CN113743807B - Scheduling information generation method, device, electronic equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a scheduling information generation method, a scheduling information generation device, electronic equipment and a computer readable medium. One embodiment of the method comprises the following steps: acquiring information to be scheduled of a target store; generating a rule file storage path; acquiring a rule file data set of a first rule file corresponding to the information to be scheduled from a database; obtaining configurable rules in a rule file data set to obtain a configurable rule set; determining configuration data of each configurable rule in the configurable rule set to obtain a configuration data set; writing the non-configurable rules, the configurable rule sets and the configuration data sets included in the rule file data sets into a second rule file; storing the second rule file under the rule file storage path; and generating scheduling information of the target store. The implementation mode realizes that the personalized rule of the store is generated under the condition that the rule is not customized for the store, and reduces the task amount and time cost of rule development.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a method, an apparatus, an electronic device, and a computer readable medium for generating scheduling information.
Background
The automatic scheduling system can automatically generate personalized scheduling information applicable to different stores. Currently, when automatically generating scheduling information, the following methods are generally adopted: rules are customized for each store.
However, when the shift information is generated in the above manner, there are often the following technical problems:
firstly, different rules are customized according to different store needs, so that the task amount and time cost of rule development are increased;
secondly, when the rules change, manual adjustment is needed for all store rules, and the task amount and time cost of later-stage rule maintenance are increased.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure 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 scheduling 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 a scheduling information generating method, the method including: acquiring information to be scheduled of a target store; generating a rule file storage path according to the information to be scheduled; acquiring a rule file data set of a first rule file corresponding to the information to be scheduled from a database, wherein the rule file data in the rule file data set is a configurable rule or an unconfigurable rule, and the rule file data set at least comprises rule file data of the configurable rule and rule file data of the unconfigurable rule; obtaining configurable rules in the rule file data set to obtain a configurable rule set; determining configuration data of each configurable rule in the configurable rule set to obtain a configuration data set; writing the non-configurable rules included in the rule file data set, the configurable rule set and the configuration data set into a second rule file; storing the second rule file in the rule file storage path; and generating scheduling information of the target store according to the second rule file.
In a second aspect, some embodiments of the present disclosure provide a scheduling information generating apparatus, the apparatus including: a first acquisition unit configured to acquire information on a target store to be scheduled; the first generation unit is configured to generate a rule file storage path according to the to-be-scheduled information; a second obtaining unit configured to obtain, from a database, a rule file data set of a first rule file corresponding to the to-be-scheduled information, where rule file data in the rule file data set is a configurable rule or an unconfigurable rule, and at least one rule file data in the rule file data set is a configurable rule and at least one rule file data in the rule file data set is an unconfigurable rule; a third obtaining unit configured to obtain a configurable rule in the rule file data set, thereby obtaining a configurable rule set; a determining unit configured to determine configuration data of each configurable rule in the configurable rule set to obtain a configuration data set; a writing unit configured to write an unconfigurable rule included in the rule file data set, and the configurable rule set and the configuration data set, into a second rule file; a storage unit configured to store the second rule file in the rule file storage path; and a second generation unit configured to generate scheduling information of the target store according to the second rule file.
