Data processing method, device, electronic device and computer readable storage medium

Abstract

The embodiments of the present invention disclose a data processing method, device, electronic device and computer-readable storage medium, which obtains characteristic data of each grid by inputting it into a pre-trained demand prediction model to obtain demand information of shared devices of each grid, obtains supply information of shared devices of each grid, and determines the resource consumption of the shared devices associated with each grid in the next time period according to the demand information and supply information of the shared devices of each grid, thereby improving the resource utilization of the shared devices.

Description

Technical Field

[0001] The present invention relates to the field of the Internet, and more specifically, to a data processing method, device, electronic device and computer-readable storage medium. Background Art

[0002] With the development of the sharing economy and the Internet, shared devices such as shared bicycles, shared cars, and shared power banks have gradually been accepted by the majority of Internet users. This way of integrating offline idle items and allowing them to provide products or services at a lower price has occupied a place in the market with its unique advantages. Therefore, how to improve the resource utilization of shared devices is an important issue. Summary of the invention

[0003] In view of this, embodiments of the present invention provide a data processing method, apparatus, electronic device, and computer-readable storage medium to improve resource utilization of shared devices.

[0004] In a first aspect, an embodiment of the present invention provides a data processing method, the method comprising:

[0005] Acquire characteristic data of each grid, the characteristic data including grid information, time period information, and demand information of shared devices of each grid in the same historical time period, the grid information including identification and environment information of the corresponding grid, and the grid corresponds to a pre-divided geographical area;

[0006] Inputting the characteristic data of each grid into a pre-trained demand prediction model to obtain demand information of shared devices of each grid;

[0007] Obtain the supply information of shared devices in each grid;

[0008] The resource consumption of the shared device associated with each grid in the next time period is determined according to the demand information and the supply information.

[0009] Optionally, determining the resource consumption associated with the shared device in each grid in the next time period according to the demand information and the supply information includes:

[0010] determining a supply-demand ratio according to the supply information and the demand information;

[0011] The resource consumption is determined according to the supply-demand ratio.

[0012] Optionally, determining the resource consumption according to the supply-demand ratio includes:

[0013] The resource consumption is determined according to the supply-demand ratio, predetermined supply-demand ratio segments, and weights corresponding to the supply-demand ratio segments.

[0014] Optionally, obtaining the supply information of the shared devices of each grid includes:

[0015] The supply information of the shared devices of each grid is determined according to the location information of each shared device.

[0016] Optionally, the location information of the shared device is determined by information reported by a user terminal when a task is completed, or by information reported by an operation and maintenance terminal.

[0017] Optionally, the demand forecasting model is trained by the following steps:

[0018] Acquire training data, wherein the training data includes information of each grid, information of each time period, and historical demand information;

[0019] Acquire the demand forecasting model according to the training data;

[0020] Among them, the historical demand information includes at least one of the average demand and median demand of shared equipment of each grid in each time period within the first predetermined time, the average demand and median demand of shared equipment of each grid in each time period within the second predetermined time, and the average demand and median demand of shared equipment of each grid in each time period within the third predetermined time.

[0021] Optionally, the information of each time period includes an identifier of the time period and a date in which the time period occurs.

[0022] Optionally, the demand forecasting model is an XGBoost regression model.

[0023] Optionally, the grids are divided according to the GeoHash method.

[0024] In a second aspect, an embodiment of the present invention provides a data processing device, the device comprising:

[0025] A characteristic data acquisition unit is configured to acquire characteristic data of each grid, wherein the characteristic data includes grid information, time period information, and demand information of shared devices of each grid in the same historical time period, wherein the grid information includes an identification and environment information of the corresponding grid, and the grid corresponds to a pre-divided geographical area;

[0026] A demand information acquisition unit is configured to input the characteristic data of each grid into a pre-trained demand prediction model to obtain demand information of shared devices of each grid;

[0027] A supply information acquisition unit, configured to acquire supply information of shared devices of each grid;

[0028] The determination unit is configured to determine the resource consumption associated with the shared device in each grid in the next time period according to the demand information and the supply information.

[0029] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used 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 as described above.

[0030] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described above is implemented.

