CN117762577B - Digital product area scheduling method and system based on gridding realization - Google Patents
Digital product area scheduling method and system based on gridding realizationInfo
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- CN117762577B CN117762577B CN202311557186.6A CN202311557186A CN117762577B CN 117762577 B CN117762577 B CN 117762577B CN 202311557186 A CN202311557186 A CN 202311557186A CN 117762577 B CN117762577 B CN 117762577B
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Abstract
The invention relates to the field of grid division, and discloses a digital product region scheduling method and a digital product region scheduling system based on gridding realization, wherein the method comprises the following steps: analyzing a scene range, a scene internal structure and scene characteristics of a regional scheduling scene, performing grid division on the regional scheduling scene to obtain a grid regional scheduling scene, and performing grid optimization on the grid regional scheduling scene to obtain an optimized grid regional scheduling scene; acquiring product data of digital products in a scene grid corresponding to the scheduling scene of the optimized grid area; identifying product scheduling requirements of digital products, creating a multi-stage feedback queue of the product scheduling requirements, and setting a scheduling strategy of the multi-stage feedback queue to obtain the multi-stage scheduling queue; and identifying grid resources of the scene grid, determining a scheduling state of the digital product, determining a state transition equation of the digital product based on the scheduling state, and executing regional scheduling of the digital product. The invention can improve the regional dispatching effect of the digital products.
Description
Technical Field
The invention relates to the field of grid division, in particular to a digital product area scheduling method and system based on grid realization.
Background
Digital product regional dispatching refers to the process of dispatching and managing digital products in a region by using a digital technology and a digital system, and the aim of digital product regional dispatching is to realize reasonable allocation of resources and efficient completion of tasks so as to improve production efficiency, reduce cost and improve user satisfaction. Through the application of the digital technology and the system, intelligent scheduling and management of digital products can be realized, and the overall operation efficiency and quality are improved.
At present, the regional dispatching of the digital product mainly analyzes and formulates a dispatching plan of the product by collecting digital product data and the state of each region, and the method generally adopts a single dispatching algorithm such as shortest job priority, first come first serve and the like to realize the dispatching of the product, so that the conditions of overlong dispatching waiting time, untimely processing of urgent tasks and the like can occur in the dispatching process, and the regional dispatching effect of the digital product is poor.
Disclosure of Invention
The invention provides a digital product area scheduling method and system based on gridding, which mainly aim to improve the monitoring effect of underground petroleum detection on detection equipment.
In order to achieve the above object, the present invention provides a digital product area scheduling method based on gridding implementation, including:
Acquiring a regional dispatching scene of a digital product, analyzing a scene range, a scene internal structure and scene characteristics of the regional dispatching scene, and analyzing grid requirements of the regional dispatching scene based on the scene range and the scene internal structure;
Based on the grid demand, carrying out grid division on the regional scheduling scene to obtain a grid regional scheduling scene, and carrying out grid optimization on the grid regional scheduling scene through the scene characteristics to obtain an optimized grid regional scheduling scene;
Configuring product identifiers of the digital products and the optimized grid area scheduling scenes, and acquiring product data of the digital products in the scene grids corresponding to the optimized grid area scheduling scenes through the product identifiers;
Based on the product data, identifying the product scheduling demand of the digital product, calculating the scheduling demand priority of the product scheduling demand, creating a multi-stage feedback queue of the product scheduling demand based on the scheduling demand priority, and setting a scheduling strategy of the multi-stage feedback queue to obtain a multi-stage scheduling queue;
and identifying grid resources of the scene grid, determining a scheduling state of the digital product based on the multi-stage scheduling queue and the grid resources, determining a state transition equation of the digital product based on the scheduling state, and executing regional scheduling of the digital product through the state transition equation.
Optionally, the analyzing the scene range, the scene internal structure and the scene characteristics of the regional dispatch scene includes:
acquiring scene data of the regional scheduling scene;
extracting geographic data in the scene data;
Calculating a scene range of the regional scheduling scene based on the geographic data;
Extracting internal structure data in the scene data;
Identifying a scene internal structure of the regional scheduling scene through the internal structure data;
calculating the scene data kurtosis of the scene data;
And extracting scene characteristics of the regional scheduling scene through the kurtosis of the scene data.
