32results about How to "Improve the rationality of distribution" patented technology

The invention reveals a method and a system for assigning review tasks automatically. The system comprises: a database obtaining module, an expert information arranging module, a task assigning scheme automatically generating module, and a task assigning scheme determining module. The database obtaining module is used to obtain a review plan and a resource information database. The expert information arranging module is used to arrange expert information. The task assigning scheme automatically generating module is used to automatically generate a task assigning scheme. The task assigning scheme determining module is used to determine the task assigning scheme. The method and the system for assigning review tasks automatically proposed by the invention are capable of increasing the assigning efficiency and the assigning reasonability. The method and the system, in consideration of the technical background and technical specialties, calculate the matching degree between the review work and the responsible person. Compared with the method relying purely on the human subjective factors, the method and the system focus more on objective details, therefore, optimizing the assigning result for assigning tasks.

The invention discloses a medical resource configuration system based on the Internet of Things, relates to the technical field of medical resource configuration, and solves the technical problem that the distribution rationality of human resources is reduced due to the fact that the human resources of medical points in each subarea cannot be analyzed in the prior art. After the human resource detection signals and the corresponding sub-regions are received through the human analysis unit, human resource demand analysis is carried out on each sub-region; the average number of patients received by each subarea medical point within the detection time threshold and the average number of patients who can complete patient diagnosis by each subarea medical point within the detection time threshold are obtained, and the service intensity of each subarea medical point is obtained; and the human resources of the medical points in each sub-region are analyzed, the distribution rationality of the human resources is improved, and meanwhile, the accuracy of resource allocation is improved, so that the working efficiency is improved.

The embodiment of the invention provides an airport and air traffic control operation plan collaborative allocation method and device. The method comprises the steps of obtaining flight data of at least one flight in a first time period; according to the flight data and the rule data, determining a target allocation resource of each flight in the first time period, with the rule data being obtained by performing analysis processing on historical flight data and historical resource allocation data to improve the resource allocation rationality as a target, and when the target allocation resource in the first time period is allocated to each flight, determining that the flight is in the second time period. Processing is carried out according to flight data, and certain limitation is carriedout according to rule data, so that the rationality of resource allocation can be effectively improved, and collaborative allocation of airport and air traffic control operation plans is realized.

The invention discloses a meal taking grid distribution method and device and a storage medium. The meal taking grid distribution method comprises the steps: acquiring grid occupation information of adesignated grid in a meal taking cabinet of a restaurant and target order information for ordering the restaurant in a meal ordering system, wherein the grid is used for storing target food corresponding to the target order information; determining a first idle grid set in an idle state according to the grid occupation information, the to-be-taken food being not placed in each grid in the first idle grid set; obtaining height information of an extraction object of the target food to be extracted according to the target order information; determining a target grid from the first idle grid setaccording to the height information; and distributing the target grid as a grid for storing the target food. According to the meal taking grid distribution method, the technical problem of poor distribution rationality of the meal taking grids is solved.

The invention discloses a construction method of an intelligent module of a manufacturing system, wherein the manufacturing system comprises a plurality of production equipment, and each production equipment is provided with the intelligent module; the construction method of the intelligent module comprises the following steps of S1, configuring corresponding embedded microprocessors according todifferent production equipment; S2, establishing the adaptation layer of the module based on the motion information and the state information of each production equipment; S3, extracting correspondingstate information and external interaction information according to the adaptation layer, and constructing an intelligent analysis layer of the module; S4, establishing an information interaction machine of the intelligent module according to the organizational relationship among the modules; and S5, enabling the constructed adaptation layer, the intelligent analysis layer and the information interaction mechanism to be written into the embedded microprocessor. The intelligent module construction method is applied to different production equipment, so that production difficulty caused by "multiple varieties, small batch and variable processes" in the production process in a discrete workshop are overcome, so that the production allocation reasonability is improved, the manual operation isreduced, and the efficiency and the reliability are improved.

