CN111340581B - Data processing method and device, readable storage medium and electronic equipment - Google Patents
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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 method comprises the steps of determining a plurality of candidate adjusting information and distribution state information used for representing the distribution state of the target object, respectively predicting corresponding second pressure coefficients after a preset time, further determining adjusting coefficients representing the predicted state information of the target object after the candidate adjusting information is applied according to the candidate adjusting information, the distribution state information and the corresponding second pressure coefficients, and selecting the target adjusting information to adjust the attribute information of the target object according to the adjusting coefficients corresponding to the candidate adjusting information. The method can predict the service quality after the distribution pressure is regulated through different candidate regulation information, and selects the optimal candidate regulation information as the target regulation information based on the prediction result to regulate the target object, thereby improving the quality and the efficiency of regulating the distribution pressure.
Description
Technical Field
The present invention relates to the field of computer software, and in particular, to a data processing method, an apparatus, a readable storage medium, and an electronic device.
Background
Currently, in the field of take-out, a method for dividing a delivery area or a delivery team is generally selected to distribute orders. The orders on the take-away software increase or decrease dramatically at certain specific times or under certain conditions, such as weather changes, peak/low meals, or event effects, which may lead to increased or decreased orders, and thus, the distribution pressure of the distribution area or the distribution team is too high or too low, and the distribution time is increased, the service quality is decreased, or the distribution team has too few tasks. For the unstable delivery pressure, there are various means for adjusting the delivery pressure, such as adjusting the deliverable radius of all merchants in the delivery grid covered by the delivery team, adjusting the expected delivery duration, and modifying the unit price or delivery fee of customers. However, when the dispensing pressure is too high, the dispensing pressure can be adjusted by manually randomly selecting one or more adjusting means, and the effect of adjusting the dispensing pressure is difficult to predict.
Disclosure of Invention
In view of this, embodiments of the present invention provide a data processing method, an apparatus, a readable storage medium, and an electronic device, which aim to determine optimal target adjustment information by predicting effects of different candidate adjustment information after adjusting delivery pressure, so as to improve quality and efficiency of a process of adjusting delivery pressure.
In a first aspect, an embodiment of the present invention discloses a data processing method, where the method includes:
determining a plurality of candidate adjusting information, wherein the candidate adjusting information comprises an object identifier and an adjusting strategy, the object identifier is used for representing a target object as an adjusting target, and the adjusting strategy is used for adjusting the attribute information of the target object;
determining distribution state information, wherein the distribution state information is used for representing a distribution state of the target object and at least comprises a first pressure coefficient, the first pressure coefficient is used for representing distribution pressure of the target object at the current moment, and the target object is a target area or a target resource set;
for each candidate adjusting information, predicting a corresponding second pressure coefficient after a preset time according to the candidate adjusting information and the first pressure coefficient;
for each candidate adjusting information, inputting the candidate adjusting information, the distribution state information and the corresponding second pressure coefficient into an adjusting coefficient model obtained by pre-training to determine a corresponding adjusting coefficient, wherein the adjusting coefficient is used for representing the predicted state information of the target object after the candidate adjusting information is applied;
selecting target adjustment information according to the adjustment coefficients corresponding to the candidate adjustment information;
and adjusting the attribute information corresponding to the target object based on the target adjusting information.
Further, for each candidate adjustment information, predicting a second pressure coefficient after a predetermined time according to the candidate adjustment information and the first pressure coefficient respectively is specifically:
and for each candidate adjusting information, inputting the candidate adjusting information and the first pressure coefficient into a pressure coefficient model obtained by pre-training to determine a corresponding second pressure coefficient, wherein the pressure coefficient model is obtained by training according to historical adjusting information, the first pressure coefficient and the second pressure coefficient after a preset time.
Further, the adjustment strategy also comprises corresponding adjustment parameters.
Further, the delivery status information further includes at least one of: distribution team characteristic information, historical order information, pressure coefficient characteristic values, weather characteristic information and order processing information.
Further, the process of training the adjustment coefficient model includes:
acquiring a plurality of historical adjustment information, distribution state information, a second pressure coefficient and state information indexes of a plurality of historical tasks corresponding to the historical adjustment information;
determining an adjusting coefficient according to each state information index;
and taking the adjusting information, the distribution state information and the second pressure coefficient as inputs, and taking the adjusting coefficient as an output to train the adjusting coefficient model.
