47 results about "Demand point" patented technology

The invention relates to the technical field of transportation scheduling, and particularly discloses an unmanned Internet of Things workshop cargo transportation robot scheduling method and system, and the method comprises the steps: receiving demand point locations inputted by an administrator, calculating the virtual distance between the demand point locations, and creating a point location distribution map; obtaining task parameters of each demand point location based on a preset matrix to obtain a task parameter table, and calculating a delivery level of each demand point location based on the task parameter table; selecting a distribution path in a point location distribution diagram according to the distribution level, and feeding back the distribution path to an administrator; and correcting the distribution path based on the feedback information of the administrator, and generating a scheduling instruction according to the corrected distribution path. According to the method, the task parameter table is obtained by counting the task parameters of each demand point location based on the matrix, the task parameter table is subjected to self-identification, the dynamic weight of each index is determined, and then the path selection process is adjusted, so that the integrating degree of the path and the actual situation is greatly improved.

The present invention discloses a method and system for generating functional test cases based on a large language model, the method comprising: obtaining a requirement document, and parsing the requirement document into an intermediate representation of a tree structure; traversing the tree path of the intermediate representation to extract fine-grained requirement points, and obtaining all fine-grained requirement points; designing prompt words for functional test cases according to the fine-grained requirement points, and generating functional test cases according to the prompt words using a large language model; performing post-processing according to the functional test cases, extracting fields of the functional test cases, and generating final functional test case results. Through the technical solution of the present invention, changes in requirements can be synchronized quickly, and each fine-grained requirement point can be extracted to generate functional test cases in a targeted manner, reducing the complexity of writing requirement documents, and saving time and manpower.

The invention discloses a medicine supply chain data management system and method based on data analysis, and relates to the technical field of data analysis. The system comprises a demand urgency evaluation module, an inventory authenticity verification module, a transportation path reliability evaluation module and an optimal allocation scheme screening module. According to the method, the demand urgency score is calculated by collecting the medical resource and material consumption data of the demand point; screening adaptive warehouses and verifying the authenticity of the inventory; evaluating the reliability of the transportation path; and a three-dimensional matrix is constructed, a comprehensive score is calculated through dynamic weighting, and an optimal'demand point-warehouse-path 'combination is screened. According to the invention, quantitative decision and global cooperation of emergency material allocation are realized, the response efficiency and accuracy are improved, and the method is suitable for material guarantee in public health emergencies.

The invention discloses a storage resource library point layout optimization method and apparatus. The method comprises the steps of obtaining a demand point position information set; performing first processing on the demand point position information to obtain first result information; performing second processing on the demand point position information to obtain second result information; and carrying out fusion processing on the first result information and the second result information to obtain library point layout optimization result information. According to the method, on the basis of the target task information and the storage resource quantity scale optimization result, optimization processing of the storage resource layout is realized, the problem that the storage resources are too dispersed is solved, and the comprehensive benefits of the storage resources are improved.

To improve accuracy of path finding.SOLUTION: A delivery planning apparatus includes an acquisition unit and an optimization unit. The acquisition unit acquires, as parameter information, the number of transportation media, the number of demand points, the number of staff members who ride on the transportation media, and maximum continuous work time indicating lengths of time in which the staff members can continuously perform activities. The optimization unit simultaneously optimizes paths of the transportation media with respect to the demand points, and allocation of the staff members with respect to the transportation media, by Branch and Bound using the parameter information.SELECTED DRAWING: Figure 1

The invention relates to the technical field of data processing, in particular to a warehouse site selection method, device and equipment and a storage medium. Basic input data and model configuration parameters of warehouse site selection are obtained, and multi-scene and multi-constraint limiting conditions are applied to warehouse site selection; the demand point address set and the alternative warehouse address set are determined through the preset address processing model, more alternative warehouses are expanded and the diversity of the supply chain is improved on the premise that the user demand is met, the site selection instance file is constructed according to the demand point address set, the alternative warehouse address set and the model configuration parameters, and the site selection efficiency is improved. And finally, by running and calculating the site selection instance file, a warehouse site selection result is obtained, so that the reasonability of warehouse site selection is improved, the technical problem of relatively high supply chain cost caused by an unreasonable warehouse site selection mode in the prior art is avoided, and the cost of the supply chain is reduced.

