CN116841709A - Wind control task grouping method and device in wind control service system and computer equipment - Google Patents
Wind control task grouping method and device in wind control service system and computer equipment Download PDFInfo
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Abstract
The invention discloses a method, a device and computer equipment for grouping wind control tasks in a wind control service system, wherein the method comprises the following steps: grouping each wind control task to be executed by using a setting algorithm, wherein the obtained grouping result meets the following conditions: the sum of the memory resources required by the wind control task included in any obtained group does not exceed the memory capacity of the wind control service system, and the number of the obtained groups is the least in all the group results which can be exhausted; performing iterative processing until a preset iteration stop condition is met by the following steps: determining a first group with the largest sum of memory resources required by the wind control task and a second group with the smallest sum of memory resources required by the wind control task from the current groups; determining a target wind control task meeting preset conditions in the first group; the target wind control task is deployed from the first group to the second group.
Description
Technical Field
The embodiment of the specification relates to the technical field of data processing, in particular to a method, a device and computer equipment for grouping wind control tasks in a wind control service system.
Background
Currently, a grouping algorithm is involved in many application scenarios, for example, application scenarios such as resource scheduling, task execution, etc. In short, the grouping algorithm solves the following problems: the N objects to be grouped are grouped, so that the sum of object members in each group does not exceed a preset threshold, the number of the groups is as small as possible, and the groups are balanced, namely the variance among the groups is as small as possible. In order to solve the problem, an exhaustion method and a greedy algorithm are proposed in the prior art, wherein the exhaustion method exhausts all grouping results, and an optimal solution is selected from all grouping results; the greedy algorithm assigns a plurality of object members to the same group as much as possible until the sum of the object members in the group does not exceed a preset threshold.
However, the computational complexity of the exhaustive approach is high, resulting in a lower grouping efficiency; greedy algorithms, while relatively efficient with respect to the exhaustive approach, do not achieve a relative balance between packets.
Disclosure of Invention
Aiming at the technical problems, the embodiment of the specification provides a wind control task grouping method, a device and computer equipment in a wind control service system, and the technical scheme is as follows:
according to a first aspect of embodiments of the present specification, there is provided a method for grouping wind control tasks in a wind control service system, the method comprising:
grouping each wind control task to be executed by using a setting algorithm, wherein the obtained grouping result meets the following conditions: the sum of the memory resources required by the wind control task included in any obtained group does not exceed the memory capacity of the wind control service system, and the number of the obtained groups is the least in all the group results which can be exhausted;
performing iterative processing until a preset iteration stop condition is met by the following steps:
determining a first group with the largest sum of memory resources required by the wind control task and a second group with the smallest sum of memory resources required by the wind control task from the current groups;
determining a target wind control task meeting preset conditions in the first group;
the target wind control task is deployed from the first group to the second group.
According to a second aspect of embodiments of the present specification, there is provided a wind control task grouping apparatus in a wind control service system, the apparatus comprising:
the initial grouping module is used for grouping all wind control tasks to be executed by using a setting algorithm, wherein the obtained grouping result meets the following conditions: the sum of the memory resources required by the wind control task included in any obtained group does not exceed the memory capacity of the wind control service system, and the number of the obtained groups is the least in all the group results which can be exhausted;
the first determining module is used for determining a first group with the largest sum of memory resources required by the wind control task and a second group with the smallest sum of memory resources required by the wind control task from the current groups;
the second determining module is used for determining a target wind control task meeting a preset condition in the first group;
a re-allocation module for allocating the target wind control task from the first group to the second group;
the first determining module, the second determining module and the re-allocation module are mutually matched to realize iterative processing until a preset iteration stop condition is met.
According to a third aspect of the embodiments of the present specification, there is provided a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the wind control task grouping method provided by the embodiments of the present specification when executing the program.
According to the technical scheme provided by the embodiment of the specification, the wind control tasks to be executed are grouped by utilizing a setting algorithm, wherein the obtained grouping result meets the following conditions: the sum of the memory resources required by the wind control task included in any obtained group does not exceed the memory capacity of the wind control service system, and the number of the obtained groups is the least in all the group results which can be exhausted; performing iterative processing until a preset iteration stop condition is met by the following steps: determining a first group with the largest sum of memory resources required by the wind control tasks included in the current groups and a second group with the smallest sum of memory resources required by the wind control tasks included in the current groups, determining a target wind control task meeting preset conditions in the first group, and allocating the target wind control task from the first group to the second group. The grouping result which meets the minimum grouping quantity and the minimum variance among the grouping can be achieved, the sum of memory resources required by the wind control tasks included in any grouping is not more than the memory capacity of the wind control service system, and the grouping efficiency is higher because the complexity of the grouping process is only related to the iteration times.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the embodiments of the disclosure.
