CN111539591B - Data processing method and device, readable storage medium and electronic equipment - Google Patents
Data processing method and device, readable storage medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a data processing method, a data processing device, a readable storage medium and electronic equipment. The embodiment of the invention determines a transport capacity resource set corresponding to a target object through at least one processor; acquiring state data of the capacity resource set and task data of the capacity resource set at a target moment through at least one processor; at least one processor determines the processing information of the target object at the target moment according to the state data and the task data; the at least one processor adjusts delivery parameters of the target object according to the processing information. By the method, the processing information of the target object can be predetermined, the distribution parameters of the target object are adjusted according to the processing information, the actual task quantity of the capacity resource set at the target moment is controlled by adjusting the distribution parameters, the actual task quantity of the capacity resource set at the target moment is prevented from exceeding the maximum task quantity, the distribution pressure of the capacity resource is reduced, and the use experience of a user is improved.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and an apparatus for data processing, a readable storage medium, and an electronic device.
Background
With the continuous development of the take-out industry, more and more convenience is brought to life. Capacity resources play an important role in the retail industry, and at one point in time, the amount of backorders for a set of capacity resources, i.e., the amount of orders that capacity resources have been taken from merchants and are ready for delivery to users, is limited. When the back singular number is larger than the back singular number threshold of the capacity resource set, and the part exceeding the back singular number threshold of the capacity resource set exceeds the delivery capacity of the capacity resource set, problems such as delivery delay and the like may occur, which causes problems such as poor user experience and large capacity resource pressure.
Disclosure of Invention
In view of this, embodiments of the present invention provide a data processing method and apparatus, a readable storage medium, and an electronic device, which can reduce distribution pressure of transportation resources and improve user experience.
In a first aspect, an embodiment of the present invention provides a data processing method, where the method includes: receiving an information processing request from a program calling interface; determining, by at least one processor, a set of capacity resources corresponding to a target object; acquiring, by the at least one processor, state data of the capacity resource set and task data of the capacity resource set at a target moment; the at least one processor determines processing information of the target object at a target moment according to the state data and the task data; the at least one processor adjusts the delivery parameters of the target object according to the processing information; and returning the delivery parameters of the target object through the program call interface.
Preferably, the at least one processor adjusts the delivery parameters of the target object according to the processing information, specifically including: the at least one processor determines a processing mode corresponding to the processing information; and the at least one processor adjusts the distribution parameters of the target object according to the processing mode.
Preferably, the obtaining, by the at least one processor, the state data of the capacity resource set and the task data of the capacity resource set at the target time specifically include: and acquiring the actual task quantity and the maximum task quantity of the transport capacity resource set through the at least one processor, and acquiring the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment.
Preferably, the acquiring the receiving task volume and the distributing task volume of the capacity resource set at the target time specifically includes: inputting the historical receiving task quantity and the historical distributing task quantity of the transport capacity resource set at the historical target moment in a set time period into a pre-trained machine learning model; and determining the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment through the machine learning model.
Preferably, the at least one processor determines, according to the state data and the task data, processing information of the target object at a target time, specifically including: the at least one processor determines a first difference value between the sum value and the distribution task amount according to the sum value of the actual task amount and the received task amount; the at least one processor determining a second difference value between the first difference value and the maximum task amount according to the first difference value; the at least one processor determines a ratio of the second difference to the received task amount according to the second difference; the at least one processor determines the ratio as the processing information.
Preferably, the determining, by the at least one processor, a processing manner corresponding to the processing information specifically includes: the at least one processor determines a processing information range corresponding to the processing information, wherein the processing information range is preset; and the at least one processor determines a corresponding processing mode according to the processing information range.
Preferably, the adjusting the delivery parameters of the target object according to the processing information specifically includes: and adjusting the distribution parameters of the target object according to the processing mode and the grade of the target object, wherein the grade of the target object is preset.
Preferably, the delivery parameters include at least one of a delivery duration, a delivery range, and a delivery tariff.
In a second aspect, an embodiment of the present invention provides an apparatus for data processing, where the apparatus includes: the receiving unit is used for receiving an information processing request from the program calling interface; the first determining unit is used for determining a transport capacity resource set corresponding to the target object through at least one processor; an obtaining unit, configured to obtain, by the at least one processor, state data of the capacity resource set and task data of the capacity resource set at a target time; the second determining unit is used for determining the processing information of the target object at the target moment by the at least one processor according to the state data and the task data; an adjusting unit, configured to adjust, by the at least one processor, delivery parameters of the target object according to the processing information; and the sending unit is used for returning the distribution parameters of the target object through the program calling interface.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium on which computer program instructions are stored, which when executed by a processor implement the method according to the first aspect or any one of the possibilities of the first aspect.