The embodiment of the invention provides a matching process allocation method and device, a server, a terminal and a storage medium, and relates to the technical field of computers. The method comprises the following steps: receiving an opposite office matching request from n user accounts, wherein the opposite office matching request is used for requesting opposite office allocation for the useraccounts and n is a positive integer, acquiring capability information respectively corresponding to the n user accounts, wherein the capability information is used for indicating competitive capabilities of the user accounts, sorting the n user accounts according to the capability information respectively corresponding to the n user accounts, and according to the sorting result of the n user accounts, sequentially allocating the n user accounts to at least one matching process, wherein the matching process is used for performing local allocation on the user accounts in the same matching process. According to the technical scheme provided by the embodiment of the invention, the fairness of the office can be improved.

The embodiment of the invention discloses a traffic prediction method and device, a model training method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a test traffic data set which comprises the traffic data of a target object in a first preset historical time period, and inputting the test traffic data set into a pre-trained traffic prediction model, and outputting a target traffic prediction result of the target object in a specified time period, the traffic prediction model being obtained by training a prediction network based on the training traffic data set, the prediction network is obtained by integrating and constructing a full connection layer of the convolutional neural network and a full connection layer of the convolutional recurrent neural network. According to the technical scheme of the embodiment of the invention, the traffic prediction scheme is greatly optimized, and the accuracy of traffic prediction is improved.

The invention discloses a resource allocation method and system, a computer readable storage medium and electronic equipment. The method comprises the steps of obtaining job feature information of a target job to be executed by a target computing cluster; determining a target computing resource type based on the job feature information; the target computing resource is allocated to the target computing cluster, the target computing resource is the computing resource needed by the target computing cluster for executing the target job, and the computing resource type of the target computing resource is the target computing resource type. The technical problem of unreasonable resource allocation caused by selecting the same kind of computing resources for all jobs in the prior art is solved.

The invention relates to a path learning planning system based on a knowledge graph and a target ontology. The path learning planning system comprises a subject data acquisition module, a student information acquisition module, an evaluation demand acquisition module, a knowledge graph construction module, a data link module, a data operation module and a data operation module. On one hand, corresponding learning plans and learning plans can be made for students in a targeted manner according to the respective requirements and knowledge reserve conditions of the students; therefore, the requirements of learning ability and learning habits of students are met; on the other hand, the learning contents are effectively combined; the knowledge points are reasonably combined to construct an efficient knowledge network, the distribution rationality of the knowledge points in the knowledge network and the comprehensiveness of the knowledge network coverage content are improved to meet different learning requirements and flexibly adjust the learning requirements, and finally, the convenience and the flexibility of students during learning are improved.

The invention provides a service resource allocation method, device and equipment. The method comprises the following steps: acquiring historical flow data in a service execution process within a preset time period; obtaining a resource allocation evaluation result of at least one service according to the historical process data; according to the resource allocation evaluation result of the at least one service, obtaining a target resource allocation evaluation result of the service in the preset time period; and performing resource allocation and / or resource allocation adjustment on the service according to the target resource allocation evaluation result. According to the scheme provided by the invention, the reasonable degree of resource allocation of services in the process mining process can be improved.

The invention discloses an interface monitoring optimization method and device, computer equipment and a storage medium. The method comprises the following steps: receiving an optimization request for preset interface monitoring; obtaining access information and request parameters of an access interface within a preset time and an interface return data volume at an interface monitoring position; optimizing an access threshold at the interface monitoring part according to the access information; inputting the access information, the request parameter and the return data volume into a preset interface performance evaluation model according to a preset first input rule to obtain a maximum return data volume value and a minimum return data volume value; and optimizing the interface performance of the interface monitoring part according to the maximum return data volume value and the minimum return data volume value. On the basis of a process optimization technology, interface monitoring is optimized through the method, interface access is safer and more stable, the error rate is lower, meanwhile, system resource allocation reasonability is improved, and resource waste is reduced.