Further, not all of the effects described above need be achieved in any of the embodiments of the present specification.
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In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present description, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flowchart of an embodiment of a method for grouping wind control tasks in a wind control service system according to an exemplary embodiment of the present disclosure;
FIG. 2 is a block diagram of an embodiment of a wind-controlled task grouping device in a wind-controlled business system according to an exemplary embodiment of the present disclosure;
FIG. 3 illustrates a more specific computing device hardware architecture diagram provided by embodiments of the present description.
Detailed Description
In order for those skilled in the art to better understand the technical solutions in the embodiments of the present specification, the technical solutions in the embodiments of the present specification will be described in detail below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification shall fall within the scope of protection.
Referring to fig. 1, a flowchart of an embodiment of a method for grouping wind control tasks in a wind control service system according to an exemplary embodiment of the present disclosure may include the following steps:
step 102: grouping objects to be grouped by using a setting algorithm, wherein the obtained grouping result meets the following conditions: the sum of the object members included in any of the resulting groupings does not exceed a preset threshold, and the number of resulting groupings is the least among all the exhaustible groupings.
In the embodiment of the present specification, the objects to be grouped may be grouped by a greedy algorithm, and the greedy algorithm is based on the following metrics: the sum of the object members included in any of the resulting groupings does not exceed a preset threshold, and the number of resulting groupings is the least among all the exhaustible groupings.
The "all the grouping results that can be exhausted" mentioned above refers to all the grouping results that can be exhausted by utilizing the exhaustion method on the premise that the sum of the object members included in any one of the obtained groupings does not exceed the preset threshold.
It can be seen that, based on the above metric, the grouping of the objects to be grouped by using the greedy algorithm can make the obtained grouping result satisfy the condition that "the sum of the object members included in any one of the obtained groupings does not exceed the preset threshold, and the number of the obtained groupings is the least among all the exhaustible grouping results".
Step 104: and determining a first group with the largest sum of the included object members and a second group with the smallest sum of the included object members from the current groups.
Step 106: and determining a target object member meeting a preset condition in the first group.
Step 108: the target object members are adapted from the first group to the second group.
Step 110: determining whether a preset iteration stop condition is met, and ending the flow if the preset iteration stop condition is met; if not, go back to step 104.
As an exemplary application scenario, in a wind control service system, tasks such as feature automatic expansion, feature metadata extraction, feature metadata filtering, etc. are included, the memory resources required by different tasks when executing are different, and the memory capacity of the system is limited, so the grouping method provided by the embodiment of the present disclosure may be applied to group the tasks to be processed, so that the number of the groups is as small as possible, and the sum of the memory resources required by each task to be processed in any group does not exceed the memory capacity of the system, and the groups are balanced as much as possible. By the processing, the processing efficiency of the wind control business system on the task can be improved, and the computing resources are saved.
In this processing scenario of the wind-controlled business system, when executing the steps 102-110, the objects to be grouped in the step 102 are actually the wind-controlled tasks to be executed in the wind-controlled business system.
For the "sum of object members" in the above steps:
since the memory capacity of the wind control service system is limited, the following needs to be considered in the process of grouping wind control tasks: the sum of the memory resources required by the wind control task meets the grouping requirement, so that in order to ensure the balance among the groupings as much as possible during the grouping, the memory resources required by each object member (namely the wind control task) need to be divided, so that the balance among the groupings can be realized as much as possible. Thus, the term "sum of object members" as used herein refers to the "sum of memory resources required for a wind-controlled task".
For the above step 102: the sum of the object members included in any obtained group does not exceed a preset threshold, and the object members are correspondingly in the wind control task processing scene, so that: the sum of the memory resources required by each task to be processed in any group does not exceed the memory capacity of the system. Thus, the "preset threshold" herein refers to: "memory capacity of the wind control service system".
The target object members for the above steps 106-108 are actually target wind control tasks in the wind control business system.
Wherein, the wind control service system is used for: based on each group after the iterative processing is completed, the wind control tasks in each group are executed, so that the wind control tasks in each wind control task group in the wind control service system are balanced as much as possible. By the processing, the processing efficiency of the wind control business system on the task can be improved, and the computing resources are saved.
Steps 104 to 110 are described as follows:
assume that M groups are obtained by performing step 102, and that the sum of the object members included in each of the M groups is A 1 、A 2 、A 3 、……、A m (wherein A1 to Am are arranged in order from small to large), at this time, the variances S of the M packets 0 2 Can be calculated by the following formula (I):
subsequently, assume that the sum of the object members is A k One object member n in this group is assigned to the sum of object members A j In this group, at this time, the variance S of the M groups 1 2 Relative to the above S 0 2 The variation of (2) can be calculated by the following formula (II):
as can be seen from the above formula (two),if it is desired to reduce the variance after regrouping, i.e. the variance S 1 2 Less than the variance S 0 2 Then satisfy A k -A j And m is more than m.