[0031] The embodiment of the present invention inputs the acquired characteristic data of each grid into a pre-trained demand prediction model to obtain the demand information of the shared equipment of each grid, obtains the supply information of the shared equipment of each grid, and determines the resource consumption of the shared equipment associated with each grid in the next time period based on the demand information and supply information of the shared equipment of each grid, thereby improving the resource utilization of the shared equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0033] Figure 1 is a flow chart of a data processing method according to an embodiment of the present invention;

[0034] Figure 2 is a schematic diagram of a training method for a demand forecasting model according to an embodiment of the present invention;

[0035] Figure 3 is a data flow diagram of the data processing method according to an embodiment of the present invention;

[0036] Figure 4 is a schematic diagram of an application scenario of the data processing method according to an embodiment of the present invention;

[0037] Figure 5 is a schematic diagram of a data processing device according to an embodiment of the present invention;

[0038] Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention is described below based on embodiments, but the present invention is not limited to these embodiments. In the detailed description of the present invention below, some specific details are described in detail. It is possible for a person skilled in the art to fully understand the present invention without the description of these details. In order to avoid confusing the essence of the present invention, known methods, processes, flows, components and circuits are not described in detail.

[0040] In addition, persons of ordinary skill in the art will appreciate that the drawings provided herein are for illustration purposes and are not necessarily drawn to scale.

[0041] Unless the context clearly requires otherwise, words such as “include”, “including” and the like in the specification should be interpreted as including rather than being exclusive or exhaustive; that is, as meaning “including but not limited to”.

[0042] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0043] Shared devices have different demands in different geographical areas and time periods. For example, shared bicycles are in greater demand during rush hours, and shared power banks are in greater demand in shopping malls on weekends or in the afternoon. In the following description, the embodiments of the present invention mainly take shared bicycles as an example, but it should be understood that the data processing method of this embodiment can be applied to shared devices with the same properties as shared bicycles.

[0044] There is an obvious tidal phenomenon in urban traffic, that is, during the morning peak period, most people move from residential areas to subway stations, bus stations, and from subway stations to office buildings, while the opposite is true during the evening peak period. Therefore, the supply and demand relationship of shared bicycles is significantly different in different time and space (geographic space) scenarios. Based on this difference, this embodiment provides a data processing method to improve the resource utilization of shared devices.

[0045] Figure 1 is a flow chart of a data processing method according to an embodiment of the present invention. Figure 1 As shown, the data processing method of this embodiment includes:

[0046] Step S110, obtaining characteristic data of each grid. The characteristic data of the grid includes grid information, time period information, and demand information of shared devices of each grid in the same historical time period. The grid information includes the identification and environmental information of the corresponding grid, and the grid corresponds to a pre-divided geographical area. The environmental information of the grid may include building information in the grid, for example, whether the grid includes residential areas, subway stations, bus stations, hospitals, schools, office buildings, shopping malls and other buildings and the number of each building included. In this embodiment, a day is divided into multiple time periods, and each time period has a unique identification. Optionally, each time period is 15 minutes. Assuming that the length of each time period is 15 minutes, and the time period to be predicted is 18:00-18:15 this afternoon, the demand information of shared devices of each grid in the same historical time period is the demand for shared devices of each grid from 18:00 to 18:15 every day in history.

[0047] In an optional implementation, this embodiment divides a regional area into multiple grids according to the GeoHash method. GeoHash is an address encoding method that can encode two-dimensional longitude and latitude data into a string. That is, GeoHash uses a string to represent the two coordinates of longitude and latitude to characterize an area, and the length of the string is used to characterize the range of the area. The longer the string length, the more accurate the range it represents, that is, the smaller the range of representation. Conversely, the shorter the string length, the larger the range of representation. Optionally, in this embodiment, a regional area is divided into multiple grids using the granularity of GeoHash7, that is, a string of length 7 is used to represent the longitude and latitude of each grid.

[0048] Step S120, inputting the characteristic data of each grid into a pre-trained demand prediction model to obtain demand information of shared equipment of each grid.

[0049] Figure 2 is a schematic diagram of a training method for a demand forecasting model according to an embodiment of the present invention. Figure 2 As shown, the demand forecasting model is trained through the following steps:

[0050] Step S121, obtain training data, the training data includes information of each grid, information of each time period, and historical demand information. In this embodiment, in the spatial dimension, a regional area is pre-divided into multiple grids according to a predetermined granularity by the GeoHash method, and each grid is given a unique identifier. In the time dimension, a day is divided into multiple time periods, and each time period is given a unique identifier. Optionally, each time period is 15 minutes, then each grid has 96 time periods every day, assuming that there are N (N>1) grids in total, then in the spatial and temporal dimensions, there are 96*N grid time period slices every day. Thus, in the acquired training data, the grid information includes the identifier and environmental information of the corresponding grid. In an embodiment, the time period information includes the identifier of the time period, the date of the time period (such as working days, weekends, holidays, and months, etc.).