The invention discloses an urban logistics unmanned aerial vehicle take-off and landing point layout optimization method and system, and belongs to the technical field of path planning, and the method comprises the steps: taking all existing express cabinets in an urban region as a candidate take-off and landing point set, and obtaining the express cabinet transformation cost of each candidate take-off and landing point; obtaining the distribution cost from each candidate take-off and landing point to the demand point; determining customer coverage of all existing express cabinets in the urban area; obtaining all paths from each candidate take-off and landing point to the demand point without passing through the no-fly zone, and determining a plurality of take-off and landing point layout schemes corresponding to the selected take-off and landing points and the delivery paths based on all the paths as to-be-optimized individuals; a multi-objective optimization model is constructed by taking minimization of the sum of the transformation cost and the distribution cost of the express cabinet and maximization of the customer coverage degree as objectives, optimization is carried out on individuals to be optimized, and the optimal take-off and landing point layout and the planning result of the demand point range of the service of the take-off and landing point layout are determined. According to the method, reasonable and efficient operation of urban unmanned aerial vehicle logistics can be realized.

The invention provides an emergency supply chain cooperative intelligent reconstruction method and device. The method comprises the following steps: acquiring demand point information; according to the demand point information, the emergency warehouse reserve, the supplier reserve, the emergency warehouse dispatching amount, the emergency material amount purchased from the supplier according to different prices and the material amount transported from the supplier to the demand point are obtained by using the scene robust model constructed in advance; wherein the scene robust model is constructed on the basis of the variance between the expected performance and the actual performance, wherein the expected performance is obtained according to the pre-disaster reserve cost expectation and the post-disaster emergency supply cost expectation in any scene; and reconstructing an emergency supply chain according to the emergency warehouse reserve, the supplier reserve, the emergency warehouse allocation volume, the emergency material volume purchased from the supplier according to different prices and the material quantity transported from the supplier to the demand point. From the perspective of pre-disaster reserve and post-disaster response, rapid repair after emergency supply chain interruption is realized, and emergency material supply is guaranteed.

The invention provides a weighted address selection method based on a maximum coverage algorithm and an all-in-one machine thereof, and relates to the technical field of material distribution network processing, and the method comprises the following steps: S1, determining a demand point set and weights thereof, the weights reflecting the importance degree or demand quantity of demand points; secondly, determining an available library station set and material limitation conditions, and determining a material demand point set and a candidate library station position set; a material demand point position set: D [i] = {1, 2,..., N}, and each demand point i has a certain material demand quantity; the scheme is clear and visual, can combine with an emergency guarantee task, integrates factors, such as road conditions, geographical environments and safety, which influence the guarantee effect into a maximum coverage algorithm, and flexibly configures the priority and weight of each influence factor, so that the calculation of the method is more accurate, and the result better meets the actual requirement; and the accuracy and the usability of the method are improved. When the method is applied to more emergency fields, the method has the flexibility of being applied to multiple fields.

The invention provides a material scheduling path planning method, electronic equipment and a storage medium, and the method comprises the steps: carrying out the analysis of real-time multi-source data, carrying out the prediction of a demand prediction result in a period of time in the future, and carrying out the planning of a material scheduling path based on the demand prediction results in a plurality of periods of time in the future. And generating a material scheduling path from each demand point to the target supply point. By analyzing the multi-source data, the accuracy of demand prediction can be improved, and more efficient material distribution and scheduling can be realized by generating the scheduling path from the demand point to the target supply point according to the demand prediction results in the plurality of time periods in the future of the current time period. In addition, rolling demand prediction and path generation are carried out in each time period, and timely response to complex demand fluctuation can be realized.

