5 results about "Evaluation period" patented technology

The invention provides a bridge support working condition early warning method and device, a storage medium and a bridge support working condition multifunctional monitoring method, and the method comprises the steps: judging the type of a to-be-evaluated bridge support, and obtaining a disease affecting the use function of the bridge support; determining a working condition evaluation index system of the bridge support based on the diseases; obtaining the evaluation grade of each evaluation index; obtaining a representative value sequence of the monitoring value of each evaluation index in the to-be-evaluated time period, and determining the weight of each evaluation index based on the representative value sequence; calculating the overall score of the working condition of the bridge support based on the evaluation grade and the weight of each evaluation index; obtaining a dynamic early warning value corresponding to the to-be-evaluated time period based on the traffic load borne by the bridge above the bridge support in the to-be-evaluated time period and the working duration of the bridge support; when the overall score of the working condition of the bridge support is larger than the dynamic early warning value, early warning is triggered. By adopting the early warning method provided by the invention, rapid and accurate early warning of the working condition of the support can be realized.

A power load curve fluctuation degree evaluation method, device and equipment, the method comprises the following steps: according to the power load value of each sampling point in the power load curve in the preset evaluation period, the average load is calculated;According to the power load value and the average load, the total fluctuation energy is calculated;According to the average load, the total fluctuation energy and the total sampling point number, the text wave coefficient is calculated to evaluate the fluctuation degree of the power load curve. By introducing the evaluation period which can be flexibly set, the fluctuation details of different time scales can be captured;At the same time, the text wave coefficient is a dimensionless index, and the value directly corresponds to the strength of the fluctuation degree, which enhances the comparability of the evaluation result, effectively improves the comprehensiveness, fineness and accuracy of the power load curve fluctuation degree evaluation.

The application provides a reinforcement learning method suitable for inhibiting performance fluctuation, a reinforcement learning intelligent agent containing an online policy network and a target policy network queue is constructed, a main experience pool and an evaluation experience pool are combined, and stable evolution and efficient convergence of a policy are realized. In an environment interaction process, the online policy network and the optimal policy in the target policy network queue are alternately sampled and obtain environment interaction information, and the environment interaction information is stored in the main experience pool. In a preset evaluation period, a parallel multi-environment rolling evaluation mechanism is used to obtain policy evaluation rewards, and the target policy network queue is dynamically updated or a policy rollback is performed based on the evaluation results. A double evaluation network is introduced, and an adversarial evaluation training mechanism based on double Q mean value and small double Q random sampling is used to inhibit value function overestimation and policy over-conservatism. Interaction data generated in the evaluation stage is stored in the evaluation experience pool, and the interaction data is used in combination with main experience pool data in the training process, so that the stability, robustness and convergence performance of the reinforcement learning training are improved.

This invention relates to the field of automated dispensing equipment management technology, specifically to an automated dispensing management system and method based on artificial intelligence. Upon detecting a new fault report, the invention triggers an assessment of the dispensing control platform within the corresponding work evaluation period. It determines whether this is due to performance degradation. The analysis involves analyzing the continuous operational distribution of the dispensing robot within the evaluation period and the frequency distribution of challenging task change nodes. Ultimately, this allows for the evaluation of the dispensing control platform's operational performance index within the evaluation period. This invention effectively monitors and addresses aging or damage caused by prolonged operation of the dispensing control platform, promptly identifying and resolving performance degradation issues.

This invention discloses a human resources performance evaluation system based on big data analysis, belonging to the field of big data technology. It organizes relevant business data of target employees within a preset evaluation period into a performance event set (Evt), a target record set (Tar), and a responsibility record set (Rol). Then, it identifies change node results (Nod) and generates interval division results (Seg). This accurately separates evaluation stages within the same preset evaluation period that change due to target or responsibility adjustments, and then generates segmented performance results (Prf), percentage results (Wgt), final performance results (Fin), and explanatory results (Exp) for each stage. This avoids the mixing of work before and after target changes, ensuring that the results of the previous stage are not diluted by the evaluation conditions of the later stage, and that the high-pressure handling work of the later stage is not averaged over by the stable tasks of the previous stage. The interval division result (Seg) clearly separates the two stages.