Optionally, the calculating the scene data kurtosis of the scene data includes:
Performing data classification on the scene data to obtain scene classification data;
Determining a data value of the scene classification data;
calculating scene data kurtosis of the scene classification data from the data values using the following formula:
Wherein F represents the scene data kurtosis of the scene classification data, A i represents the ith type of scene classification data, Represents the data average value of the scene classification data of the i-th type, B represents the number of the scene classification data of the i-th type, and θ i represents the standard deviation of the scene classification data of the i-th type.
Optionally, the analyzing the grid requirement of the area scheduling scene based on the scene range and the scene internal structure includes:
determining a grid threshold of the regional scheduling scene through the scene range;
analyzing the structural attribute of the internal structure of the scene;
determining the grid quantity and division mode of the regional scheduling scene through the structure attribute;
and analyzing the grid requirement of the regional scheduling scene based on the grid threshold, the grid number and the division mode.
Optionally, grid optimization is performed on the grid region scheduling scene through the scene feature to obtain an optimized grid region scheduling scene, which includes:
Extracting optimization demand characteristics from the scene characteristics;
Based on the optimized demand characteristics, analyzing grid correlation of the grid area scheduling scene corresponding to the scene grid;
Constructing a grid optimization strategy of the grid area scheduling scene based on the grid correlation;
And carrying out grid optimization on the grid area scheduling scene through the grid optimization strategy to obtain an optimized grid area scheduling scene.
Optionally, the analyzing the grid correlation of the grid area scheduling scene corresponding to the scene grid based on the optimized demand characteristic includes:
Performing sequence marking on the scene grid to obtain a sequence grid;
calculating the feature correlation value of the grid of the scene corresponding to the grid regional scheduling scene by using the following formula through the optimization demand features:
wherein Q a,d represents a feature correlation value of the a-th scene mesh and the D-th scene mesh, D a,c represents a c-th optimization demand feature corresponding to the a-th scene mesh, and D d,v represents a D-th optimization demand feature of the D-th scene mesh;
and analyzing the grid correlation of the grid corresponding to the scene grid of the grid regional scheduling scene through the characteristic correlation value.
Optionally, the identifying, based on the product data, a product scheduling requirement of the digital product includes:
Analyzing product characteristics and user product behaviors of the digital product through the product data;
analyzing customer requirements of the digital product through the user product behavior;
Constructing a product demand prediction model of the digital product based on the product characteristics and the customer demands;
And predicting the product scheduling demand of the digital product through the product demand prediction model.
Optionally, the calculating the scheduling requirement priority of the product scheduling requirement includes:
Determining a priority impact factor of the product scheduling requirement;
setting factor weights of the priority impact factors;
weighting the product scheduling demand by the factor weight and the priority influence factor to obtain a demand priority weight;
and identifying the scheduling demand priority of the product scheduling demand through the demand priority weight.
Optionally, the determining a state transition equation of the digital product based on the scheduling state includes:
Determining current parameters of the digital product through the scheduling state;
Calculating a change coefficient of the digital product based on the scheduling state and the current parameter by using the following formula:
dx(m)＝f(r(m),s(m))dt
Wherein dx (m) represents a coefficient of variation of the digital product at time m, r (m) represents a state of the digital product at time m, s (m) represents an s-th current parameter of the digital product at time m, and f represents a state function of the digital product;
and determining a state transition equation of the digital product through the change coefficient.
In order to solve the above problems, the present invention further provides a digital product area scheduling system based on gridding implementation, the system comprising:
The grid demand analysis module is used for acquiring a regional scheduling scene of the digital product, analyzing a scene range, a scene internal structure and scene characteristics of the regional scheduling scene, and analyzing grid demand of the regional scheduling scene based on the scene range and the scene internal structure;
The grid optimization module is used for carrying out grid division on the regional scheduling scene based on the grid requirements to obtain a grid regional scheduling scene, and carrying out grid optimization on the grid regional scheduling scene through the scene characteristics to obtain an optimized grid regional scheduling scene;
The product data acquisition module is used for configuring product identifiers of the digital products and the optimized grid area scheduling scenes, and acquiring product data of the digital products in the scene grids corresponding to the optimized grid area scheduling scenes through the product identifiers;
The demand priority calculating module is used for identifying the product scheduling demand of the digital product based on the product data, calculating the scheduling demand priority of the product scheduling demand, creating a multi-stage feedback queue of the product scheduling demand based on the scheduling demand priority, and setting a scheduling strategy of the multi-stage feedback queue to obtain a multi-stage scheduling queue;
and the product area scheduling module is used for identifying grid resources of the scene grid, determining the scheduling state of the digital product based on the multi-stage scheduling queue and the grid resources, determining a state transition equation of the digital product based on the scheduling state, and executing area scheduling of the digital product through the state transition equation.