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 causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantages: according to the scheduling information generation method of some embodiments of the present disclosure, flexible rule configuration can be achieved for different stores by using the rule file template, so that the task amount and time cost of rule development are reduced. Specifically, the reason why the task amount and time cost of rule development are increased is that: different rules need to be customized for different stores. Based on this, the scheduling information generating method of some embodiments of the present disclosure first obtains the scheduling information of the target store. And then, generating a rule file storage path according to the information to be scheduled. Therefore, a unique rule file storage path can be generated according to the acquired store waiting scheduling information, and the store personalized rule file which is automatically generated can be stored. And then, acquiring a rule file data set of the first rule file corresponding to the to-be-scheduled information from a database, wherein the rule file data in the rule file data set is a configurable rule or an unconfigurable rule, and at least one rule file data which is the configurable rule and at least one rule file data which is the unconfigurable rule are included in the rule file data set. Thus, by acquiring rule data in the rule file templates in the database, support can be provided for generating store scheduling information. And then, obtaining the configurable rules in the rule file data set to obtain a configurable rule set. Thus, the obtained configurable rule set may provide support for automatically generating personalized rule files for different stores. Then, the configuration data of each configurable rule in the configurable rule set is determined, and a configuration data set is obtained. Thus, the obtained configuration data set may provide data support for generating store scheduling information. And then writing the non-configurable rules included in the rule file data set, the configurable rule set and the configuration data set into a second rule file. Thus, the automatic generation of the store personalized rule file is realized. And then, storing the second rule file in the rule file storage path. Therefore, the rule file of the store can be automatically generated aiming at different stores, flexible configuration of the rules of different stores is realized, different rules do not need to be customized for different stores, and the task amount and time cost of rule development are reduced. And finally, generating the scheduling information of the target store according to the second rule file. Thus, the store scheduling information can be generated according to the automatically generated rule file of the store, and the automatic generation of the store scheduling information can be realized. And because different rules do not need to be customized for different stores, the task amount and time cost of rule development are reduced.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a schematic illustration of one application scenario of a scheduling information generating method of some embodiments of the present disclosure;
FIG. 2 is a flow chart of some embodiments of a scheduling information generating method according to the present disclosure;
FIG. 3 is a schematic diagram of the structure of some embodiments of a scheduling information generating apparatus according to the present disclosure;
fig. 4 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 should be understood that the present 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 so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such 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 one application scenario of a scheduling information generating method of some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may acquire the to-be-scheduled information 102 of the target store. The computing device 101 may then generate a rule file storage path 103 from the to-be-scheduled information 102 described above. Then, the computing device 101 may obtain, from the database 104, a rule file data set 105 of the first rule file corresponding to the to-be-scheduled information 102, where rule file data in the rule file data set 105 is a configurable rule 1051 or an unconfigurable rule 1052, and at least one rule file data in the rule file data set 105 is a configurable rule 1051 and at least one rule file data in the rule file data set 105 is an unconfigurable rule 1052. Thereafter, the computing device 101 may obtain the configurable rules 1051 in the rule file data set 105 described above, resulting in a configurable rule set 106. Thereafter, the computing device 101 may determine configuration data for each of the configurable rules 1051 in the set of configurable rules 106 described above, resulting in a set of configuration data 107. Thereafter, the computing device 101 may write the non-configurable rules 1052 included in the rule file data set 105, as well as the configurable rule set 106 and the configuration data set 107, to the second rule file 108. Thereafter, the computing device 101 may deposit the second rule file 108 under the rule file storage path 103. Finally, the computing device 101 may generate the scheduling information 109 for the target store according to the second rule file 108.
The computing device 101 may be hardware or software. When the computing device is hardware, the computing device may be implemented as a distributed cluster formed by a plurality of 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 listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention 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 desired for an implementation.
With continued reference to fig. 2, a flow 200 of some embodiments of a scheduling information generating method according to the present disclosure is shown. The scheduling information generation method comprises the following steps:
step 201, obtaining information to be scheduled of a target store.
In some embodiments, an executing subject of the scheduling information generating method (e.g., computing device 101 shown in fig. 1) may obtain the scheduling information of the target store. The information to be scheduled may be post information of the waiting for the scheduled work. For example, the information to be scheduled may be information of a store cashier waiting for a scheduled shift.
