CN111832600A - Data processing method and device, electronic equipment and computer readable storage medium - Google Patents
Data processing method and device, electronic equipment and computer readable storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a data processing method, a data processing device, electronic equipment and a computer readable storage medium, wherein acquired feature data of each grid are input into a pre-trained demand prediction model to acquire demand information of shared equipment of each grid, supply information of the shared equipment of each grid is acquired, and resource consumption of the shared equipment in each grid in the next time period is determined according to the demand information and the supply information of the shared equipment of each grid, so that the resource utilization rate of the shared equipment can be improved.
Description
Technical Field
The present invention relates to the field of internet, and more particularly, to a data processing method, apparatus, electronic device, and computer-readable storage medium.
Background
With the development of sharing economy and the internet, sharing equipment such as a sharing bicycle, a sharing automobile and a sharing charger are gradually accepted by the majority of internet users. The way that the spare goods under the integration line provide products or services at a lower price is taken as a place for market with unique advantages. Therefore, how to improve the resource utilization rate of the shared device is an important issue.
Disclosure of Invention
In view of this, embodiments of the present invention provide a data processing method, an apparatus, an electronic device, and a computer-readable storage medium, so as to improve resource utilization of a shared device.
In a first aspect, an embodiment of the present invention provides a data processing method, where the method includes:
acquiring characteristic data of each grid, wherein the characteristic data comprises grid information, time period information and requirement information of shared equipment of each grid in the same historical time period, the grid information comprises identification and environment information of the corresponding grid, and the grid corresponds to a pre-divided geographical area;
inputting the characteristic data of each grid into a pre-trained demand prediction model to obtain demand information of shared equipment of each grid;
acquiring supply information of shared equipment of each grid;
and determining the resource consumption of the shared equipment in each grid in the next period according to the demand information and the supply information.
Optionally, determining, according to the demand information and the supply information, resource consumption amounts of the sharing devices associated in each grid at the next time period includes:
determining a supply-demand ratio according to the supply information and the demand information;
and determining the resource consumption according to the supply-demand ratio.
Optionally, determining the resource consumption amount according to the supply-demand ratio includes:
and determining the resource consumption according to the supply-demand ratio, the predetermined supply-demand ratio sections and the weight corresponding to each supply-demand ratio section.
Optionally, the obtaining the provisioning information of the sharing device of each grid includes:
and determining the supply information of the sharing equipment of each grid according to the position information of each sharing equipment.
Optionally, the location information of the sharing device is determined by the reported information of the user terminal when the task is completed, or determined by the reported information of the operation and maintenance terminal.
Optionally, the demand prediction model is trained by the following steps:
acquiring training data, wherein the training data comprises grid information, time interval information and historical demand information;
training according to the training data to obtain the demand prediction model;
the historical demand information comprises at least one of a demand mean value and a demand median value of the shared devices of each grid in each period in first preset time, a demand mean value and a demand median value of the shared devices of each grid in each period in second preset time, and a demand mean value and a demand median value of the shared devices of each grid in each period in third preset time.
Optionally, the time period information includes an identifier of a time period and a date of the time period.
Optionally, the demand prediction model is an XGBoost regression model.
Optionally, each grid is divided according to a GeoHash method.
In a second aspect, an embodiment of the present invention provides a data processing apparatus, where the apparatus includes:
the system comprises a characteristic data acquisition unit, a data processing unit and a data processing unit, wherein the characteristic data acquisition unit is configured to acquire characteristic data of each grid, the characteristic data comprises grid information, time period information and requirement information of shared equipment of each grid in the same historical time period, the grid information comprises identification and environment information of corresponding grids, and the grids correspond to pre-divided geographic areas;
the demand information acquisition unit is configured to input the characteristic data of each grid into a pre-trained demand prediction model and acquire demand information of the shared equipment of each grid;
a provisioning information acquisition unit configured to acquire provisioning information of the shared devices of each mesh;
a determining unit configured to determine, in accordance with the demand information and the supply information, a resource consumption amount of the shared device associated in each grid at a next period.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is used to store one or more computer program instructions, where the one or more computer program instructions are executed by the processor to implement the method described above.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method as described above.
