CN113139803A - Order information generation method and device, electronic equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses an order information generation method, an order information generation device, electronic equipment and a computer readable medium. One embodiment of the method comprises: in response to receiving order information submitted by a user, detecting the order type of the order information; selecting at least one value factor extraction model matched with the order type from a preset value factor extraction model group according to the detected order type; extracting value factor information from the order information through at least one value factor extraction model; and generating order value information corresponding to the order information based on the value factor information. The implementation mode ensures the consistency of the charging items of the charging formula, solves the problem that the charging items need to be configured repeatedly, and reduces the configuration workload of service personnel; meanwhile, the probability of configuration errors of service personnel is reduced, and the charging cost is reduced.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to an order information generation method, an order information generation device, electronic equipment and a computer readable medium.
Background
The charging system is an internal office platform constructed based on network and OA software, and is widely applied to the business field and the charging field. Currently, the common charging method of the charging system is as follows: and directly accessing the service event information of the service system, charging by combining the service event information with the configured charging formula and quotation of the corresponding service, and generating a charging certificate.
However, the following technical problems generally exist in the above manner: because the charging items of the charging formula are inconsistent, the charging items need to be configured repeatedly, the configuration workload of service personnel is increased, the probability of configuration errors of the service personnel is increased, and the charging cost is higher.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose order information generation methods, apparatuses, electronic devices, and computer readable media to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide an order information generating method, including: in response to receiving order information submitted by a user, detecting the order type of the order information; selecting at least one value factor extraction model matched with the order type from a preset value factor extraction model group according to the detected order type; extracting value factor information from the order information through the at least one value factor extraction model; and generating order value information corresponding to the order information based on the value factor information.
Optionally, the selecting, according to the detected order type, at least one value factor extraction model matched with the order type from a preset value factor extraction model group includes: and in response to the fact that the order type of the order information is detected to be a first type, selecting an order value factor extraction model and an event value factor extraction model which are matched with the first type from a preset value factor extraction model group.
Optionally, the value factor information includes: an order value factor and an event value factor.
Optionally, the extracting, by the at least one value factor extraction model, value factor information from the order information includes: extracting order value factors from the order information through the order value factor extraction model; and extracting the event value factor from the order information through the event value factor extraction model.
Optionally, the event value factor includes an origin, a destination, and a weight of the item, and the order value factor includes a total value of the item.
Optionally, the generating order value information corresponding to the order information based on the value factor information includes: searching starting value information and unit weight value information corresponding to the starting place and the destination from a preset value information table; generating an order value attribute value according to the weight of the article, the starting value information and the unit weight value information; searching value interval rate information matched with the total value of the article from a preset value interval rate information table to be used as target value interval rate information; and generating order value information corresponding to the first type based on the target value interval rate information and the order value attribute value.
Optionally, the method further includes: and sending the order value information corresponding to the first type to a payment device of a user corresponding to the destination.
Optionally, the selecting, according to the detected order type, at least one value factor extraction model matched with the order type from a preset value factor extraction model group includes: and in response to the fact that the order type of the order information is detected to be a second type, selecting an event value factor extraction model matched with the second type from a preset value factor extraction model group.
Optionally, the extracting, by the at least one value factor extraction model, value factor information from the order information includes: and extracting the event value factor from the order information through the event value factor extraction model.
Optionally, the generating order value information corresponding to the order information based on the value factor information includes: and generating order value information corresponding to the second type according to the event value factor.
Optionally, the method further includes: and sending the order value information corresponding to the second type to the payment equipment corresponding to the user.
In a second aspect, some embodiments of the present disclosure provide an order information generating apparatus, the apparatus including: the detection unit is configured to respond to the order information submitted by the user and detect the order type of the order information; the selecting unit is configured to select at least one value factor extraction model matched with the order type from a preset value factor extraction model group according to the detected order type; an extracting unit configured to extract value factor information from the order information through the at least one value factor extraction model; and a generating unit configured to generate order value information corresponding to the order information based on the value factor information.