As can be seen from the above equation (II), if the variance is reduced after the regrouping, the variance S is also known 1 2 Less than the variance S 0 2 And the Ak-Aj is more than m.
Assume again that the sum of the object members is A k One object member n in this group is assigned to the sum of object members A i In this group, where A i ＞A j And assume A i ＝A j +k (k is greater than 0), at this time, the variance S of the M packets 2 2 Relative to the above S 0 2 The variation of (c) can be calculated by the following formula (III):
combining equation (II) and equation (III) can know that the variance S 2 2 Less than the variance S 1 2 Thus, the following can be concluded: if it is desired to make the variance after regrouping as small as possible, m can be reassigned to the grouping where the sum of the object members is smallest. Based on this, in step 104, the current respective groups may be sorted in order of decreasing sum of object members included therein, a group in which the sum of object members is largest is determined, and a group in which the sum of object members is smallest is referred to as a first group and a group in which the sum of object members is smallest is referred to as a second group for convenience of description.
Subsequently, in step 106, an object member satisfying the preset condition may be determined in the first group, and for convenience of description, the object member satisfying the preset condition is referred to as a target object member, and further, in step 108, the target object member may be relocated from the first group to the second group.
Based on the above-mentioned "from aboveThe equation (two) shows that if it is desired to reduce the variance after regrouping, the variance S 1 2 Less than the variance S 0 2 Then satisfy A k -A j The description of > m "can be understood that, in the embodiment of the present specification, the preset condition may be: the object members are less than a specified value that is the difference between the sum of the object members included in the first group and the sum of the object members included in the second group. It follows that by reassigning one target member of the first group that satisfies the preset condition to the second group, the variance after the regrouping can be reduced.
Wherein, the object member is smaller than the specified value, which means that the size of the object member needs to be considered when grouping the object members. In particular, in an application scenario of processing a wind control task in a wind control service system, when an object member is the wind control task, the "size of the object member" herein actually refers to the "size of a memory resource required by the wind control task". At this time, the preset conditions include: the memory resources required by the wind control tasks are smaller than a specified value, wherein the specified value is the difference value between the sum of the memory resources required by the wind control tasks included in the first group and the sum of the memory resources required by the wind control tasks included in the second group.
Further, in the first group, there may be more than one object member satisfying the preset condition, in which case the smallest one of the object members satisfying the preset condition may be determined as the above-described target object member. By this processing, fine adjustment between packets can be realized, and balance between packets can be gradually improved.
For example, still described in the context of a processing scenario of a wind-controlled business system, in this processing scenario of a wind-controlled business system, when an object member is a wind-controlled task, the size of the object member is the size of the memory resource required for the wind-controlled task, so "the smallest of the object members" herein is actually the smallest of the memory resources required for the wind-controlled task ".
In the processing scenario of the wind control service system, the step 106 is actually: determining a target wind control task meeting a preset condition in the first group, if more than one object member (wind control task) meeting the preset condition is needed, executing the following steps:
determining wind control tasks meeting preset conditions in the first group; if the number of the wind control tasks meeting the preset conditions is more than two, determining the minimum memory resource required in the wind control tasks meeting the preset conditions as a target wind control task.
Subsequently, in step 110, it is determined whether a preset iteration stop condition is satisfied, and if so, the flow may be ended, and a final grouping result is obtained; if not, the method may return to step 104, and continue to perform a round of iterative processing, that is, continue to optimize the grouping result.
In one embodiment, the iteration stop condition may be: the iteration number reaches a preset number threshold.
In another embodiment, the iteration stop condition may be: the grouping result after the current iteration processing is the same as the grouping result after the previous iteration processing, namely the grouping result is not changed any more.
According to the technical scheme provided by the embodiment of the specification, the objects to be grouped are grouped by utilizing a setting algorithm, wherein the obtained grouping result meets the condition: the sum of the object members included in any obtained group does not exceed a preset threshold, and the number of the obtained groups is the least in all the exhaustible grouping results; performing iterative processing until a preset iteration stop condition is met by the following steps: the method comprises the steps of determining a first group with the largest sum of object members included in the current groups and a second group with the smallest sum of object members included in the current groups, determining a target object member meeting preset conditions in the first group, and allocating the target object member from the first group to the second group, so that a grouping result with the smallest number of groups and smallest variance among the groups can be obtained, the sum of the object members in the groups does not exceed a preset threshold, and the grouping efficiency is higher because the complexity of a grouping process is only related to the iteration times.