According to the embodiment of the invention, the data basis can be provided for later grid division by analyzing the scene range, the scene internal structure and the scene characteristics of the regional scheduling scene; according to the embodiment of the invention, based on the grid demand, the regional scheduling scene is subjected to grid division, so that the regional scheduling scene can be gridded by obtaining a grid regional scheduling scene; according to the embodiment of the invention, grid optimization is carried out on the grid area scheduling scene through the scene characteristics, so that the optimized grid area scheduling scene can be obtained, and the saving of storage space, the improvement of calculation efficiency, the simplification of data analysis, the improvement of visual effect and the improvement of data quality can be brought; according to the embodiment of the invention, the product identification of the digital product and the product identification of the optimized grid area scheduling scene are configured, the product data of the digital product in the scene grid corresponding to the optimized grid area scheduling scene can be collected through the product identification, the tracking and the data recording of the digital product are facilitated, the reliability of the data is improved, further, the product with urgent need can be preferentially scheduled through calculating the scheduling demand priority of the product scheduling demand, the scheduling effect of the digital product is improved, finally, the scheduling state of the digital product is determined based on the multi-stage scheduling queue and the grid resource, and a data basis can be provided for the later scheduling of the digital product, so that the scheduling reliability of the digital product is improved, the regional scheduling of the digital product can be realized through executing the state transition equation, and the reliability of the digital product scheduling is improved. Therefore, the digital product region scheduling method and the system based on gridding realization can improve the region scheduling effect of digital products.
Drawings
Fig. 1 is a flow chart of a digital product area scheduling method based on gridding implementation according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a digital product area scheduling system based on a gridding implementation according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device of a digital product area scheduling system based on gridding implementation according to an embodiment of the present invention
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a digital product region scheduling method based on gridding realization. The execution main body of the digital product region scheduling method based on gridding implementation comprises at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the digital product region scheduling method implemented based on gridding may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flowchart of a digital product area scheduling method based on gridding implementation according to an embodiment of the present invention is shown. In this embodiment, the digital product region scheduling method based on gridding implementation includes:
S1, acquiring a regional dispatching scene of a digital product, analyzing a scene range, a scene internal structure and scene characteristics of the regional dispatching scene, and analyzing grid requirements of the regional dispatching scene based on the scene range and the scene internal structure.
In the embodiment of the invention, the digital product refers to various products and services in digital form, including but not limited to software, application programs, electronic books, music, videos, games and the like, and the regional scheduling scene refers to a scene for scheduling the digital product.
Further, the embodiment of the invention can provide a data basis for later grid division by analyzing the scene range, the scene internal structure and the scene characteristics of the regional scheduling scene. The scene range refers to a geographic range designed by the regional scheduling scene, the scene internal structure refers to an internal organization of the regional scheduling scene, such as an organization of user equipment, a server, network equipment and the like, and the scene feature refers to scene feature attributes such as user distribution, network state and the like of the regional scheduling scene.
As one embodiment of the present invention, the analyzing the scene range, the scene internal structure and the scene characteristics of the regional dispatch scene includes: acquiring scene data of the regional scheduling scene; extracting geographic data in the scene data; calculating a scene range of the regional scheduling scene based on the geographic data; extracting internal structure data in the scene data; identifying a scene internal structure of the regional scheduling scene through the internal structure data; calculating the scene data kurtosis of the scene data; and extracting scene characteristics of the regional scheduling scene through the kurtosis of the scene data.
The scene data refers to a data set related to the regional scheduling scene, such as user behavior data, product data, scene geographic position data and the like, the geographic data refers to data representing the geographic position of the regional scheduling scene, and the scene data kurtosis refers to a kurtosis value of the scene data calculated through kurtosis.
Further, in an optional embodiment of the present invention, the calculating the scene data kurtosis of the scene data includes: performing data classification on the scene data to obtain scene classification data; determining a data value of the scene classification data; calculating scene data kurtosis of the scene classification data from the data values using the following formula:
Wherein F represents the scene data kurtosis of the scene classification data, A i represents the ith type of scene classification data, Represents the data average value of the scene classification data of the i-th type, B represents the number of the scene classification data of the i-th type, and θ i represents the standard deviation of the scene classification data of the i-th type.