Optionally, the selecting unit is further configured to: and in response to the fact that the order type of the order information is detected to be a first type, selecting an order value factor extraction model and an event value factor extraction model which are matched with the first type from a preset value factor extraction model group.
Optionally, the value factor information includes: an order value factor and an event value factor.
Optionally, the extracting unit is further configured to: extracting order value factors from the order information through the order value factor extraction model; and extracting the event value factor from the order information through the event value factor extraction model.
Optionally, the event value factor includes an origin, a destination, and a weight of the item, and the order value factor includes a total value of the item.
Optionally, the generating unit is further configured to: searching starting value information and unit weight value information corresponding to the starting place and the destination from a preset value information table; generating an order value attribute value according to the weight of the article, the starting value information and the unit weight value information; searching value interval rate information matched with the total value of the article from a preset value interval rate information table to be used as target value interval rate information; and generating order value information corresponding to the first type based on the target value interval rate information and the order value attribute value.
Optionally, the apparatus further comprises: a transmitting unit configured to transmit the order value information corresponding to the first type to a payment apparatus of a user corresponding to the destination.
Optionally, the selecting unit is further configured to: and in response to the fact that the order type of the order information is detected to be a second type, selecting an event value factor extraction model matched with the second type from a preset value factor extraction model group.
Optionally, the extracting unit is further configured to: and extracting the event value factor from the order information through the event value factor extraction model.
Optionally, the generating unit is further configured to: and generating order value information corresponding to the second type according to the event value factor.
Optionally, the apparatus further comprises: and an information sending unit configured to send the order value information corresponding to the second type to a payment device corresponding to the user.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The above embodiments of the present disclosure have the following advantages: by the order information generation method of some embodiments of the present disclosure, the consistency of the charging items of the charging formula is ensured, the problem that the charging items need to be configured repeatedly is solved, and the configuration workload of business personnel is reduced; meanwhile, the probability of configuration errors of service personnel is reduced, and the charging cost is reduced. Specifically, the reason why the charging cost is high is that: because the charging items of the charging formula are inconsistent, the charging items need to be configured repeatedly, the configuration workload of the service personnel is increased, and the probability of configuration errors of the service personnel is increased. Based on this, the order information generating method of some embodiments of the present disclosure first detects an order type of the order information in response to receiving the order information submitted by the user. Therefore, different charging processing can be carried out on the order information according to different order types. And then, selecting at least one value factor extraction model matched with the order type from a preset value factor extraction model group according to the detected order type. Therefore, an extraction model matched with the order type can be selected, and support is provided for converting the order information into a standard charging event (value factor) subsequently. In addition, the preset value factor extraction model group can cover the charging factors (value factors) related to the same charging items, so that the reusability of the charging models (value factor extraction models) can be improved to the maximum extent. And then, extracting value factor information from the order information through the at least one value factor extraction model. Therefore, the consistency of the charging items (value factor information) in the charging formula is ensured. And finally, generating order value information corresponding to the order information based on the value factor information. Therefore, the consistency of the charging items of the charging formula is ensured, the problem that the charging items need to be configured repeatedly is solved, and the configuration workload of service personnel is reduced; meanwhile, the probability of configuration errors of service personnel is reduced, and the charging cost is reduced.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of one application scenario of the order information generation method of some embodiments of the present disclosure;
FIG. 2 is a schematic diagram of a value factor extraction model of an order information generation method of some embodiments of the present disclosure;
FIG. 3 is a flow diagram of some embodiments of an order information generation method according to the present disclosure;
FIG. 4 is a flow diagram of further embodiments of an order information generation method according to the present disclosure;
FIG. 5 is a schematic diagram of an order value factor extraction model of the order information generation method of some embodiments of the present disclosure;
FIG. 6 is a flow diagram of still further embodiments of an order information generation method according to the present disclosure;