Further, the distribution team characteristic information includes a specific distribution staff ratio, an existing single quantity in a preset period, an incoming single quantity in the preset period, and an average pressure coefficient of N preset periods, where N is a preset integer.
Further, the historical order information comprises a historical delivery duration characteristic value and a historical customer order characteristic value.
Further, the pressure coefficient characteristic value is a difference value between the first pressure coefficient and the current city pressure coefficient.
Further, the weather feature information includes weather and an entry feature value corresponding to the weather.
Further, the order processing information comprises an order entry characteristic value and a current order entry and exit speed.
Further, for each candidate adjustment information, selecting target adjustment information according to the adjustment coefficient corresponding to the candidate adjustment information includes:
acquiring at least one candidate adjusting message of which the corresponding adjusting coefficient meets a preset condition;
sequencing the acquired candidate adjusting information according to a preset priority sequence;
and determining the candidate adjusting information with the highest priority as the target adjusting information.
Further, for each candidate adjustment information, selecting target adjustment information according to the adjustment coefficient corresponding to the candidate adjustment information includes:
sorting the candidate adjusting information from large to small according to the corresponding adjusting coefficients;
and determining candidate adjusting information corresponding to the maximum adjusting coefficient as target adjusting information.
In a second aspect, an embodiment of the present invention discloses a data processing apparatus, where the apparatus includes:
the device comprises a first information determining module, a second information determining module and a third information determining module, wherein the first information determining module is used for determining a plurality of candidate adjusting information, the candidate adjusting information comprises an object identifier and an adjusting strategy, the object identifier is used for representing a target object serving as an adjusting target, and the adjusting strategy is used for adjusting attribute information of the target object;
a second information determining module, configured to determine distribution state information, where the distribution state information is used to characterize a distribution state of the target object and at least includes a first pressure coefficient, the first pressure coefficient is used to characterize a distribution pressure of the target object at a current time, and the target object is a target area or a target resource set;
the first prediction module is used for predicting a corresponding second pressure coefficient after a preset time for each candidate adjusting information according to the candidate adjusting information and the first pressure coefficient;
the second prediction module is used for inputting the candidate adjusting information, the distribution state information and the corresponding second pressure coefficient into a pre-trained adjusting coefficient model respectively for each candidate adjusting information to determine the corresponding adjusting coefficient, wherein the adjusting coefficient is used for representing the predicted state information of the target object to which the candidate adjusting information is applied;
the information screening module is used for selecting target adjusting information according to the adjusting coefficient corresponding to each candidate adjusting information;
and the attribute adjusting module is used for adjusting the attribute information corresponding to the target object based on the target adjusting information.
Further, the first prediction module is specifically:
and the first prediction unit is used for inputting each piece of regulation information and the first pressure coefficient into a pressure coefficient model obtained by pre-training so as to determine a corresponding second pressure coefficient, and the pressure coefficient model is obtained by training according to historical regulation information, the first pressure coefficient and the second pressure coefficient after a preset time.
Further, the adjustment strategy also comprises corresponding adjustment parameters.
Further, the delivery status information further includes at least one of: distribution team characteristic information, historical order information, pressure coefficient characteristic values, weather characteristic information and order processing information.
Further, the process of training the adjustment coefficient model includes:
acquiring a plurality of historical adjustment information, distribution state information, a second pressure coefficient and state information indexes of a plurality of historical tasks corresponding to the historical adjustment information;
determining an adjusting coefficient according to each state information index;
and taking the adjusting information, the distribution state information and the second pressure coefficient as inputs, and taking the adjusting coefficient as an output to train the adjusting coefficient model.
Further, the distribution team characteristic information includes a specific distribution staff ratio, an existing single quantity in a preset period, an incoming single quantity in the preset period, and an average pressure coefficient of N preset periods, where N is a preset integer.
Further, the historical order information comprises a historical delivery duration characteristic value and a historical customer order characteristic value.
Further, the pressure coefficient characteristic value is a difference value between the first pressure coefficient and the current city pressure coefficient.
Further, the weather feature information includes weather and an entry feature value corresponding to the weather.
Further, the order processing information comprises an order entry characteristic value and a current order entry and exit speed.
Further, the information screening module comprises:
the information acquisition unit is used for acquiring at least one candidate adjusting information of which the corresponding adjusting coefficient meets a preset condition;
the first sequencing unit is used for sequencing the acquired candidate adjusting information according to a preset priority order;
and the first information determining unit is used for determining the candidate adjusting information with the highest priority as the target adjusting information.
