22 results about "Budget allocation" patented technology

To provide a system for maximizing the effect and ROI by optimizing targetting, creative, or budget allocation of an advertisement.SOLUTION: The system includes: means for optimizing targetting, creative, or budget allocation of an advertisement; means for grasping preferences or actions of a target audience by data analysis and prediction capabilities and developing an advertisement strategy; and means for monitoring and optimizing the performance of the advertisement in real time.SELECTED DRAWING: Figure 1

The invention discloses a budget allocation strategy generation method and device, equipment and a storage medium, and the method comprises the steps: carrying out the disassembly of a total budget amount according to a budget period and each time period, and obtaining a budget disassembly result; performing target disassembly according to the delivery target type and the industry to which the enterprise belongs to obtain a target disassembly result; combining the budget disassembling result and the target disassembling result to obtain a preliminary budget allocation framework; embedding historical delivery data of the same industry and the optimal practice of the industry into a rule engine; and based on the preliminary budget allocation framework, the static basic data and the dynamic real-time data, generating at least two types of alternative budget allocation strategies by adopting a mode of fusing a machine learning model and an embedded rule engine. According to the method, the client side data, the platform side data and the industry side data are systematically integrated, and the distribution strategy is generated through fusion of the rule engine and the machine learning model, so that adaptive fusion and real-time optimization of the strategy are realized, and the generation efficiency is remarkably improved.

The invention relates to the field of carbon market policy design, and discloses a dynamically optimized carbon pricing strategy and budget allocation collaborative game model. Comprising a participant modeling module, a carbon pricing dynamic adjustment module, a budget allocation optimization module and a game evolution module. By establishing a cost function and a revenue function of multiple subjects such as a government and an enterprise and combining a dynamic carbon price function and a budget constraint condition, optimal resource allocation among multiple industries is realized. According to the method, a master-slave game method, a Nash equilibrium method and a reinforcement learning method are adopted, so that carbon pricing and budget allocation are co-evolved in multiple periods, and the dynamic regulation capability and excitation compatibility of the system are enhanced. Compared with the prior art, the policy execution consistency is improved, the total emission reduction cost is reduced, and the method is suitable for application scenes of regional carbon markets and multi-enterprise alliance.

The application provides a media information effect attribution and budget intelligent allocation method and system, relates to the technical field of media information, and comprises the following steps: constructing a conversion path by analyzing a user touch sequence and calculating attribution contribution weights of each media channel, and then obtaining an attribution return on investment. Based on this, a budget allocation optimization model is constructed to maximize the overall return, and a target budget scheme is generated for execution. After execution, the conduction effect of budget adjustment is analyzed through a cause-effect tracking map, and feedback is fed back to optimize the attribution weight calculation, so that dynamic closed-loop optimization and accurate effect evaluation of budget allocation are realized.

We provide the system. [Solution] Means for obtaining information on salary receipts, expenses, and savings, A means of integrating the acquired information to create a user profile, A means of analyzing spending patterns and proposing the optimal budget allocation, A means of automatically allocating the remaining amount after deducting fixed expenses and budget from the aforementioned salary to savings or investments, A means of generating and providing financial data as a report in natural language, A means of generating answers to user questions and providing educational content, A system that includes this.

Neural Network-Based Predictive Budget Allocation Computer Device for Multi-Channel Social Advertising

The disclosure relates to a method and system for optimizing budget allocation for an application. The method includes, for a project of one or more projects associated with the application, and for an environment of one or more environments, receiving a parameter score level corresponding to a parameter, based on relevance of the parameter to the environment, wherein the parameter score level corresponds to a range of parameter score values. Further, for the project and for the environment, a mean environment budget value is derived based on the range of parameter score values corresponding to the set of parameters, and determining a project budget value based on summation of the mean environment budget value and an associated environment weightage corresponding to the one or more environments. The method further includes determining an application budget value based on summation of the project budget values.

The invention discloses a budget allocation optimization method based on big data analysis, and particularly relates to the technical field of budget management. According to the method, historical data of multi-channel budget expenditure is collected, a budget allocation basic data set is constructed, budget execution difference features are extracted, grouping identification is performed by using a clustering algorithm, and a key feature region is determined; extracting budget sensitivity features in combination with external economic index data; the budget sensitivity features are analyzed based on a random forest algorithm, and influence weights of the budget sensitivity features are determined; establishing a budget allocation optimization initial model, and optimizing a budget resource configuration parameter combination; generating a multi-scene simulation scheme of budget configuration, and evaluating a budget execution expected effect; and based on the budget execution expected effect and the preset budget execution efficiency threshold, selecting the optimal budget configuration scheme, and outputting the budget optimization allocation strategy, thereby realizing dynamic optimization of the budget allocation process, and being suitable for budget management in a complex multi-channel environment.