According to the embodiment of the invention, the acquired feature data of each grid is input into the pre-trained demand prediction model to acquire the demand information of the shared equipment of each grid, the supply information of the shared equipment of each grid is acquired, and the resource consumption of the shared equipment in each grid in the next time period is determined according to the demand information and the supply information of the shared equipment of each grid, so that the resource utilization rate of the shared equipment can be improved.
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The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a data processing method of an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method of training a demand prediction model according to an embodiment of the present invention;
FIG. 3 is a data flow diagram of a data processing method of an embodiment of the present invention;
FIG. 4 is a schematic diagram of an application scenario of a data processing method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an electronic device of an embodiment of the invention.
Detailed Description
The present invention will be described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present invention.
Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout the description, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
The shared devices have different needs in different geographical areas and time periods. For example, the shared bicycle has a large demand during the period of work and work, the shared charger has a large demand during the market on weekends or during the period of afternoon, and the like. In the following description, the embodiment of the present invention mainly takes a sharing bicycle as an example, but it should be understood that the data processing method of the present embodiment can be applied to a sharing device having the same property as the sharing bicycle.
Urban traffic has obvious tidal phenomena, that is, most people are transferred from a cell to a subway station, a bus station and from the subway station to an office building in the early peak period, and vice versa in the late peak period. Therefore, under different time and space (geographic space) scenes, the supply and demand relationships of the shared bicycle have significant differences, and according to the difference, the embodiment provides a data processing method to improve the resource utilization rate of the shared device.
Fig. 1 is a flowchart of a data processing method of an embodiment of the present invention. As shown in fig. 1, the data processing method of the present embodiment includes:
step S110, feature data of each grid is obtained. The grid characteristic data comprises grid information, time period information and requirement information of shared equipment of each grid in the same historical time period, the grid information comprises identification and environment information of the corresponding grid, and the grid corresponds to a pre-divided geographic area. The environment information of the grid may include building information in the grid, for example, whether buildings such as residential districts, subway stations, bus stations, hospitals, schools, office buildings, shopping malls, and the like, and the number of the buildings included in the grid are included in the grid. In this embodiment, the time of day is equally divided into a plurality of periods, each having a unique identification. Optionally, each period is 15 min. Assuming that the time length of each time interval is 15min, and the time interval to be predicted is 18:00-18:15 in the afternoon of this day, the demand information of the shared devices of each grid in the same historical time interval is the demand of the shared devices of each grid in each historical time interval of 18:00-18:15 each day.
In an alternative implementation manner, the embodiment divides a region into a plurality of grids according to the GeoHash method. The GeoHash is an address coding method, which can code two-dimensional longitude and latitude data into a character string. That is, the GeoHash represents two coordinates of longitude and latitude by one character string, which is used for representing a region, and the length of the character string is used for representing the range of the region, the longer the length of the character string is, the more precise the range of representation is, that is, the smaller the range of representation is, and conversely, the shorter the length of the character string is, the larger the range of representation is. Optionally, in this embodiment, a geographic area is divided into a plurality of grids by using the granularity of the GeoHash7, that is, the longitude and latitude of each grid are represented by a character string with a length of 7.
Step S120, inputting the characteristic data of each grid into a pre-trained demand prediction model, and obtaining the demand information of the shared equipment of each grid.
FIG. 2 is a schematic diagram of a method for training a demand prediction model according to an embodiment of the present invention. In an alternative implementation, as shown in FIG. 2, the demand prediction model is trained by:
step S121, training data is obtained, wherein the training data comprises grid information, time interval information and historical demand information. In this embodiment, in the spatial dimension, a region is divided into a plurality of grids according to a predetermined granularity by a GeoHash method in advance, and a unique identifier is given to each grid. In the time dimension, the time of day is equally divided into a plurality of periods, and each period is given a unique identifier. Optionally, each epoch is 15min, then each grid has 96 epochs per day, and assuming a total of N (N >1) grids, then in the spatial and temporal dimensions, there are 96 × N grid epoch slices per day. Thus, in the acquired training data, the mesh information includes the identification of the corresponding mesh and the environmental information. In an embodiment, the period information includes an identification of the period, the date on which the period is located (e.g., weekday, weekend, holiday, month, etc.).