[0051] The historical demand information includes at least one of the average demand and median demand of the shared equipment in each grid period within the first predetermined time, the average demand and median demand of the shared equipment in each grid period within the second predetermined time, and the average demand and median demand of the shared equipment in each grid period within the third predetermined 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 of the shared equipment of grid 1 in the time period 18:00-18:15 in the last 14 days is: the average value of the demand in the time period 18:00-18:15 of each day in the last 14 days.

[0052] For each grid period, the demand for the grid period is the number of users who want to associate with the shared devices, such as the number of scanned shared bike QR codes. Optionally, multiple scans by the same user are recorded as one demand, and as long as the code is scanned, it can be recorded as one demand regardless of whether the ride is completed. In this way, the demand for shared devices in the grid period can be obtained relatively accurately.

[0053] Step S122, training and obtaining a demand forecasting model based on the training data. In an optional implementation, the demand forecasting model is an XGBoost regression model. The XGBoost model is a supervised model that can construct and optimize the objective function and can also customize some loss functions. Therefore, the demand forecasting model of this embodiment uses the XGBoost regression model, which can more accurately obtain the demand for shared devices in each grid in the next time period.

[0054] Step S130, obtaining the supply information of the shared devices of each grid. The supply information of the shared devices of the grid includes the number of shared devices that are functioning normally and not in use in the grid. Optionally, the supply information of the shared devices of each grid is determined according to the location information of each shared device.

[0055] In an optional implementation, the location information of the shared device is determined by the reporting information of the user terminal when the task is completed. That is to say, when the user finishes renting the shared device, the user terminal reports the message of the end of the lease, and the current location information of the shared device (that is, the location where the user ends the lease) is obtained based on the message of the end of the lease reported by the user terminal. In addition, when the terminal of the operation and maintenance personnel moves the shared device, the relevant information of the moved shared device will be reported through the operation and maintenance terminal. Therefore, the location information of the relevant shared device can also be obtained based on the reporting information of the operation and maintenance terminal. In another optional implementation, the current location information of each shared device can be obtained by controlling the shared device to periodically report its own location information. It should be understood that this embodiment is not limited to the above-mentioned method of obtaining the location information of the shared device, and other methods that can achieve the above-mentioned functions can be applied to this embodiment.

[0056] It should be understood that there is no specific execution order for step S120 and step S130 , and step S130 may be executed before step S120 , or after step S120 , or simultaneously with step S120 , and this embodiment does not limit this.

[0057] Step S140: determining the resource consumption of the associated shared devices in each grid in the next time period according to the demand information and the supply information.

[0058] In an optional implementation, step S140 may include: determining the supply-demand ratio of each grid in the next time period according to the supply information and demand information of each grid in the next time period, and determining the resource consumption of the associated shared devices in each grid in the next time period according to the supply-demand ratio. Optionally, in this embodiment, the resource consumption is determined according to the supply-demand ratio, the predetermined supply-demand ratio segments, and the weights corresponding to the supply-demand ratio segments.

[0059] For example, assuming that the reference resource consumption is x (x>0), the correspondence table between each supply and demand ratio segment and the corresponding weight is shown in Table (1). It should be understood that Table (1) is only exemplary, and the supply and demand ratio segments, corresponding weights, and the correspondence between the supply and demand ratio segments and the corresponding weights can be adjusted according to the actual application scenario.

[0060] Supply and Demand Ratio Weight 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

[0061] Therefore, assuming that the supply-demand ratio calculated in grid 1 is 0.55, the resource consumption of the associated shared device in grid 1 in the next period can be adjusted to 1.5*x. In this way, the resource utilization rate of the shared device can be effectively improved.

[0062] The embodiment of the present invention inputs the acquired characteristic data of each grid into a pre-trained demand prediction model to obtain the demand information of the shared equipment of each grid, obtains the supply information of the shared equipment of each grid, and determines the resource consumption of the shared equipment associated with each grid in the next time period based on the demand information and supply information of the shared equipment of each grid, thereby improving the resource utilization of the shared equipment.