The invention discloses a wireless resource adaptive distribution method and device. The method comprises calculating wireless communication quality parameters of terminals in a real-time manner, and determining, based on the wireless communication quality parameters of the terminals, the wireless resource quantity needed by the terminals and distributing wireless resources to the terminals. According to the scheme, wireless communication quality parameters of all terminals can be calculated in a real-time manner, wireless resources can be distributed reasonably for different resource demands of different terminals, and the reasonable resource distribution degree and the resource utilization rate can be improved under a condition that the conversation quality of users is guaranteed and the cost of hardware is not increased.

The embodiment of the invention provides a medical resource allocation method and device and a storage medium. In the embodiment of the invention, a medical record of a to-be-served patient can be obtained in response to a deployment trigger event; predicting resource demand information of the to-be-served patient according to the medical record; and allocating medical resources according to the resource demand information. Therefore, the assessment accuracy of the disease development condition of the patient can be improved by fusing big data, so that the resource demand condition of the patient can be predicted more accurately. On the basis, the medical resources can be allocated based on the prediction result, so that the utilization rate and allocation rationality of the medical resources are improved, and fine management of the medical resources is realized.

The embodiment of the invention provides a resource allocation method and device for network slices, relates to the technical field of communication, and can improve the allocation rationality of frequency domain resources in a cell in the network slice. The method comprises: according to a cell identifier of a target cell, determining an allocation rule corresponding to the target cell from a plurality of allocation rules, the allocation rule being used for determining frequency domain resources occupied by a network slice in the target cell; and determining frequency domain resources occupied by M network slices in the target cell according to the allocation rule corresponding to the target cell, M being a positive integer.

The invention discloses a charging pile distribution method and related equipment, and the method comprises the steps: obtaining the current state data of at least one target vehicle and the current charging data of at least one target charging pile; firstly, inputting current state data of at least one target vehicle and current charging data of at least one target charging pile into a reinforcement learning model, and obtaining a charging pile distribution result output by the reinforcement learning model is obtained, wherein the charging pile distribution result can represent a matching relationship between at least one target vehicle and at least one target charging pile; and obtaining a charging feedback result of the at least one target vehicle, updating the reinforcement learning model according to the charging feedback result of the at least one target vehicle, and returning to execute the steps of obtaining the current state data of the at least one target vehicle and the current charging data of the at least one target charging pile and performing subsequent steps, until a preset stop condition is reached. Therefore, the distribution rationality of the charging piles canbe effectively improved.

The embodiment of the invention provides a resource allocation method and device for a network slice, relates to the technical field of communication, and can improve the allocation rationality of frequency domain resources in a cell in the network slice. The method comprises the following steps: acquiring identification information of a network slice, a frequency domain resource initial position of the network slice in a target cell and a bandwidth of the network slice, wherein the identification information of the network slice is used for determining a frequency domain resource allocation direction of the network slice in at least one cell; determining a frequency domain resource allocation direction of the network slice in the target cell according to the cell identifier of the target cell and the identifier information of the network slice; and determining the frequency domain resource of the network slice in the target cell according to the frequency domain resource allocation direction of the network slice in the target cell, the frequency domain resource initial position of the network slice in the target cell and the bandwidth of the network slice.

The embodiment of the invention discloses a police officer deployment and control method, a device and equipment and a storage medium, relates to the technical field of data processing, and aims to reasonably determine a plurality of deployment and control positions and the number and equipment of police officers at each deployment and control position within the occurrence time of a major event. The method comprises the following steps: a server side obtains a plurality of alarm records in a preset period before the occurrence time of a major event from an alarm subsystem, determines a target key person in the plurality of alarm records, and predicts the movement track of the target key person in the occurrence time of the major event; then, the server side matches the predicted movement track with a plurality of early warning positions generated by the three-dimensional geographic information subsystem for the major event, determines a plurality of monitoring positions and danger levels of different monitoring positions in a security plan area of the major event, and finally determines the security plan area of the major event according to the danger levels of the different monitoring positions. The number of police officers and police officer equipment at the monitoring position are distributed within the occurrence time of a major event.