In some optional implementations of some embodiments, the information to be scheduled may include: total identity, store identity, job identity and target algorithm identity. And the executing body can acquire the information to be shifted of the target store in response to the fact that the current system time is the same as the preset time. Wherein the total identification is used for uniquely identifying a merchant. For example, the total identification may be 0001. The store identifier is used for uniquely identifying one store. For example, the store identifier may be 0002. The job identification is used to uniquely identify one job. For example, the job identifier may be cashier identifier 0003. The target algorithm identifier is the name of the scheduling algorithm corresponding to the job identifier. For example, the target algorithm identifier may be a name cashier of a cashier shift algorithm corresponding to the cashier identifier 0003. The current system time may be a system time of the execution subject. For example, the current system time may be 10:30:10 of the thursday, and the preset time may be a preset time. For example, the predetermined time may be 17:00:00 of sunday.
And 202, generating a rule file storage path according to the information to be scheduled.
In some embodiments, the execution body may generate a rule file storage path according to the to-be-scheduled information. The rule file storage path is a path for storing the rule file.
Therefore, a unique rule file storage path can be generated according to the acquired store waiting scheduling information, and the store personalized rule file which is automatically generated can be stored.
In some optional implementations of some embodiments, the execution body may sequentially use the total identifier, the store identifier, the job identifier, and the target algorithm identifier as respective nodes of a storage path, to generate the storage path of the rule file. The execution body may sequentially use the total identifier, the store identifier, the job identifier, and the target algorithm identifier as each node of the storage path in order. For example, the rule file storage path described above may be 0001/0002/0003/cashier.
Step 203, a rule file data set of a first rule file corresponding to the information to be scheduled is obtained from the database.
In some embodiments, the executing body may acquire a rule file data set of the first rule file corresponding to the to-be-scheduled information from the database, where rule file data in the rule file data set is a configurable rule or an unconfigurable rule, and at least one rule file data in the rule file data set is a configurable rule and at least one rule file data in the rule file data set is an unconfigurable rule. The first rule file may be a rule file template in dll format stored in a database. The execution body may first query and obtain the first rule file from the database according to the corresponding relationship between the to-be-scheduled information and the rule file template stored in the database, and then obtain rule file data of the first rule file. For example, the rule file data may include (the longest continuous length of a shift is equal to a first preset threshold, the maximum number of days of work per week is equal to a second preset threshold, and the formals will take priority of scheduling). The above-mentioned "the longest continuous length of the shift is equal to the first preset threshold value" and "the maximum number of days of work per week is equal to the second preset threshold value" may be a configurable rule. The above-mentioned "formal job priority scheduling" may be an unconfigurable rule.
Thus, by acquiring rule data in the rule file templates in the database, support can be provided for generating store scheduling information.
Step 204, obtaining configurable rules in the rule file data set to obtain a configurable rule set.
In some embodiments, the executing entity may obtain the configurable rule in the rule file data set, to obtain a configurable rule set. For example, the set of configurable rules may include (the longest continuous length of a shift is equal to a first preset threshold and the maximum number of days of work per week is equal to a second preset threshold).
Thus, the obtained configurable rule set may provide support for automatically generating personalized rule files for different stores.
In some optional implementations of some embodiments, the executing body may obtain the configurable rule in the rule file data set according to the configuration code in the first rule file, to obtain a configurable rule set. Wherein the configuration code is a code for configuring the configurable rule stored in the first rule file in advance. The configuration code may be a code written using freemaker syntax. The configuration code may be used to determine whether to turn on the current configurable rule, the configurable parameters of the current configurable rule, and the constraint level of the current configurable rule. The constraint levels include hard constraints and soft constraints. The hard constraint may be a constraint that cannot be broken. For example, the hard constraint may be "do not shift leave staff". The soft constraint may be a constraint that should not be broken if not necessary. For example, the soft constraint may be "working 6 days at maximum per week".
Thus, a differentiated rule file may be generated by retrieving configurable rules in a rule file template. When the rules change, all store rules can be updated only by changing the rule file templates in the database, so that the task amount and time cost of later-stage rule maintenance are reduced.
In step 205, configuration data for each configurable rule in the set of configurable rules is determined, resulting in a set of configuration data.