The present application relates to the field of transportation engineering, and discloses a truck-unmanned aerial vehicle joint distribution path planning method and system for emergency supplies in an urban waterlogging scene, which divides demand points into type I demand points, type II demand points and type III demand points based on road interruption data, the quantity of supplies required by each demand point, the maximum load capacity of unmanned aerial vehicles and the maximum range of unmanned aerial vehicles; minimizes the latest time at which all demand points are serviced as an objective function, and constructs a truck-unmanned aerial vehicle joint distribution path planning model with demand point access, flow balance, path coordination, time, truck load capacity, unmanned aerial vehicle range, path access order and variable relationship as constraint conditions; the truck-unmanned aerial vehicle joint distribution path planning model is optimized and solved to obtain a joint distribution path scheme. The present application can provide an efficient and reliable joint distribution scheme for emergency decision-making in a waterlogging scene, thereby improving the overall efficiency and response speed of emergency supply distribution.

The application discloses a kind of electric heavy truck battery swap station planning method, device and readable storage medium, comprising: first, based on preset heavy truck trip data simulation freight process, generate battery swap demand data set by battery swap demand trigger mechanism;Demand point clustering is combined with service area information to form initial alternative site;The demand coverage of each site is calculated by nearest neighbor search, and sites with insufficient coverage are screened out;According to the sliding window algorithm, the number of batteries is determined according to the distribution of battery swap request time;After eliminating single-battery sites, the remaining sites are selected to cover the remaining demand using the greedy algorithm with coverage frequency priority to form the final planning set;Finally, the evaluation indexes including replacement rate, planning cost and battery swap loss time are output through the simulation model to realize the scientific evaluation of planning scheme.

The embodiment of the invention discloses an agricultural chemical drop-and-pull transportation method, device and equipment and a medium. A specific embodiment of the method comprises the following steps: performing parameter initialization on a collected user demand point information set to generate an initialized user demand point information set; generating a value-assigned path set according to the initialized user demand point set; generating a length adjustment taboo table according to the updated value assigned path; in response to determining that the number of iteration processing times does not reach the preset number of iteration times, continuing to execute the iteration processing step; and performing service mapping on the optimal path scheme to generate an executable transportation scheme. According to the embodiment, different transportation tasks can be rapidly adapted, flexible allocation of transportation resources is achieved, and the transportation efficiency and the resource utilization rate are improved. Dependence on storage facilities on the site is effectively reduced, meanwhile, the efficiency of loading and unloading operation is improved, and material dispatching on the site is optimized.

The present invention relates to an efficient optimal facility location determination method for convex demand position demand points implemented to arrange a plurality of facilities as close as possible to given demand locations when positions of the demand locations satisfy a convex position condition. According to the efficient optimal facility location determination method for convex position demand points of the present invention, when demand locations satisfy a convex position condition, it is possible to quickly calculate a method of efficiently arranging a plurality of facilities in a very short time. In addition, according to the present invention, even if positions of demand locations do not satisfy a convex position condition, it is possible to approximate arrangement of facilities, and to be applied to various fields such as a location selection of a warehouse or data clustering.

The invention relates to the technical field of urban planning, and discloses a police force layout method and device based on multi-source data fusion, and the method comprises the steps: obtaining the road network data of a target region; analyzing the road network data to determine a candidate police station set; the method comprises the steps of determining a demand point set and a demand weight corresponding to each demand point by analyzing region POI data corresponding to a target region, and determining police demands under each demand level based on the demand weights; based on the geographic positions of the candidate local police station set and the demand point set, calculating the passing time of each local police station in the candidate local police station set to each demand point in the demand point set in different time periods, and generating a passing time set; based on the passing time set, analyzing the service efficiency of each local police station to each demand point in different scenes, and generating a service efficiency set; by applying the method, the police dispatching time of the urban police station can be shortened, and the public security prevention and control efficiency of the police station can be improved.