The invention discloses a medical resource allocation system based on the Internet of Things. The invention relates to the technical field of medical resource allocation, and solves the problem that the prior art cannot analyze the human resources of medical points in each sub-area, resulting in a reasonable allocation of human resources. In order to solve the technical problem of reduced sex, after the human resource analysis unit receives the human resource detection signal and the corresponding sub-area, it analyzes the human resource demand of each sub-area, and obtains the average number of patients received by each sub-area medical point within the detection time threshold and The average number of patients who can complete patient diagnosis in each sub-regional medical point within the detection time threshold can obtain the service intensity of each regional medical point; analyze the human resources of each sub-regional medical point to improve the rationality of human resource allocation , while improving the accuracy of resource allocation, thereby improving work efficiency.

The invention relates to a ship loading operation system, a control method and device and computer equipment. The ship loading operation control method comprises the steps that a material throwing shovel of a ship loader is controlled to carry out ship loading on a target loading point in a preset working posture; and if the actual loading capacity of the target loading point is matched with the simulated loading capacity corresponding to the target loading point, the ship loader is controlled to move to the next loading point, the next loading point serves as the target loading point, the step of controlling the material throwing shovel of the ship loader to carry out ship loading on the target loading point in the preset working posture is executed again, and the total loading capacity of goods entering a cabin is matched with the simulated total loading capacity. The total simulation loading amount and the simulation loading amount of each loading point are obtained according to ship loading operation simulation of a preset loading height carried out by the material throwing shovel of the ship loader in a preset working posture. The phenomenon of uneven material distribution caused by observation is eliminated, and the distribution reasonability of the materials in the cabin is improved.

The application relates to a ship loading operation system, control method, device and computer equipment. The loading operation control method includes: controlling the throwing shovel of the ship loader to load the target loading point with a preset working posture; if the actual loading amount of the target loading point matches the simulated loading amount corresponding to the target loading point, then control The ship loader moves to the next loading point and takes the next loading point as the target loading point, and returns to control the throwing shovel of the ship loader to load the target loading point with the preset working posture to continue until the cargo entering the cabin The total loading of matches the simulated total loading. Among them, the total amount of simulated loading and the simulated loading of each loading point are obtained according to the simulation of the ship loading operation at the preset loading height with the throwing shovel of the ship loader at the preset working posture. It eliminates the phenomenon of uneven material distribution caused by observation, and improves the rationality of material distribution in the cabin.

The invention discloses an optimization method, device, computer equipment and storage medium for interface monitoring. The method includes: receiving an optimization request for preset interface monitoring; acquiring access information, request parameters and interface of the access interface within a preset time The interface at the monitoring place returns the data volume; the access threshold at the interface monitoring place is optimized according to the access information; the access information, the request parameter and the returned data volume are input into the preset interface performance evaluation model according to the preset first input rule, Obtain the highest and lowest returned data volume values; optimize the interface performance at the interface monitoring site according to the highest and lowest returned data volume values. The invention is based on the process optimization technology, and the interface monitoring is optimized by the method, which not only makes the interface access safer, more stable, and has a lower error rate, but also improves the rationality of system resource allocation and reduces the waste of resources.

The embodiment of the present application discloses police deployment and control methods, devices, equipment, and storage media, which relate to the field of data processing technology and aim to reasonably determine multiple deployment locations, the number of police officers and the number of police officers at each deployment location within the time of a major event. equipment. The method includes: the server obtains a plurality of alarm records from the alarm subsystem within a preset period before the occurrence of a major event, and determines the target key personnel in the multiple alarm records, and predicts that the target key personnel will be involved in the major event. The activity trajectory within the occurrence time; then the server matches the predicted activity trajectory with the multiple early warning locations generated by the 3D geographic information subsystem for the major event, and determines multiple control locations in the security plan area of ​​the major event and The danger levels of different control positions, and finally, according to the danger levels of different control positions, the number of police officers and police equipment in the control position are allocated within the time of major incidents.