Further, the information screening module comprises:
the second sorting unit is used for sorting the candidate adjusting information from large to small according to the corresponding adjusting coefficient;
and the second information determining unit is used for determining the candidate adjusting information corresponding to the maximum adjusting coefficient as the target adjusting information.
In a third aspect, an embodiment of the present invention discloses a computer-readable storage medium for storing computer program instructions, which when executed by a processor implement the method according to any one of the first aspect.
In a fourth aspect, an embodiment of the present invention discloses an electronic device, including a memory and a processor, the memory being configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of the first aspect.
The method comprises the steps of determining a plurality of candidate adjusting information and distribution state information used for representing the distribution state of the target object, respectively predicting corresponding second pressure coefficients after a preset time, further determining adjusting coefficients representing the predicted state information of the target object after the candidate adjusting information is applied according to the candidate adjusting information, the distribution state information and the corresponding second pressure coefficients, and selecting the target adjusting information to adjust the attribute information of the target object according to the adjusting coefficients corresponding to the candidate adjusting information. The method can predict the service quality after the distribution pressure is regulated through different candidate regulation information, and selects the optimal candidate regulation information as the target regulation information based on the prediction result to regulate the target object, thereby improving the quality and the efficiency of regulating the distribution pressure.
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 data processing method according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of predicting a second pressure coefficient according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating determining an adjustment factor according to an embodiment of the present invention;
FIG. 4 is a diagram of a system of a data processing method according to an embodiment of the present invention;
FIG. 5 is a diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The present invention will be described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details. Well-known methods, procedures, and procedures have not been described in detail so as not to obscure the present invention.
Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout this specification, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention, and as shown in fig. 1, the data processing method includes:
and step S100, determining a plurality of candidate adjusting information.
Specifically, the candidate adjustment information may be sent by the client to the server, and includes an object identifier and an adjustment policy, where the object identifier is used to characterize a target object as an adjustment target, and the adjustment policy is used to adjust attribute information of the target object. Wherein the adjustment strategy may further comprise an adjustment parameter. For example, in the internet takeout platform, the adjustment policy may be policy contents such as adjusting a delivery range, adjusting a delivery duration of a promised customer, adjusting a unit price of a customer, or adjusting a delivery fee, and the adjustment parameter corresponds to each policy content, for example, when the adjustment policy is adjusting the delivery range, the corresponding adjustment parameter is an increased or decreased delivery distance; when the adjustment strategy is to adjust the delivery time of the promised customer, the corresponding adjustment parameter is increased or decreased delivery time; when the adjustment strategy is to adjust the unit price of the passenger, the corresponding adjustment parameter is the increased or decreased commodity price; when the adjustment strategy is to adjust the delivery fee, the corresponding adjustment parameter is to increase or decrease the delivery fee price. For example, when the distribution pressure of the distribution team is too large, the adjustment strategy can be set to reduce the distribution range of the merchant, prolong the distribution time of the promised customer, increase the unit price of the goods in the platform, increase the distribution cost of the merchant and the like; when the distribution pressure of the distribution team is too small, the adjustment strategy can be set to increase the distribution range of the merchant, reduce the distribution time of the promised customer, reduce the unit price of the goods in the platform, reduce the distribution cost of the merchant and the like. The target object is an object that the candidate adjustment information is expected to be adjusted. For example, in an internet takeaway platform, the target object may be a predetermined delivery area or a predetermined set of delivery devices.
And step S200, determining distribution state information.
In particular, the amount of the solvent to be used,
the distribution state information of the target object at least includes a first pressure coefficient, the first pressure coefficient is used for representing the distribution pressure of the target object at the current moment, and the target object is a target area or a target resource set. When the target object is a target area preset by a server, the first pressure coefficient is used for representing the distribution pressure of the target area at the current moment, and can be determined by the number of tasks in the target area at the moment and the number of task processing devices in the target area at the moment; when the target object is a preset target resource set, the first pressure coefficient is used for representing the distribution pressure of the target resource set at the current moment, and the distribution pressure can be determined by the number of tasks to be processed of the target resource set at the current moment and the number of currently available task processing devices in the target resource set. The determination means may be, for example, a quotient of the number of tasks and the number of available task processing devices.
