390results about How to "Improve resource utilization efficiency" patented technology

The invention relates to the technical field of intelligent spectrum regulation and control, and discloses an intelligent spectrum regulation and control system based on multi-modal data fusion and dynamic optimization, which comprises a multi-modal data acquisition module, a real-time data processing module, a dynamic weight distribution algorithm, a deep reinforcement learning regulation and control algorithm and a distributed collaborative optimization module. According to the system, real-time fusion and dynamic priority adjustment of multi-dimensional data are realized through acquisition of multi-modal data (environment spectrum, temperature and humidity, carbon dioxide concentration and user behavior) and dynamic weight distribution based on LSTM (Long Short Term Memory); dynamic optimization of a spectrum regulation and control strategy is realized by combining a deep reinforcement learning model and utilizing a weighted reward function of a spectrum output error and energy consumption, and efficient light supplement and low energy consumption requirements are balanced; a distributed optimization algorithm with edge computing and cloud collaboration is adopted, computing resources are dynamically allocated, task processing efficiency is greatly improved, and system power consumption is reduced.

The invention relates to the technical field of big data storage, in particular to a storage control method and device based on big data. The method comprises the following steps: acquiring an original data stream and application demand information; performing data dynamic correlation analysis on the original data flow according to the application demand information to obtain a dynamic update data correlation graph; performing feature vector generation on the dynamic update data association graph to obtain a data feature vector; performing intelligent quality evaluation on the data feature vector to obtain a data quality label; performing timeliness evaluation according to the data feature vector, and performing access popularity evaluation to obtain a timeliness score and an access popularity score; and acquiring system resource load condition information. The storage resource utilization rate, the data access performance and the overall efficiency of the system are improved by solving the limitation of a traditional method in the aspects of static data management and resource allocation.

The invention provides an erasure code compatible read-write method and system based on a bidirectional data access agency, and relates to the technical field of distributed storage, and the method comprises the steps: employing multi-agent reinforcement learning to construct a heterogeneous resource portrait and optimize task allocation, combining a space-time graph convolutional network and a federated learning framework to construct a data format conversion model, and constructing an erasure code compatible read-write model; data features are analyzed through an encoder based on an attention mechanism, a fragmentation strategy is generated, self-adaptive encoding is achieved through a capsule neural network, data integrity verification and repairing are conducted through a double-layer space attention network and a graph attention network, the data access efficiency of a heterogeneous storage system is improved, data format compatibility is enhanced, and the data access efficiency is improved. And the data integrity is ensured.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention relates to the technical field of distributed file processing, in particular to a distributed file processing method and system, and the method comprises the following steps: based on a task distribution node and a data exchange interface, extracting path characteristics of a task input / output file, analyzing a file exchange sequence of a task path, and calculating a topological structure and length of the path. According to the method, the flexibility and the adaptability of task allocation are realized by combining task state monitoring and real-time adjustment of dependent path weights and task priority marks, resource allocation and a short-time task sequence are optimized by utilizing request frequency and time period records, high-frequency request resource conflicts are effectively relieved, the resource allocation efficiency is improved, and the resource allocation efficiency is improved. Through node historical resource distribution data and time distribution trend analysis, task distribution is adjusted and resource loads are balanced in combination with differences, the stability and balance of the system are enhanced, a file transmission path is optimized based on a load state and a task execution path, and a concurrent task distribution strategy is improved.

The invention provides an aerial remote sensing multi-platform collaborative observation task planning and optimization method and system, and relates to the technical field of collaborative planning, and the method comprises the steps: receiving the observation demand information of a target region and the resource information of a plurality of aerial remote sensing platforms, building a multi-platform collaborative observation task model based on a deep reinforcement learning algorithm, generating an initial task planning scheme by utilizing the constructed multi-platform collaborative observation task model, optimizing the initial task planning scheme by adopting an improved multi-target particle swarm optimization algorithm, and distributing corresponding specific execution instructions to each aerial remote sensing platform through a security encrypted communication link; state information, including positions, postures, energy levels and equipment working states, fed back by the aerial remote sensing platforms is received and processed in real time, and a current task planning scheme is dynamically adjusted according to the real-time state information based on a federated learning framework by utilizing edge nodes and cloud cooperative computing capability, so that adaptive optimization of tasks is realized.

