CN117455192A - Department store matching method, device, equipment and storage medium - Google Patents
Department store matching method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for matching department stores, belonging to the technical field of goods transportation, wherein the method comprises the following steps: generating an initial adaptive goods source list according to a current goods source request of a target driver; determining department delivery data according to the initial fit source list and the department delivery matching prediction set; according to the historical exposure data of each adaptive cargo source in the initial adaptive cargo source list, determining the non-delivery data of the historical cargo source; and determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources. The invention reduces the ineffective exposure rate of the goods sources on the logistics platform and improves the performance efficiency of drivers.
Description
Technical Field
The present invention relates to the field of cargo transportation technologies, and in particular, to a method, an apparatus, a device, and a storage medium for matching cargo.
Background
With the development of highway transportation, unbalance of supply and demand becomes a common problem in a highway freight scene, and the market phenomenon that vehicles cannot find goods and people cannot find goods often occurs.
The existing department-cargo matching method cannot effectively utilize limited online flow resources on a logistics platform, cannot adapt to supply and demand conditions changing in real time, and easily causes a lot of ineffective exposure and resource waste, so that the performance efficiency is reduced.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for matching a commodity, which are used for uniformly utilizing resources on a logistics platform, reducing the invalid exposure rate of a commodity source on the logistics platform and improving the performance efficiency of a driver.
According to an aspect of the present invention, there is provided a shipment matching method, the method comprising:
generating an initial adaptive goods source list according to a current goods source request of a target driver;
determining department delivery data according to the initial fit source list and the department delivery matching prediction set;
according to the historical exposure data of each adaptive cargo source in the initial adaptive cargo source list, determining the non-delivery data of the historical cargo source;
and determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources.
According to another aspect of the present invention, there is provided a shipment matching apparatus, comprising:
the initial goods source list determining module is used for generating an initial adapting goods source list according to the current goods source request of the target driver;
the department-delivery data determining module is used for determining department-delivery data according to the initial adaptive goods source list and the department-delivery matching prediction set;
the non-delivery data determining module is used for determining non-delivery data of the historical goods sources according to the historical exposure data of each of the matched goods sources in the initial matched goods source list;
the target goods source list determining module is used for determining a target adaptive goods source list corresponding to a target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the order matching method of any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a department store matching method of any embodiment of the present invention.
According to the technical scheme, an initial adaptive goods source list is generated according to a current goods source request of a target driver; determining department delivery data according to the initial fit source list and the department delivery matching prediction set; according to the historical exposure data of each adaptive cargo source in the initial adaptive cargo source list, determining the non-delivery data of the historical cargo source; and determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources. According to the technical scheme, the current request data is determined according to the current goods source request of the target driver; then, combining the current request data and future data (namely, department store matching prediction set) to determine department store transaction data; meanwhile, according to the historical goods source exposure data, determining historical goods source non-delivery data of the goods source in the current request data; and determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources. The method realizes global comprehensive overall planning of limited resources on the logistics platform, realizes prediction of new on-shelf sources and drivers on the logistics platform, not only can adapt to supply and demand conditions of real-time change on the logistics platform, but also can uniformly utilize the limited resources on the logistics platform, thereby reducing ineffective exposure rate of the sources on the logistics platform and improving performance efficiency of the drivers.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for matching a shipment according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a method for matching a shipment according to a second embodiment of the present invention;
fig. 3 is a schematic structural view of a cargo matching device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing the order matching method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "target," "history," and "initial," etc. in the description of the present invention and the claims and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In addition, it should be noted that, in the technical solution of the present invention, the current cargo source request of the target driver and the historical exposure data of each adapted cargo source in the initial adapted cargo source list are collected, stored, used, processed, transmitted, provided, disclosed and other processes, which all conform to the regulations of the related laws and regulations, and do not violate the public welfare.
Example 1
Fig. 1 is a flowchart of a cargo matching method provided in an embodiment of the present invention, where the method may be implemented by a cargo matching device, the device may be implemented in hardware and/or software, and the device may be configured in an electronic device, and the electronic device may be a server of a logistics platform. As shown in fig. 1, the method includes:
s101, generating an initial adaptive goods source list according to a current goods source request of a target driver.