In some embodiments, the executing entity may determine configuration data for each configurable rule in the set of configurable rules to obtain a set of configuration data. The configuration data may be data configured for the configurable rule. For example, the configuration data set may include { [ on, 4, soft constraint ], [ on, 6, soft constraint ] }. The above [ on, 4, soft constraint ] is data configured for a configurable rule "the longest continuous length of shift is equal to a first preset threshold" in the above configurable rule set (the longest continuous length of shift is equal to a first preset threshold, and the maximum number of days of work per week is equal to a second preset threshold). The "on" in the above [ on, 4, soft constraint ] is a state of setting the configurable rule "the longest continuous time of the shift is equal to the first preset threshold" on ". The "4" in the above [ on, 4, soft constraint ] is to set the first preset threshold value of the configurable rule "the longest continuous time of shift is equal to the first preset threshold value" to be "4" hours. The "soft constraint" in the above [ on, 4, soft constraint ] is to set the constraint level of the configurable rule "the longest continuous length of shift is equal to the third preset threshold" as "soft constraint". The above [ on, 6, soft constraint ] is data configured for the configurable rule "maximum number of days of work per week is equal to the second preset threshold" in the above configurable rule set (maximum continuous length of shift is equal to the first preset threshold, maximum number of days of work per week is equal to the second preset threshold). The "on" in the above [ on, 6, soft constraint ] is to set the configurable rule "the maximum number of days of operation per week is equal to the second preset threshold" on "state. The "6" in the above [ on, 6, soft constraint ] is that the second preset threshold value of the configurable rule "the maximum number of days of work per week is equal to the second preset threshold value" is "6" days ". The "soft constraint" in the above [ on, 6, soft constraint ] is to set the constraint level of the configurable rule "the maximum number of days of operation per week is equal to the second preset threshold" as "soft constraint".
Thus, the obtained configuration data set may provide data support for generating store scheduling information.
In some optional implementations of some embodiments, the executing body determining configuration data of each configurable rule in the configurable rule set to obtain a configuration data set may include the following steps:
and firstly, acquiring a target configuration data set of the target store from a database according to the information to be scheduled.
The execution body can query and acquire the configuration data of the target store from a database according to the information to be scheduled to obtain a target configuration data set. The target configuration data set is a data set obtained by acquiring data customized by the target store for the configurable rule set (the longest continuous time of a shift is equal to a first preset threshold value, and the maximum working days of each week is equal to a second preset threshold value). For example, the target configuration data set may include { [ on, 4], [ on, 6] }. The above [ on, 4] is the data customized by the target store for the configurable rule "the longest continuous length of shift is equal to the first preset threshold" in the configurable rule set (the longest continuous length of shift is equal to the first preset threshold, the maximum working days per week is equal to the second preset threshold). The "on" in the above [ on, 4] is a state in which the configurable rule "the longest continuous time of the shift is set equal to the first preset threshold" on ". The "4" in the above [ on, 4] is to set the first preset threshold value of the configurable rule "the longest continuous time of shift is equal to the first preset threshold value" to be "4" hours. The above [ on, 6] is the data customized by the target store for the configurable rule "the maximum number of days of work per week is equal to the second preset threshold" in the configurable rule set (the longest continuous length of a shift is equal to the first preset threshold, the maximum number of days of work per week is equal to the second preset threshold). The "on" in the above [ on, 6] is to set the configurable rule "the maximum number of days of operation per week is equal to the second preset threshold" on "state. The "6" in the above [ on, 6] is to set the second preset threshold value of the configurable rule "the maximum number of days of work per week is equal to the second preset threshold value" to "6" days.
And step two, obtaining default data of each configurable rule in the first rule file to obtain a default data set.