In a fourth 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 following steps: receiving an information processing request from a program calling interface; determining, by at least one processor, a set of capacity resources corresponding to a target object; acquiring, by the at least one processor, state data of the capacity resource set and task data of the capacity resource set at a target moment; the at least one processor determines processing information of the target object at a target moment according to the state data and the task data; the at least one processor adjusts the delivery parameters of the target object according to the processing information; and returning the delivery parameters of the target object through the program call interface.
Preferably, the processor further performs the steps of: and the at least one processor determines a processing mode corresponding to the processing information.
Preferably, the processor further performs the steps of: the at least one processor determines a processing mode corresponding to the processing information; and the at least one processor adjusts the distribution parameters of the target object according to the processing mode.
Preferably, the processor specifically executes the following steps: and acquiring the actual task quantity and the maximum task quantity of the transport capacity resource set through the at least one processor, and acquiring the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment.
Preferably, the processor specifically executes the following steps: inputting the historical receiving task quantity and the historical distributing task quantity of the transport capacity resource set at the historical target moment in a set time period into a pre-trained machine learning model; and determining the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment through the machine learning model.
Preferably, the processor specifically executes the following steps: the at least one processor determines a first difference value between the sum value and the distribution task amount according to the sum value of the actual task amount and the received task amount; the at least one processor determining a second difference value between the first difference value and the maximum task amount according to the first difference value; the at least one processor determines a ratio of the second difference to the received task amount according to the second difference; the at least one processor determines the ratio as the processing information.
Preferably, the processor specifically executes the following steps: the at least one processor determines a processing information range corresponding to the processing information, wherein the processing information range is preset; and the at least one processor determines a corresponding processing mode according to the processing information range.
Preferably, the processor specifically executes the following steps: and adjusting the distribution parameters of the target object according to the processing mode and the grade of the target object, wherein the grade of the target object is preset.
Preferably, the delivery parameters include at least one of a delivery duration, a delivery range, and a delivery tariff.
The embodiment of the invention receives the information processing request from the program calling interface; determining, by at least one processor, a set of capacity resources corresponding to a target object; acquiring, by the at least one processor, state data of the capacity resource set and task data of the capacity resource set at a target moment; the at least one processor determines processing information of the target object at a target moment according to the state data and the task data; the at least one processor adjusts the delivery parameters of the target object according to the processing information; and returning the delivery parameters of the target object through the program call interface. By the method, the processing mode of the target object can be predetermined, the distribution parameters of the target object are adjusted according to the processing mode, the actual task quantity of the capacity resource set at the target moment is controlled by adjusting the distribution parameters, the actual task quantity of the capacity resource set at the target moment is prevented from exceeding the maximum task quantity, the distribution pressure of the capacity resource is reduced, and the use experience of a user is improved.
Drawings
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 method of data processing according to a first embodiment of the present invention;
FIG. 2 is a diagram of an application scenario of a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a data processing apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The present disclosure is described below based on examples, but the present disclosure is not limited to only these examples. In the following detailed description of the present disclosure, certain specific details are set forth. It will be apparent to those skilled in the art that the present disclosure 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 disclosure.
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 and the claims, 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 disclosure, 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 disclosure, "a plurality" means two or more unless otherwise specified.