The target driver refers to a driver who finds goods on the logistics platform. The current cargo source request refers to a cargo finding request sent by a target driver to the logistics platform. The initial fit source list refers to a list consisting of a plurality of sources on the logistics platform that satisfy the current source request.
Specifically, according to the current goods source request of the target driver, a plurality of goods sources meeting the current goods source request can be extracted from the logistics platform, and the goods sources are stored according to a certain rule, so that an initial adaptive goods source list is obtained.
For example, according to the current source request of the target driver, a plurality of sources meeting the current source request can be extracted from the logistics platform, and the sources are stored in ascending order according to source numbers, so that an initial adaptive source list is obtained.
S102, determining the department delivery data according to the initial fit source list and the department delivery matching prediction set.
The department cargo matching prediction set refers to a data set consisting of an optimal matching driver list of all new on-shelf cargo sources on the logistics platform. The new shelving source refers to a newly released source of a cargo owner on the logistics platform. It should be noted that the number of new shelving sources is greater than or equal to one. The optimal matching driver list refers to a matching driver list with optimal information on-shelf goods sources. The department-delivery data refers to a data set consisting of exposure delivery rates between each of the adaptive delivery sources and the matched drivers in the initial adaptive delivery source list; alternatively, the department store data may be presented in a matrix. The adaptive cargo source refers to a cargo source on the logistics platform, which is adaptive to a target driver. It should be noted that the initial source list includes at least one source of the adapted good.
Optionally, determining an optimal matching driver list of each matching cargo source in the initial matching cargo source list from the cargo matching prediction set to obtain cargo matching data; and determining the department delivery data according to the initial matching source list and the department delivery matching data.
The department-cargo matching data refer to data of matched drivers corresponding to the matched cargo sources; alternatively, the department store matching data may be presented in a matrix, for example, the department store matching data may be a matrix with the adapted source as a row and the driver as a column.
Specifically, for each of the source of the initial source of lading, extracting an optimally matched driver list of the source of lading from the source of lading matching prediction set; combining the optimal matching driver lists of all the matched goods sources in the initial matched goods source list to obtain the matching data of the goods; and determining the department-delivery data according to the initial matching source list and the department-delivery matching data based on the department-delivery exposure yield model. The exposure yield model of the department stores is a model for calculating the exposure yield between a driver and a goods source, can be preset according to actual business requirements, and is not particularly limited in the embodiment of the invention.
It can be appreciated that the exposure yield of the department stores is taken as an important basis for measuring the income, and has important influence on the performance of the goods sources. Thus, determining department delivery data based on the initial fit source list and the department match prediction set may provide critical data support for subsequent determination of the target fit source list.
S103, determining the non-delivery data of the historical goods sources according to the historical exposure data of each of the matched goods sources in the initial matched goods source list.
Optionally, the historical exposure data includes a set of historical source exposure drivers, a historical department store yield, and a historical department store exposure value. The historical goods source exposure driver set refers to a data set consisting of historical matched drivers of the adapted goods source. History matched drivers refer to drivers that the source of the adapted cargo has been matched. The historical department store yield refers to the exposure yield between the adapted source and its history matched driver. The historical department store exposure value is used for representing whether the adaptive goods source is exposed to a certain historical matched driver; alternatively, the historical department store exposure value may be 0 or 1. For example, if the historical cargo exposure value between the adapted cargo source a and the history matching driver 1 is 1, it indicates that the adapted cargo source a is exposed to the history matching driver 1; if the historical driver exposure value between the adapted source A and the history matching driver 1 is 0, it indicates that the adapted source A is not exposed to the history matching driver 1.
Specifically, for each adapted cargo source in the initial adapted cargo source list, determining the non-delivery probability of the adapted cargo source according to the historical cargo source exposure driver set, the historical delivery rate and the historical delivery exposure value of the adapted cargo source; and determining historical goods source non-delivery data according to the non-delivery probability of each adapted goods source in the initial adapted goods source list.
For the ith adaptive cargo source in the initial adaptive cargo source list, according to the historical cargo source exposure driver set, the historical department delivery rate and the historical department delivery exposure value of the ith adaptive cargo source, the non-delivery probability of the ith adaptive cargo source is determined according to the following formula:
wherein j is the j-th history matching driver in the history goods source exposure driver set of the i-th adaptive goods source, A (i, j) is the history goods yield rate between the i-th adaptive goods source and the j-th history matching driver, a ij Historical department exposure, s, between the ith adapted source and the jth history matched driver i The probability of non-arrival for the ith adapted source.