[0063] Figure 3 : is a data flow diagram of the data processing method of an embodiment of the present invention. Figure 3 As shown, the acquired characteristic data of each grid is input into the pre-trained demand prediction model 31 to obtain the demand information of the shared equipment of each grid. Among them, the characteristic data of the grid includes grid information, time period information and the demand information of the shared equipment of each grid in the same historical period. The grid information includes the identification and environmental information of the corresponding grid, and the grid corresponds to the pre-divided geographical area. Among them, the environmental information of the grid may include the building information in the grid, for example, whether the grid includes residential areas, subway stations, bus stations, hospitals, schools, office buildings, shopping malls and other buildings and the number of each building included. In this embodiment, the time of a day is divided into multiple time periods, and each time period is optionally 15 minutes. Assuming that the length of each time period is 15 minutes, and the time period to be predicted is 18:00-18:15 this afternoon, the demand information of the shared equipment of each grid in the same historical period is the demand for shared equipment of each grid from 18:00 to 18:15 every day in history.

[0064] Optionally, the demand forecasting model is trained by the acquired training data, and the training data includes information of each grid, information of each time period, and historical demand information. Optionally, the demand forecasting model is an XGBoost regression model. Among them, the historical demand information includes at least one of the demand mean and median of the shared equipment of each grid in each time period within the first predetermined time, the demand mean and median of the shared equipment of each grid in each time period within the second predetermined time, and the demand mean and median of the shared equipment of each grid in each time period within the third predetermined 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 shared equipment of grid 1 in the time period 18:00-18:15 in the last 14 days is: the average demand for the time period 18:00-18:15 every day in the last 14 days. Among them, for each grid time period, the demand for the grid time period is the number of users who want to be associated with the shared equipment, such as the number of scanned shared bicycle QR codes. Optionally, multiple scans of the same user are recorded as one demand, and as long as the code is scanned, it can be recorded as one demand regardless of whether the ride is completed. In this way, the demand for shared equipment in the grid period can be obtained relatively accurately. It should be understood that due to the different demand for shared equipment on weekdays and rest days (weekends and holidays) in urban traffic, the date information of the time period (such as date, weekday mark, rest day mark, etc.) can be introduced when training the model to further improve the accuracy of demand prediction.

[0065] In this embodiment, the supply information of the shared devices of each grid in the next time period is obtained according to the supply information acquisition unit 32. The supply information of the shared devices of the grid includes the number of shared devices that are functioning normally and not in use in the grid. Optionally, the supply information of the shared devices of each grid is determined according to the location information of each shared device.

[0066] In an optional implementation, the location information of the shared device is determined by the reporting information of the user terminal when the task is completed. That is to say, when the user finishes renting the shared device, the user terminal reports the message of the end of the lease, and the current location information of the shared device (that is, the location where the user ends the lease) is obtained based on the message of the end of the lease reported by the user terminal. In addition, when the terminal of the operation and maintenance personnel moves the shared device, the relevant information of the moved shared device will be reported through the operation and maintenance terminal. Therefore, the location information of the relevant shared device can also be obtained based on the reporting information of the moving terminal. In another optional implementation, the current location information of each shared device can be obtained by controlling the shared device to periodically report its own location information. It should be understood that this embodiment is not limited to the above-mentioned method of obtaining the location information of the shared device, and other methods that can achieve the above-mentioned functions can be applied to this embodiment.

[0067] The determination unit 33 determines the resource consumption of the associated shared devices in each grid in the next time period according to the acquired demand information and supply information. Optionally: the supply-demand ratio of each grid in the next time period is determined according to the supply information and demand information of each grid in the next time period, and the resource consumption of the associated shared devices in each grid in the next time period is determined according to the supply-demand ratio. Optionally, in this embodiment, the resource consumption is determined according to the supply-demand ratio, the predetermined supply-demand ratio segments, and the weights corresponding to the supply-demand ratio segments.

[0068] The embodiment of the present invention inputs the acquired characteristic data of each grid into a pre-trained demand prediction model to obtain the demand information of the shared equipment of each grid, obtains the supply information of the shared equipment of each grid, and determines the resource consumption of the shared equipment associated with each grid in the next time period based on the demand information and supply information of the shared equipment of each grid, thereby improving the resource utilization of the shared equipment.