Wherein the default data set is a data set stored in the database in advance and defining the configurable rule set (the longest continuous time length of a shift is equal to a first preset threshold value, and the maximum working days per week is equal to a second preset threshold value). The default data set may include { [ on, 7, soft constraint ], [ on, 5, soft constraint ] }. The above [ on, 7, soft constraint ] is a data set stored in advance in the database defining a configurable rule "the longest continuous length of shift is equal to a first preset threshold" in the above configurable rule set (the longest continuous length of shift is equal to a first preset threshold, the maximum number of days of work per week is equal to a second preset threshold). The "on" in the above [ on, 7, soft constraint ] is a default state defining the configurable rule "the longest continuous length of shift is equal to the first preset threshold" is an "on" state. "7" in the above [ on, 7, soft constraint ] is a default first preset threshold defining a configurable rule "the longest continuous length of a shift is equal to the first preset threshold" is "7" hours. The "soft constraint" in the above [ on, 7, soft constraint ] is that the default constraint level defining the configurable rule "the longest continuous length of shift is equal to the first preset threshold" is "soft constraint". The above [ on, 5, soft constraint ] is a data set stored in advance in the database defining a configurable rule "maximum number of days per week is equal to a second preset threshold" among the above configurable rule sets (maximum continuous length of shift is equal to a first preset threshold, maximum number of days per week is equal to a second preset threshold). The "on" in the above [ on, 5, soft constraint ] is a default state defining the configurable rule "the maximum number of days of operation per week is equal to the second preset threshold" is an "on" state. "5" in the above [ on, 5, soft constraint ] is a default second preset threshold defining the configurable rule "maximum number of days of operation per week equals the second preset threshold" is "5" days. The "soft constraint" in the above [ on, 5, soft constraint ] is a default constraint level defining a configurable rule "maximum number of days of operation per week is equal to a second preset threshold" is "soft constraint".
Thirdly, selecting and preserving the target configuration data set and the default data set to obtain the configuration data set.
The reservation selection process is a process of selecting one of the values in the target configuration data set and the default data set for reservation for the same characteristic of each of the configurable rules in the configurable rule set. The characteristic may be a characteristic set by the configuration code. The execution body may select a value in the target configuration data set when the target configuration data set contains the value of the characteristic, and select a default value in the default data set when the target configuration data set does not contain the value of the characteristic.
For example, for the characteristic "first preset threshold" of the configurable rule "the longest continuous length of shift is equal to the first preset threshold" in the above-mentioned configurable rule set (the longest continuous length of shift is equal to the first preset threshold, the maximum number of days of work per week is equal to the second preset threshold), the value of the above-mentioned target configuration data set { [ on, 4], [ on, 6] } is "4". The value of the characteristic "first preset threshold" is 4. For the "constraint level of the current configurable rule" of the characteristic "the longest continuous length of the shift is equal to the first preset threshold value" of the configurable rule in the configurable rule set (the longest continuous length of the shift is equal to the first preset threshold value, the maximum working days per week is equal to the second preset threshold value), the corresponding value is not defined in the target configuration data set { [ on, 4], [ on, 6] }, so the "soft constraint" in the default data set { [ on, 7, soft constraint ], [ on, 5, soft constraint ] } is selected as the value of the "constraint level of the current configurable rule" of the characteristic. And so on, selecting and reserving the target configuration data set { [ on, 4], [ on, 6] } and the default data set { [ on, 7, soft constraint ], [ on, 5, soft constraint ] } to obtain { [ on, 4, soft constraint ], [ on, 6, soft constraint ] }.
Thus, by selecting and retaining the target configuration data set and the default data set, personalized data in the personalized rule file of the store can be automatically generated.
And step 206, writing the non-configurable rules, the configurable rule set and the configuration data set which are included in the rule file data set into a second rule file.
In some embodiments, the execution body may write the non-configurable rule included in the rule file data set and the configurable rule set and the configuration data set into a second rule file. Thus, the automatic generation of the store personalized rule file is realized.