And then, the obtained non-delivery probability of each adapted goods source in the initial adapted goods source list is used as historical goods source non-delivery data.
And S104, determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources.
Wherein, the department's exposure rate refers to the current exposure rate between the adapted goods source and its matched driver. The number of drivers refers to the total number of matched drivers in the department store data; correspondingly, the number of the goods sources refers to the total number of the adapted goods sources in the department store data. It should be noted that, the adapted sources in the department store data and the adapted sources in the history store data refer to the same batch of adapted sources, and the number of the adapted sources is the same.
Specifically, a target adaptive goods source list corresponding to a target driver can be determined based on the goods matching optimization model according to the goods delivery data, the historical goods source non-delivery data, the goods exposure rate, the number of drivers and the number of goods sources. The department store matching optimization model can be preset according to actual business requirements, and the department store matching optimization model is not particularly limited in the embodiment of the invention.
According to the technical scheme, an initial adaptive goods source list is generated according to a current goods source request of a target driver; determining department delivery data according to the initial fit source list and the department delivery matching prediction set; according to the historical exposure data of each adaptive cargo source in the initial adaptive cargo source list, determining the non-delivery data of the historical cargo source; and determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources. According to the technical scheme, the current request data is determined according to the current goods source request of the target driver; then, combining the current request data and future data (namely, department store matching prediction set) to determine department store transaction data; meanwhile, according to the historical goods source exposure data, determining historical goods source non-delivery data of the goods source in the current request data; and determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources. The method realizes global comprehensive overall planning of limited resources on the logistics platform, realizes prediction of new on-shelf sources and drivers on the logistics platform, not only can adapt to supply and demand conditions of real-time change on the logistics platform, but also can uniformly utilize the limited resources on the logistics platform, thereby reducing ineffective exposure rate of the sources on the logistics platform and improving performance efficiency of the drivers.
Example two
Fig. 2 is a flowchart of a method for matching a shipment provided in the second embodiment of the present invention, and the present embodiment further optimizes a target adapted shipment list corresponding to a target driver according to the shipment data, the historical shipment non-shipment data, the shipment exposure, the number of drivers and the number of sources, based on the foregoing embodiment, so as to provide an alternative embodiment. In the embodiments of the present invention, parts not described in detail may be referred to for related expressions of other embodiments. As shown in fig. 2, the method includes:
s201, generating an initial adaptive goods source list according to a current goods source request of a target driver.
S202, determining department delivery data according to the initial fit source list and the department delivery matching prediction set.
S203, determining the non-delivery data of the historical goods sources according to the historical exposure data of each of the matched goods sources in the initial matched goods source list.
S204, determining cargo source constraint conditions and driver constraint conditions according to the number of cargo sources which can be exposed by the driver, the number of cargo source exposable drivers, the number of drivers and the number of cargo sources.
Wherein, the number of goods sources which can be exposed to the driver refers to the number of goods sources which can be exposed to the driver at most; optionally, the number of sources of the exposing goods that can be used by the driver can be preset according to the actual service requirement, and the invention is not limited in particular. The number of drivers that a cargo source can expose to refers to the number of drivers that a cargo source can be exposed to at most; optionally, the number of the goods source exposable drivers can be preset according to the actual service requirement, and the invention is not limited in particular. The number of drivers refers to the total number of matched drivers in the department store data; correspondingly, the number of the goods sources refers to the total number of the adapted goods sources in the department store data. It should be noted that, the adapted sources in the department store data and the adapted sources in the history store data refer to the same batch of adapted sources, and the number of the adapted sources is the same.
Specifically, the cargo source constraint condition can be determined according to the number of cargo sources that can be exposed by the driver, the number of drivers and the number of cargo sources, specifically by the following formula:
wherein j represents the j-th matched driver, i represents the i-th matched cargo source, m is the number of cargo sources, n is the number of drivers, m and n are both positive integers, x is the number of cargo sources which can be exposed to light, and beta ij For the current department exposure value between the ith adapted source and the jth matching driver, beta ij The value of (2) is 0 or 1.