Usually, the takeout system or the takeout platform allocates the tasks received by the target object to the corresponding capacity resource sets for distribution, but at one moment, the back-order quantity of one capacity resource set is limited, wherein the back-order quantity is the quantity of the tasks which are obtained from the merchants and prepared to be distributed to the users. It is assumed that the threshold of the amount of the back number of one capacity resource set at a set time is 100, but the number of tasks suddenly increases due to the set time reaching the peak time of a meal, the number of tasks that the capacity resource set needs to process at the set time is 110, the number of tasks exceeds the threshold of the amount of the back number of the capacity resource set by 10, which may also be called as a blast list 10, and the capacity of the capacity resource set is exceeded, so the 10 list of the blast list may have problems such as delivery delay, poor user experience, high capacity of the capacity resource, and the like. In the prior art, in order to solve the problems, after the occurrence of the single explosion, the task quantity is regulated and controlled at the next moment of the single explosion, for example, if a blast occurs in the number of backorders for a first instance of the capacity resource set, then at the next instance of the first instance, i.e. the number of tasks is regulated at the second moment, for example, the number of tasks at the second moment is reduced, specifically, the number of tasks can be reduced in any way of increasing the distribution time length, reducing the distribution range or increasing the distribution self-fee, however, in the prior art, all the handling modes adopted when the task quantity is regulated and controlled when the transportation capacity resources are integrated are the same, for example, capacity resource set 1 has a number of tasks at a first time of 120, a threshold number of tasks of 100, the method comprises the following steps that (1) a single order is exploded by 20 at the first moment, so that the number of tasks needs to be regulated and controlled at the second moment, and the specific processing mode is that the distribution range is reduced to 70% of the original distribution range; the number of tasks of the capacity resource set 2 at the first moment is 130 single, the threshold value of the number of back pieces is 100 single, and the list is exploded by 30 single at the first moment, so that the number of tasks needs to be regulated and controlled at the second moment, and the specific processing mode still reduces the distribution range to 70% of the original distribution range; or the number of tasks of the capacity resource set 1 at the first moment is 120, the threshold of the number of back pieces is 100, and the number of back pieces is 20, so that the number of tasks needs to be regulated at the second moment, and the specific processing mode is to delay the distribution time length by 5 minutes on the basis of the original distribution time length; the number of tasks of the capacity resource set 2 at the first moment is 130 single, the threshold value of the number of back pieces is 100 single, and the list is exploded for 30 single at the first moment, so that the number of tasks needs to be regulated and controlled at the second moment, and the specific processing mode is to delay the distribution time length by 5 minutes on the basis of the original distribution time length. And the task quantity is regulated and controlled by adopting the same processing mode under different single explosion conditions, so that the regulation and control effect is poor.
In the embodiment of the present invention, the merchant may also be referred to as a target object, a target merchant, and the embodiment of the present invention does not limit the merchant. Fig. 1 is a flowchart of a data processing method according to a first embodiment of the present invention. As shown in fig. 1, the method specifically comprises the following steps:
and step S100, receiving an information processing request from the program calling interface.
Step S101, determining a capacity resource set corresponding to a target object through at least one processor.
Specifically, the transportation capacity resource set is composed of a plurality of transportation capacity resources, and the transportation capacity resources may be human or machine, which is not limited in the embodiment of the present invention.
Step S102, acquiring state data of the transport capacity resource set and task data of the transport capacity resource set at a target moment through the at least one processor.
Specifically, the actual task amount and the maximum task amount of the capacity resource set are obtained through the at least one processor, and the receiving task amount and the distributing task amount of the capacity resource set at the target moment are obtained.
For example, if the target time is 9/20/9/00 in 2019, the historical target time is 19/9 in 2019; alternatively, 9:00 of any day before 20/9/2019 may be referred to as a historical target time, which is not limited by the present invention.
In the embodiment of the present invention, the receiving task amount and the delivering task amount of the capacity resource set at the target time may be determined in the following manner:
inputting the historical receiving task quantity and the historical distributing task quantity of the transport capacity resource set at the historical target moment in a set time period into a pre-trained machine learning model; and determining the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment through the machine learning model.
For example, the historical received task quantity and the historical distributed task quantity at 9:00 each day within 90 days before 9 and 20 days in 2019 are selected and input into the machine learning model, and then the received task quantity and the distributed task quantity at 9:00 days in 20 days in 2019 and 20 months are predicted. And the prediction is carried out by adopting a machine learning model, so that the prediction accuracy is higher.
And selecting the historical received task quantity and the historical distributed task quantity of the historical target time of the previous day of the target time as the received task quantity and the distributed task quantity of the transport capacity resource set.
For example, the historical received task amount and the historical delivered task amount at the time of one day before 2019, 9 and 20 days, that is, 9, 19, 9:00 days in 2019, 9 and 19 months are taken as the received task amount and the delivered task amount at 9:00 days in 2019, 20 months.
And selecting the average value of the historical received task quantity and the historical distributed task quantity of the historical target time of the set time period before the target time as the received task quantity and the distributed task quantity of the capacity resource set.
For example, assuming that the set time period is 10 days, the average of the historical received task amount and the historical delivered task amount from 20 days before 9 months and 20 days in 2019, i.e., from 9 months and 10 days and 9:00 hours in 2019 to 9 months and 19 days and 9:00 hours in 2019 is taken as the received task amount and the delivered task amount at 9 days and 20 days in 2019. The set time period may also be 5 days, 15 days, or 30 days, which is not limited in the present invention.