The historical demand information comprises at least one of a demand mean value and a demand median value of the shared devices in each grid period in a first preset time, a demand mean value and a demand median value of the shared devices in each grid period in a second preset time, and a demand mean value and a demand median value of the shared devices in each grid period in a third preset time. Optionally, the first predetermined time is the last 56 days, the second predetermined time is the last 28 days, and the third predetermined time is the last 14 days. For example, the average demand for grid 1 shared devices over time period 18:00-18:15 over the last 14 days is: average of demand from 18:00 to 18:15 on each day for the last 14 days.
Wherein, for each grid period, the required amount of the grid period is the number that the user wants to associate with the sharing device, for example, the number of scanning the sharing bicycle two-dimensional code. Optionally, the multiple code scanning of the same user is recorded as 1 time of requirement, and as long as the code scanning is finished, whether the riding is finished or not can be recorded as 1 time of requirement. Thus, the demand of the shared device for the grid period can be relatively accurately obtained.
And step S122, training and acquiring a demand forecasting model according to the training data. In an alternative implementation, the demand prediction model is an XGBoost regression model. The XGboost model is a supervision model, can construct and optimize a target function, and can also define some loss functions in a user-defined mode, so that the demand forecasting model of the embodiment adopts the XGboost regression model, and can accurately acquire the demand of the shared equipment of each grid in the next period.
In step S130, supply information of the shared devices of each mesh is acquired. Wherein the provisioning information for the shared devices of the grid includes the number of shared devices that are functioning properly and are not in use within the grid. Optionally, the supply information of the sharing device of each grid is determined according to the location information of each sharing device.
In an alternative implementation, the location information of the sharing device is determined by the reported information of the ue when the task is completed. That is, when the user rents the sharing device, the user terminal reports the message of the end of the renting, and the current location information of the sharing device (that is, the location where the user ends the renting) is obtained according to the message of the end of the renting reported by the user terminal. Moreover, after the terminal of the operation and maintenance personnel moves the sharing device, the operation and maintenance terminal reports the related information for moving the sharing device, so that the position information of the related sharing device can be obtained according to the reported information of the operation and maintenance terminal. In another alternative implementation manner, the current location information of each sharing device may be obtained by controlling the sharing device to periodically report its own location information. It should be understood that the present embodiment is not limited to the above-described method for acquiring the location information of the sharing device, and other methods capable of implementing the above-described functions may be applied to the present embodiment.
It should be understood that step S120 and step S130 do not have a sequential execution order, and step S130 may be executed before step S120, after step S120, or simultaneously with step S120, which is not limited in this embodiment.
And step S140, determining resource consumption of the associated shared devices in each grid in the next period according to the demand information and the supply information.
In an alternative implementation, step S140 may include: and determining the supply-demand ratio of each grid in the next period according to the supply information and the demand information of each grid in the next period, and determining the resource consumption of the associated shared equipment in each grid in the next period according to the supply-demand ratio. Optionally, in this embodiment, the resource consumption amount is determined according to the supply-demand ratio, a predetermined supply-demand ratio segment, and a weight corresponding to each supply-demand ratio segment.
For example, assuming that the reference resource consumption amount is x (x >0), each supply and demand ratio segment and the corresponding weight correspondence table are shown in table (1), it should be understood that table (1) is only exemplary, and the supply and demand ratio segment, the corresponding weight, and the supply and demand ratio segment and the corresponding weight correspondence relationship may be adjusted according to the actual application scenario.
| Supply-demand ratio | Weight of | Adjusted resource consumption |
| ≥1 | 1.0 | 1.0*x |
| [0.75,1) | 1.25 | 1.25*x |
| [0.5,0.75) | 1.5 | 1.5*x |
| [0.25,0.5) | 2.0 | 2.0*x |
| [0,0.25) | 3.0 | 3.0*x |
Thus, assuming that the calculated supply-to-demand ratio in grid 1 is 0.55, the resource consumption of the associated shared devices in grid 1 for the next time period may be adjusted to 1.5 x. Therefore, the resource utilization rate of the shared equipment can be effectively improved.
According to the embodiment of the invention, the acquired feature data of each grid is input into the pre-trained demand prediction model to acquire the demand information of the shared equipment of each grid, the supply information of the shared equipment of each grid is acquired, and the resource consumption of the shared equipment in each grid in the next time period is determined according to the demand information and the supply information of the shared equipment of each grid, so that the resource utilization rate of the shared equipment can be improved.