To provide a planning AI household account book for the purpose of allocating a fund to an activity to which a user applies passion, and to provide optimum budget distribution and a fund plan in the course.SOLUTION: Means for inputting and transmitting hobbies and interests to a server; means for obtaining user data from a database; means for analyzing user data and updating prioritized information to the database; means for receiving, automatically categorizing, and updating user transaction information to the database; and means for obtaining revenue and expense information from the database; A system comprising: means for calculating and updating surplus funds to a database; means for calculating and updating budgets to a database based on user goals; means for simulating future allocation of funds; means for generating and transmitting simulation results to a server; means for generating and transmitting advice to a user based on user goals and financial conditions; and means for encrypting and storing data to a database.SELECTED DRAWING: Figure 1

The present application relates to the technical field of electronic commerce, and more particularly to a user journey attribution and budget self-optimization method for e-commerce global investment, comprising the following steps: step 1: collecting multi-channel user behavior event streams, taking the difference between the cumulative amount of conversion event condition strength values of the original sequence and the counterfactual residual sequence in the unified prediction interval as the unified window counterfactual conversion risk drop; step 2: for each target channel, obtaining the net causal attribution coefficient through multi-fold cross fitting double machine learning; step 3: constructing a cumulative attribution revenue concave utility function for each channel, forming an iterative closed loop of attribution modeling, causal debiasing and budget allocation. The present application can finely capture the timing incentive effect and decay characteristics of cross-channel touchpoints, effectively eliminate the confounding interference of user self-selection bias on attribution estimation, and ensure that the long-term cumulative revenue of the budget allocation strategy gradually approaches the theoretical optimal level in a non-stationary investment environment.

A system and method for generating a budgetary recommendation for a user-selected target activity are disclosed. The method includes enabling a user to select a target activity from a plurality of activities. Next, the method includes receiving first information associated with a set of preferences that corresponds to the selected target activity. Next, the method includes retrieving second information associated with the selected target activity and the first information. Next, the method includes analyzing, using a recommendation engine, the first information and the second information to determine a budgetary allocation. Next, the method includes generating a preliminary budgetary recommendation based on the determined budgetary allocation. Next, the method includes rendering, via a display, the preliminary budgetary recommendation to receive a user input. Next, the method includes generating a final budgetary recommendation based on the user input received in response to the preliminary budgetary recommendation.

The present invention discloses a data collection method for insufficient participation and budget allocation in a crowd intelligence network. It solves the problem of insufficient number of participants by hiring agents to introduce their social neighbors to participate in data collection, and takes into account that participants will be more engaged when performing tasks of interest, and the closer the relationship between participants and their agents, the stronger the restriction by the agents, and the higher the quality of task completion. The present invention quantifies these two factors as the basic interest and cooperation tightness attributes of social neighbors, estimates the value of social neighbors to the platform through these two attributes, thereby obtaining the influence value of the agents on the platform, and selects agents in non-increasing order of the influence value to expand the participant set. During the selection process, the present invention dynamically allocates the budgets for hiring agents and data collectors according to the real-time task completion rate. Compared with the existing inventions, the present invention considers the problems of insufficient number of participants and budget allocation, and improves the revenue of the platform and the utilization rate of the budget.

The invention provides a budget allocation method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining historical bubbling data within a preset time period; the original feature vectors of the historical bubbling data are clustered, a plurality of clusters are obtained, and each cluster corresponds to an implicit scene; based on the historical bubbling data, learning a price elasticity curve of each implicit scene; and based on the learned price elasticity curve of each implicit scene, determining an optimal budget allocation strategy of each implicit scene under the total subsidy budget limit. According to the method and the device, the implicit scene division is obtained based on the representation clustering of the implicit low-dimensional space for the obtained original feature vector of the historical bubble data, and the price elasticity curve of each implicit scene is learned, so that the optimal budget allocation strategy of different implicit scenes is formulated, the labor cost is effectively saved, and the subsidy resource utilization rate is improved.

The invention discloses a budget splitting strategy determination method and device, electronic equipment and a program product, and relates to the technical field of artificial intelligence, and the determination method comprises the steps: extracting a business data set from a database, converting business data into a budget limit value based on a preset rule, and adding the budget limit value into a corresponding budget pool, under the condition that it is detected that a user submits a budget splitting strategy on a front-end page, calling a target model based on a preset interface, analyzing the budget splitting strategy, outputting a classification result of the budget splitting strategy and budget allocation data, and under the condition that the category to which the budget splitting strategy belongs is an abnormal category, performing budget allocation on the budget splitting strategy. And judging whether the detection score of the abnormal category is greater than a preset threshold, and when the detection score of the abnormal category is greater than the preset threshold, adjusting the budget splitting strategy based on the budget allocation data to obtain a target budget splitting strategy. The technical problem that the budget splitting strategy cannot be accurately determined in the prior art is solved.