FIG. 7 is a schematic block diagram of some embodiments of an order information generation apparatus according to the present disclosure;
FIG. 8 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of an application scenario of an order information generation method according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may detect an order type 103 of the order information 102 in response to receiving the order information 102 submitted by the user. Here, the order information 102 may refer to express order information, and may include, but is not limited to, at least one of the following: a delivery address, a package weight (item weight), a sender information, a recipient information, a carrier information (express company information), a delivery time, an order total value (value attribute value of item), and the like. Here, the order type 103 may refer to a payment type of the express order, and may include, but is not limited to, at least one of: a first type, a second type, a third type, etc. Here, the first type may characterize the recipient payment. Here, the second type may be to characterize the sender payment. Here, the third type may represent that the sender settled the payment within a preset time, for example, within 30 days. Next, the computing device 101 may select, from a set of preset value factor extraction models 104, at least one value factor extraction model 105 matching the detected order type 103, according to the detected order type 103. Here, referring to fig. 2, the value factor extraction model in the value factor extraction model group 104 may refer to a mapping conversion table that converts each field in the order information 102 into value factor information. The computing device 101 may then extract value factor information 106 from the order information 102 via the at least one value factor extraction model 105. Here, value factor information 106 may characterize various billing terms, which may include, but are not limited to, at least one of: origin, destination, weight, payer, payee, etc. Finally, the computing device 101 may generate order value information 107 corresponding to the order information 102 based on the value factor information 106.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to fig. 3, a flow 300 of some embodiments of an order information generation method according to the present disclosure is shown. The order information generation method comprises the following steps:
In some embodiments, an executive (e.g., computing device 101 shown in fig. 1) of an order information generation method may detect an order type of order information submitted by a user in response to receiving the order information. Here, the order information may refer to express order information, and may include, but is not limited to, at least one of the following: a delivery address, a package weight (item weight), a sender information, a recipient information, a carrier information (express company information), a delivery time, an order total value (value attribute value of item), and the like. Here, the order type may refer to a payment type of the express order, and may include, but is not limited to, at least one of: a first type, a second type, a third type, etc. Here, the first type may characterize the recipient payment. Here, the second type may be to characterize the sender payment. Here, the third type may represent that the sender settled the payment within a preset time, for example, within 30 days.
As an example, the order information received by the execution subject may be:
{ [ mailing address: WW street number 56 of ZZ district, YY city, XX province ];
[ recipient address: NN area FF street No. 12, KK city, XY ];
[ wrapping weight: 5KG ];
[ sender information: xxx ];
[ recipient information: yyy ];
[ carrier information: some east express company ];
[ total value of order: 100 elements ] } - [ second type ].
The execution subject may detect that the order type of the order information is: [ second type ].
In some embodiments, the execution principal may select at least one value factor extraction model matching the order type from a preset value factor extraction model group. Here, the value factor extraction model in the value factor extraction model group may refer to a mapping conversion table that converts each field in the order information into the value factor information. In practice, the preset value factor extraction model set may include, but is not limited to, at least one of: an order value factor extraction model, an event value factor extraction model (see fig. 2), a periodic value factor extraction model, and the like. Here, the order value factor extraction model is applied to the first type of order information. Here, the event value factor extraction model is applicable to any type (including a first type, a second type, and a third type) of order information. Here, the periodic value factor extraction model is applied to the third type of order information.
As an example, the execution subject may select an event value factor extraction model matching the order type from a preset value factor extraction model group { order value factor extraction model, event value factor extraction model, periodic value factor extraction model }.
In some embodiments, first, the execution principal may find the same fields in the order information as the various fields included in the event value factor extraction model. Then, the field mapping which is the same as that included in the event value factor extraction model in the order information is converted into value factor information. Here, the value factor information may be that each value factor (charging parameter) for the charging formula is included. Optionally, the value factor information may include an order value factor and an event value factor. Here, the order value factor may be a billing item required to include a first type of billing formula. Here, the event value factor may be a billing item required to include various types of billing formulas.