Wherein the scene classification data may be implemented by a clustering function.
Further, the embodiment of the invention analyzes the grid demand of the regional scheduling scene based on the scene range and the scene internal structure, and can accurately judge the position of the digital product and the corresponding grid resource through gridding, thereby realizing the efficient scheduling of the digital product. The grid requirement refers to grid conditions for carrying out grid division on the regional scheduling scene.
As one embodiment of the present invention, the analyzing the grid requirement of the regional scheduling scene based on the scene range and the scene internal structure includes: determining a grid threshold of the regional scheduling scene through the scene range; analyzing the structural attribute of the internal structure of the scene; determining the grid quantity and division mode of the regional scheduling scene through the structure attribute; and analyzing the grid requirement of the regional scheduling scene based on the grid threshold, the grid number and the division mode.
The grid threshold value refers to a range of gridding the area scheduling scene, the structural attribute refers to an attribute characteristic of an internal structure of the area scheduling scene, the grid number refers to the grid number required for grid division of the area, and the division mode refers to a method for grid division, such as a square grid, a hexagonal grid, a rectangular grid and the like.
S2, based on the grid requirements, carrying out grid division on the regional scheduling scene to obtain a grid regional scheduling scene, and carrying out grid optimization on the grid regional scheduling scene through the scene characteristics to obtain an optimized grid regional scheduling scene.
According to the embodiment of the invention, based on the grid demand, the regional scheduling scene is subjected to grid division, so that the regional scheduling scene can be gridded by obtaining the grid regional scheduling scene. The grid area scheduling scene refers to dividing the area scheduling scene into small grid scenes. The meshing of the regional scheduling scenes may be performed by a Geographic Information System (GIS).
Further, according to the embodiment of the invention, the grid optimization is performed on the grid area scheduling scene through the scene characteristics, so that the optimized grid area scheduling scene can be obtained, and the saving of storage space, the improvement of calculation efficiency, the simplification of data analysis, the improvement of visual effect and the improvement of data quality can be brought. The optimized grid area scheduling scene is a scheduling scene obtained by performing grid combination optimization on the grid area scheduling scene.
As an embodiment of the present invention, the performing grid optimization on the grid region scheduling scene through the scene feature to obtain an optimized grid region scheduling scene includes: extracting optimization demand characteristics from the scene characteristics; based on the optimized demand characteristics, analyzing grid correlation of the grid area scheduling scene corresponding to the scene grid; constructing a grid optimization strategy of the grid area scheduling scene based on the grid correlation; and carrying out grid optimization on the grid area scheduling scene through the grid optimization strategy to obtain an optimized grid area scheduling scene.
The optimization demand feature refers to determining scene features to be considered according to a specific grid area scheduling scene. For example, characteristics such as load condition, communication overhead, energy consumption and the like of the grid units can be considered, wherein the grid correlation refers to connection between the grid areas corresponding to the grid area scheduling scene, and the grid optimization strategy refers to the strategy for carrying out combination, adjustment and the like on the scene grids.
Further, in an optional embodiment of the present invention, the analyzing the grid correlation of the grid area scheduling scene corresponding to the scene grid based on the optimization requirement feature includes: performing sequence marking on the scene grid to obtain a sequence grid; calculating the feature correlation value of the grid of the scene corresponding to the grid regional scheduling scene by using the following formula through the optimization demand features:
wherein Q a,d represents a feature correlation value of the a-th scene mesh and the D-th scene mesh, D a,c represents a c-th optimization demand feature corresponding to the a-th scene mesh, and D d,v represents a D-th optimization demand feature of the D-th scene mesh;
and analyzing the grid correlation of the grid corresponding to the scene grid of the grid regional scheduling scene through the characteristic correlation value.
S3, configuring product identifiers of the digital products and the optimized grid area scheduling scenes, and acquiring product data of the digital products in the scene grids corresponding to the optimized grid area scheduling scenes through the product identifiers.
According to the embodiment of the invention, the product identification of the digital product and the product identification of the optimized grid area scheduling scene are configured, and the product data of the digital product in the grid of the scene corresponding to the optimized grid area scheduling scene is acquired through the product identification, so that the tracking and the data recording of the digital product can be conveniently performed, and the reliability of the data is improved. The product identifier refers to an identifier which can represent the uniqueness of a digital product, such as a two-dimensional code, a bar code and the like, and the product data refers to data of the digital product collected by a sensor, such as running state data, sales data and the like.