And fourthly, assuming that the target time is a holiday, selecting the average value of the historical received task quantity and the historical distributed task quantity of the historical target time of any other holiday before the target time, or the historical received task quantity and the historical distributed task quantity of the same holiday as the received task quantity and the distributed task quantity of the capacity resource set.
For example, assuming that the target time is 9:00 in 2019, 10, 1 and mid-autumn, the average of the historical received task volume and the historical distributed task volume of the historical target time in the morning and mid-autumn festival is selected as the received task volume and the distributed task volume in 2019, 10, 1, 9:00 in month; alternatively, the historical received task amount and the historical distributed task amount at the target time of 2018, 10, month, 1, day, 9:00 are taken as the received task amount and the distributed task amount at the target time of 2019, 10, month, 1, day, 9: 00.
And a fifth mode, assuming that the target time is special weather, selecting the average value of the historical receiving task amount and the historical delivery task amount of the historical target time with the same special weather at the target time as the receiving task amount and the delivery task amount of the transport capacity resource set.
In the embodiments of the present invention, other special cases may exist, and the present invention is not described in detail.
In the embodiment of the invention, the receiving task amount and the distributing task amount of the target time are determined through the method.
For example, the actual task amount of the capacity resource set corresponding to the target object at the current time is 90, and the maximum task amount is 100, where the maximum task amount may also be referred to as a background number threshold, the received task amount at the target time is 40, and the distributed task amount is 20.
Step S103, the at least one processor determines the processing information of the target object at the target time according to the state data and the task data.
Optionally, in a logistics scenario, the processing information may be a regulatory factor.
Specifically, the at least one processor determines a first difference value between the sum value and the distribution task amount according to the sum value of the actual task amount and the received task amount; the at least one processor determining a second difference value between the first difference value and the maximum task amount according to the first difference value; the at least one processor determines a ratio of the second difference to the received task amount according to the second difference; the at least one processor determines the ratio as the processing information.
For example, it is assumed that the actual task amount of the capacity resource set corresponding to the target object is 90, the maximum task amount is 100, the received task amount is 40, and the distributed task amount is 20; the sum of the actual task amount and the received task amount is 90+ 40-130; and determining that a first difference value between the sum and the distribution task amount is 130-20-110, and a second difference value between the first difference value and the maximum task amount is 110-100-10, wherein 10 is the explosive amount at the target moment, and the ratio of the second difference value 10 to the receiving task amount 40 is 0.25, wherein 0.25 is the processing information.
Step S104, the at least one processor adjusts the distribution parameters of the target object according to the processing information.
For example, different configuration parameters may be configured according to different processing information, where the configuration parameters may be a delivery duration, a delivery range, and a delivery tariff, assuming that the configuration parameters are shown in table 1:
TABLE 1
Table 1 is only a simple example, and there are configuration parameters corresponding to other processing information in specific use, for example, the processing information is a numerical value such as 0.15, 0.3, 0.35, or 0.45. When the processing information is 0.1, the distribution time length is increased by 5 minutes; when the processing information is 0.2, the distribution time length is increased by 5 minutes, and the distribution range is reduced to 70% of the original distribution range; when the processing information is 0.4, the distribution duration is increased by 5 minutes, the distribution range is reduced to 70 percent of the original distribution range, and the distribution charge is increased by 3 yuan; when the processing information is 0.6, the distribution time length is increased by 5 minutes, the distribution range is reduced to 50 percent of the original distribution range, and the distribution charge is increased by 3 yuan.
In this embodiment of the present invention, step S104 specifically further includes: the at least one processor determines a processing mode corresponding to the processing information; and the at least one processor adjusts the distribution parameters of the target object according to the processing mode. The at least one processor determines a processing information range corresponding to the processing information, wherein the processing information range is preset; and the at least one processor determines a corresponding processing mode according to the processing information range.