As an example, the order information may be:
{ [ mailing address: WW street number 56 of ZZ district, YY city, XX province ];
[ recipient address: NN area FF street No. 12, KK city, XY ];
[ wrapping weight: 5KG ];
[ sender information: xxx ];
[ recipient information: yyy ];
[ carrier information: some east express company ];
[ total value of order: 100 elements ] } - [ second type ].
Converting the same field mapping in the order information as the field mapping included in the event value factor extraction model into value factor information through the event value factor extraction model (as shown in FIG. 2):
{ [ origin: WW street number 56 of ZZ district, YY city, XX province ];
[ destination: NN area FF street No. 12, KK city, XY ];
[ weight of article: 5KG ];
[ payer: xxx ];
[ payee: east express company ] }.
And 304, generating order value information corresponding to the order information based on the value factor information.
In some embodiments, first, the execution subject may look up each of the value factors included in the value factor information from a preset value information table for the unit weight value information of the corresponding value factor. In practice, the value information table may include: origin, destination, value per unit weight information. The unit weight value information may represent value information corresponding to a unit package weight. For example, the unit weight value information may be "7 yuan per 5kg express fee". The executive body may then determine an order value attribute value for the order information. And finally, combining the 'payer' and 'payee' in the value factor information with the order value attribute value to generate order value information corresponding to the order information.
As an example, the value information table may be:
| origin | Destination | Information of unit weight value |
| City of YY | KK City | The express fee of every 5KG is 7 yuan |
| City of YY | TT market | The express fee of every 5KG is 6 yuan |
The value factor information may be:
{ [ origin: WW street number 56 of ZZ district, YY city, XX province ];
[ destination: NN area FF street No. 12, KK city, XY ];
[ weight: 5KG ];
[ payer: xxx ];
[ payee: east express company ] }.
Thus, the execution agent can search the value information table for "origin" in the value information table corresponding to "WW street 56 number of ZZ district of YY city, XX province" and "FF street 12 number of NN district of KK city, XY province" in the origin included in the value factor information: the city of YY; destination: and the unit weight value information of KK city is 7 yuan per 5kg of express fee. Then, the execution agent may set the order value attribute value (express fee) of the value factor "weight 5 KG" in the value factor information to "7-yuan". Finally, the executing agent may send the value factor information "payer: xxx "and payee: some east express company "combines with the order value attribute value to generate order value information" corresponding to the order information [ payer: xxx ] - [ payee: express in the east of the country ] - [ order value attribute value: 7-membered ] ".
The above embodiments of the present disclosure have the following advantages: by the order information generation method of some embodiments of the present disclosure, the consistency of the charging items of the charging formula is ensured, the problem that the charging items need to be configured repeatedly is solved, and the configuration workload of business personnel is reduced; meanwhile, the probability of configuration errors of service personnel is reduced, and the charging cost is reduced. Specifically, the reason why the charging cost is high is that: because the charging items of the charging formula are inconsistent, the charging items need to be configured repeatedly, the configuration workload of the service personnel is increased, and the probability of configuration errors of the service personnel is increased. Based on this, the order information generating method of some embodiments of the present disclosure first detects an order type of the order information in response to receiving the order information submitted by the user. Therefore, different charging processing can be carried out on the order information according to different order types. And then, selecting at least one value factor extraction model matched with the order type from a preset value factor extraction model group according to the detected order type. Therefore, an extraction model matched with the order type can be selected, and support is provided for converting the order information into a standard charging event (value factor) subsequently. In addition, the preset value factor extraction model group can cover the charging factors (value factors) related to the same charging items, so that the reusability of the charging models (value factor extraction models) can be improved to the maximum extent. And then, extracting value factor information from the order information through the at least one value factor extraction model. Therefore, the consistency of the charging items (value factor information) in the charging formula is ensured. And finally, generating order value information corresponding to the order information based on the value factor information. Therefore, the consistency of the charging items of the charging formula is ensured, the problem that the charging items need to be configured repeatedly is solved, and the configuration workload of service personnel is reduced; meanwhile, the probability of configuration errors of service personnel is reduced, and the charging cost is reduced.