The invention provides a demand priority determination method and device, equipment, a storage medium and a program product, can be applied to the technical field of artificial intelligence, relates to business architecture, and can be applied to the field of financial science and technology. The method comprises the following steps: acquiring demand investigation information; on the basis of a natural language processing technology, a demand matrix is generated according to demand investigation information, and the demand matrix comprises m demand points, the initial weight of each demand point, the mention frequency corresponding to each demand point, the number of related users and semantic criticality obtained through calculation according to the initial weight, the mention frequency and the number of the related users; m is an integer greater than or equal to 1; according to the demand investigation information and m demand points in the demand matrix, utilizing an emotion analysis algorithm to calculate the emotion intensity of each demand point; calculating the comprehensive weight of each demand point according to the semantic criticality and emotion intensity of each demand point; and determining the priorities of the m demand points according to the sorting of the comprehensive weights of the m demand points. The invention further provides a method.

The invention discloses a fundamental research-oriented flight test demand analysis method, which comprises the following steps of: classifying test tasks according to research contents; according to the types of the test tasks, test demand point carding is carried out, a test verification demand point list is established, and the test verification demand point list is subjected to tagging processing; according to the labeled test verification demand point list, integration and classification are carried out according to label contents, and a test demand matrix is formed; based on the test demand matrix, contribution degree evaluation is carried out, and a demand quantitative evaluation result is obtained; performing test design according to a demand quantitative evaluation result; and according to the test design result, selecting a flight test platform, determining test times and test loads, determining a test target range and a test trajectory, and forming a flight test scheme. According to the method, the requirement analysis can be ensured to be in place, and the test result effectively supports the project research work.

The invention discloses a material storage warehouse-distribution center-demand point coupling optimization-oriented material distribution center site selection method and device, and relates to the field of traffic road networks, and the method comprises the steps: generating a plurality of blocks according to road network data; constructing a demand point driven multi-distribution center site selection model by taking the block as a demand point of material distribution and taking the lowest total transportation cost from the distribution center to the demand point as a target; constructing a multi-distribution-center site selection optimization model by taking the service bearing capacity difference minimization of the distribution centers as a target; constructing a multi-distribution center site selection model driven by dynamic material allocation by taking the lowest transportation cost of dynamic material allocation as a target; and integrating the models and carrying out optimization solution to obtain the optimal position of each distribution center. According to the method, the optimal site selection scheme of the distribution center of the research area is determined from multiple aspects of the transportation cost from the distribution center to the demand point, the service bearing capacity difference of the distribution center and the transportation cost of dynamic material allocation.

The application relates to the technical field of supply chain regulation, and particularly discloses a supply chain space intelligent regulation method and system of a digital cloud warehouse, which comprises the following steps: acquiring a supply point of a product and passenger flow information of the product, and creating a supply layer according to the supply point and the passenger flow information; acquiring order information of the product, inserting a demand point into the supply layer according to the order information, and obtaining a supply-demand feature map; generating a buffer node based on the supply-demand feature map of different time periods, and generating a supply chain regulation instruction pointing to the buffer node; the supply information and the demand information are subjected to two-dimensional processing, the supply-demand feature map is constructed, supply-demand analysis is carried out in the supply-demand feature map, the buffer point is determined, the buffer point is taken as a new temporary supply point, a supply chain update request is synchronously generated, a new supply chain control instruction is determined, and thus the regulation process is realized, so that the overall supply efficiency is greatly improved.

The application discloses a session group establishment method, system, device and storage medium. The application stores the association information of a plurality of demand points of an application in a development version in an online document. A target object can initiate a group creation operation for a target demand point in the online document, and a group configuration page is displayed to the target object. In the group configuration page, an initial creation option is provided. The target object can perform a first interaction operation on the initial creation option to determine to establish a session group. In the group configuration page, a group name determined according to the target demand point, a pending group member matched according to the association information of the target demand point and a creation component are displayed to the target object. The target object can determine that the group name and the pending group member are correct, and perform a second interaction operation on the creation component, thereby completing the task of creating a session group in an instant messaging application. The application can improve the efficiency of establishing a session group and can be widely applied to the technical field of software application.