The invention discloses an enterprise cost optimization and budget management method based on big data analysis. The method comprises the steps that S1, real-time data of all departments are collected and preprocessed; s2, constructing a graph structure model of enterprise cost and budget, distributing a preliminary feature vector for each node, and constructing a node feature matrix; s3, the node feature matrix is processed through multi-layer convolution operation, and graph embedding representation of the nodes is generated; s4, based on the graph embedding representation generated by the graph convolutional network, performing budget optimization by using an adaptive simulated annealing algorithm, and outputting a budget allocation scheme; s5, automatically adjusting budget and resource configuration of each department and project of the enterprise; s6, displaying various information of the enterprise in real time through a visual decision support system; and S7, continuously optimizing cost management and budget control of the enterprise. According to the method, an efficient and scientific optimization scheme can be provided in enterprise cost optimization and budget management, and remarkable technical values and economic benefits are brought to practical application.

A method for managing budget allocation for an event is described. The method comprises obtaining data pertaining to the event from a vendee. The event is allocated to a vendor for completion of tasks associated with the event. The data is analyzed to define a scope of the event. An overall budget is evaluated for the event based on the scope of the event. The overall budget is divided into multiple sub-budgets. A sub-budget from the multiple sub-budgets is allocated to a corresponding task of the one or more tasks based on the division of the overall budget. A notification is transmitted to devices associated with the vendor and the vendee. The notification indicates the budget allocated for the corresponding task.

The present invention discloses a budget allocation optimization method based on big data analysis, which specifically relates to the technical field of budget management. The method collects historical data of budget expenditures from multiple channels, constructs a basic budget allocation data set, extracts budget execution difference characteristics, uses a clustering algorithm to perform group identification, and determines key feature areas; combines external economic indicator data to extract budget sensitivity characteristics; analyzes the budget sensitivity characteristics based on a random forest algorithm to determine the influence weights of the budget sensitivity characteristics; establishes an initial model for budget allocation optimization, and optimizes the combination of budget resource configuration parameters; generates a multi-scenario simulation plan for budget configuration, and evaluates the expected effect of budget execution; selects the optimal budget configuration plan based on the expected effect of budget execution and a preset budget execution efficiency threshold, and outputs a budget optimization allocation strategy, thereby realizing dynamic optimization of the budget allocation process and being suitable for budget management in a complex multi-channel environment.

This invention relates to the field of programmatic advertising technology. More specifically, it relates to a budget allocation and pacing control method based on campaign value feedback. The method first divides the advertising campaign period into multiple time intervals according to a preset timeframe; it obtains the initial target spending for each time interval and confirms the actual spending and campaign result value of the ads already placed within each time interval, constructing a campaign time-series dataset; it calculates the unit budget value efficiency based on the campaign time-series dataset, constructs a relative score for each time interval, and constructs a target spending curve based on the relative score; it obtains the actual cumulative spending for each time interval to calculate the cumulative spending deviation, and determines the update direction of the campaign adjustment factor based on the sign direction of the cumulative spending deviation; based on the collected and calculated change in the ratio between actual spending and target spending and the change in the campaign adjustment factor, it calculates effectiveness evaluation data.

The invention discloses a budget management method and device for cloud resources, a server, a computer readable storage medium and a computer program product. According to the method, a budget full-life-cycle management scheme of cloud resources is provided, budget life cycle circulation is achieved through the processes of budget collection, budget auditing, budget implementation, budget conversion, budget settlement and the like, transparent budget control can ensure that resources are allocated and used according to plans, coordination and consistency of resource requirements and budget allocation are ensured, and the budget management efficiency is improved. Therefore, comprehensive cloud resource management and budget management are realized; the method further provides an efficient budget conversion process, corresponding solutions are provided for whether resources are executed according to expectation or not, budget conversion is carried out on application items which are not executed according to expectation by maintaining the actual cost within the preset range of the budget cost, cooperation between different departments can be promoted through flexible resource scheduling, and the efficiency of budget conversion is improved. Therefore, efficient circulation of the budget among different resources is ensured, and the overall operation level of an enterprise is improved.

This invention relates to the field of advertising technology, specifically to a method for intelligent advertising placement and budget allocation in cross-border e-commerce based on reinforcement learning. The invention defines different agents based on different product categories and target markets. The decision-making process of each agent is constructed using an MDP model. Each agent independently deploys an Actor network, while a central server deploys a global Critic network. Each agent is trained locally, and the Critic parameters of each agent after local training are uploaded to the central server. The central server trains the global Critic network to maximize the multi-objective global reward function and then distributes the trained global Critic network to each agent, obtaining optimized Actor parameters for each agent and outputting the advertising action strategy. This invention achieves reasonable advertising placement by considering global rewards, product characteristics, and target market conditions.

We provide the system. [Solution] A method for obtaining real-time information on the usage status of meeting facilities, searching for available meeting facilities, and suggesting them to users, A means for analyzing legal or human resources documents using natural language processing to identify important sections and generate revision suggestions, A method for collecting financial data, analyzing past spending patterns, and proposing the optimal budget allocation, A means to secure real-time information on the reservation status of public resources from citizens and suggest the optimal usage time, A means of generating feedback to notify citizens of important changes and risks in administrative documents, A system that includes this.