Fig. 3 is a data flow diagram of a data processing method according to an embodiment of the present invention. As shown in fig. 3, the acquired feature data of each grid is input to a demand prediction model 31 trained in advance, and demand information of the shared device of each grid is acquired. The grid characteristic data comprises grid information, time period information and requirement information of shared equipment of each grid in the same historical time period, the grid information comprises identification and environment information of the corresponding grid, and the grid corresponds to a pre-divided geographic area. The environment information of the grid may include building information in the grid, for example, whether buildings such as residential districts, subway stations, bus stations, hospitals, schools, office buildings, shopping malls, and the like, and the number of the buildings included in the grid are included in the grid. In the present embodiment, the time of day is equally divided into a plurality of periods, and each period is optionally 15 min. Assuming that the time length of each time interval is 15min, and the time interval to be predicted is 18:00-18:15 in the afternoon of this day, the demand information of the shared devices of each grid in the same historical time interval is the demand of the shared devices of each grid in each historical time interval of 18:00-18:15 each day.
Optionally, the demand prediction model is trained through the acquired training data, where the training data includes grid information, time interval information, and historical demand information. Optionally, the demand prediction model is an XGBoost regression model. The historical demand information comprises at least one of a demand mean value and a demand median value of the shared devices of each grid in each period in first preset time, a demand mean value and a demand median value of the shared devices of each grid in each period in second preset time, and a demand mean value and a demand median value of the shared devices of each grid in each period in third preset time. Optionally, the first predetermined time is the last 56 days, the second predetermined time is the last 28 days, and the third predetermined time is the last 14 days. For example, the average demand for grid 1 shared devices over time period 18:00-18:15 over the last 14 days is: average of demand from 18:00 to 18:15 on each day for the last 14 days. Wherein, for each grid period, the required amount of the grid period is the number that the user wants to associate with the sharing device, for example, the number of scanning the sharing bicycle two-dimensional code. Optionally, the multiple code scanning of the same user is recorded as 1 time of requirement, and as long as the code scanning is finished, whether the riding is finished or not can be recorded as 1 time of requirement. Thus, the demand of the shared device for the grid period can be relatively accurately obtained. It should be understood that, in urban traffic, shared device demand amounts on weekdays and holidays (weekends and holidays) are different, so that when model training is performed, date information (such as date, weekday identification, holiday identification and the like) of a time period can be introduced to further improve the accuracy of demand prediction.
In the present embodiment, the provisioning information of the shared device for each grid in the next period is acquired according to the provisioning information acquisition unit 32. Wherein the provisioning information for the shared devices of the grid includes the number of shared devices that are functioning properly and are not in use within the grid. Optionally, the supply information of the sharing device of each grid is determined according to the location information of each sharing device.
In an alternative implementation, the location information of the sharing device is determined by the reported information of the ue when the task is completed. That is, when the user rents the sharing device, the user terminal reports the message of the end of the renting, and the current location information of the sharing device (that is, the location where the user ends the renting) is obtained according to the message of the end of the renting reported by the user terminal. Moreover, after the terminal of the operation and maintenance personnel moves the sharing device, the operation and maintenance terminal reports the related information for moving the sharing device, so that the position information of the related sharing device can be obtained according to the reported information of the mobile terminal. In another alternative implementation manner, the current location information of each sharing device may be obtained by controlling the sharing device to periodically report its own location information. It should be understood that the present embodiment is not limited to the above-described method for acquiring the location information of the sharing device, and other methods capable of implementing the above-described functions may be applied to the present embodiment.
The determination unit 33 determines the resource consumption amount of the associated shared device in each grid at the next period based on the acquired demand information and supply information. Optionally: and determining the supply-demand ratio of each grid in the next period according to the supply information and the demand information of each grid in the next period, and determining the resource consumption of the associated shared equipment in each grid in the next period according to the supply-demand ratio. Optionally, in this embodiment, the resource consumption amount is determined according to the supply-demand ratio, a predetermined supply-demand ratio segment, and a weight corresponding to each supply-demand ratio segment.
According to the embodiment of the invention, the acquired feature data of each grid is input into the pre-trained demand prediction model to acquire the demand information of the shared equipment of each grid, the supply information of the shared equipment of each grid is acquired, and the resource consumption of the shared equipment in each grid in the next time period is determined according to the demand information and the supply information of the shared equipment of each grid, so that the resource utilization rate of the shared equipment can be improved.