In some optional implementations of some embodiments, the execution body may render the non-configurable rule included in the rule file data set, the configurable rule set, and the configuration data set with a template engine to obtain a configurable rule data set, and write the configurable rule data set into the second rule file. For example, the execution body may combine the non-configurable rule included in the rule file data set, the configurable rule set, and the configuration data set into rule data conforming to the format of the dll file syntax by using a template engine freemaker, obtain a configurable rule data set, and write the configurable rule data set into the second rule file.
Step 207, storing the second rule file under the rule file storage path.
In some embodiments, the execution body may store the second rule file in the rule file storage path.
Therefore, the rule file of the store can be automatically generated aiming at different stores, flexible configuration of the rules of different stores is realized, different rules do not need to be customized for different stores, and the task amount and time cost of rule development are reduced.
And step 208, generating scheduling information of the target store according to the second rule file.
In some embodiments, the executive may generate the scheduling information of the target store using an Optaplanner (lightweight planning scheduling engine) according to the rule file.
Thus, the store scheduling information can be generated according to the automatically generated rule file of the store, and the automatic generation of the store scheduling information can be realized.
Alternatively, the execution subject may transmit the scheduling information to a terminal device of the target store. For example, the execution subject may transmit the scheduling information to a computer terminal of the store.
The above embodiments of the present disclosure have the following advantages: according to the scheduling information generation method of some embodiments of the present disclosure, flexible rule configuration can be achieved for different stores by using the rule file template, so that the task amount and time cost of rule development are reduced. Specifically, the reason why the task amount and time cost of rule development are increased is that: different rules need to be customized for different stores. Based on this, the scheduling information generating method of some embodiments of the present disclosure first obtains the scheduling information of the target store. And then, generating a rule file storage path according to the information to be scheduled. Therefore, a unique rule file storage path can be generated according to the acquired store waiting scheduling information, and the store personalized rule file which is automatically generated can be stored. And then, acquiring a rule file data set of the first rule file corresponding to the to-be-scheduled information from a database, wherein the rule file data in the rule file data set is a configurable rule or an unconfigurable rule, and at least one rule file data which is the configurable rule and at least one rule file data which is the unconfigurable rule are included in the rule file data set. Thus, by acquiring rule data in the rule file templates in the database, support can be provided for generating store scheduling information. And then, obtaining the configurable rules in the rule file data set to obtain a configurable rule set. Thus, the obtained configurable rule set may provide support for automatically generating personalized rule files for different stores. Then, the configuration data of each configurable rule in the configurable rule set is determined, and a configuration data set is obtained. Thus, the obtained configuration data set may provide data support for generating store scheduling information. And then writing the non-configurable rules included in the rule file data set, the configurable rule set and the configuration data set into a second rule file. Thus, the automatic generation of the store personalized rule file is realized. And then, storing the second rule file in the rule file storage path. Therefore, the rule file of the store can be automatically generated aiming at different stores, flexible configuration of the rules of different stores is realized, different rules do not need to be customized for different stores, and the task amount and time cost of rule development are reduced. And finally, generating the scheduling information of the target store according to the second rule file. Thus, the store scheduling information can be generated according to the automatically generated rule file of the store, and the automatic generation of the store scheduling information can be realized. And because different rules do not need to be customized for different stores, the task amount and time cost of rule development are reduced.
With further reference to fig. 3, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of a scheduling information generating apparatus, which correspond to those method embodiments shown in fig. 2, and which are particularly applicable in various electronic devices.