Meanwhile, the constraint conditions of the drivers can be determined according to the number of the exposable drivers of the goods sources, the number of the drivers and the number of the goods sources by the following formula:
wherein j represents the j-th matched driver, i represents the i-th matched cargo source, m is the number of cargo sources, n is the number of drivers, m and n are both positive integers, y is the number of cargo source exposable drivers, and beta ij For the current department exposure value between the ith adapted source and the jth matching driver, beta ij The value of (2) is 0 or 1.
S205, constructing a target department-cargo matching function according to department-cargo transaction data, historical source non-transaction data, department-cargo exposure rate, driver quantity and source quantity.
Wherein, the department's exposure rate refers to the current exposure rate between the adapted goods source and its matched driver.
Specifically, according to the department delivery data, the historical department source non-delivery data, the department delivery exposure rate, the number of drivers and the number of sources, the following target department delivery matching function is constructed:
wherein m is the number of goods sources, n is the number of drivers, m and n are both positive integers, j represents the j-th matched driver, i represents the i-th matched goods source, s i For the probability of non-arrival of the ith adapted source,for the exposure rate of the department's goods between the ith adapted goods source and the jth matched driver, A 1 (i, j) is the current exposure yield between the ith adapted source and the jth matching driver.
S206, determining a target adaptive goods source list corresponding to the target driver according to the goods source constraint condition, the driver constraint condition and the target department-goods matching function.
Specifically, a target adaptive source list corresponding to a target driver can be determined based on the operation optimization solver according to the source constraint condition, the driver constraint condition and the target cargo matching function.
According to the technical scheme, the target matching source list corresponding to the target driver is determined by solving the maximized global optimal solution of the target cargo matching function, global comprehensive overall planning of limited resources on the logistics platform is achieved, prediction of new cargo sources and drivers on the logistics platform is achieved, supply and demand conditions changing in real time on the logistics platform can be adapted, limited resources on the logistics platform can be utilized in a balanced mode, and therefore invalid exposure rate of the cargo sources on the logistics platform is reduced, and the performance efficiency of the drivers is improved.
On the basis of the above embodiment, as an alternative manner of the embodiment of the present invention, before generating the initial adaptive source list according to the current source request of the target driver, it may further include: for each new on-shelf goods source, responding to the on-shelf message of the new on-shelf goods source, triggering a recall matching driver algorithm, and obtaining a recall driver list of the new on-shelf goods source; carrying out optimization processing on the recall driver list to obtain an optimal matched driver list of the new on-shelf goods source; and determining a department store matching prediction set according to the optimal matching driver lists of the new shelving sources.
The new shelving source refers to a source newly released by a cargo owner on the logistics platform. The recall matching driver algorithm can be preset according to actual service requirements, and the recall matching driver algorithm is not particularly limited in the embodiment of the invention. It should be noted that the recall driver list includes at least k recall drivers. Wherein k is a super parameter, k is a positive integer, and is used for representing recall level, and can be preset according to actual service requirements, and generally k is ten thousand levels. Recall drivers refer to drivers who are most likely to be in contact with the newly-shelved source.
Specifically, for each new on-shelf goods source, responding to the on-shelf message of the new on-shelf goods source, triggering a recall matching driver algorithm to obtain a recall driver list of the new on-shelf goods source; determining the matching degree of each recall driver in the recall driver list and the new stock of the newly-placed stock source; according to the matching degree of the goods and the screening condition, carrying out optimization processing on the recall driver list to obtain an optimal matched driver list of the new on-shelf goods source; more specifically, whether the matching degree of each recall driver in the recall driver list and the new on-shelf goods source meets the screening condition is sequentially detected, and recall drivers meeting the screening condition in the recall driver list are removed, so that an optimally matched driver list of the new on-shelf goods source is obtained; and further taking a data set formed by the optimally matched driver lists of a plurality of new on-shelf goods sources as a department-goods matching prediction set. It should be noted that the number of new loading sources on the logistics platform is greater than or equal to one.
The matching degree of the goods for the department refers to the matching degree between a new goods source on shelf and a recall driver of the new goods source. The screening condition may be preset according to an actual service requirement, for example, the screening condition may be that a recall driver whose matching degree of the driver is smaller than a threshold value of matching degree is removed from a list of recall drivers, which is not specifically limited by the embodiment of the present invention. The matching degree threshold value can be preset according to actual service requirements, and the matching degree threshold value is not particularly limited by the invention.