Fig. 4 is a schematic view of a data processing method according to an embodiment of the present invention. As shown in fig. 4, the regional area 4 is divided into a plurality of grids in advance according to the GeoHash method. Wherein, grid 41 includes a cell a, and grid 42 includes a subway B. Assuming that the current time is 07:59, if the resource consumption amount of the grids 41 and 42 in the next period (i.e., 08:00-08:15) is determined by the data processing method of the embodiment, the acquired grid information, period information, and demand information of the shared bicycle in the same historical period of the grids 41 and 42 are input into a pre-trained demand prediction model to predict the demand information of the grids 41 and 42 in the next period, the supply information of the shared bicycle in the grids 41 and 42 is determined based on the information reported by the user terminal and the operation and maintenance terminal, and the resource consumption amount of the grids 41 and 42 in the next period is determined based on the pre-determined supply-demand ratio segments, the weights corresponding to the supply-demand ratio segments, and the calculated acquired supply-demand ratios. Taking the reference resource consumption as 1 yuan, the supply-demand ratio segments and the corresponding weights in table (1) as examples, assuming that the supply-demand ratio corresponding to grid 41 is 0.3 and the supply-demand ratio corresponding to grid 42 is 2.0, in the next time period, the resource consumption corresponding to grid 41 is 2 yuan and the resource consumption corresponding to grid 42 is 1 yuan. That is, if the starting resource consumption amount of the user renting the shared vehicle at the cell a of the grid 41 is 2 yuan, the starting resource consumption amount of the user renting the shared vehicle at the subway B of the grid 42 is 1 yuan. Therefore, the corresponding resource consumption can be determined according to the supply-demand ratio of the shared bicycle, and the resource utilization rate of the shared equipment is improved.
The present embodiment is described by taking a shared bicycle as an example, and it should be understood that other shared devices, such as a shared automobile, a shared charger, and the like, can all apply the data processing method of the present embodiment.
According to the embodiment of the invention, the acquired feature data of each grid is input into the pre-trained demand prediction model to acquire the demand information of the shared equipment of each grid, the supply information of the shared equipment of each grid is acquired, and the resource consumption of the shared equipment in each grid in the next time period is determined according to the demand information and the supply information of the shared equipment of each grid, so that the resource utilization rate of the shared equipment can be improved.
FIG. 5 is a schematic diagram of a data processing apparatus of an embodiment of the invention. As shown in fig. 5, the data processing apparatus of the present embodiment includes a characteristic data acquisition unit 51, a demand information acquisition unit 52, a supply information acquisition unit 53, and a determination unit 54.
The feature data obtaining unit 51 is configured to obtain feature data of each grid, the feature data including grid information including an identification of a corresponding grid and environmental information, period information, and requirement information of a shared device of each grid at a same period in history, the grid corresponding to a pre-divided geographical area. Optionally, each grid is divided according to a GeoHash method.
The demand information obtaining unit 52 is configured to input the feature data of each grid to a pre-trained demand prediction model, and obtain demand information of the shared device of each grid. Optionally, the demand prediction model is an XGBoost regression model.
The provisioning information acquisition unit 53 is configured to acquire provisioning information of the shared devices of each grid in the next period. Optionally, the supply information obtaining unit 53 is further configured to determine supply information of the sharing devices of each grid according to the position information of each sharing device. Optionally, the location information of the sharing device is determined by the reported information of the user terminal when the task is completed, or determined by the reported information of the operation and maintenance terminal.
The determination unit 54 is configured to determine the resource consumption amount of the shared device associated in each grid at the next period according to the demand information and the supply information. In an alternative implementation, the determining unit 54 is further configured to determine a supply-demand ratio according to the supply information and the demand information, and determine the resource consumption amount according to the supply-demand ratio. Optionally, the determining unit 54 further determines the resource consumption amount according to the supply-demand ratio, the predetermined supply-demand ratio segments, and the weight corresponding to each supply-demand ratio segment.
According to the embodiment of the invention, the acquired feature data of each grid is input into the pre-trained demand prediction model to acquire the demand information of the shared equipment of each grid, the supply information of the shared equipment of each grid is acquired, and the resource consumption of the shared equipment in each grid in the next time period is determined according to the demand information and the supply information of the shared equipment of each grid, so that the resource utilization rate of the shared equipment can be improved.