With further reference to FIG. 4, a flow diagram of further embodiments of an order information generation method according to the present disclosure is shown. The order information generation method comprises the following steps:
In some embodiments, the specific implementation of step 401 and the technical effect brought by the implementation may refer to step 301 in those embodiments corresponding to fig. 3, which are not described herein again.
In some embodiments, an executing entity (e.g., the computing device 101 shown in fig. 1) of the order information generating method may select an order value factor extraction model (e.g., fig. 5) and an event value factor extraction model (e.g., fig. 2) matching the first type from a preset value factor extraction model group in response to detecting that the order type of the order information is the first type. Here, the order value factor extraction model is applied to the first type of order information. Here, the event value factor extraction model is applicable to any type (including the second type, the first type, and the third type) of order information.
In some embodiments, first, the execution principal may find the same fields in the order information as the various fields included in the order value factor extraction model. Then, the same field mapping included in the order information and the order value factor extraction model is converted into the order value factor. Wherein the order value factor comprises an item value attribute value. Here, the order value factor may further include: payer and payee.
As an example, the order information may be:
{ [ mailing address: WW street number 56 of ZZ district, YY city, XX province ];
[ recipient address: NN area FF street No. 12, KK city, XY ];
[ wrapping weight: 5KG ];
[ sender information: xxx ];
[ recipient information: yyy ];
[ carrier information: some east express company ];
[ total value of order: 100-membered ] } - [ first type ].
The same field mapping included in the order information by the order value factor extraction model (as in fig. 5) is converted into an order value factor by the order value factor extraction model:
{ [ payer: xxx ]; [ payee: some east express company ]; [ total value of order: 100 yuan).
In some embodiments, first, the execution principal may find the same fields in the order information as the various fields included in the event value factor extraction model. Then, the field mapping which is the same as that included in the event value factor extraction model in the order information is converted into the event value factor. Wherein the event value factors include origin, destination and weight of the item. Here, the event value factor may further include, but is not limited to: payer and payee.
As an example, the order information may be:
{ [ mailing address: WW street number 56 of ZZ district, YY city, XX province ];
[ recipient address: NN area FF street No. 12, KK city, XY ];
[ wrapping weight: 5KG ];
[ sender information: xxx ];
[ recipient information: yyy ];
[ carrier information: some east express company ];
[ total value of order: 100-membered ] } - [ first type ].
Converting the same field mapping in the order information as the field mapping included in the event value factor extraction model into an event value factor through an event value factor extraction model (as shown in FIG. 2):
{ [ origin: WW street number 56 of ZZ district, YY city, XX province ];
[ destination: NN area FF street No. 12, KK city, XY ];
[ weight of article: 5KG ];
[ payer: xxx ];
[ payee: east express company ] }.
In some embodiments, the execution subject may search starting value information and unit weight value information corresponding to the starting place and the destination from a preset value information table. Here, the value information in the value information table may include: origin, destination, starting value information (starting price), and unit weight value information.
As an example, the value information table may be:
the execution subject may search the value information table for "origin" in the value information table corresponding to "WW street 56 number of ZZ district of YY city, XX province" and "FF street 12 number of NN district of KK city, XY province" in the origin included in the value factor information: the city of YY; destination: KK City "starting value information" 10 Yuan "and unit weight value information" 3 Yuan/KG ".
And 406, generating an order value attribute value according to the weight of the article, the starting value information and the unit weight value information.
In some embodiments, the executive agent may generate the order value attribute value by the following formula: Σ is starting value information (origin, destination) + (weight of article-starting weight) × unit weight value information (origin, destination). Here, Σ represents an order value attribute value. Here, the starting weight may be a starting weight that represents the starting value information.