[0069] Figure 4 Schematic diagram of a data processing method according to an embodiment of the present invention. Figure 4 As shown, the regional area 4 is pre-divided into multiple grids according to the GeoHash method. Among them, the grid 41 includes the community A, and the grid 42 includes the subway B. Assuming that the current time is 07:59 in the morning, if the data processing method of this embodiment is to be used to determine the resource consumption corresponding to the grid 41 and the grid 42 in the next time period (that is, 08:00-08:15 in the morning), the grid information, time period information of the grid 41 and the grid 42, and the demand information of the shared bicycles in the same historical time period are input into the pre-trained demand prediction model to predict the demand information of the grid 41 and the grid 42 in the next time period, and determine the supply information of the shared bicycles in the grid 41 and the grid 42 based on the information reported by the user terminal and the operation and maintenance terminal, and determine the resource consumption of the grid 41 and the grid 42 in the next time period based on the predetermined supply and demand ratio segmentation, the weight corresponding to each of the supply and demand ratio segments, and the calculated supply and demand ratio. Taking the reference resource consumption as 1 yuan, the supply-demand ratio segmentation and the corresponding weights in Table (1) as an example, 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, then in the next 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 of a user renting a shared bicycle at Community A in grid 41 is 2 yuan, the starting resource consumption of a user renting a shared bicycle at Subway B in grid 42 is 1 yuan. Therefore, the corresponding resource consumption can be determined according to the supply-demand ratio of the shared bicycle, thereby improving the resource utilization of shared devices.

[0070] This embodiment is described using a shared bicycle as an example. It should be understood that other shared devices, such as shared cars, shared power banks, etc., can also apply the data processing method of this embodiment.

[0071] The embodiment of the present invention inputs the acquired characteristic data of each grid into a pre-trained demand prediction model to obtain the demand information of the shared equipment of each grid, obtains the supply information of the shared equipment of each grid, and determines the resource consumption of the shared equipment associated with each grid in the next time period based on the demand information and supply information of the shared equipment of each grid, thereby improving the resource utilization of the shared equipment.

[0072] Figure 5 Schematic diagram of a data processing device according to an embodiment of the invention. Figure 5 As shown, the data processing device of this embodiment includes a feature data acquisition unit 51 , a demand information acquisition unit 52 , a supply information acquisition unit 53 and a determination unit 54 .

[0073] The characteristic data acquisition unit 51 is configured to acquire characteristic data of each grid, the characteristic data including grid information, time period information, and demand information of shared devices of each grid in the same historical time period, the grid information including identification and environment information of the corresponding grid, and the grid corresponds to a pre-divided geographical area. Optionally, each grid is divided according to the GeoHash method.

[0074] The demand information acquisition unit 52 is configured to input the characteristic data of each grid into a pre-trained demand prediction model to obtain demand information of shared devices of each grid. Optionally, the demand prediction model is an XGBoost regression model.

[0075] The supply information acquisition unit 53 is configured to acquire the supply information of the shared devices of each grid in the next time period. Optionally, the supply information acquisition unit 53 is further configured to determine the supply information of the shared devices of each grid according to the location information of each shared device. Optionally, the location information of the shared device is determined by the reporting information of the user terminal when the task is completed, or by the reporting information of the operation and maintenance terminal.

[0076] The determination unit 54 is configured to determine the resource consumption associated with the shared device in each grid in the next time period according to the demand information and the supply information. In an optional implementation, the determination unit 54 is further configured to determine the supply-demand ratio according to the supply information and the demand information, and determine the resource consumption according to the supply-demand ratio. Optionally, the determination unit 54 further determines the resource consumption according to the supply-demand ratio, a predetermined supply-demand ratio segment, and a weight corresponding to each supply-demand ratio segment.

[0077] The embodiment of the present invention inputs the acquired characteristic data of each grid into a pre-trained demand prediction model to obtain the demand information of the shared equipment of each grid, obtains the supply information of the shared equipment of each grid, and determines the resource consumption of the shared equipment associated with each grid in the next time period based on the demand information and supply information of the shared equipment of each grid, thereby improving the resource utilization of the shared equipment.