Further, the delivery status information further includes at least one of: distribution team characteristic information, historical order information, pressure coefficient characteristic values, weather characteristic information and order processing information. The distribution team characteristic information comprises a specific distribution personnel proportion, an existing single quantity in a preset period, an incoming single quantity in the preset period and an average pressure coefficient of N preset periods, wherein N is a preset integer. The historical order information is used for characterizing the historical orders, and comprises a historical delivery time characteristic value and a historical customer order characteristic value, wherein the characteristic value can be an average value, a median value and the like used for representing the delivery time or the price characteristic of the historical orders. The pressure coefficient characteristic value is used for representing the current distribution pressure characteristic of the distribution team and is the difference value between the first pressure coefficient and the current city pressure coefficient. The weather characteristic information is used for representing the influence of weather on distribution, and comprises weather and entry characteristic values corresponding to the weather, wherein the characteristic values are the ratio of entry quantities of the weather in a preset period to historical entry quantity average values of the preset period. The order processing information is used for representing the efficiency of the distribution team for processing orders and comprises an order entering characteristic value and a current order entering and exiting speed, wherein the order entering characteristic value is the ratio of the order entering amount of the current time period in a preset period to the historical order entering amount average value of the preset period.
And step S300, for each candidate adjusting information, predicting a corresponding second pressure coefficient after a preset time according to the candidate adjusting information and the first pressure coefficient.
Specifically, the second pressure coefficient is a pressure coefficient of the target object after a predetermined time elapses from the time when the candidate adjustment information is applied, and is used to characterize a distribution pressure adjustment condition of the candidate adjustment information on the target object. In this embodiment of the present invention, the second pressure coefficient corresponding to each candidate adjustment information may be determined by inputting each candidate adjustment information and the first pressure coefficient into a pressure coefficient model obtained through pre-training, respectively. The pressure coefficient model is obtained by training according to historical adjustment information, a first pressure coefficient and a second pressure coefficient after a preset time, namely, the adjustment information and the first pressure coefficient which are obtained historically are used as the input of the pressure coefficient model, and the second pressure coefficient after the preset time is used as the output to train the pressure coefficient model. The pressure coefficient model may be, for example, a neural network model such as a recurrent neural network, a convolutional neural network, or the like.
Fig. 2 is a schematic diagram of predicting the second pressure coefficient according to an embodiment of the present invention, and as shown in fig. 2, when the second pressure coefficient is predicted, the adjustment information and the first pressure coefficient are input into the pressure coefficient model 20, and the second pressure coefficient is output.
And step S400, inputting the candidate adjusting information, the distribution state information and the corresponding second pressure coefficient into an adjusting coefficient model obtained by pre-training respectively for each candidate adjusting information to determine the corresponding adjusting coefficient.
Specifically, the server determines corresponding adjustment coefficients by inputting the candidate adjustment information, the delivery status information and the second pressure coefficient into an adjustment coefficient model, wherein the adjustment coefficients are used for representing the predicted status information of the target object after the delivery team applies the adjustment information. The adjustment coefficient model may be, for example, a neural network model such as a cyclic neural network or a convolutional neural network, and is determined by historical adjustment information, delivery status information, a second pressure coefficient, and status information indexes of a plurality of historical tasks corresponding to the historical adjustment information, where the status information indexes may be determined according to evaluation and scoring of each task by a user, and used to represent service quality of the corresponding task.
When the adjustment coefficient model is trained, historical adjustment information, distribution state information, a second pressure coefficient and state information indexes of a plurality of historical tasks corresponding to the historical adjustment information are determined, and then an adjustment coefficient is determined according to the state information indexes, for example, the adjustment coefficient can be determined by calculating an average value of all the state information indexes. And finally, taking the historical adjustment information, the distribution state information and the second pressure coefficient as input, and taking the corresponding adjustment coefficient as output to train the adjustment coefficient model. Wherein the delivery status information includes at least one parameter of a first pressure coefficient and team characteristic information, historical order information, a pressure coefficient characteristic value, weather characteristic information, and order processing information.
Fig. 3 is a schematic diagram illustrating determining an adjustment coefficient according to an embodiment of the present invention, and as shown in fig. 3, when an adjustment coefficient is predicted, the candidate adjustment information, the delivery status information, and the second pressure coefficient are input into the adjustment coefficient model 30, and a corresponding adjustment coefficient is output.
And S500, selecting target adjustment information according to the adjustment coefficients corresponding to the candidate adjustment information.
Specifically, after the adjustment coefficients corresponding to the candidate adjustment information are determined, the optimal candidate adjustment information is selected as the target adjustment information according to the adjustment coefficients.