S4, identifying product scheduling requirements of the digital product based on the product data, calculating scheduling requirement priority of the product scheduling requirements, creating a multi-stage feedback queue of the product scheduling requirements based on the scheduling requirement priority, and setting a scheduling strategy of the multi-stage feedback queue to obtain a multi-stage scheduling queue.
According to the embodiment of the invention, based on the product data, the product scheduling requirement of the digital product is identified to clearly schedule the direction, so that the scheduling efficiency of the product is improved. The product scheduling requirement refers to the requirement of reasonably arranging and scheduling resources, tasks or activities of the digital product according to actual conditions and requirements.
As one embodiment of the present invention, the identifying the product scheduling requirement of the digital product based on the product data includes: analyzing product characteristics and user product behaviors of the digital product through the product data; analyzing customer requirements of the digital product through the user product behavior; constructing a product demand prediction model of the digital product based on the product characteristics and the customer demands; and predicting the product scheduling demand of the digital product through the product demand prediction model.
The product characteristics refer to characteristic attributes of the digital product, such as characteristics of sales volume, sales trend, sales area, product specification and the like of the product, the user product behavior refers to action behavior of a user on the digital product, such as purchasing, using, browsing and the like, and the product demand prediction model refers to a model trained through machine learning and used for predicting scheduling demands of the user on the digital product.
Further, by calculating the scheduling demand priority of the product scheduling demand, the embodiment of the invention can schedule the product with urgent demand preferentially through priority scheduling, thereby improving the scheduling effect on the digital product. The scheduling requirement priority refers to the emergency degree of the product scheduling requirement.
As one embodiment of the present invention, the calculating the scheduling requirement priority of the product scheduling requirement includes: determining a priority impact factor of the product scheduling requirement; setting factor weights of the priority impact factors; weighting the product scheduling demand by the factor weight and the priority influence factor to obtain a demand priority weight; and identifying the scheduling demand priority of the product scheduling demand through the demand priority weight.
The priority influence factor refers to factors having influence on the priority of the product scheduling demand, such as emergency degree, profit, etc., the factor weight refers to the degree of influence on the priority, and the demand priority weight refers to the accumulated weight of the product scheduling demand on the priority influence factor.
Further, the embodiment of the invention establishes the multi-stage feedback queue of the product scheduling demands based on the scheduling demand priority, and can divide the demands into different levels through the priority to perform multi-queue simultaneous scheduling, thereby improving the scheduling efficiency of the digital products. The multi-stage feedback queues are queues for dividing the product scheduling demands into different priority levels.
Further, the embodiment of the invention sets the scheduling policy of the multistage feedback queue, obtains the multistage scheduling queue, can allocate proper scheduling modes in different queues to realize efficient scheduling of the digital product, and improves the scheduling reliability. The multi-stage scheduling queue refers to a set of demand queues with different scheduling modes for different priority queues, for example, a scheduling algorithm with priority is adopted for a high priority queue, a scheduling algorithm with time slice rotation is adopted for a medium priority queue, and a scheduling algorithm with first-come first-served is adopted for a low priority queue in the multi-stage scheduling queue.
S5, identifying grid resources of the scene grid, determining the scheduling state of the digital product based on the multi-stage scheduling queue and the grid resources, determining a state transition equation of the digital product based on the scheduling state, and executing regional scheduling of the digital product through the state transition equation.
In the embodiment of the present invention, the grid resources refer to corresponding resources in the scene grid, such as personnel resources, product inventory resources, and the like.
Further, according to the embodiment of the invention, based on the multi-stage scheduling queue and the grid resource, the scheduling state of the digital product is determined, so that a data basis can be provided for product scheduling in the later stage, and the scheduling reliability of the digital product is improved. The scheduling state refers to a state of the digital product before scheduling, such as an operation state, a product position and the like.
As an embodiment of the present invention, the determining the scheduling status of the digital product based on the multi-level scheduling queue and the grid resource may be implemented by extracting scheduling status data of the digital product.
Further, the embodiment of the invention determines the state transition equation of the digital product based on the scheduling state, so that the scheduling path of the digital product can be formulated, thereby improving the reliability of the scheduling of the digital product. The state transition equation is an equation used for simulating a scheduling route of the digital product.