The invention discloses a private domain user management method and system for multiple touch channels, a medium and a program product, and the method comprises the steps: obtaining the historical behavior data of a user in each touch channel; identifying the behavior relevance of the user among the arriving channels; constructing an inter-channel behavior mapping relation of migration paths of the user among the touch channels based on behavior relevance; according to the behavior mapping relation between the channels, the overlapping ratio of user behaviors between the reaching channels, the difference of conversion effects and the overlapping ratio of time sequences are calculated; based on the overlapping rate, the difference and the coincidence degree, determining the reaching channel complementation degree; grouping and clustering the reaching channels according to the reaching channel complementation degree to obtain a plurality of channel collaboration units; and the optimal time difference of the arriving users in each channel cooperation unit is calculated, and the marketing resources are allocated among the arriving channels based on the optimal time difference, so that the accuracy of private domain operation management is improved.

The invention discloses a networking X-band weather radar cooperative adaptive control method, and relates to the technical field of weather radars. The problems that in the prior art, real-time state data and historical data cannot be fully utilized to construct an accurate radar performance model, and deep analysis on environment characteristics is lacked when control parameters are dynamically adjusted are solved. According to the method, the operation state and historical data of the radar system are collected and analyzed, the radar performance model is established, and the control parameters are dynamically optimized, so that the observation performance and the resource utilization efficiency of the radar system are remarkably improved, state information sharing and cooperative adjustment among the radar systems are realized based on a cooperative control mechanism of a communication link, and the reliability of the system is improved. The adaptability and reliability of the system are effectively enhanced, main factors influencing the system performance can be identified by constructing an intelligent performance evaluation module, model parameters are dynamically adjusted, constraint conditions are optimized, and long-term efficient operation of the radar network under complex environmental conditions is ensured.

The invention provides a task queue-based heterogeneous resource pool real-time scheduling method, which comprises the following steps of: splitting tasks transmitted at random moments into sub-task sets with topological relations, adding the tasks into a queue to open up a track, and sequencing and numbering according to priorities; the bottom layer chip is interconnected, a resource pool monitor is added to monitor the state of resources, a heterogeneous resource pool is constructed, and data is provided for a scheduling system; the scheduling system obtains the real-time states of the queue and the resource pool, monitors the occurrence of events, makes a dynamic decision, sets an optimization target, and schedules the required computing resources for the subtasks according to the priority in the queue. According to the method, the parallel computing advantage of the FPGA chip is fully exerted, high-speed communication in the heterogeneous resource pool is ensured, the resource utilization efficiency of the resource pool is greatly improved, the total task set execution time delay can be effectively reduced, and meanwhile, the heterogeneous resource pool can achieve the load balancing effect.

The invention provides a high-low voltage isolated energy storage battery management system, and relates to the technical field of battery management systems or other related fields, the system comprises a power supply isolation unit, a high voltage acquisition unit and a communication isolation unit, the power supply isolation unit is used for providing working voltage for the high voltage acquisition unit, and the communication isolation unit is used for providing communication voltage for the high voltage acquisition unit; the power supply isolation unit comprises a low-voltage power supply, a first transformer, a voltage conditioning circuit, a second transformer, a diode module and a voltage stabilizer, and the high-voltage acquisition unit is used for acquiring battery cluster information, converting a signal format of the battery cluster information into a digital signal and transmitting the battery cluster information to the communication isolation unit; the communication isolation unit is used for transmitting the battery cluster information to the micro-processing chip, the micro-processing chip generates a battery cluster control instruction based on the battery cluster information, and the communication isolation unit comprises a third transformer, a fourth transformer and a bridging chip. According to the invention, the technical problem that high-voltage and low-voltage isolation cannot be carried out on a high-voltage energy storage battery management system in the prior art is solved.