In an optional implementation manner of the embodiment of the present invention, the determining the target adjustment information includes:
step S510, obtaining at least one candidate adjustment information whose corresponding adjustment coefficient satisfies a preset condition.
Specifically, the server determines an adjustment coefficient satisfying a preset condition, and acquires at least one candidate adjustment information corresponding thereto. For example, when the output of the adjustment coefficient model is an adjustment coefficient 1 for representing that the predicted state information is qualified, or an adjustment coefficient 0 for representing that the predicted state information is unqualified, the adjustment coefficient with a set value of 1 may be the adjustment coefficient meeting the preset condition. When the output of the adjustment coefficient model is a probability value for representing the qualification of the predicted state information, an adjustment coefficient larger than a threshold value may be set as an adjustment coefficient that satisfies a predetermined condition. And after the adjustment coefficient is determined, acquiring candidate adjustment information corresponding to the adjustment coefficient.
And step S520, sequencing the acquired candidate adjusting information according to a preset priority sequence.
Specifically, the server sets the priority of each type of candidate adjustment information in advance, and in this embodiment, the priority order of the candidate adjustment information may be set based on the adjustment policy included in each candidate adjustment information, and then the priority order of each candidate adjustment information including the same adjustment policy may be further set based on the adjustment parameter included in each adjustment policy. And the server sorts the obtained candidate adjusting information according to a preset priority strategy. For example, the priority order may be set to include candidate adjustment information in which the adjustment policy is to adjust the delivery range, to adjust the delivery fee, and to adjust the delivery duration in turn. The priority order of the candidate adjusting information of the included adjusting strategy for adjusting the distribution range can be arranged from small to large according to the adjusting coefficient, the priority order of the candidate adjusting information of the included adjusting strategy for adjusting the distribution fee can be arranged from small to large according to the adjusting coefficient, and the priority order of the candidate adjusting information of the included adjusting strategy for adjusting the distribution duration can be arranged from small to large according to the adjusting coefficient.
Step S530, determining the candidate adjustment information with the highest priority as the target adjustment information.
Specifically, the candidate adjustment information with the highest priority is determined as the target adjustment information among the candidate adjustment information sorted according to the priority.
In another optional implementation manner of the embodiment of the present invention, the determining the target adjustment information includes:
step S510', rank the candidate adjustment information according to the corresponding adjustment coefficients from large to small.
Specifically, when the adjustment coefficient is a numerical value and the larger the adjustment coefficient is, the better the corresponding adjustment effect is represented, the candidate adjustment information is sorted from large to small according to the corresponding adjustment coefficient.
Step S520', the candidate adjustment information corresponding to the maximum adjustment coefficient is determined as the target adjustment information.
And in specific time, determining the target adjustment information with the maximum corresponding adjustment coefficient in the sorted candidate adjustment information. Optionally, when the number of the candidate adjustment information with the largest adjustment coefficient is greater than 1, the candidate adjustment information with the highest priority may be determined as the target adjustment information from the plurality of candidate adjustment information with the largest adjustment coefficient according to a preset priority order.
And S600, adjusting the attribute information corresponding to the target object based on the target adjusting information.
Specifically, after target adjustment information is determined, the target object is adjusted based on an adjustment policy included in the target adjustment information and an adjustment parameter included in the adjustment policy. For example, when the target object is a preset target area, the delivery range of each shop within the target object may be reduced by 1km or the delivery cost of each shop within the target object may be increased by 2 yuan based on the target adjustment information. The server may process the task corresponding to the target object based on the adjusted attribute information after adjusting the attribute information corresponding to the target object based on the target adjustment information.
The method of the embodiment of the invention respectively predicts the corresponding second pressure coefficients after the preset time passes by determining a plurality of candidate adjusting information and the distribution state information used for representing the distribution state of the target object, further determines the adjusting coefficient representing the predicted state information of the target object after the candidate adjusting information is applied according to the candidate adjusting information, the distribution state information and the corresponding second pressure coefficients, and then selects the target adjusting information to adjust the attribute information of the target object according to the adjusting coefficient corresponding to the candidate adjusting information. The method can predict the service quality after the distribution pressure is regulated through different candidate regulation information, and selects the optimal candidate regulation information as the target regulation information based on the prediction result to regulate the target object, thereby improving the quality and the efficiency of regulating the distribution pressure.