The invention provides an emergency team management method and device, a storage medium and equipment, and the method comprises the steps: obtaining the label information of team members and the emergency attributes of a plurality of emergency teams, and enabling the label information to comprise the historical emergency events and professional skill information of the team members; according to the label information and an emergency team matching rule, calculating a management matching rate between the team member and the plurality of emergency teams; and according to the management matching rate, sorting the matching degrees of the team members and the plurality of emergency teams, obtaining the emergency team with the highest matching degree, and allocating the team members to the emergency team with the highest matching degree. According to the emergency team management method and device, the storage medium and the equipment, the objectivity and the definition of emergency team distribution are ensured through the label information and the emergency team matching rule, so that the team member distribution reasonability of the emergency team is improved.

The invention relates to a self-adaptive adjustment method and device for channel scanning time. The method comprises the following steps: acquiring a historical AP number of a channel according to ahistorical scanning result of the channel; and determining the scanning time of the channel according to the historical AP number. The scanning time of a current channel is determined according to a historical scanning result of the channel. According to the self-adaptive adjustment method and device for channel scanning time, the scanning time of the channel with a large number of APs is longer,the scanning time slice is prevented from being wasted on the channel with a small number of APs or even without APs, the distribution rationality of the channel scanning time is improved, more APs can be scanned by using less time, and the utilization of the scanning time slice is more efficient.

The invention discloses an intelligent distribution and storage system for warehouse logistics. The system comprises a storage space division module, a goods information input module, a goods storagestatistics module, a management cloud server and a display terminal, wherein the management cloud server is connected with the storage space division module, the goods information input module, the goods storage statistics module and the display terminal, the goods storage statistics module comprises a reader, a processor, a goods database and a goods information statistics unit, and the processoris connected with the reader, the goods database and the goods information statistics unit. The invention provides an intelligent distribution and storage system for warehouse logistics. The goods information input module and the goods storage statistics module are integrated through the management cloud server, the goods storing and taking efficiency of managers is improved, the goods storage difficulty degree is reduced, the goods storing and taking safety is greatly improved. The system has the advantages of being high in goods storage position distribution rationality, good in storage effect and high in storage space utilization rate.

The invention relates to a power load distribution method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a power utilization rule of a target power station in a current region within a first preset duration; generating target prediction data according to the power utilization rule in the first preset duration and the reference power utilization rule of a non-target power station in the current region in the second preset duration or the reference power utilization rule of any power station in the non-current region in the third preset duration; organizing the target prediction data to generate prediction operation data and a power distribution scheme of the target power station; and performing power distribution on the target power station according to the predicted operation data and the at least one power distribution scheme. Compared with a traditional mode of carrying out power distribution through a parameter table, the scheme utilizes the power utilization rule of the target power station and the reference power utilization rule of other power stations to predict the operation data of the target power station, and carries out power distribution on the target power station according to the predicted data, so that the effect of improving the reasonability of power load distribution is realized.

The invention provides an improved KNN-based procuratorial officer personalized recommendation method, and relates to the technical field of personalized recommendation. According to the method, the to-be-recommended procurators are subjected to procurator expected workload analysis and procurator expected workload prediction of the current case handling, personalized procurator recommendation is performed, and distribution of new cases is achieved. The case allocation rationality is improved, and the case handling efficiency is improved.

The invention reveals a method and a system for assigning review tasks automatically. The system comprises: a database obtaining module, an expert information arranging module, a task assigning scheme automatically generating module, and a task assigning scheme determining module. The database obtaining module is used to obtain a review plan and a resource information database. The expert information arranging module is used to arrange expert information. The task assigning scheme automatically generating module is used to automatically generate a task assigning scheme. The task assigning scheme determining module is used to determine the task assigning scheme. The method and the system for assigning review tasks automatically proposed by the invention are capable of increasing the assigning efficiency and the assigning reasonability. The method and the system, in consideration of the technical background and technical specialties, calculate the matching degree between the review work and the responsible person. Compared with the method relying purely on the human subjective factors, the method and the system focus more on objective details, therefore, optimizing the assigning result for assigning tasks.