The invention relates to the technical field of audio processing, and discloses a multi-mode audio processing method and system based on an SOC. The method comprises the following steps: inputting multiple paths of audio input signals into an ARM processor of an SOC chip for processing, and generating a preprocessed audio data stream; inputting the preprocessed audio data stream into a basic audio processing stream executed by a digital signal processor (DSP) and a multi-modal feature extraction stream executed by an artificial intelligence (AI) accelerator in parallel; performing adaptive noise reduction processing and multi-channel audio signal sound mixing processing in the basic audio processing stream to generate a basic processing feature stream; performing multi-dimensional feature extraction and scene type identification in the multi-modal feature extraction stream to generate a high-level feature stream; according to the method, the processing capability of the SOC chip on different types of audio signals is improved, the continuity and smoothness of the reconstructed audio signals are ensured, and signal distortion is effectively avoided.

The invention provides a task unloading method for an emergency rescue scene under a space-air-ground integrated network, and relates to the technical field of calculation unloading, and the method comprises the steps: unifying the feature description of task unloading; establishing a joint optimization problem model by taking minimization of unloading time delay and power consumption as an optimization target; determining a task unloading priority based on a firefly algorithm; constructing a reward mechanism of a deep reinforcement learning model according to the task unloading priority, and solving the joint optimization problem model by using a deep reinforcement learning algorithm to obtain a plurality of task unloading schemes; performing simulation verification based on the task unloading scheme to obtain evaluation data; parameters of the joint optimization problem model are corrected according to the evaluation data, and a task unloading optimal model is obtained; and solving the optimal task unloading model to obtain a final task unloading scheme. Reasonable and effective resource allocation is carried out on a large number of tasks which emerge in an emergency rescue scene and need to be processed in real time, and the scheduling efficiency is improved.

The invention relates to the technical field of information resource security management, and particularly discloses a security management method and system based on computer information resources and a storage medium, and the method comprises the steps: obtaining user identification information of a user, extracting a reference evaluation information set, analyzing a user behavior abnormal evaluation value, and judging a user behavior tag. If the user behavior label is judged to be in a potential risk mode, communication end current limiting is carried out, abnormal secondary verification is started, a user behavior abnormal verification evaluation value is analyzed, user behavior label re-judgment is carried out, and user permission locking measures are determined. Potential risks can be perceived in time, the situation that potential safety hazards are gradually expanded under the condition that the potential safety hazards are not perceived is avoided, and the user permission locking accuracy is improved. For a potential risk mode, communication end current limiting is carried out and secondary verification is started, so that network activities of abnormal users are effectively limited, security risks are reduced, the risk degree of user behaviors is comprehensively and accurately judged, and the stability of the system and the security of information resources are guaranteed.

The invention discloses a virtual network bandwidth dynamic regulation and control method and system based on a user portrait, electronic equipment and a storage medium, and belongs to the technical field of computers. In order to solve the problem of poor user experience caused by acceleration of a virtual private network due to fixed bandwidth of the virtual network, the invention provides a dynamic regulation and control method for bandwidth of the virtual network based on a user portrait, which comprises the following steps of: collecting a user acceleration log, and constructing a behavior data set of a corresponding user; according to the behavior data set, user characteristics of the user in a recent first preset period are obtained, and the user characteristics comprise a user peak use time period and a corresponding bandwidth, a low peak use time period and a corresponding bandwidth, and a bandwidth connection duration; determining a user bandwidth prediction value of the next first preset period of the corresponding user according to the user characteristics; and / or, according to the behavior data set of all users of the same edge node and based on a random forest algorithm, constructing a bandwidth prediction model of the corresponding edge node; taking behavior data in the latest second preset period in the behavior data set of all users corresponding to the edge node as input, and obtaining output of the corresponding bandwidth prediction model as a node bandwidth prediction value of the next second preset period of the corresponding edge node; the first preset period is smaller than the second preset period. According to the invention, through deep analysis of user behaviors and demands, user portraits are constructed, bandwidth is intelligently allocated and adjusted, and the utilization efficiency of network resources and user experience can be improved.