As shown in fig. 3, the scheduling information generating apparatus 300 of some embodiments includes: a first acquisition unit 301, a first generation unit 302, a second acquisition unit 303, a third acquisition unit 304, a determination unit 305, a writing unit 306, a storage unit 307, and a second generation unit 308. Wherein the first acquiring unit 301 is configured to acquire information to be scheduled of a target store; the first generating unit 302 is configured to generate a rule file storage path according to the to-be-scheduled information; the second obtaining unit 303 is configured to obtain, from a database, a rule file data set of the first rule file corresponding to the to-be-scheduled information, where rule file data in the rule file data set is a configurable rule or an unconfigurable rule, and at least one rule file data in the rule file data set is a configurable rule and at least one rule file data in the rule file data set is an unconfigurable rule; the third obtaining unit 304 is configured to obtain the configurable rule in the rule file data set, so as to obtain a configurable rule set; the determining unit 305 is configured to determine configuration data of each configurable rule in the configurable rule set to obtain a configuration data set; the writing unit 306 is configured to write the non-configurable rule included in the rule file data set, the configurable rule set, and the configuration data set into a second rule file; the storing unit 307 is configured to store the second rule file under the rule file storage path; the second generating unit 308 is configured to generate the scheduling information of the target store according to the second rule file.
It will be appreciated that the elements described in the apparatus 300 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting benefits described above with respect to the method are equally applicable to the apparatus 300 and the units contained therein, and are not described in detail herein.
Referring now to FIG. 4, a schematic diagram of a configuration of an electronic device 400 (e.g., computing device 101 shown in FIG. 1) suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 4 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 4, the electronic device 400 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 401, which may perform various suitable actions and processes according to a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
In general, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, magnetic tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 shows an electronic device 400 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 4 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts 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 shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 409, or from storage 408, or from ROM 402. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 401.
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. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 present 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, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication 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 networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated 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: and acquiring the information to be scheduled of the target store. And generating a rule file storage path according to the information to be scheduled. And acquiring a rule file data set of a first rule file corresponding to the to-be-scheduled information from a database, wherein the rule file data in the rule file data set is a configurable rule or an unconfigurable rule, and at least one rule file data of the configurable rule and at least one rule file data of the unconfigurable rule are included in the rule file data set. And obtaining the configurable rules in the rule file data set to obtain a configurable rule set. And determining configuration data of each configurable rule in the configurable rule set to obtain a configuration data set. And writing the non-configurable rules included in the rule file data set, the configurable rule set and the configuration data set into a second rule file. And storing the second rule file in the rule file storage path. And generating scheduling information of the target store according to the second rule file.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts 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 means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes a first acquisition unit, a first generation unit, a second acquisition unit, a third acquisition unit, a determination unit, a writing unit, a storage unit, and a second generation unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the first acquisition unit may also be described as "a unit that acquires information on a target store to be scheduled".
The functions described above herein 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: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being 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 technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (8)
1. A scheduling information generation method comprises the following steps:
acquiring information to be scheduled of a target store;
generating a rule file storage path according to the information to be scheduled;
acquiring a rule file data set of a first rule file corresponding to the to-be-scheduled information from a database, wherein the rule file data in the rule file data set is a configurable rule or an unconfigurable rule, at least one rule file data of the configurable rule and at least one rule file data of the unconfigurable rule are included in the rule file data set, and the rule file data comprises: the longest continuous time of the shift is equal to a first preset threshold, the maximum working days per week is equal to a second preset threshold, the formal work is given priority to schedule, the longest continuous time of the shift is equal to the first preset threshold, the maximum working days per week is equal to the second preset threshold, the configurable rule is set, and the formal work is given priority to schedule is not configured rule;
obtaining configurable rules in the rule file data set to obtain a configurable rule set;
determining configuration data of each configurable rule in the configurable rule set to obtain a configuration data set;
Writing the non-configurable rules included in the rule file data set and the configurable rule set and the configuration data set into a second rule file;
storing the second rule file under the rule file storage path;
generating scheduling information of the target store according to the second rule file;
the obtaining the configurable rule in the rule file data set to obtain the configurable rule set includes:
acquiring configurable rules in the rule file data set according to configuration codes in the first rule file to obtain a configurable rule set, wherein the configuration codes are codes which are stored in the first rule file in advance and are used for configuring the configurable rules;
wherein the determining the configuration data of each configurable rule in the configurable rule set to obtain a configuration data set includes:
acquiring a target configuration data set of the target store from a database according to the information to be scheduled;
acquiring default data of each configurable rule in the first rule file to obtain a default data set;
and selecting and reserving the target configuration data set and the default data set to obtain a configuration data set, wherein the selecting and reserving process is a process of reserving one value selected by the target configuration data set and the default data set according to the same characteristic of each configurable rule in the configurable rule set, the characteristic is set by the configuration code, the value in the target configuration data set is selected when the target configuration data set contains the value of the characteristic, and the default value in the default data set is selected when the target configuration data set does not contain the value of the characteristic.