As one embodiment of the present invention, the determining a state transition equation of the digital product based on the scheduling state includes: determining current parameters of the digital product through the scheduling state; calculating a change coefficient of the digital product based on the scheduling state and the current parameter by using the following formula:
dx(m)＝f(r(m)，s(m))dt
Wherein dx (m) represents a coefficient of variation of the digital product at time m, r (m) represents a state of the digital product at time m, s (m) represents an s-th current parameter of the digital product at time m, and f represents a state function of the digital product;
and determining a state transition equation of the digital product through the change coefficient.
The current parameter refers to a parameter of the current state of the digital product, such as a coordinate, stability and the like, the change coefficient represents a tiny change amount of the system state r (m) along with the time m, and dt represents a change rate or speed of the state r (m) in unit time. By integrating the differential equation, specific values of the state at different points in time can be found.
Further, according to the embodiment of the invention, the regional dispatching of the digital product can be implemented through the state transition equation, so that the dispatching reliability of the digital product is improved.
According to the embodiment of the invention, the data basis can be provided for later grid division by analyzing the scene range, the scene internal structure and the scene characteristics of the regional scheduling scene; according to the embodiment of the invention, based on the grid demand, the regional scheduling scene is subjected to grid division, so that the regional scheduling scene can be gridded by obtaining a grid regional scheduling scene; according to the embodiment of the invention, grid optimization is carried out on the grid area scheduling scene through the scene characteristics, so that the optimized grid area scheduling scene can be obtained, and the saving of storage space, the improvement of calculation efficiency, the simplification of data analysis, the improvement of visual effect and the improvement of data quality can be brought; according to the embodiment of the invention, the product identification of the digital product and the product identification of the optimized grid area scheduling scene are configured, the product data of the digital product in the scene grid corresponding to the optimized grid area scheduling scene can be collected through the product identification, the tracking and the data recording of the digital product are facilitated, the reliability of the data is improved, further, the product with urgent need can be preferentially scheduled through calculating the scheduling demand priority of the product scheduling demand, the scheduling effect of the digital product is improved, finally, the scheduling state of the digital product is determined based on the multi-stage scheduling queue and the grid resource, and a data basis can be provided for the later scheduling of the digital product, so that the scheduling reliability of the digital product is improved, the regional scheduling of the digital product can be realized through executing the state transition equation, and the reliability of the digital product scheduling is improved. Therefore, the digital product region scheduling method based on gridding implementation can improve the region scheduling effect of digital products.
Fig. 2 is a functional block diagram of a digital product area scheduling system based on gridding implementation according to an embodiment of the present invention.
The digital product area scheduling system 200 based on gridding implementation of the present invention may be installed in an electronic device. Depending on the functions implemented, the digital product area scheduling system 200 implemented based on gridding may include a grid demand analysis module 201, a grid optimization module 202, a product data acquisition module 203, a product data acquisition module 204, and a product area scheduling module 205. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The grid demand analysis module 201 is configured to obtain a regional scheduling scene of a digital product, analyze a scene range, a scene internal structure, and scene characteristics of the regional scheduling scene, and analyze grid demand of the regional scheduling scene based on the scene range and the scene internal structure;
the grid optimization module 202 is configured to perform grid division on the regional scheduling scene based on the grid requirement to obtain a grid regional scheduling scene, and perform grid optimization on the grid regional scheduling scene through the scene feature to obtain an optimized grid regional scheduling scene;
The product data collection module 203 is configured to configure product identifiers of the digital product and the optimized grid area scheduling scene, and collect product data of the digital product in a scene grid corresponding to the optimized grid area scheduling scene through the product identifiers;
The demand priority calculating module 204 is configured to identify a product scheduling demand of the digital product based on the product data, calculate a scheduling demand priority of the product scheduling demand, create a multi-stage feedback queue of the product scheduling demand based on the scheduling demand priority, and set a scheduling policy of the multi-stage feedback queue to obtain a multi-stage scheduling queue;
The product area scheduling module 205 is configured to identify grid resources of the scene grid, determine a scheduling state of the digital product based on the multi-level scheduling queue and the grid resources, determine a state transition equation of the digital product based on the scheduling state, and execute area scheduling of the digital product according to the state transition equation.