The invention relates to the technical field of intelligent power grids, in particular to an inspection vehicle tracking and communication optimization method and system, and the method comprises the steps: constructing an RIS-ISAC-NOMA system framework oriented to an intelligent power grid application scene, converting a non-line-of-sight NLOS path into an equivalent virtual line-of-sight LOS path through a reconfigurable intelligent metasurface RIS in a non-line-of-sight NLOS scene, and carrying out the tracking and communication optimization of an inspection vehicle through the virtual line-of-sight LOS path. A propagation path of an ISAC signal is adjusted in real time, and a vehicle tracking and predicting method based on an EKF algorithm is designed, so that efficient vehicle real-time sensing and communication are realized; a multi-inspection vehicle power distribution optimization scheme is provided in combination with the NOMA technology, the communication rate and the resource utilization efficiency of the system are further improved, meanwhile, the simulation result also verifies the advantages of the proposed system in the aspects of communication performance and resource utilization, and the overall efficiency of the system can be remarkably improved while the pilot signal overhead is reduced.

The invention provides a remote sensing multispectral imaging system for agricultural monitoring, which relates to the technical field of space remote sensing, and realizes accurate selection by indicating an economic crop determination module, analyzing historical soil salinity and water shortage data and selecting sensitive crops; the correlation evaluation module calculates a water shortage index change rate, analyzes correlation between historical data and current data, and improves prediction accuracy; the value range determination module divides soil salinity intervals and standardizes management data in combination with evaluation coefficients; the indication area determination module realizes accurate monitoring by using a multispectral image and enhances representativeness; the humidity evaluation module analyzes local spectral data to quantify the soil humidity; the prediction model construction module predicts the salinity content by using machine learning, and can accurately predict dynamic changes of soil salinity and moisture; the correlation analysis module verifies real-time monitoring data in combination with the prediction model; and the division module optimizes a soil management strategy according to the water shortage index and the humidity coefficient.

The invention discloses a cloud storage acceleration method and system based on edge computing, and relates to the field of electrical digital data process.The method comprises the steps that a file access track ring is constructed; extracting historical access files as a first associated file set; calculating the access time difference of each historical access file; determining a plurality of association clusters and two association file sets according to the access time difference; performing cyclic displacement in the second associated file set to obtain a displacement associated cluster; extracting a third associated file set; determining a prefetching file; setting an annular cache queue, and determining a prefetching position according to the position information; when the space of the annular cache queue is smaller than a preset value, the prefetched file corresponding to the prefetched position in the preset interval of the annular cache queue is reserved, and a final prefetched file is obtained; and when it is detected that the user accesses the target file, loading the final prefetched file from the cloud storage server to the edge server. The method and device are used for solving the problem that the cache hit rate of the associated files is reduced, and the cloud storage acceleration speed is increased.

The invention discloses a system and a method for resisting a pseudo link layer protocol attack in an electric power communication network, and relates to the technical field of communication. The system comprises: a switching unit, which is used for receiving all data frames transmitted from an Ethernet port; the data identification module analyzes the protocol type and the destination address of the data frame, and forwards the data frame to the service processing unit or directly discards the data frame; the buffer area module sets an independent buffer area for each link layer protocol, and dynamically adjusts the size of the buffer area based on the protocol characteristic weight and the equipment memory resource through the configuration unit; the buffer area management module monitors the state of a buffer area in real time and triggers data discarding operation when the buffer area is fully loaded; and the data extraction module extracts the data frames from the buffer area according to a set time interval and analyzes and forwards the data frames in combination with a priority rule. According to the method, the size and the time interval of the buffer area can be dynamically adjusted, the resource utilization efficiency is improved, the pseudo link layer protocol attack is effectively resisted, and the security and the real-time performance of an electric power communication network are guaranteed.