Fig. 4 is a schematic diagram of a data processing method system according to an embodiment of the present invention, and as shown in fig. 4, the system for implementing the data processing method includes a server 40, clients 41 and 42 connected via a network, where the server 40 determines an adjustment coefficient corresponding to each candidate adjustment information by using the data processing method according to the embodiment of the present invention to obtain attribute information of an adjustment target object of target adjustment information, receives to-be-processed tasks sent by the client 41 after adjustment, and distributes the to-be-processed tasks to distribution devices 43 for processing. Fig. 5 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention, and as shown in fig. 5, the apparatus includes an information determining module 50, a second information determining module 51, a first predicting module 52, a second predicting module 53, an information filtering module 54, and an attribute adjusting module 55.
Specifically, the first information determining module 50 is configured to determine a plurality of candidate adjustment information, where the candidate adjustment information includes an object identifier and an adjustment policy, where the object identifier is used to characterize a target object as an adjustment target, and the adjustment policy is used to adjust attribute information of the target object. The second information determining module 51 is configured to determine distribution status information, where the distribution status information is used to characterize a distribution status of the target object, and at least includes a first pressure coefficient, where the first pressure coefficient is used to characterize a distribution pressure of the target object at the current time, and the target object is a target area or a target resource set. The first prediction module 52 is configured to, for each candidate adjustment information, predict a corresponding second pressure coefficient after a predetermined time according to the candidate adjustment information and the first pressure coefficient. The second prediction module 53 is configured to, for each candidate adjustment information, input the candidate adjustment information, the delivery status information, and the corresponding second pressure coefficient into an adjustment coefficient model obtained through pre-training to determine a corresponding adjustment coefficient, where the adjustment coefficient is used to represent the prediction status information of the target object to which the candidate adjustment information is applied. The information filtering module 54 is configured to select target adjustment information according to the adjustment coefficients corresponding to the candidate adjustment information. The attribute adjusting module 55 is configured to adjust attribute information corresponding to the target object based on the target adjusting information.
Further, the first prediction module is specifically:
and the first prediction unit is used for inputting each piece of regulation information and the first pressure coefficient into a pressure coefficient model obtained by pre-training so as to determine a corresponding second pressure coefficient, and the pressure coefficient model is obtained by training according to historical regulation information, the first pressure coefficient and the second pressure coefficient after a preset time.
Further, the adjustment strategy also comprises corresponding adjustment parameters.
Further, the delivery status information further includes at least one of: distribution team characteristic information, historical order information, pressure coefficient characteristic values, weather characteristic information and order processing information.
Further, the process of training the adjustment coefficient model includes:
acquiring a plurality of historical adjustment information, distribution state information, a second pressure coefficient and state information indexes of a plurality of historical tasks corresponding to the historical adjustment information;
determining an adjusting coefficient according to each state information index;
and taking the adjusting information, the distribution state information and the second pressure coefficient as inputs, and taking the adjusting coefficient as an output to train the adjusting coefficient model.
Further, the distribution team characteristic information includes a specific distribution staff ratio, an existing single quantity in a preset period, an incoming single quantity in the preset period, and an average pressure coefficient of N preset periods, where N is a preset integer.
Further, the historical order information comprises a historical delivery duration characteristic value and a historical customer order characteristic value.
Further, the pressure coefficient characteristic value is a difference value between the first pressure coefficient and the current city pressure coefficient.
Further, the weather feature information includes weather and an entry feature value corresponding to the weather.
Further, the order processing information comprises an order entry characteristic value and a current order entry and exit speed.
Further, the information screening module comprises:
the information acquisition unit is used for acquiring at least one candidate adjusting information of which the corresponding adjusting coefficient meets a preset condition;
the first sequencing unit is used for sequencing the acquired candidate adjusting information according to a preset priority order;
and the first information determining unit is used for determining the candidate adjusting information with the highest priority as the target adjusting information.
Further, the information screening module comprises:
the second sorting unit is used for sorting the candidate adjusting information from large to small according to the corresponding adjusting coefficient;
and the second information determining unit is used for determining the candidate adjusting information corresponding to the maximum adjusting coefficient as the target adjusting information.
The device of the embodiment of the invention predicts the corresponding second pressure coefficients after the preset time is passed respectively by determining a plurality of candidate adjusting information and the delivery state information used for representing the delivery state of the target object, further determines the adjusting coefficient representing the predicted state information of the target object after the candidate adjusting information is applied according to the candidate adjusting information, the delivery state information and the corresponding second pressure coefficients, and selects the target adjusting information to adjust the attribute information of the target object according to the adjusting coefficient corresponding to the candidate adjusting information. The method can predict the service quality after the distribution pressure is regulated through different candidate regulation information, and selects the optimal candidate regulation information as the target regulation information based on the prediction result to regulate the target object, thereby improving the quality and the efficiency of regulating the distribution pressure.