The invention discloses a multimodal transport cross-border logistics path optimization method and system based on demand and tax rate uncertainty, a medium and a processor, and relates to the technical field of cross-border logistics, comprising: constructing a cross-border logistics network system, the system comprising a supply point, a domestic transit point, a foreign transit point and a most total user demand point; model assumption and related variables are determined, the related variables comprise parameters and decision variables, the parameters comprise deterministic parameters and uncertainty parameters, a multi-objective optimization model is established, and the purpose is to minimize the total transportation cost and the total transportation time; constructing constraint conditions of the multi-objective optimization model; and solving the multi-objective optimization model by adopting a hybrid algorithm of a genetic algorithm and a hill climbing method to obtain an optimal transportation scheme. According to the method, the uncertainty of the demand and the tax rate is comprehensively considered, the multi-target optimization model is established, the actual situation of cross-border logistics can be comprehensively reflected, and the accuracy and practicability of path optimization are improved.

The invention relates to the technical field of logistics management, in particular to an intelligent site selection method for urban and rural parcel loading and unloading sites based on a radiation range, and the method comprises the steps: obtaining geographical coordinates, business attributes and passing time sequence data of routes of demand points and candidate sites; calculating a reference transit time, a peak transit time, and a route instability index based on the time series data; calculating a demand priority index based on the business attribute and matching a dynamic service timeliness threshold; constructing a traffic cost function after asymmetric risk adjustment, and applying a penalty related to priority and instability to a service failure condition in which peak time exceeds an aging threshold; urban and rural demand points are separated based on priorities, and a station with the lowest total risk cost is searched by applying a K-center point algorithm and using a traffic cost function. Through the technical scheme of the invention, the service toughness of the selected site can be high, and the practical stability of the radiation range under various time-varying conditions is ensured.

The invention discloses a business demand analysis method and device, electronic equipment and a computer program product, the method is realized based on a producer-consumer model, and the method comprises the following steps: obtaining and analyzing a code submission log to obtain demand information corresponding to the code submission log; generating a demand point list corresponding to the code submission log according to the demand information corresponding to the code submission log; obtaining a total demand point list of a business demand side; and according to the total demand point list and the demand point list corresponding to the code submission log, analyzing the development condition of the business demand. According to the business demand analysis method, statistical analysis can be comprehensively and visually performed on the code submission logs corresponding to the demand points by adopting the producer-consumer model, and the missing and entrainment conditions of the iterative demand points are automatically, efficiently and accurately found, so that related personnel can know the development conditions of business demands in time; therefore, submission and combination of codes are effectively promoted, and the online success rate is improved.

The invention discloses an electric power metering equipment multi-constraint intelligent scheduling method, system, equipment and medium, and belongs to the technical field of electric power metering, and the method comprises the steps: building a metering equipment demand point, supply point, transportation time and safety intelligent scheduling model, and calculating the demand point importance index of the metering equipment based on the demand point importance index and the metering equipment demand rate; a demand priority is calculated through weighted double-target proximity; a particle swarm is initialized and divided into a main swarm and multiple dynamic subswarms, the multiple dynamic subswarms are independently evolved and periodically recombined, the main swarm updates the speed through a dynamic jitter strategy and learns optimal particles of the subswarms, and a progressive confinement strategy is adopted to adaptively adjust and constrain a feasible region range; and screening supply points meeting the latest arrival time limit, generating a transportation queue according to a time urgency descending order, and outputting a scheduling scheme through multi-subgroup collaborative optimization. According to the invention, an efficient, reliable and flexible solution is provided in the production and transportation scheduling of the electric power metering equipment, and the overall operation efficiency and the service quality can be obviously improved.

This invention relates to a method, apparatus, and medium for parallel scheduling optimization of drones and vehicles. The method includes: Step S1: Creating a set of demand points, etc.; Step S2: Sort all demand points in the demand point set and add a first symbol to the end of the initial sequence to obtain an initial node sequence; Step S3: Based on drone range constraints, etc., traverse and filter to obtain ultra-far points, etc., and move them after the first symbol to obtain an encoded sequence corresponding to the initial node sequence; Step S4: Decode the encoded sequence to obtain a decoded sequence; Step S5: Calculate the drone completion time and vehicle completion time of the decoded sequence, and further obtain the corresponding total completion time; Step S6: Apply random perturbation to the encoded sequence, and repeat steps S4 and S5, and take the encoded sequence with the minimum total completion time as the optimization result. Compared with the prior art, this invention can optimize short-term, high-burst, and short-distance scattered delivery scenarios.