By way of example, the above-mentioned article weight may be "5 KG". The starting value information may be "10 yuan". The initial weight may be "1 KG". The above value per unit weight information may be "3 yuan/KG". Through the above formula, an order value attribute value of "22 yuan" is generated.
In some embodiments, the execution subject may look up value section rate information including a total value of the item from a preset value section rate information table as target value section rate information. Here, the value section rate information in the value section rate information table may include an item total value (in units of yuan) and a rate.
As an example, the value section rate information table may be:
| total value of goods | Rate of charge |
| (0-100] | 0.1％ |
| (100-200] | 0.15％ |
| … | … |
Accordingly, the execution subject can search for value section rate information (0-100; 0.1% ") including the total value of the article" 100 Yuan "from a preset value section rate information table as target value section rate information.
In some embodiments, first, the execution subject may determine a product of a rate included in the target value interval rate information and a total value of the item as a rate value attribute value. Then, the execution principal may determine the sum of the rate value attribute value and the order value attribute value as order value information.
As an example, first, the product "0.1 yuan" of the above-described rate "0.1%" and the item total value "100 yuan" may be determined as the rate value attribute value. Then, the sum "22.1-tuple" of the rate value attribute value "0.1-tuple" and the order value attribute value "22-tuple" may be determined as the above-described first type of order value information.
In some embodiments, the executing entity may send the order value information corresponding to the first type to a payment device of a user corresponding to the destination.
As an example, the first type of order value information "22.1 yuan" may be transmitted to the payment device "cell phone" of the user "recipient" corresponding to the destination "NN area FF street number 12" of KK city, XY province ".
As can be seen from fig. 4, compared to the description of some embodiments corresponding to fig. 3, the flow 400 in some embodiments corresponding to fig. 4 may split the first type of order information into different billing events through different extraction models. Therefore, configuration work of service personnel can be reduced, and meanwhile, the situation that calculation is wrong due to different configurations of a plurality of same charging items of the system caused by configuration errors is also reduced. In addition, the consistency of the charging items of the charging formula is ensured, the problem that the charging items need to be configured repeatedly is solved, the probability of configuration errors of service personnel is reduced, and the charging cost is reduced.
With further reference to fig. 6, a flow diagram of still further embodiments of an order information generation method according to the present disclosure is shown. The order information generation method comprises the following steps:
In some embodiments, the specific implementation of step 601 and the technical effect brought by the implementation may refer to step 301 in those embodiments corresponding to fig. 3, which are not described herein again.
In some embodiments, an executing entity (e.g., the computing device 101 shown in fig. 1) of the order information generating method may select an event value factor extraction model (e.g., fig. 2) matching the second type from a preset value factor extraction model group in response to detecting that the order type of the order information is the second type. Here, the event value factor extraction model is applicable to any type (including the second type, the first type, and the third type) of order information.
In some embodiments, first, the execution principal may find the same fields in the order information as the various fields included in the event value factor extraction model. Then, the field mapping which is the same as that included in the event value factor extraction model in the order information is converted into the event value factor. Wherein the event value factors include origin, destination and weight of the item. Here, the event value factor may further include, but is not limited to: payer and payee.
As an example, the order information may be:
{ [ mailing address: WW street number 56 of ZZ district, YY city, XX province ];
[ recipient address: NN area FF street No. 12, KK city, XY ];
[ wrapping weight: 5KG ];
[ sender information: xxx ];
[ recipient information: yyy ];
[ carrier information: some east express company ];
[ total value of order: 100 elements ] } - [ second type ].
Converting the same field mapping in the order information as the field mapping included in the event value factor extraction model into an event value factor through an event value factor extraction model (as shown in FIG. 2):
{ [ origin: WW street number 56 of ZZ district, YY city, XX province ];
[ destination: NN area FF street No. 12, KK city, XY ];
[ weight of article: 5KG ];
[ payer: xxx ];
[ payee: east express company ] }.