For example, as shown in table 2, different processing information ranges correspond to different processing modes, which are specifically as follows:
TABLE 2
Specifically, in table 2, T represents the delivery duration, and when the control coefficient is greater than 0 and less than 0.15, the delivery duration is increased by 5 minutes in the processing mode; when the regulation and control coefficient is more than or equal to 0.15 and less than 0.3, the processing mode is that the distribution time length is increased by 5 minutes, and the distribution range is reduced to 70 percent of the original distribution range; when the regulation and control coefficient is more than or equal to 0.13 and less than 0.5, the processing mode is that the distribution time length is increased by 5 minutes, the distribution range is reduced to 70 percent of the original distribution range, and the distribution charge is increased by 3 yuan; when the regulation and control coefficient is more than or equal to 0.5 and less than 1, the processing mode is that the distribution time length is increased by 5 minutes, the distribution range is reduced to 50 percent of the original distribution range, and the distribution charge is increased by 3 yuan; when the regulation and control coefficient is more than 1, the processing mode is that the distribution time length is increased by 5 minutes, the distribution range is reduced to 50 percent of the original distribution range, and the distribution charge is increased by 5 yuan. The processing mode can reduce the task generated by the user.
The setting of the processing manner in the above embodiment is related to an actual use situation, in the actual use situation, the delivery duration T may be increased by 5 minutes, 10 minutes, and the like, the delivery range may be reduced to 70%, 50%, and the like of the original delivery range, the delivery tariff may be increased by 3 yuan, 5 yuan, and the like, and the coarse-grained manner is used for adjustment.
In an embodiment of the present invention, specifically, a delivery parameter of the target object is adjusted according to the processing manner and the grade of the target object, where the grade of the target object is preset, and the delivery parameter includes at least one of a delivery duration, a delivery range, and a delivery tariff, where the grade corresponding to the target is set according to an importance of the target object, and the importance corresponding to the target is related to a value of the target object, task data, card meal pressure, and a timeout rate.
In the embodiment of the present invention, target objects may also be classified, and the corresponding processing manners of target objects of different levels are different, so that delivery parameters of the target objects may be set more specifically, assuming that the target objects are classified into 3 levels, specifically, a level 1 target object, a level 2 target object, and a level 3 target object, where the higher the level is, the higher the importance of the target object is, the higher the accuracy requirement on the delivery parameters is, and the specific processing manner of target objects of different levels is as shown in table 3 below:
TABLE 3
Specifically, in table 3 above, T represents a delivery duration, and when the adjustment coefficient is greater than 0 and less than 0.15, the processing manner of the 3-level target object is that the delivery duration is increased by 5 minutes, and the processing manner of the 1-level target object and the 2-level target object is none, that is, no adjustment is performed; when the regulation and control coefficient is more than or equal to 0.15 and less than 0.3, the processing mode of the 3-level target object is that the distribution time length is increased by 5 minutes, the distribution range is reduced to 70 percent of the original distribution range, and the processing modes of the 1-level target object and the 2-level target object are not adjusted; when the regulation and control coefficient is more than or equal to 0.3 and less than 0.5, the processing mode of the 3-level target object is that the distribution time length is increased by 5 minutes, the distribution range is reduced to 70 percent of the original distribution range, the distribution charge is increased by 3 yuan, and the processing mode of the 1-level target object and the 2-level target object is not adjusted; when the regulation and control coefficient is more than or equal to 0.5 and less than 1, the processing mode of the 3-level target object is that the distribution time length is increased by 5 minutes, the distribution range is reduced to 50 percent of the original distribution range, the distribution charge is increased by 3 yuan, the processing mode of the 1-level target object and the 2-level target object is that the distribution time length is increased by 5 minutes, the distribution range is reduced to 70 percent of the original distribution range, and the distribution charge is increased by 3 yuan; when the regulation and control coefficient is larger than 1, the processing mode of the 3-level target object is that the distribution time length is increased by 5 minutes, the distribution range is reduced to 50 percent of the original distribution range, the distribution charge is increased by 5 yuan, the distribution time length is increased by 5 minutes, the distribution range is reduced to 50 percent of the original distribution range, and the distribution charge is increased by 3 yuan. By the method, the back order number of the transport capacity resource set at the target moment can be reduced, the pressure of resource distribution is reduced, and the use experience of a user is improved.
In the embodiment of the present invention, when the processing information value is low, for example, between (0, 0.15), when the configuration parameter is adjusted, only the delivery duration needs to be adjusted, the delivery duration is increased by 5 minutes, the back number quantity of the capacity resource set is reduced at the target time, and the back number capacity of the capacity resource set can process the current back number quantity; when the processing information value is between [0.15, 0.3), only adjusting the distribution time length cannot completely relieve the current pressure, because the adjustment force needs to be increased, and further, on the basis of increasing the distribution time length, the distribution range is reduced, and the number of received tasks is reduced; likewise, when the value of the physical information is between [0.3, 0.5), merely adjusting the delivery duration and reducing the delivery range does not completely alleviate the current pressure, since the delivery tariff needs to be increased, further reducing the number of tasks received.