It can be understood that the prediction is carried out on drivers who possibly get into contact with the newly-loaded goods sources on the logistics platform, so that a goods-distribution matching prediction set is obtained, and the prediction on future exposure drivers of the goods sources on the logistics platform is realized.
Example III
Fig. 3 is a schematic structural diagram of a department store matching device according to a third embodiment of the present invention, where the embodiment is applicable to a situation of performing balanced allocation on limited resources on a logistics platform in a freight scenario, and the device may be implemented in a form of hardware and/or software, and may be configured in an electronic device, where the electronic device may be a server of the logistics platform. As shown in fig. 3, the apparatus includes:
an initial source list determining module 301, configured to generate an initial adapted source list according to a current source request of a target driver;
a department-delivery data determining module 302, configured to determine department-delivery data according to the initial fit source list and the department-delivery matching prediction set;
the non-delivery data determining module 303 is configured to determine non-delivery data of the historical sources according to the historical exposure data of each of the adaptive sources in the initial adaptive source list;
the target goods source list determining module 304 is configured to determine a target adapted goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources.
According to the technical scheme, an initial adaptive goods source list is generated according to a current goods source request of a target driver; determining department delivery data according to the initial fit source list and the department delivery matching prediction set; according to the historical exposure data of each adaptive cargo source in the initial adaptive cargo source list, determining the non-delivery data of the historical cargo source; and determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources. According to the technical scheme, the current request data is determined according to the current goods source request of the target driver; then, combining the current request data and future data (namely, department store matching prediction set) to determine department store transaction data; meanwhile, according to the historical goods source exposure data, determining historical goods source non-delivery data of the goods source in the current request data; and determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources. The method realizes global comprehensive overall planning of limited resources on the logistics platform, realizes prediction of new on-shelf sources and drivers on the logistics platform, not only can adapt to supply and demand conditions of real-time change on the logistics platform, but also can uniformly utilize the limited resources on the logistics platform, thereby reducing ineffective exposure rate of the sources on the logistics platform and improving performance efficiency of the drivers.
Optionally, the department store transaction data determining module 302 is specifically configured to:
determining an optimal matching driver list of each matching cargo source in the initial matching cargo source list from the cargo matching prediction set to obtain cargo matching data;
and determining the department delivery data according to the initial matching source list and the department delivery matching data.
Optionally, the apparatus further comprises:
the recall driver list determining module is used for triggering a recall matching driver algorithm for each new on-shelf goods source in response to the on-shelf message of the new on-shelf goods source before generating an initial adaptive goods source list according to the current goods source request of the target driver to obtain a recall driver list of the new on-shelf goods source;
the optimal matching driver list determining module is used for carrying out optimization processing on the recall driver list to obtain an optimal matching driver list of the new on-shelf goods source;
and the department-cargo matching prediction set determining module is used for determining the department-cargo matching prediction set according to the optimal matching driver lists of the plurality of new shelving sources.
Optionally, the optimal matching driver list determining module is specifically configured to:
determining the matching degree of each recall driver in the recall driver list and the new stock of the newly-placed stock source;
and carrying out optimization processing on the recall driver list according to the matching degree of the goods and the screening condition to obtain an optimal matching driver list of the new on-shelf goods source.
Optionally, the historical exposure data includes a set of historical source exposure drivers, a historical department store yield, and a historical department store exposure value.
Optionally, the non-delivery data determining module 303 is specifically configured to:
for each adaptive cargo source in the initial adaptive cargo source list, determining the non-delivery probability of the adaptive cargo source according to the historical cargo source exposure driver set, the historical delivery rate and the historical delivery exposure value of the adaptive cargo source;
and determining historical goods source non-delivery data according to the non-delivery probability of each adapted goods source in the initial adapted goods source list.
Optionally, the target source list determining module 304 is specifically configured to:
determining cargo source constraint conditions and driver constraint conditions according to the number of cargo sources which can be exposed by a driver, the number of cargo source exposable drivers, the number of drivers and the number of cargo sources;
constructing a target department-cargo matching function according to department-cargo delivery data, historical source non-delivery data, department-cargo exposure rate, driver quantity and source quantity;
and determining a target adaptive goods source list corresponding to the target driver according to the goods source constraint condition, the driver constraint condition and the target goods-department matching function.