In some embodiments, first, the execution principal may generate an order value attribute value by the following formula: Σ is starting value information (origin, destination) + (weight of article-starting weight) × unit weight value information (origin, destination). Here, Σ represents an order value attribute value. Here, the starting weight may be a starting weight that represents the starting value information. Then, the order value attribute value may be determined as the above-described second type of order value information.
As an example, first, the above-mentioned article weight may be "5 KG". The starting value information may be "10 yuan". The initial weight may be "1 KG". The above value per unit weight information may be "3 yuan/KG". Through the above formula, an order value attribute value of "22 yuan" is generated. Then, the order value attribute value of "22-tuple" may be determined as the above-described second type of order value information.
In some embodiments, the executing entity may send the order value information corresponding to the second type to a payment device corresponding to the user.
As an example, the above-mentioned second type of order value information "22 yuan" may be transmitted to the payment device "cell phone" with the above-mentioned user "sender".
As can be seen from fig. 6, compared to the description of some embodiments corresponding to fig. 3, the flow 600 in some embodiments corresponding to fig. 6 may convert the second type of order information into a billing event through the event value factor extraction model. Therefore, configuration work of service personnel can be reduced, and meanwhile, the situation that calculation is wrong due to different configurations of a plurality of same charging items of the system caused by configuration errors is also reduced.
With further reference to fig. 7, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an order information generating apparatus, which correspond to those shown in fig. 3, and which may be applied in various electronic devices.
As shown in fig. 7, the order information generating apparatus 700 of some embodiments includes: a detection unit 701, a selection unit 702, an extraction unit 703, and a generation unit 704. The detecting unit 701 is configured to detect an order type of order information submitted by a user in response to receiving the order information; the selecting unit 702 is configured to select at least one value factor extraction model matching the detected order type from a preset value factor extraction model group according to the detected order type; the extracting unit 703 is configured to extract value factor information from the order information through the at least one value factor extraction model; the generating unit 704 is configured to generate order value information corresponding to the order information based on the value factor information.
In some optional implementations of some embodiments, the selecting unit 702 is further configured to: and in response to the fact that the order type of the order information is detected to be a first type, selecting an order value factor extraction model and an event value factor extraction model which are matched with the first type from a preset value factor extraction model group.
Optionally, the value factor information includes: an order value factor and an event value factor.
In some optional implementations of some embodiments, the extracting unit 703 is further configured to: extracting order value factors from the order information through the order value factor extraction model; and extracting the event value factor from the order information through the event value factor extraction model.
Optionally, the event value factor includes an origin, a destination, and a weight of the item, and the order value factor includes a total value of the item.
In some optional implementations of some embodiments, the generating unit 704 is further configured to: searching starting value information and unit weight value information corresponding to the starting place and the destination from a preset value information table; generating an order value attribute value according to the weight of the article, the starting value information and the unit weight value information; searching value interval rate information matched with the total value of the article from a preset value interval rate information table to be used as target value interval rate information; and generating order value information corresponding to the first type based on the target value interval rate information and the order value attribute value.
Optionally, the apparatus 700 further comprises: a transmitting unit configured to transmit the order value information corresponding to the first type to a payment apparatus of a user corresponding to the destination.
In some optional implementations of some embodiments, the selecting unit 702 is further configured to: and in response to the fact that the order type of the order information is detected to be a second type, selecting an event value factor extraction model matched with the second type from a preset value factor extraction model group.
In some optional implementations of some embodiments, the extracting unit 703 is further configured to: and extracting the event value factor from the order information through the event value factor extraction model.
In some optional implementations of some embodiments, the generating unit 704 is further configured to: and generating order value information corresponding to the second type according to the event value factor.
Optionally, the apparatus 700 further comprises: and an information sending unit configured to send the order value information corresponding to the second type to a payment device corresponding to the user.