To improve accuracy of path finding.SOLUTION: A delivery planning apparatus includes an acquisition unit and an optimization unit. The acquisition unit acquires, as parameter information, the number of transportation media, the number of demand points, the number of staff members who ride on the transportation media, and maximum continuous work time indicating lengths of time in which the staff members can continuously perform activities. The optimization unit simultaneously optimizes, under a constraint that staff members who ride on the transportation media are present in a same base, paths of the transportation media with respect to the demand points, and allocation of the staff members with respect to the transportation media, by Branch and Bound using the parameter information.SELECTED DRAWING: Figure 1

The invention relates to a national logistics hub emergency function layout double-layer optimization method considering demand uncertainty, and belongs to the field of emergency logistics planning. The method comprises the following steps: firstly, constructing a national logistics hub-emergency material collection and distribution center-demand point three-level network; a bilevel planning model is established, the upper layer takes the minimization of the total cost of the system as a target, the lower layer takes the maximization of the demand satisfaction rate as a target, and the demand uncertainty is processed by a triangular fuzzy number; converting the model into a deterministic form through an expected value operator, converting the double-layer model into a single-layer nonlinear model by using a KKT condition, and linearizing the single-layer nonlinear model into a mixed integer linear programming model through a large M method and a binary variable; and finally, outputting an optimal scheme by using a standard mathematical programming solver. The method solves the problems of mismatching of decision levels, weak uncertainty response and low solving efficiency in the prior art, realizes balance of economic cost and rescue effect, and is suitable for rapid planning of post-disaster emergency logistics.

The application provides a community and enterprise cooperative meal assistance facility site selection optimization method and system, the method comprises the following steps: obtaining geographic information data, population statistics data and meal assistance demand data of demand points and candidate facility points from a multi-source heterogeneous data set; performing data preprocessing and demand uncertainty modeling processing on the geographic information data, population statistics data and meal assistance demand data to obtain a safety demand value of each demand point; according to the safety demand value of each demand point, a mixed integer linear programming model is established with the target of maximizing the effective coverage of the number of elderly demand persons, the model contains budget allocation constraints, facility capacity constraints, service range constraints and service uniqueness constraints; a reinforcement learning site selection environment is constructed according to the model objective function and constraint conditions, and based on a hybrid solving algorithm that fuses deep reinforcement learning and multi-level heuristic search, an elderly meal assistance facility site selection optimization scheme is generated. The application realizes efficient and reliable coverage of meal assistance service demand.

The embodiments of the present application provide a camera layout method and device, which relate to the technical field of surveillance camera site selection and layout. The camera layout method may include: obtaining a supply point set and a demand point set within a target monitoring area; obtaining an effective monitoring area and an installation posture set of a camera; establishing a camera layout model based on the supply point set, the demand point set, the effective monitoring area of ​​the camera, and the installation posture set of the camera; solving the camera layout model to obtain the minimum number of camera installations, and the installation position and installation posture of each camera when the number of camera installations takes the minimum value. The technical solution provided by the embodiments of the present application can solve the problem of poor layout effect of the camera layout method based on personal subjective experience in the prior art.

The invention discloses a service function configuration method and device, equipment and a storage medium, and relates to the technical field of smart communities, and the method comprises the steps: determining matching demand points corresponding to service functions, and determining the function matching degree of the service functions according to a matching degree coefficient determination rule and the number of the matching demand points corresponding to the service functions; determining elastic demand information between a current demand point and each matched demand point, wherein the current demand point is any demand point except the matched demand point in the current region; determining a matching result of the current demand point and the service function according to the elastic demand information between the current demand point and each matched demand point and the function matching degree of the service function; and when the matching result is determined to be matched, configuring the service function to the current demand point. According to the technical scheme, the service function is configured to the demand point actually needing the service function, the service expenditure is reduced while the resident service experience is improved, and the service function configuration effect is improved.