The department store matching device provided by the embodiment of the invention can execute the department store matching method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the department store matching methods.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the order matching method.
In some embodiments, the order matching method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM12 and/or the communication unit 19. When the computer program is loaded into RAM13 and executed by processor 11, one or more steps of the order matching method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the order matching method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of matching a shipment, comprising:
generating an initial adaptive goods source list according to a current goods source request of a target driver;
determining department store transaction data according to the initial fit goods source list and the department store matching prediction set;
according to the historical exposure data of each adaptive goods source in the initial adaptive goods source list, determining the non-delivery data of the historical goods sources;
and determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources.
2. The method of claim 1, wherein determining department store deal data based on the initial fit source list and the department store match prediction set comprises:
determining an optimal matching driver list of each matching cargo source in the initial matching cargo source list from a cargo matching prediction set to obtain cargo matching data;
and determining the department delivery data according to the initial matching source list and the department delivery matching data.
3. The method of claim 1, wherein prior to said generating an initial list of adapted sources based on a current source request of a target driver, the method further comprises:
for each new on-shelf goods source, responding to the on-shelf message of the new on-shelf goods source, triggering a recall matching driver algorithm, and obtaining a recall driver list of the new on-shelf goods source;
optimizing the recall driver list to obtain an optimal matched driver list of the new on-shelf goods source;
and determining a department store matching prediction set according to the optimal matching driver lists of the new shelving sources.
4. The method of claim 3, wherein optimizing the list of recall drivers to obtain a list of best matching drivers for the new on-shelf source comprises:
determining the matching degree of each recall driver in the recall driver list and the new stock of the newly-placed stock source;
and carrying out optimization processing on the recall driver list according to the matching degree and the screening condition of the goods, and obtaining an optimal matching driver list of the new on-shelf goods source.
5. The method of claim 1, wherein the historical exposure data includes a set of historical source exposure drivers, a historical department store yield, and a historical department store exposure value.
6. The method of claim 5, wherein determining historical source non-delivery data based on historical exposure data for each of the adapted sources in the initial adapted source list comprises:
for each adaptive cargo source in the initial adaptive cargo source list, determining the non-delivery probability of the adaptive cargo source according to the historical cargo source exposure driver set, the historical delivery rate and the historical delivery exposure value of the adaptive cargo source;
and determining historical goods source non-delivery data according to the non-delivery probability of each adapted goods source in the initial adapted goods source list.
7. The method of claim 1, wherein the determining the target source-of-adaptation list corresponding to the target driver based on the pick-up data, the historical source-of-not-pick data, the pick exposure, the number of drivers, and the number of sources comprises:
determining cargo source constraint conditions and driver constraint conditions according to the number of cargo sources which can be exposed by a driver, the number of cargo source exposable drivers, the number of drivers and the number of cargo sources;
constructing a target department-cargo matching function according to the department-cargo delivery data, the historical source non-delivery data, the department-cargo exposure rate, the number of drivers and the number of sources;
and determining a target adaptive goods source list corresponding to the target driver according to the goods source constraint condition, the driver constraint condition and the target department-cargo matching function.
8. A department store matching device, comprising:
the initial goods source list determining module is used for generating an initial adapting goods source list according to the current goods source request of the target driver;
the department-delivery data determining module is used for determining department-delivery data according to the initial adaptive goods source list and the department-delivery matching prediction set;
the non-delivery data determining module is used for determining non-delivery data of the historical goods sources according to the historical exposure data of each of the matched goods sources in the initial matched goods source list;
and the target goods source list determining module is used for determining a target adaptive goods source list corresponding to the target driver according to the goods delivery data, the historical goods source non-delivery data, the goods delivery exposure rate, the number of drivers and the number of goods sources.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the department store matching method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the department store matching method of any one of claims 1-7.
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| CN202311543585.7A CN117455192A (en) | 2023-11-20 | 2023-11-20 | Department store matching method, device, equipment and storage medium |
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| CN202311543585.7A CN117455192A (en) | 2023-11-20 | 2023-11-20 | Department store matching method, device, equipment and storage medium |
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