It will be understood that the elements described in the apparatus 700 correspond to various steps in the method described with reference to fig. 3. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 700 and the units included therein, and will not be described herein again.
Referring now to FIG. 8, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1)800 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device in some embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, an electronic device 800 may include a processing means (e.g., central processing unit, graphics processor, etc.) 801 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The processing apparatus 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage 808 including, for example, magnetic tape, hard disk, etc.; and a communication device 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 illustrates an electronic device 800 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 8 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through communications device 809, or installed from storage device 808, or installed from ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: in response to receiving order information submitted by a user, detecting the order type of the order information; selecting at least one value factor extraction model matched with the order type from a preset value factor extraction model group according to the detected order type; extracting value factor information from the order information through the at least one value factor extraction model; and generating order value information corresponding to the order information based on the value factor information.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes a detection unit, a selection unit, an extraction unit, and a generation unit. Where the names of the units do not in some cases constitute a limitation on the units themselves, for example, the selection unit may also be described as a "unit that selects at least one value factor extraction model matching the detected order type from a preset set of value factor extraction models".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (12)
1. An order information generating method includes:
in response to receiving order information submitted by a user, detecting the order type of the order information;
selecting at least one value factor extraction model matched with the order type from a preset value factor extraction model group according to the detected order type;
extracting value factor information from the order information through the at least one value factor extraction model;
and generating order value information corresponding to the order information based on the value factor information.
2. The method of claim 1, wherein selecting at least one value factor extraction model from a preset set of value factor extraction models matching the order type according to the detected order type comprises:
and in response to the fact that the order type of the order information is detected to be a first type, selecting an order value factor extraction model and an event value factor extraction model which are matched with the first type from a preset value factor extraction model group.
3. The method of claim 2, wherein the value factor information comprises: an order value factor and an event value factor; and
the extracting value factor information from the order information through the at least one value factor extracting model includes:
extracting order value factors from the order information through the order value factor extraction model;
and extracting an event value factor from the order information through the event value factor extraction model.
4. The method of claim 3, wherein the event value factors include origin, destination, and item weight, the order value factor including an item total value; and
generating order value information corresponding to the order information based on the value factor information, wherein the generating comprises:
searching starting value information and unit weight value information corresponding to the starting place and the destination from a preset value information table;
generating an order value attribute value according to the weight of the article, the starting value information and the unit weight value information;
searching value interval rate information matched with the total value of the article from a preset value interval rate information table to serve as target value interval rate information;
and generating order value information corresponding to the first type based on the target value interval rate information and the order value attribute value.
5. The method of claim 4, wherein the method further comprises:
and sending the order value information corresponding to the first type to a payment device of a user corresponding to the destination.
6. The method of claim 1, wherein selecting at least one value factor extraction model from a preset set of value factor extraction models matching the order type according to the detected order type comprises:
and in response to the fact that the order type of the order information is detected to be a second type, selecting an event value factor extraction model matched with the second type from a preset value factor extraction model group.
7. The method of claim 6, wherein said extracting value factor information from said order information via said at least one value factor extraction model comprises:
and extracting an event value factor from the order information through the event value factor extraction model.
8. The method of claim 7, wherein said generating order value information corresponding to said order information based on said value factor information comprises:
and generating order value information corresponding to the second type according to the event value factor.
9. The method of claim 8, wherein the method further comprises:
and sending the order value information corresponding to the second type to a payment device corresponding to the user.
10. An order information generating apparatus comprising:
the detection unit is configured to respond to the order information submitted by the user and detect the order type of the order information;
a selecting unit configured to select at least one value factor extraction model matching the order type from a preset value factor extraction model group according to the detected order type;
an extraction unit configured to extract value factor information from the order information through the at least one value factor extraction model;
a generating unit configured to generate order value information corresponding to the order information based on the value factor information.
11. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.
12. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-9.
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