2. The method of claim 1, wherein the information to be scheduled comprises: total identification, store identification, job identification and target algorithm identification; and
the obtaining the information to be scheduled of the target store comprises the following steps:
and acquiring the information to be scheduled of the target store in response to the fact that the current system time is the same as the preset time.
3. The method of claim 1, wherein the method further comprises:
and sending the scheduling information to the terminal equipment of the target store.
4. The method of claim 2, wherein the generating a rule file storage path according to the information to be scheduled comprises:
and sequentially taking the total identifier, the store identifier, the job identifier and the target algorithm identifier as each node of a storage path to generate the storage path of the rule file.
5. The method of claim 4, wherein the writing the non-configurable rules included in the rule file data set and the configurable rule set and the configuration data set to a second rule file comprises:
and rendering the non-configurable rules, the configurable rule set and the configuration data set contained in the rule file data set by using a template engine to obtain the configurable rule data set, and writing the configurable rule data set into the second rule file.
6. A scheduling information generating apparatus comprising:
a first acquisition unit configured to acquire information on a target store to be scheduled;
the first generation unit is configured to generate a rule file storage path according to the information to be scheduled;
a second obtaining unit, configured to obtain, from a database, a rule file data set of a first rule file corresponding to the to-be-scheduled information, where rule file data in the rule file data set is a configurable rule or an unconfigurable rule, and at least one rule file data that is a configurable rule and at least one rule file data that is an unconfigurable rule are included in the rule file data set, and the rule file data includes: the longest continuous time of the shift is equal to a first preset threshold, the maximum working days per week is equal to a second preset threshold, the formal work is given priority to schedule, the longest continuous time of the shift is equal to the first preset threshold, the maximum working days per week is equal to the second preset threshold, the configurable rule is set, and the formal work is given priority to schedule is not configured rule;
the third acquisition unit is configured to acquire the configurable rules in the rule file data set to obtain a configurable rule set;
A determining unit configured to determine configuration data of each configurable rule in the configurable rule set to obtain a configuration data set;
a writing unit configured to write an unconfigurable rule included in the rule file data set and the configurable rule set and the configuration data set into a second rule file;
a storing unit configured to store the second rule file under the rule file storage path;
a second generation unit configured to generate scheduling information of the target store according to the second rule file;
the obtaining the configurable rule in the rule file data set to obtain the configurable rule set includes:
acquiring configurable rules in the rule file data set according to configuration codes in the first rule file to obtain a configurable rule set, wherein the configuration codes are codes which are stored in the first rule file in advance and are used for configuring the configurable rules;
wherein the determining the configuration data of each configurable rule in the configurable rule set to obtain a configuration data set includes:
acquiring a target configuration data set of the target store from a database according to the information to be scheduled;
Acquiring default data of each configurable rule in the first rule file to obtain a default data set;
and selecting and reserving the target configuration data set and the default data set to obtain a configuration data set, wherein the selecting and reserving process is a process of reserving one value selected by the target configuration data set and the default data set according to the same characteristic of each configurable rule in the configurable rule set, the characteristic is set by the configuration code, the value in the target configuration data set is selected when the target configuration data set contains the value of the characteristic, and the default value in the default data set is selected when the target configuration data set does not contain the value of the characteristic.
7. 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, causes the one or more processors to implement the method of any of claims 1 to 5.
8. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1 to 5.
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