b are spaces whose parts or whole parts do not overlap. At least one of the side walls 323, 324 has a protrusion part protruded to the space S4a side. A minimum separation distance between the side walls 323, 324 in the space S4a is shorter than that between the side walls 323, 324 in the space S4b. The holding member 320 has a storage part for storing a prescribed article.SELECTED DRAWING: Figure 20

The invention relates to the technical field of power distribution network fault early warning systems and methods, in particular to an intelligent power distribution network fault early warning system and method based on the Internet of Things, and the system comprises a sensing unit, a data processing unit, a data analysis unit, a fault early warning unit, a monitoring management unit, and a personnel inspection subsystem unit. The sensing unit is used for installing a current sensor, a voltage sensor, a temperature sensor and a humidity sensor at key node positions of a transformer, a switch cabinet and a line of the power distribution network, the current sensor is used for monitoring the current in the line in real time, the voltage sensor is used for monitoring the voltage value, and the temperature sensor is used for monitoring the operating temperature of equipment; faults are analyzed and judged from multiple angles, misjudgment and missed judgment are reduced, whether fault hidden dangers exist or not is judged more accurately, the operation state of equipment is evaluated in real time through monitoring of the Internet of Things, the frequency of manual inspection is appropriately reduced, the labor cost is saved, and the resource utilization efficiency is improved.

The invention discloses a memory test system and method, a board card and a server. The memory test system comprises an out-of-band management unit and a target chip, the out-of-band management unit sends a pressure test instruction to the target chip through the preset communication interface, the pressure test instruction comprises dynamically adjusted test strength parameters, and the pressure test instruction is generated based on a preset instruction set; the target chip performs calibration verification on the target memory in response to the pressure test instruction; after the calibration verification is passed, a test load is applied to the target memory according to the test strength parameter, and a cyclic pressure test is executed; the target chip collects test data generated in the pressure test process and transmits the test data back to the out-of-band management unit through the preset communication interface, and the out-of-band management unit generates structured test data according to the test data. Compared with the prior art, the situation that normal services are interrupted due to testing in a traditional testing method can be avoided, and the availability and stability of the system in the testing process are guaranteed.

The invention provides an intelligent power grid safety early warning system and a monitoring method in the technical field of intelligent power grids, which are used for solving the problem of safety early warning and monitoring function defects of the existing imperfect intelligent power grids, and comprise an information acquisition unit, a data management unit, an abnormity analysis unit and a safety early warning unit, the method comprises the steps of collecting power grid real-time data and line equipment data; constructing an adjacent matrix and a target matrix, calculating and analyzing to obtain a node weight and a target correlation degree, and analyzing by using a discrete model to obtain a dispersion index; a data evaluation value is obtained, and a data abnormal value is obtained through analysis; and generating intelligent power grid safety early warning information and a disaster recovery strategy according to the data abnormal value and the line equipment data. By constructing an intelligent power grid monitoring and early warning system, power grid equipment with potential safety problems is predicted in real time, the operation and maintenance efficiency is improved, the method can be suitable for power grid systems of different scales, the influence of faults on power grid operation is reduced, and large-area power failure events are effectively prevented.

An offline pre-training and online fine-tuning apparatus based on reinforcement learning includes a data management unit collecting and processing data for offline reinforcement learning in advance; an offline model training unit training an offline policy network and an offline state-action value function network using a previously collected dataset that includes a state, an action, a next state, a reward, and an accumulated reward collected by the data management unit; and an online model training unit performing fine-tuning to update parameters of the offline policy network using an online dataset that includes action information determined based on state information acquired through interaction with the offline policy network and an environment, state information at a next time point according to the action information, and the reward, and the previously collected dataset.

The invention discloses a coal mine disaster prediction system based on big data. The system comprises a main disaster monitoring data standard management unit used for establishing a main disaster monitoring data management standard; the main disaster monitoring data acquisition and storage unit is used for acquiring monitoring data from each disaster early warning system and uniformly storing the monitoring data; the main disaster monitoring information integrated display unit is used for combining the monitored data and displaying and analyzing the data; the main disaster early-warning visual display unit is supported by a GIS platform, integrates monitoring and early-warning data of different positions and types, integrates disaster early-warning index results of all specialties, and carries out visual analysis and display of multiple disaster types, multiple dimensions and multiple indexes. According to the coal mine disaster prediction system based on big data, abnormal early screening, automatic locking, backtracking analysis and trend prediction of various disasters can be realized on the same platform interface, and technical support is provided for safety production.