Fig. 6 is a schematic diagram of an electronic device of an embodiment of the invention. As shown in fig. 6, the electronic device shown in fig. 6 is a general-purpose data processing apparatus including a general-purpose computer hardware structure including at least a processor 61 and a memory 62. The processor 61 and the memory 62 are connected by a bus 63. The memory 62 is adapted to store instructions or programs executable by the processor 61. The processor 61 may be a stand-alone microprocessor or a collection of one or more microprocessors. Thus, the processor 61 implements the processing of data and the control of other devices by executing instructions stored by the memory 62 to perform the method flows of embodiments of the present invention as described above. The bus 63 connects the above components together, and also connects the above components to a display controller 64 and a display device and an input/output (I/O) device 65. Input/output (I/O) devices 65 may be a mouse, keyboard, modem, network interface, touch input device, motion sensing input device, printer, and other devices known in the art. Typically, the input/output device 65 is connected to the system through an input/output (I/O) controller 66.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus (device) or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may employ a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations of methods, apparatus (devices) and computer program products according to embodiments of the application. It will be understood that each flow in the flow diagrams can be implemented by computer program instructions.
These computer program instructions may be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
Another embodiment of the invention is directed to a non-transitory storage medium storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A method of data processing, the method comprising:
acquiring characteristic data of each grid, wherein the characteristic data comprises grid information, time period information and requirement information of shared equipment of each grid in the same historical time period, the grid information comprises identification and environment information of the corresponding grid, and the grid corresponds to a pre-divided geographical area;
inputting the characteristic data of each grid into a pre-trained demand prediction model to obtain demand information of shared equipment of each grid;
acquiring supply information of shared equipment of each grid;
and determining the resource consumption of the shared equipment in each grid in the next period according to the demand information and the supply information.
2. The method of claim 1, wherein determining resource consumption amounts for associating the shared devices in each grid for a next period of time based on the demand information and the supply information comprises:
determining a supply-demand ratio according to the supply information and the demand information;
and determining the resource consumption according to the supply-demand ratio.
3. The method of claim 2, wherein determining the resource consumption based on the supply-to-demand ratio comprises:
and determining the resource consumption according to the supply-demand ratio, the predetermined supply-demand ratio sections and the weight corresponding to each supply-demand ratio section.
4. The method of claim 1, wherein obtaining provisioning information for shared devices for each grid comprises:
and determining the supply information of the sharing equipment of each grid according to the position information of each sharing equipment.
5. The method according to claim 4, wherein the location information of the sharing device is determined by the reported information of the user terminal when the task is completed, or determined by the reported information of the operation and maintenance terminal.
6. The method of claim 1, wherein the demand prediction model is trained by:
acquiring training data, wherein the training data comprises grid information, time interval information and historical demand information;
training according to the training data to obtain the demand prediction model;
the historical demand information comprises at least one of a demand mean value and a demand median value of the shared devices of each grid in each period in first preset time, a demand mean value and a demand median value of the shared devices of each grid in each period in second preset time, and a demand mean value and a demand median value of the shared devices of each grid in each period in third preset time.
7. The method of claim 6, wherein the time period information comprises an identification of the time period and a date on which the time period is located.
8. The method of claim 1, wherein the demand prediction model is an XGBoost regression model.
9. The method according to claim 1, wherein the grids are divided according to a GeoHash method.
10. A data processing apparatus, characterized in that the apparatus comprises:
the system comprises a characteristic data acquisition unit, a data processing unit and a data processing unit, wherein the characteristic data acquisition unit is configured to acquire characteristic data of each grid, the characteristic data comprises grid information, time period information and requirement information of shared equipment of each grid in the same historical time period, the grid information comprises identification and environment information of corresponding grids, and the grids correspond to pre-divided geographic areas;
the demand information acquisition unit is configured to input the characteristic data of each grid into a pre-trained demand prediction model and acquire demand information of the shared equipment of each grid;
a provisioning information acquisition unit configured to acquire provisioning information of the shared devices of each mesh;
a determining unit configured to determine, in accordance with the demand information and the supply information, a resource consumption amount of the shared device associated in each grid at a next period.
11. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any of claims 1-9.
12. A computer-readable storage medium on which computer program instructions are stored, which computer program instructions, when executed by a processor, are to implement a method according to any one of claims 1-9.
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