In the embodiment of the present invention, the values of the various configuration parameters corresponding to the processing mode are determined according to the actual application conditions, which is beneficial to offline regulation, and the processing information range is determined by the values of the configuration parameters, for example, assuming that the capacity resource set receives 1000 tasks at the target time, and the predicted number of the blast orders is 150, the distribution duration is increased by 5 minutes, and the distribution range is reduced to 70% of the original distribution range, and at the target time, the number of 150 tasks can be reduced, and the processing information is 0.15; assuming that the delivery time length is increased by 5 minutes, the delivery range is narrowed to 50% of the far delivery range, and the delivery charge is increased by 3 yuan, the number of tasks can be reduced by 500 to 1000, and the processing information is between [0.5, 1), that is, the node selection of each processing information range is related to the setting of the processing mode.
Fig. 2 is an application scenario diagram of a second embodiment of the present invention, where the present invention includes a server, or referred to as a takeout platform or a takeout system, a target object terminal and a user terminal, where the target object terminal receives a task of the user terminal and then allocates the task to a capacity resource terminal, and specifically, the message interaction is forwarded by the server, or the message interaction is directly sent between two device terminals, and the message interaction is performed by receiving a message processing request from a program call interface; determining, by at least one processor, a set of capacity resources corresponding to a target object; acquiring, by the at least one processor, state data of the capacity resource set and historical task data of the capacity resource set at a historical target time; the at least one processor determines processing information of a target object at a target moment according to the state data and the historical task data, wherein the processing information corresponds to a preset processing mode; the at least one processor adjusts the delivery parameters of the target object according to the processing information; and returning the delivery parameters of the target object through the program call interface. By the method, the processing information of the target object can be predetermined, the distribution parameters of the target object are adjusted according to the processing information, the actual task quantity of the capacity resource set at the target moment is controlled by adjusting the distribution parameters, the actual task quantity of the capacity resource set at the target moment is prevented from exceeding the maximum task quantity, the distribution pressure of the capacity resource is reduced, and the use experience of a user is improved.
Fig. 3 is a schematic diagram of a data processing apparatus according to a third embodiment of the present invention. As shown in fig. 3, the apparatus of the present embodiment includes a receiving unit 31, a first determining unit 32, an acquiring unit 33, a second determining unit 34, an adjusting unit 35, and a transmitting unit 36.
The receiving unit 31 is configured to receive an information processing request from a program calling interface; a first determining unit 32, configured to determine, by at least one processor, a capacity resource set corresponding to the target object; an obtaining unit 33, configured to obtain, by the at least one processor, status data of the capacity resource set and task data of the capacity resource set at a target moment; a second determining unit 34, configured to determine, by the at least one processor, processing information of the target object at the target time according to the state data and the task data; an adjusting unit 35, configured to adjust, by the at least one processor, a delivery parameter of the target object according to the processing information; and the sending unit 36 is configured to return the delivery parameters of the target object through the procedure call interface.
Further, the second determination unit is further configured to: the at least one processor determines a processing mode corresponding to the processing information; and the at least one processor adjusts the distribution parameters of the target object according to the processing mode.
Further, the obtaining unit is specifically configured to: and acquiring the actual task quantity and the maximum task quantity of the transport capacity resource set through the at least one processor, and acquiring the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment.
Further, the obtaining unit is specifically configured to: inputting the historical receiving task quantity and the historical distributing task quantity of the transport capacity resource set at the historical target moment in a set time period into a pre-trained machine learning model; and determining the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment through the machine learning model.
Further, the second determining unit is specifically configured to: the at least one processor determines a first difference value between the sum value and the distribution task amount according to the sum value of the actual task amount and the received task amount; the at least one processor determining a second difference value between the first difference value and the maximum task amount according to the first difference value; the at least one processor determines a ratio of the second difference to the received task amount according to the second difference; the at least one processor determines the ratio as the processing information.
Further, the second determining unit is specifically configured to: the at least one processor determines a processing information range corresponding to the processing information, wherein the processing information range is preset; and the at least one processor determines a corresponding processing mode according to the processing information range.
Further, the adjusting unit is specifically configured to: and adjusting the distribution parameters of the target object according to the processing mode and the grade of the target object, wherein the grade of the target object is preset.
Further, the delivery parameters include at least one of a delivery duration, a delivery range, and a delivery tariff.