Fig. 6 is a schematic diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 6, in this embodiment, the electronic device includes a server, a terminal, and the like. As shown, the electronic device includes: at least one processor 61; a memory 60 communicatively coupled to the at least one processor; and a communication component 62 communicatively coupled to the storage medium, the communication component 62 receiving and transmitting data under control of the processor; the memory 60 stores instructions executable by the at least one processor 61, and the instructions are executed by the at least one processor 61 to implement the data processing method according to the embodiment of the present invention.
In particular, the memory 60, as 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 61 executes various functional applications of the device and data processing by executing nonvolatile software programs, instructions, and modules stored in the memory, that is, implements the above-described data processing method.
The memory 60 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 60 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, the memory 60 optionally includes memory located remotely from the processor 61, 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 60, which when executed by the one or more processors 61, perform the data processing method 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.
The present invention also relates to a computer-readable storage medium for storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (24)
1. A method of data processing, the method comprising:
determining a plurality of candidate adjusting information, wherein the candidate adjusting information comprises an object identifier and an adjusting strategy, the object identifier is used for representing a target object as an adjusting target, and the adjusting strategy is used for adjusting the attribute information of the target object;
determining distribution state information, wherein the distribution state information is used for representing a distribution state of the target object and at least comprises a first pressure coefficient, the first pressure coefficient is used for representing distribution pressure of the target object at the current moment, and the target object is a target area or a target resource set;
for each candidate adjusting information, predicting a corresponding second pressure coefficient after a preset time according to the candidate adjusting information and the first pressure coefficient;
for each candidate adjusting information, inputting the candidate adjusting information, the distribution state information and the corresponding second pressure coefficient into an adjusting coefficient model obtained by pre-training to determine a corresponding adjusting coefficient, wherein the adjusting coefficient is used for representing the predicted state information of the target object after the candidate adjusting information is applied;
selecting target adjustment information according to the adjustment coefficients corresponding to the candidate adjustment information;
adjusting attribute information corresponding to a target object based on the target adjustment information;
wherein the selecting target adjustment information according to the adjustment coefficient corresponding to the candidate adjustment information includes:
acquiring at least one candidate adjusting message of which the corresponding adjusting coefficient meets a preset condition;
sequencing the acquired candidate adjusting information according to a preset priority sequence;
and determining the candidate adjusting information with the highest priority as the target adjusting information.
2. The method according to claim 1, wherein, for each candidate adjustment information, predicting the second pressure coefficient after a predetermined time from the candidate adjustment information and the first pressure coefficient respectively is specifically:
and for each candidate adjusting information, inputting the candidate adjusting information and the first pressure coefficient into a pressure coefficient model obtained by pre-training to determine a corresponding second pressure coefficient, wherein the pressure coefficient model is obtained by training according to historical adjusting information, the first pressure coefficient and the second pressure coefficient after a preset time.
3. The method of claim 1, wherein the adjustment strategy further comprises a corresponding adjustment parameter.
4. The method of claim 1, wherein the delivery status information further comprises at least one of: distribution team characteristic information, historical order information, pressure coefficient characteristic values, weather characteristic information and order processing information.
5. The method of claim 1, wherein the process of training the adjustment coefficient model comprises:
acquiring a plurality of historical adjustment information, distribution state information, a second pressure coefficient and state information indexes of a plurality of historical tasks corresponding to the historical adjustment information;
determining an adjusting coefficient according to each state information index;
and taking the adjusting information, the distribution state information and the second pressure coefficient as inputs, and taking the adjusting coefficient as an output to train the adjusting coefficient model.
6. The method as claimed in claim 4, wherein the characteristic information of the delivery team comprises a specific delivery personnel ratio, an existing single amount in a predetermined period, an incoming single amount in a predetermined period, and an average pressure coefficient for N predetermined periods, wherein N is a predetermined integer.
7. The method of claim 4, wherein the historical order information comprises a historical delivery duration characteristic and a historical customer order characteristic.
8. The method of claim 4, wherein the pressure coefficient characteristic is a difference between the first pressure coefficient and a current city pressure coefficient.
9. The method of claim 4, wherein the weather feature information comprises weather, and incoming feature values corresponding to the weather.