The invention relates to the field of intelligent manufacturing, and discloses an electronic auxiliary material processing parameter intelligent optimization method and system, and the method comprises the steps: firstly obtaining a to-be-processed electronic auxiliary material, determining the corresponding initial processing parameter, collecting the real-time operation data of preset processing equipment, carrying out the classification processing, obtaining a classification data set, extracting a core parameter subset, and carrying out the processing of the core parameter subset; then generating a machining process curve of the core parameter subset, analyzing a slope to obtain a slope change sequence, finding out a mutation point and calculating a mutation influence value, collecting fluctuation characteristics associated with the mutation point, determining fluctuation ranges of different machining stages, and calculating an auxiliary material stable value so as to determine a machining direction and identify a corresponding machining adjustment strategy; and after the strategy is implemented, monitoring effect data in real time, comparing the effect data with preset target data to obtain an effect comparison parameter, further querying potential improvement factors, determining improvement grades, setting grade indexes, and finally generating an optimization scheme adaptive to the to-be-processed electronic auxiliary materials. The production efficiency of the electronic auxiliary materials can be improved.

The invention belongs to the technical field of cloud computing, and discloses a lightweight multi-domain hyper-fusion platform construction method compatible with heterogeneous hardware resources, which adopts a lightweight hyper-fusion platform architecture compatible with the heterogeneous hardware resources, converts physical resources into virtual resources on a virtual machine based on a KVM (Kernel-based Virtual Machine), shields the difference of underlying hardware, and improves the performance of the platform. According to the method, heterogeneous storage resources are integrated based on Ceph, and storage devices of different manufacturers are integrated into a unified storage pool through a distributed storage technology without being limited by types of bottom hardware devices. Through the SDN technology, the OpenStack network service can decouple a network control plane and hardware, and unified management of different network devices is realized. Through a virtualization technology and an automatic management tool, a simplified elastic expansion solution is provided, and changes of resource demands are easily coped with.

The invention provides an intelligent spectrum allocation method and system for a self-adaptive signal-to-noise ratio of a C + L optical network, and relates to the technical field of optical fiber communication, and the method comprises the steps: initializing a C + L wave band elastic optical network, and determining nodes, links, spectrum slots and time slot resources; receiving a service request, and defining a source node, a destination node, time slot requirements and capacities; calculating a link weight according to a transmission distance and spectrum occupation, and selecting an optimal path; determining a modulation format according to the path distance, and calculating a required spectrum gap; high-order modulation is allocated to a C wave band, and low-order modulation is allocated to an L wave band; ensuring that spectrum slots are continuous and links are consistent, otherwise, the request fails; and selecting the spectrum block distribution with the highest OSNR, comparing the OSNR with a threshold value, and determining whether the request is successful or not. According to the invention, the transmission quality of the service request is improved, and resource allocation can be carried out more comprehensively and accurately.

The invention relates to the technical field of intelligent control, and discloses an air conditioner energy-saving control method, device and system and a storage medium, and the method is applied to edge equipment and comprises the steps that real-time environment state data are obtained, and the real-time environment state data and alternative regulation and control parameters are input into a preset parameter decision model; the preset decision parameter model comprises a temperature prediction sub-model and an energy consumption prediction sub-model; performing temperature prediction through the temperature prediction sub-model, and determining candidate regulation and control parameters based on a temperature prediction result; performing energy consumption prediction through the energy consumption prediction sub-model, and determining a target regulation and control parameter based on an energy consumption prediction result; and the target regulation and control parameters are sent to a power and environment monitoring unit, so that the power and environment monitoring unit executes air conditioner control operation. According to the method, parameter decision is carried out by introducing the edge device, dependence on the cloud can be reduced, quick response and low delay are realized, and meanwhile, the reliability of the target regulation and control parameter is guaranteed through the temperature prediction sub-model and the energy consumption prediction sub-model.