In detail, each module in the digital product area scheduling system 200 based on gridding implementation in the embodiment of the present invention adopts the same technical means as the digital product area scheduling method based on gridding implementation in the drawings when in use, and can produce the same technical effects, which are not described herein.
The embodiment of the invention provides electronic equipment for realizing a digital product region scheduling method based on gridding realization.
Referring to fig. 3, the electronic device may include a processor 30, a memory 31, a communication bus 32, and a communication interface 33, and may further include a computer program stored in the memory 31 and executable on the processor 30, such as a digital product area scheduling method program implemented based on meshing.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The program stored in the memory of the electronic device and based on the digital product region scheduling method realized by meshing is a combination of a plurality of instructions, and when the program runs in the processor, the digital product region scheduling method realized based on meshing can be realized.
Specifically, the specific implementation method of the above instruction by the processor may refer to descriptions of related steps in the corresponding embodiment of the drawings, which are not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement a digital product area scheduling method based on a meshing implementation.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A digital product area scheduling method based on gridding implementation, the method comprising:
Acquiring a regional dispatching scene of a digital product, analyzing a scene range, a scene internal structure and scene characteristics of the regional dispatching scene, and analyzing grid requirements of the regional dispatching scene based on the scene range and the scene internal structure;
Based on the grid demand, carrying out grid division on the regional scheduling scene to obtain a grid regional scheduling scene, and carrying out grid optimization on the grid regional scheduling scene through the scene characteristics to obtain an optimized grid regional scheduling scene;
grid optimization is performed on the grid area scheduling scene through the scene characteristics to obtain an optimized grid area scheduling scene, which comprises the following steps:
Extracting optimization demand characteristics from the scene characteristics;
Based on the optimized demand characteristics, analyzing grid correlation of the grid area scheduling scene corresponding to the scene grid;
Constructing a grid optimization strategy of the grid area scheduling scene based on the grid correlation;
Grid optimization is carried out on the grid area scheduling scene through the grid optimization strategy, so that an optimized grid area scheduling scene is obtained;
based on the optimized demand characteristics, analyzing grid correlation of the grid area scheduling scene corresponding to the scene grid, including:
Performing sequence marking on the scene grid to obtain a sequence grid;
calculating the feature correlation value of the grid of the scene corresponding to the grid regional scheduling scene by using the following formula through the optimization demand features:
wherein Q a,d represents a feature correlation value of the a-th scene mesh and the D-th scene mesh, D a,c represents a c-th optimization demand feature corresponding to the a-th scene mesh, and D d,v represents a D-th optimization demand feature of the D-th scene mesh;
analyzing grid correlation of a grid corresponding to the grid of the scene of the grid regional scheduling scene through the characteristic correlation value;
the optimization demand features are scene features which need to be considered are determined according to specific grid area scheduling scenes, the grid correlation is the relation between the corresponding scene grids of the grid area scheduling scenes, and the grid optimization strategy is the strategy for combining, adjusting and optimizing the scene grids;
Configuring product identifiers of the digital products and the optimized grid area scheduling scenes, and acquiring product data of the digital products in the scene grids corresponding to the optimized grid area scheduling scenes through the product identifiers;
Based on the product data, identifying the product scheduling demand of the digital product, calculating the scheduling demand priority of the product scheduling demand, creating a multi-stage feedback queue of the product scheduling demand based on the scheduling demand priority, and setting a scheduling strategy of the multi-stage feedback queue to obtain a multi-stage scheduling queue;
Identifying grid resources of the scene grid, determining a scheduling state of the digital product based on the multi-stage scheduling queue and the grid resources, determining a state transition equation of the digital product based on the scheduling state, and executing regional scheduling of the digital product through the state transition equation;
the determining a state transition equation of the digital product based on the scheduling state includes:
Determining current parameters of the digital product through the scheduling state;
Calculating a change coefficient of the digital product based on the scheduling state and the current parameter by using the following formula:
dx(m)＝f(r(m),s(m))dt
Wherein dx (m) represents a coefficient of variation of the digital product at time m, r (m) represents a state of the digital product at time m, s (m) represents an s-th current parameter of the digital product at time m, and f represents a state function of the digital product;
The current parameter refers to a parameter of the current state of the digital product, the change coefficient represents a tiny change quantity of a system state r (m) along with time m, and dt represents a change rate or speed of the state r (m) in unit time;
and determining a state transition equation of the digital product through the change coefficient.