10. The method of claim 4, wherein the order processing information includes an order entry characteristic value and a current order entry and exit speed.
11. The method according to claim 1, wherein for each candidate adjustment information, selecting target adjustment information according to the adjustment coefficient corresponding to the candidate adjustment information comprises:
sorting the candidate adjusting information from large to small according to the corresponding adjusting coefficients;
and determining candidate adjusting information corresponding to the maximum adjusting coefficient as target adjusting information.
12. A data processing apparatus, characterized in that the apparatus comprises:
the device comprises a first information determining module, a second information determining module and a third information determining module, wherein the first information determining module is used for determining a plurality of candidate adjusting information, the candidate adjusting information comprises an object identifier and an adjusting strategy, the object identifier is used for representing a target object serving as an adjusting target, and the adjusting strategy is used for adjusting attribute information of the target object;
a second information determining module, configured to determine distribution state information, where the distribution state information is used to characterize a distribution state of the target object and at least includes a first pressure coefficient, the first pressure coefficient is used to characterize a distribution pressure of the target object at a current time, and the target object is a target area or a target resource set;
the first prediction module is used for predicting a corresponding second pressure coefficient after a preset time for each candidate adjusting information according to the candidate adjusting information and the first pressure coefficient;
the second prediction module is used for inputting the candidate adjusting information, the distribution state information and the corresponding second pressure coefficient into a pre-trained adjusting coefficient model respectively for each candidate adjusting information to determine the corresponding adjusting coefficient, wherein the adjusting coefficient is used for representing the predicted state information of the target object to which the candidate adjusting information is applied;
the information screening module is used for selecting target adjusting information according to the adjusting coefficient corresponding to each candidate adjusting information;
an attribute adjusting module, configured to adjust attribute information corresponding to the target object based on the target adjustment information:
wherein, the information screening module includes:
the information acquisition unit is used for acquiring at least one candidate adjusting information of which the corresponding adjusting coefficient meets a preset condition;
the first sequencing unit is used for sequencing the acquired candidate adjusting information according to a preset priority order;
and the first information determining unit is used for determining the candidate adjusting information with the highest priority as the target adjusting information.
13. The apparatus of claim 12, wherein the first prediction module is specifically:
and the first prediction unit is used for inputting each piece of regulation information and the first pressure coefficient into a pressure coefficient model obtained by pre-training so as to determine a corresponding second pressure coefficient, and the pressure coefficient model is obtained by training according to historical regulation information, the first pressure coefficient and the second pressure coefficient after a preset time.
14. The apparatus of claim 12, wherein the adjustment strategy further comprises a corresponding adjustment parameter.
15. The apparatus of claim 12, wherein the delivery status information further comprises at least one of: distribution team characteristic information, historical order information, pressure coefficient characteristic values, weather characteristic information and order processing information.
16. The apparatus of claim 12, wherein the process of training the adjustment coefficient model comprises:
acquiring a plurality of historical adjustment information, distribution state information, a second pressure coefficient and state information indexes of a plurality of historical tasks corresponding to the historical adjustment information;
determining an adjusting coefficient according to each state information index;
and taking the adjusting information, the distribution state information and the second pressure coefficient as inputs, and taking the adjusting coefficient as an output to train the adjusting coefficient model.
17. The apparatus of claim 15, wherein the characteristic information of the distribution team comprises a ratio of specific distribution personnel, an amount of the existing units in a predetermined period, an amount of the incoming units in the predetermined period, and an average pressure coefficient for N predetermined periods, wherein N is a predetermined integer.
18. The apparatus of claim 15, wherein the historical order information comprises a historical delivery duration characteristic and a historical customer order characteristic.
19. The apparatus of claim 15, wherein the pressure coefficient characteristic is a difference between the first pressure coefficient and the current city pressure coefficient.
20. The apparatus of claim 15, wherein the weather feature information comprises weather, and incoming feature values corresponding to the weather.
21. The apparatus of claim 15 wherein said order processing information includes an order entry characteristic and a current order entry and exit speed.
22. The apparatus of claim 12, wherein the information filtering module comprises:
the second sorting unit is used for sorting the candidate adjusting information from large to small according to the corresponding adjusting coefficient;
and the second information determining unit is used for determining the candidate adjusting information corresponding to the maximum adjusting coefficient as the target adjusting information.
23. A computer readable storage medium storing computer program instructions, which when executed by a processor implement the method of any one of claims 1-11.
24. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any of claims 1-11.
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