8 results about "Response period" patented technology

The invention relates to the technical field of address allocation, in particular to an automatic discovery and dynamic address allocation method for 485 bus equipment, which comprises the following steps: a host verifies a broadcast address and transmits an instruction to a bus, captures a delay response of a slave to establish a response queue, analyzes dense signals and peels off conflict fragments, dynamically adjusts sampling to generate rhythm mapping, and sends the rhythm mapping to the slave; address instructions are issued according to the sequence, the slave machines check, update and return receipts, the host machine collects and generates an allocation table, monitors signal drift, adjusts time sequence parameters, reconfigures response time periods, updates a synchronization state and completes recording. According to the method, a mapping mechanism based on a broadcast response time sequence is established, stable node distinguishing is obtained through signal multi-dimensional analysis, independent extraction and sequence self-correction are achieved through differential allocation and dynamic sampling, a self-adaptive communication structure is constructed, unified recognition pairing and rhythm coordination are achieved in address allocation, and the self-organization capacity is remarkably improved; concurrency conflicts are reduced, precision is improved, and communication consistency and efficient automatic addressing in a dynamic environment are ensured.

The invention relates to the technical field of data management, in particular to a data processing and storage management method and system based on artificial intelligence, and the method comprises the following steps: obtaining task state information and processing progress, recognizing and processing a lag item, calculating and processing an emergency quantized value to generate a priority label, analyzing an access log, recognizing the change of a reading mode, and storing the access log. And generating popularity perception information according to the clustering data access mode, and performing multi-channel scheduling jump distribution configuration in combination with priority and popularity information. According to the method, quantitative evaluation is established according to the response deadline and the task emergency level, system processing blockage caused by delayed data backlog is avoided, high-frequency fluctuation items of an access mode are recognized through a feature extraction means of reading frequency change and reading time sequence fluctuation, and a trained recognizer is introduced to perform clustering analysis, so that the accuracy and the reliability of the system are improved. Dynamic perception and classification of data popularity change trends are realized, and joint matching is performed on task emergency levels and data popularity perception information, so that the suitability and timeliness of data scheduling and storage strategies are ensured.

Provide a system. 【Solution means】 The identification information of a specific information processing service, A function for monitoring the non-response period set by the user, A function for notifying the user when a non-response is detected, A function for notifying a pre-specified proxy when there is no response from the user, A function for canceling the account of the information processing service or erasing numerical information based on the recorded identification information after obtaining the proxy's approval, A function for notifying after all procedures are completed, Installed on a mobile information terminal, a function for automating operations during the non-response period, A function for periodically communicating with the proxy via a cloud environment, A system including the above.

The invention discloses a baseline load estimation method for electricity market transaction and a storage medium, and the method comprises the steps: carrying out the registration of demand response resources, and classifying the response capability and characteristics; evaluating each type of demand response resource capability; carrying out preliminary estimation on a day-ahead demand response user proportion and an intra-day demand response user proportion in the cluster; dividing day-ahead demand response users and intra-day demand response users in the cluster; searching a control group user most similar to the day-ahead demand response; performing baseline load estimation for the day-ahead demand response user; searching a transaction scene which is most similar to the response day in the historical days of the demand response user in the day; obtaining a change relationship between the load capacity declared by the user in the non-demand response time period and the actual load in the most similar transaction scene; estimating the baseline load of the response time period by combining the obtained change relation with the amount of demand response user declaration in the day; and performing cluster user baseline load estimation by combining day-ahead and intra-day demand response baseline load estimation results.

The application relates to the technical field of address allocation, in particular to a 485 bus equipment automatic discovery and dynamic address allocation method, wherein a host verifies a broadcast address and transmits an instruction to a bus, a slave time-delay response is captured to establish a response queue, intensive signals are analyzed and conflict fragments are stripped, a sampling generation rhythm map is dynamically adjusted, an address instruction is issued according to sequencing, the slave checks and updates and returns a feedback, the host collects and generates an allocation table, signal drift is monitored and timing parameters are adjusted, a response period is reconfigured, a synchronization state is updated and record is completed. According to the application, a mapping mechanism based on broadcast response timing is established, stable node differentiation is obtained through multi-dimensional signal analysis, independent extraction and sequence self-checking are realized through differentiated allocation and dynamic sampling, an adaptive communication structure is constructed, pairing and rhythm coordination are uniformly identified in address allocation, self-organizing capability is significantly improved, concurrent conflict is reduced and precision is improved, and communication consistency and efficient automatic addressing in a dynamic environment are ensured.

The application relates to a multi-modal large model-based training and inference integrated video analysis method and system, which solves the technical problems of high customization cost, long response period for new requirements of existing small models, and serious dependence on computing power of cascaded large model schemes, and difficulty in real-time processing of multiple video streams. A multi-task dynamic plug-in large model weight sharing inference method is adopted, a special large language model is used to convert user natural language requirements into standardized detection prompt words, and independent lightweight plug-ins that can be quickly fine-tuned through small sample data are generated for each detection task. During inference, the basic large model weight is shared among multiple tasks, and only the plug-in corresponding to the task is loaded for feature fusion and calculation. The application can achieve the positive effects of one-sentence generation of new algorithms and small-sample minute-level rapid scene optimization, and can support high-concurrency real-time analysis of hundreds of video streams with a single acceleration card, significantly improving algorithm iteration efficiency and reducing development and deployment costs.

This invention discloses a decision optimization system and method based on dynamic labels, belonging to the field of decision optimization technology. The system includes: a dynamic label time series construction module, a time series feature discrimination module, an intervention node localization module, and a time series deduplication intervention module. The method collects multi-dimensional time series features at a preset period, generates dynamic labels through normalization and weighted fusion, and constructs a time series set. Stable and abnormal time series features are distinguished by calculating time series deviation and feature fluctuation values. When anomalies are identified, the optimal response period is constructed with the anomaly time as the endpoint. The relative change rate of adjacent labels is calculated, and intervention nodes are determined based on local maxima. Then, the intervention overlap is calculated through a time series deduplication window to avoid duplicate interventions. This invention can improve the accuracy of anomaly identification and the timeliness of intervention, reduce redundant operations, and is suitable for various time series data-driven intelligent decision-making scenarios.

The invention relates to the technical field of smart home, in particular to an intelligent closestool control method and system based on the Internet of Things, and the method comprises the following steps: determining an urgent weight based on the instruction input of a user terminal in combination with a response time limit and a channel delay state, adjusting an instruction sequence, and screening transmittable tasks; and collecting gas concentration and gas flow data to construct peculiar smell change characteristics, and distributing an execution sequence according to a power proportion and a response period to form a linkage trigger response state. According to the method, instruction priorities are dynamically sorted through bandwidth loads and cache occupation conditions, resource conflict tendencies can be identified in a concurrent instruction set and orderly scheduling is carried out, concentration increment and airflow velocity information are fused in a sensing data processing stage, a peculiar smell accumulation trend identification path is established based on cavity structure characteristics, and a peculiar smell identification result is obtained. The instruction execution rhythm is subjected to hierarchical processing, and the cross-cycle comparison logic of the peculiar smell state is introduced in the execution feedback process, so that the control system has the capability of sensing the behavior mode change and the capability of reconstructing the execution path.