Fig. 4 is a schematic diagram of an electronic device according to a fourth embodiment of the present invention. In this embodiment, the electronic device is a server. It should be understood that other electronic devices, such as raspberry pies, are also possible. As shown in fig. 4, the electronic device: at least one processor 401; and a memory 402 communicatively coupled to the at least one processor 401; and a communication component 403 communicatively coupled to the scanning device, the communication component 403 receiving and transmitting data under control of the processor 401; wherein the memory 402 stores instructions executable by the at least one processor 401 to perform, by the at least one processor 401: receiving an information processing request from a program calling interface; determining, by at least one processor, a set of capacity resources corresponding to a target object; acquiring, by the at least one processor, state data of the capacity resource set and task data of the capacity resource set at a target moment; the at least one processor determines processing information of the target object at a target moment according to the state data and the task data; the at least one processor adjusts the delivery parameters of the target object according to the processing information; and returning the delivery parameters of the target object through the program call interface.
Further, the processor performs the steps of: the at least one processor determines a processing mode corresponding to the processing information; and the at least one processor adjusts the distribution parameters of the target object according to the processing mode.
Further, the processor specifically executes the following steps: and acquiring the actual task quantity and the maximum task quantity of the transport capacity resource set through the at least one processor, and acquiring the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment.
Further, the processor specifically executes the following steps: inputting the historical receiving task quantity and the historical distributing task quantity of the transport capacity resource set at the historical target moment in a set time period into a pre-trained machine learning model; and determining the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment through the machine learning model.
Further, the processor specifically executes the following steps: the at least one processor determines a first difference value between the sum value and the distribution task amount according to the sum value of the actual task amount and the received task amount; the at least one processor determining a second difference value between the first difference value and the maximum task amount according to the first difference value; the at least one processor determines a ratio of the second difference to the received task amount according to the second difference; the at least one processor determines the ratio as the processing information.
Further, the processor specifically executes the following steps: the at least one processor determines a processing information range corresponding to the processing information, wherein the processing information range is preset; and the at least one processor determines a corresponding processing mode according to the processing information range.
Further, the processor specifically executes the following steps: and adjusting the distribution parameters of the target object according to the processing mode and the grade of the target object, wherein the grade of the target object is preset.
Further, the delivery parameters include at least one of a delivery duration, a delivery range, and a delivery tariff.
Specifically, the electronic device includes: one or more processors 401 and a memory 402, one processor 401 being exemplified in fig. 4. The processor 401 and the memory 402 may be connected by a bus or other means, and fig. 4 illustrates the connection by a bus as an example. Memory 402, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 401 executes various functional applications of the device and data processing, i.e., implements the above-described data processing method, by executing nonvolatile software programs, instructions, and modules stored in the memory 402.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a list of options, etc. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, which may be connected to an external device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 402 and, when executed by the one or more processors 401, perform the method of data processing in any of the method embodiments described above.
The product can execute the method provided by the embodiment of the application, has corresponding functional modules and beneficial effects of the execution method, and can refer to the method provided by the embodiment of the application without detailed technical details in the embodiment.
A fifth embodiment of the invention is directed to a non-volatile 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.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific embodiments for practicing the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
Claims (14)
1. A method of data processing, the method comprising:
receiving an information processing request from a program calling interface;
determining, by at least one processor, a set of capacity resources corresponding to a target object;
acquiring, by the at least one processor, state data of the capacity resource set and task data of the capacity resource set at a target moment;
the at least one processor determines processing information of the target object at a target moment according to the state data and the task data;
the at least one processor adjusts the delivery parameters of the target object according to the processing information;
returning the distribution parameters of the target object through the program calling interface;
the obtaining, by the at least one processor, the state data of the capacity resource set and the task data of the capacity resource set at the target time specifically includes:
acquiring the actual task quantity and the maximum task quantity of the transport capacity resource set through the at least one processor, and acquiring the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment;
the at least one processor determines processing information of the target object at the target moment according to the state data and the task data, and specifically includes:
the at least one processor determines a first difference value between the sum value and the distribution task amount according to the sum value of the actual task amount and the received task amount;
the at least one processor determining a second difference value between the first difference value and the maximum task amount according to the first difference value;
the at least one processor determines a ratio of the second difference to the received task amount according to the second difference;
the at least one processor determines the ratio as the processing information.
2. The method of claim 1, wherein the at least one processor adjusting the delivery parameters of the target object based on the processing information comprises:
the at least one processor determines a processing mode corresponding to the processing information;
and the at least one processor adjusts the distribution parameters of the target object according to the processing mode.