The invention relates to an automatic blanking metering method and system for aluminum box packaging, and the method comprises the following steps: inputting a target packaging weight in a human-computer interface through an operator, and setting the target packaging weight of each aluminum box; the actual weight of the aluminum box is detected in real time through a weighing sensor, and the detected actual weight is transmitted to a PLC; the PLC calculates an error value according to the difference between the target weight and the actual weight; a PI D control algorithm is used for adjusting the blanking rate; the PLC controller dynamically adjusts the working state of the blanking equipment according to an adjusting signal output by a PI D control algorithm; in the discharging process, the PLC adjusts PI D control parameters in real time according to new feedback data; when the actual weight of the aluminum box reaches the target weight, the system automatically stops discharging; according to the scheme, real-time feedback and dynamic adjustment can be achieved in the discharging process, so that the packaging weight of the aluminum box is accurately controlled, and the problems that in the prior art, discharging precision is insufficient, the process is unstable, and manual intervention is too much are solved.

The invention relates to the technical field of agricultural information and seed and seedling cultivation, and discloses a peanut planting method for relieving continuous cropping obstacles of peanuts. The method comprises the following steps: constructing initial digital twins of a land parcel, and synchronously constructing a disease-resistant peanut variety screening and seedling pretreatment system; historical continuous cropping information, soil physicochemical and microorganism data, meteorological time sequence data and seedling cultivation parameters are fused, the system is driven to evolve into operation state digital twins, and seedling-soil-environment collaborative simulation is achieved. Evaluating the continuous cropping obstacle risk and the seedling adaptability based on the twinborn body, and generating a comprehensive diagnosis report containing a nutrient imbalance index, pathogenic bacteria abundance prediction, a root exudates cumulative effect and a seedling disease-resistant adaptation coefficient; according to the method, differentiated seed and seedling optimization, soil improvement and cultivation strategies are implemented, and management measures are dynamically optimized through real-time data feedback in a growth cycle. Through cooperative driving of digital twinning and seed and seedling cultivation, source prevention, control and intervention of continuous cropping obstacles are achieved.

The invention discloses a molybdenum disulfide acid leaching purification process, relates to the technical field of molybdenum disulfide purification, and aims to solve the problems of waste acid recycling, wastewater treatment and equipment corrosion in the molybdenum disulfide purification process. The molybdenum disulfide acid leaching purification process comprises the following steps: S1, acid purification: adding a molybdenum concentrate raw material into a reaction kettle; adding reaction acid into the reaction kettle according to a certain solid-to-liquid ratio, heating the reaction kettle to react for a period of time, and cooling to dissolve impurities in the molybdenum concentrate; s2, filter pressing and cleaning: conveying the acid-washed material to a plate frame for filter pressing to form a filter cake, cleaning the filter cake with clear water for 2-4 times until the wastewater is neutral, and conveying the filter cake to the plate frame for filter pressing again; s3, repeating acid purification and filter-pressing cleaning for several times until various acids are used up in the process; and S4, drying: putting the cleaned filter cake into a drying system for drying to obtain the high-purity molybdenum disulfide with the purity of more than or equal to 99%. According to the acid leaching purification process, impurities in molybdenum disulfide are selectively dissolved, the purity of molybdenum disulfide is remarkably improved, and the industrial application requirement is met.

