The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm. The invention can provide load dispatch scheme suitable for current microgrid and reduce operating cost of residential microgrid.

A method for providing candidate recommendations for an open position based on problem solving proficiency and culture matching includes: receiving job posting data from a first computing system, the job posting data including employer culture markers and job criteria values; receiving candidate information for a candidate, the candidate information including candidate culture markers, text submissions, and candidate values; determining a culture match score by comparing the employer culture markers against the candidate culture markers; determining a problem solving proficiency level for the candidate by applying a problem solving algorithm to the text submissions; and transmitting a hiring recommendation for the candidate to the first computing system based on the determined culture match score and problem solving proficiency level for the candidate and the criteria values.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

A schedule data creation device creates schedule data that define an execution schedule for executing a plurality of jobs of different job types in chronological order. An input/output unit acquires table data in which the schedule conditions are described in tabular format for each combination of the job types, and acquires request data that specify a plurality of jobs for which the schedule data are to be created. A schedule data creation device arranges a plurality of jobs in chronological order based on the table data such that each of the jobs specified in the request data meets the schedule conditions to create the schedule data. The input/output unit outputs the schedule data and the table data.

A method including generating, by a state engine from data describing behaviors of users in an environment external to the state engine, an executable process. An agent executes the executable process by determining, from the data describing the behaviors of the users, a problem of at least some of the users, and selects, based on the problem, a chosen action to alter the problem. At a first time, a first electronic communication describing the chosen action to the at least some of the users is transmitted. Ongoing data describing ongoing behaviors of the users is monitored. A reward is generated based on the ongoing data to change a parameter of the agent. The parameter of the agent is changed to generate a modified agent. The modified agent executes the executable process to select a modified action. At a second time, a second electronic communication describing the modified action is transmitted.

Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

Aspects described herein utilize a resource management model and a resource utilization model, such that simulations may be performed accordingly. For example, a resource management time period may be a timeframe during which resource management is simulated according to the resource management model, while a resource utilization time period may be the timeframe in which resource utilization is simulated. Optimizing a resource allocation based on such simulations may be computationally expensive, for example due to the number of simulation iterations associated with each optimization iteration. Accordingly, a simulation may utilize one or more gates, which may identify a simulation state that is indicative of a degraded optimization iteration. When such a sub-optimal iteration is identified, the simulation may be interrupted and a subsequent optimization iteration may be simulated instead. Thus, processing time may be reduced for iterations that are unlikely to ultimately result in an optimized resource allocation.

Aspects of the present disclosure relate to a partnership configuration engine. In examples, a configuration engine may maintain configuration information associated with one or more providers for which a partnership service provides computing functionality. For example, a partnership service may provide a website, generate electronic communications, and/or perform data processing associated with a third-party provider. The configuration engine may maintain a set of configuration items associated with such a third-party provider, such that functionality of the partnership service is configured accordingly. Thus, as a result of maintaining such configuration information, the partnership service may be configured to provide functionality for any number of providers. Further, data duplication may be reduced and complexity may be reduced as a result of such a configuration information repository.

Systems and methods for determining precise pick-up locations for passengers who have requested autonomous vehicle rides. In particular, systems and methods are provided for using wireless signals to determine user location. In some examples, wireless ranging technology, such as Ultra Wide Band (UWB), is used to determine the user location. Wireless transceivers are used to determine a mobile device's range, and range information from multiple transceivers is used to determine the mobile's device's position. In some examples, triangulation is used to determine user location, such as triangulation between one or more wireless transceivers and the mobile device. In various examples, wireless transceivers are installed on autonomous vehicles, and in some examples, wireless transceivers are installed in various static locations (e.g., on buildings, lamp posts, or other structures.