3. The method according to claim 2, wherein the obtaining of the receiving task volume and the delivering task volume of the capacity resource set at the target time specifically includes:
inputting the historical receiving task quantity and the historical distributing task quantity of the transport capacity resource set at the historical target moment in a set time period into a pre-trained machine learning model;
and determining the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment through the machine learning model.
4. The method of claim 2, wherein the determining, by the at least one processor, the processing manner corresponding to the processing information specifically includes:
the at least one processor determines a processing information range corresponding to the processing information, wherein the processing information range is preset;
and the at least one processor determines a corresponding processing mode according to the processing information range.
5. The method of claim 4, wherein the adjusting the delivery parameters of the target object according to the processing information specifically comprises:
and adjusting the distribution parameters of the target object according to the processing mode and the grade of the target object, wherein the grade of the target object is preset.
6. The method of claim 1, wherein the delivery parameters include at least one of a delivery duration, a delivery range, and a delivery tariff.
7. An apparatus for data processing, the apparatus comprising:
the receiving unit is used for receiving an information processing request from the program calling interface;
the first determining unit is used for determining a transport capacity resource set corresponding to the target object through at least one processor;
an obtaining unit, configured to obtain, by the at least one processor, state data of the capacity resource set and task data of the capacity resource set at a target time;
the second determining unit is used for determining the processing information of the target object at the target moment by the at least one processor according to the state data and the task data;
an adjusting unit, configured to adjust, by the at least one processor, delivery parameters of the target object according to the processing information;
the sending unit is used for returning the distribution parameters of the target object through the program calling interface;
the acquiring unit is specifically configured to acquire, by the at least one processor, an actual task amount and a maximum task amount of the capacity resource set, and acquire a received task amount and a distributed task amount of the capacity resource set at the target time;
the second determining unit is specifically configured to determine, by the at least one processor, a first difference between the sum and the distribution task amount according to the sum of the actual task amount and the received task amount; the at least one processor determining a second difference value between the first difference value and the maximum task amount according to the first difference value; the at least one processor determines a ratio of the second difference to the received task amount according to the second difference; the at least one processor determines the ratio as the processing information.
8. A computer-readable storage medium on which computer program instructions are stored, which, when executed by a processor, implement the method of any one of claims 1-6.
9. 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 perform the steps of:
receiving an information processing request from a program calling interface;
determining, by at least one processor, a set of capacity resources corresponding to a target object;
acquiring, by the at least one processor, state data of the capacity resource set and task data of the capacity resource set at a target moment;
the at least one processor determines processing information of the target object at a target moment according to the state data and the task data;
the at least one processor adjusts the delivery parameters of the target object according to the processing information;
returning the distribution parameters of the target object through the program calling interface;
wherein the processor specifically executes the following steps:
acquiring the actual task quantity and the maximum task quantity of the transport capacity resource set through the at least one processor, and acquiring the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment;
wherein the processor specifically executes the following steps:
the at least one processor determines a first difference value between the sum value and the distribution task amount according to the sum value of the actual task amount and the received task amount;
the at least one processor determining a second difference value between the first difference value and the maximum task amount according to the first difference value;
the at least one processor determines a ratio of the second difference to the received task amount according to the second difference;
the at least one processor determines the ratio as the processing information.
10. The electronic device of claim 9, wherein the processor further performs the steps of:
the at least one processor determines a processing mode corresponding to the processing information;
and the at least one processor adjusts the distribution parameters of the target object according to the processing mode.
11. The electronic device of claim 9, wherein the processor is further configured to perform the steps of:
inputting the historical receiving task quantity and the historical distributing task quantity of the transport capacity resource set at the historical target moment in a set time period into a pre-trained machine learning model;
and determining the receiving task quantity and the distributing task quantity of the transport capacity resource set at the target moment through the machine learning model.
12. The electronic device of claim 10, wherein the processor is further configured to perform the steps of:
the at least one processor determines a processing information range corresponding to the processing information, wherein the processing information range is preset;
and the at least one processor determines a corresponding processing mode according to the processing information range.
13. The electronic device of claim 12, wherein the processor is further configured to perform the steps of:
and adjusting the distribution parameters of the target object according to the processing mode and the grade of the target object, wherein the grade of the target object is preset.
14. The electronic device of claim 9, wherein the delivery parameters include at least one of a delivery duration, a delivery range, and a delivery tariff.
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