2. The digital product area scheduling method of claim 1, wherein the analyzing the scene range, scene internal structure, and scene characteristics of the area scheduling scene comprises:
acquiring scene data of the regional scheduling scene;
extracting geographic data in the scene data;
Calculating a scene range of the regional scheduling scene based on the geographic data;
Extracting internal structure data in the scene data;
Identifying a scene internal structure of the regional scheduling scene through the internal structure data;
calculating the scene data kurtosis of the scene data;
And extracting scene characteristics of the regional scheduling scene through the kurtosis of the scene data.
3. The digital product area scheduling method based on gridding implementation of claim 2, wherein the calculating the scene data kurtosis of the scene data comprises:
Performing data classification on the scene data to obtain scene classification data;
Determining a data value of the scene classification data;
calculating scene data kurtosis of the scene classification data from the data values using the following formula:
Wherein F represents the scene data kurtosis of the scene classification data, A i represents the ith type of scene classification data, Represents the data average value of the scene classification data of the i-th type, B i represents the number of the scene classification data of the i-th type, and θ i represents the standard deviation of the scene classification data of the i-th type.
4. The digital product area scheduling method of claim 1, wherein the analyzing the grid requirements of the area scheduling scene based on the scene range and the scene internal structure comprises:
determining a grid threshold of the regional scheduling scene through the scene range;
analyzing the structural attribute of the internal structure of the scene;
determining the grid quantity and division mode of the regional scheduling scene through the structure attribute;
and analyzing the grid requirement of the regional scheduling scene based on the grid threshold, the grid number and the division mode.
5. The digital product area scheduling method of claim 1, wherein the identifying product scheduling requirements for the digital product based on the product data comprises:
Analyzing product characteristics and user product behaviors of the digital product through the product data;
analyzing customer requirements of the digital product through the user product behavior;
Constructing a product demand prediction model of the digital product based on the product characteristics and the customer demands;
And predicting the product scheduling demand of the digital product through the product demand prediction model.
6. The digital product area scheduling method of claim 1, wherein said calculating a scheduling demand priority of said product scheduling demand comprises:
Determining a priority impact factor of the product scheduling requirement;
setting factor weights of the priority impact factors;
weighting the product scheduling demand by the factor weight and the priority influence factor to obtain a demand priority weight;
and identifying the scheduling demand priority of the product scheduling demand through the demand priority weight.
7. A digital product area scheduling system based on a gridding implementation, for performing the digital product area scheduling method based on a gridding implementation according to any of claims 1-6, the system comprising:
The grid demand analysis module is used for acquiring a regional scheduling scene of the digital product, analyzing a scene range, a scene internal structure and scene characteristics of the regional scheduling scene, and analyzing grid demand of the regional scheduling scene based on the scene range and the scene internal structure;
The grid optimization module is used for carrying out grid division on the regional scheduling scene based on the grid requirements to obtain a grid regional scheduling scene, and carrying out grid optimization on the grid regional scheduling scene through the scene characteristics to obtain an optimized grid regional scheduling scene;
The product data acquisition module is used for configuring product identifiers of the digital products and the optimized grid area scheduling scenes, and acquiring product data of the digital products in the scene grids corresponding to the optimized grid area scheduling scenes through the product identifiers;
The demand priority calculating module is used for identifying the product scheduling demand of the digital product based on the product data, calculating the scheduling demand priority of the product scheduling demand, creating a multi-stage feedback queue of the product scheduling demand based on the scheduling demand priority, and setting a scheduling strategy of the multi-stage feedback queue to obtain a multi-stage scheduling queue;
and the product area scheduling module is used for identifying grid resources of the scene grid, determining the scheduling state of the digital product based on the multi-stage scheduling queue and the grid resources, determining a state transition equation of the digital product based on the scheduling state, and executing area scheduling of the digital product through the state transition equation.
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| CN116863102A (en) * | 2023-06-01 | 2023-10-10 | 北京卡普拉科技有限公司 | Grid cell division method, device and equipment for robot |
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| CN114999161A (en) * | 2022-07-29 | 2022-09-02 | 河北博士林科技开发有限公司 | Be used for intelligent traffic jam edge management system |
| CN116863102A (en) * | 2023-06-01 | 2023-10-10 | 北京卡普拉科技有限公司 | Grid cell division method, device and equipment for robot |
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