[0078] Figure 6 Schematic diagram of an electronic device according to an embodiment of the present invention. Figure 6 As shown, Figure 6 The electronic device shown is a general data processing device, which includes a general computer hardware structure, which at least includes a processor 61 and a memory 62. The processor 61 and the memory 62 are connected via a bus 63. The memory 62 is suitable for storing instructions or programs executable by the processor 61. The processor 61 can be an independent microprocessor or a collection of one or more microprocessors. Thus, the processor 61 executes the instructions stored in the memory 62, thereby executing the method flow of the embodiment of the present invention as described above to realize the processing of data and the control of other devices. The bus 63 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to the display controller 64 and the display device and the input / output (I / O) device 65. The input / output (I / O) device 65 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a somatosensory input device, a printer, and other devices known in the art. Typically, the input / output device 65 is connected to the system via an input / output (I / O) controller 66.

[0079] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices (equipment) or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may adopt a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] The present application is described with reference to flowcharts of methods, apparatuses (devices) and computer program products according to embodiments of the present application. It should be understood that each process in the flowchart can be implemented by computer program instructions.

[0081] These computer program instructions may be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device that implements the process Figure 1 A function specified in a process or multiple processes.

[0082] These computer program instructions may also be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce the instructions for implementing the process Figure 1 A device that specifies functions in a process or multiple processes.

[0083] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program, wherein the computer-readable program is used for a computer to execute part or all of the above method embodiments.

[0084] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire characteristic data of each grid, the characteristic data including grid information, time period information, and demand information of shared devices of each grid in the same historical time period, the grid information including an identifier and environmental information of the corresponding grid, the grid corresponding to a pre-divided geographical area, the environmental information including a building type and the number of buildings corresponding to each building type, the demand information including the demand quantity of the shared device, and the demand quantity is the number of users associated with the shared device; Inputting the characteristic data of each grid into a pre-trained demand prediction model to obtain demand information of shared devices of each grid; Obtain the supply information of shared devices in each grid; Determine the resource consumption of the shared device associated with each grid in the next time period according to the demand information and the supply information; Determining the resource consumption of the shared device associated with each grid in the next time period according to the demand information and the supply information includes: determining a supply-demand ratio according to the supply information and the demand information; The resource consumption is determined according to the supply-demand ratio, predetermined supply-demand ratio segments, and weights corresponding to the supply-demand ratio segments.

2. The method according to claim 1, characterized in that Obtaining the supply information of shared devices of each grid includes: The supply information of the shared devices of each grid is determined according to the location information of each shared device.

3. The method according to claim 2, characterized in that The location information of the shared device is determined by the reporting information of the user terminal when the task is completed, or by the reporting information of the operation and maintenance terminal.

4. The method according to claim 1, characterized in that: The demand forecasting model is trained by the following steps: Acquire training data, wherein the training data includes information of each grid, information of each time period, and historical demand information; Acquire the demand forecasting model according to the training data; Among them, the historical demand information includes at least one of the average demand and median demand of shared equipment of each grid in each time period within the first predetermined time, the average demand and median demand of shared equipment of each grid in each time period within the second predetermined time, and the average demand and median demand of shared equipment of each grid in each time period within the third predetermined time.

5. The method according to claim 4, characterized in that The information of each time period includes an identifier of the time period and the date of the time period.

6. The method according to claim 1, characterized in that The demand forecasting model is an XGBoost regression model.

7. The method according to claim 1, characterized in that The grids are divided according to the GeoHash method.

8. A data processing device, characterized in that: The device comprises: a characteristic data acquisition unit, configured to acquire characteristic data of each grid, the characteristic data including grid information, time period information, and demand information of shared devices of each grid in the same historical time period, the grid information including an identifier and environmental information of the corresponding grid, the grid corresponding to a pre-divided geographical area, the environmental information including a building type and a number of buildings corresponding to each building type, the demand information including a demand quantity of the shared device, and the demand quantity being the number of users associated with the shared device; A demand information acquisition unit is configured to input the characteristic data of each grid into a pre-trained demand prediction model to obtain demand information of shared devices of each grid; A supply information acquisition unit, configured to acquire supply information of shared devices of each grid; a determination unit configured to determine the resource consumption associated with the shared device in each grid in the next time period according to the demand information and the supply information; The determining unit is specifically configured to: determining a supply-demand ratio according to the supply information and the demand information; The resource consumption is determined according to the supply-demand ratio, predetermined supply-demand ratio segments, and weights corresponding to the supply-demand ratio segments.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used 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 according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions, when executed by a processor, implement the method according to any one of claims 1 to 7.

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