The invention discloses a wind-light storage configuration method considering transient voltage constraint and inertia constraint, and the method comprises the steps: obtaining a wind-light typical output scene and a direct-current power transmission plan power through employing a k-means clustering method based on wind-light output and receiving end load data, employing a bilevel programming model for wind-light-storage capacity configuration, and obtaining a wind-light storage capacity configuration model; the upper-layer model takes the minimization of the total investment cost as an optimization target, and constructs a transient overvoltage constraint, an inertia support constraint and a capacity constraint based on the short-circuit capacity; and the lower layer model takes the operation cost of the thermal power generating unit and the new energy power abandoning cost as targets, considers the power balance constraint, the operation constraint and the like of the wind-light-storage channel direct current power transmission delivery system, introduces the power transmission confidence probability constraint, and realizes the measurement of the delivery reliability of the wind-light storage channel. An improved grey wolf optimization algorithm is adopted, a Logistic-Tent mapping opposite learning mechanism is added, a mutation operator, a crossover operator and a selection operator are introduced in the differential mutation process of the algorithm, the population number and category richness in the algorithm are improved, and the solving speed is increased.

The invention relates to the technical field of concrete performance detection, and discloses a concrete condensation state prediction and maintenance method based on multi-sensor fusion. The method aims at solving the problems that an existing concrete condensation state judgment method is destructive and cannot reflect the real state in a structure in real time, and maintenance measures cannot dynamically adapt to actual working condition changes. The method comprises the following steps: deploying a multi-sensor combination in concrete to collect data in real time, pre-processing the data, inputting the pre-processed data into a long-short-term memory network model, and predicting a condensation state and early strength; and the maintenance equipment is automatically controlled according to the prediction result. According to the method, feedback optimization is also performed on the model by utilizing the final actual measurement intensity. Lossless and real-time monitoring and accurate prediction of the internal state of the concrete are realized, automatic and refined closed-loop regulation and control can be performed on the maintenance operation, and the scientificity and efficiency of construction management are improved.

The present invention realizes the method of ecological operation of MaaS platform based on big model evaluation technology, including: determining the HTTP service interface and functional requirements of the model to be evaluated, and preparing the data set related to the model function; configuring the HTTP service address and OpenAPI interface document of the model to be evaluated; using the big model tool to generate the request parameter template through the prompt word, and replacing the file parameters with the placeholder; using the JMeter tool to set up the test environment; running the JMeter stress test process; writing Groovy scripts or using big model tools to generate code; using the script to read the generated VOC format message and compare it with the original data set. The present invention not only improves the model quality of the MaaS platform from a technical level, but also provides all-round improvements in operation management and user experience, laying a solid foundation for the long-term development of the platform.

The invention discloses a mobile robot performance system popularity racking method based on an improved TabNet algorithm. Comprising the following steps: collecting historical order data; the historical order data is preprocessed; a TabNet algorithm is improved, and a popularity model based on the improved TabNet algorithm is constructed; training a popularity model based on an improved TabNet algorithm by using the preprocessed data set, calculating a popularity score of each SKU, and performing real-time data updating and dynamic recalculation of the popularity score; and according to the SKU popularity score obtained through calculation, goods are put on different goods shelf areas. The invention provides a novel mobile robot performance system popularity shelving method based on an improved TabNet algorithm, so that high-popularity SKUs can be effectively identified and shelved in a concentrated manner through dynamic feature selection and a popularity scoring mechanism, thereby improving the order line hit rate of an RMFS system and optimizing the sorting efficiency.

The invention relates to a sea area management and control early warning evidence obtaining method based on video intelligent analysis, and the method comprises the steps: collecting equipment parameters: collecting internal and external parameters of equipment and environment parameters; equipment screening control is carried out, controllable and controllable equipment with a good detection effect is selected through equipment state analysis and target detection effectiveness analysis, and follow-up monitoring of a target area is realized through pan-tilt control; performing video intelligent analysis, determining a follow-up concerned target through mapping matching of a video intelligent analysis model and different coordinate system positioning information, and performing accurate linkage and video snapshot by fine tuning equipment; according to early warning and evidence obtaining association, structured attribute analysis is performed on a target and an area detected in a video capturing process, and an evidence obtaining result is added to early warning information with an association relationship, so that repeated evidence obtaining is avoided. According to the invention, the accuracy and efficiency of early warning evidence obtaining can be effectively improved, the utilization efficiency of data and resources is improved, and powerful technical support is provided for sea area management and control.