It will be appreciated by those skilled in the art that the above-described application of the grouping method provided in the embodiments of the present disclosure to the wind-controlled service system is merely an example, and in practical applications, the grouping method may also be applied to other application scenarios, for example, application scenarios involving resource scheduling, which are not limited in the embodiments of the present disclosure.
Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a wind control task grouping device in a wind control service system, referring to fig. 2, which is a block diagram of an embodiment of the wind control task grouping device in a wind control service system provided in an exemplary embodiment of the present disclosure, where the device may include: an initial grouping module 21, a first determination module 22, a second determination module 23, and a re-allocation module 24.
Wherein, the initial grouping module 21 may be configured to group the objects to be grouped by using a setting algorithm, wherein the obtained grouping result satisfies the following conditions: the sum of the object members included in any obtained group does not exceed a preset threshold, and the number of the obtained groups is the least in all the exhaustible grouping results;
a first determining module 22, configured to determine, from among the current multiple groups, a first group having a largest sum of object members included therein and a second group having a smallest sum of object members included therein;
a second determining module 23, configured to determine, in the first group, a target object member that satisfies a preset condition;
a re-provisioning module 24 may be used to provision the target object member from the first group to the second group.
The initial grouping module 21 is specifically configured to:
grouping objects to be grouped by using a greedy algorithm under a preset measurement standard, wherein the measurement standard comprises the following steps: the sum of the object members included in any of the resulting groupings does not exceed a preset threshold, and the number of resulting groupings is the least among all the exhaustible groupings.
In an embodiment, the preset iteration stop condition includes one of the following:
the iteration times reach a preset time threshold;
the grouping result after the current iteration processing is the same as the grouping result after the previous iteration processing.
In an embodiment, the preset condition includes:
the object members are less than a specified value, wherein the specified value is a difference between a sum of the object members included in the first group and a sum of the object members included in the second group.
In an embodiment, each of the wind control tasks includes:
the feature automatic unfolding task, the feature metadata extraction task or the feature source data filtering task.
In an embodiment, the wind control service system is configured to:
based on each group after the iterative process is completed, the wind control task in each group is executed.
In an embodiment, the second determining module 23 includes (not shown in fig. 2):
a first determining submodule, configured to determine an object member that satisfies a preset condition in the first group;
and the second determining submodule is used for determining the minimum one of the object members meeting the preset conditions as the target object member if the object members meeting the preset conditions are more than two.
It will be appreciated that the initial grouping module 21, the first determining module 22, the second determining module 23, and the re-allocation module 24 may be configured in the apparatus as four modules with independent functions, as shown in fig. 2, or may be configured in the apparatus separately, and thus the configuration shown in fig. 2 should not be construed as limiting the embodiment of the present disclosure.
In addition, the implementation process of the functions and roles of each module in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
The embodiments of the present disclosure also provide a computer device at least including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the foregoing grouping method when executing the program. The method at least comprises the following steps: grouping objects to be grouped by using a setting algorithm, wherein the obtained grouping result meets the following conditions: the sum of the object members included in any obtained group does not exceed a preset threshold, and the number of the obtained groups is the least in all the exhaustible grouping results; performing iterative processing until a preset iteration stop condition is met by the following steps: determining a first group with the largest sum of the included object members and a second group with the smallest sum of the included object members from the current groups; determining a target object member meeting a preset condition in the first group; the target object member is adapted from the first group to the second group.
FIG. 3 is a schematic diagram of a more specific hardware architecture of a computer device according to an embodiment of the present disclosure, where the device may include: a processor 310, a memory 320, an input/output interface 330, a communication interface 340, and a bus 350. Wherein the processor 310, the memory 320, the input/output interface 330 and the communication interface 340 are communicatively coupled to each other within the device via a bus 350.
The processor 310 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 320 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 320 may store an operating system and other application programs, and when implementing the techniques provided by the embodiments of the present disclosure via software or firmware, the associated program code is stored in memory 320 and invoked for execution by processor 310.
The input/output interface 330 is used for connecting with an input/output module to realize information input and output. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
The communication interface 340 is used to connect to a communication module (not shown in the figure) to enable communication interaction between the present device and other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 350 includes a path to transfer information between components of the device (e.g., processor 33, memory 320, input/output interface 330, and communication interface 340).
It should be noted that although the above device only shows the processor 310, the memory 320, the input/output interface 330, the communication interface 340, and the bus 350, in the implementation, the device may further include other components necessary to achieve normal operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The present specification embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the foregoing grouping method. The method at least comprises the following steps: grouping objects to be grouped by using a setting algorithm, wherein the obtained grouping result meets the following conditions: the sum of the object members included in any obtained group does not exceed a preset threshold, and the number of the obtained groups is the least in all the exhaustible grouping results; performing iterative processing until a preset iteration stop condition is met by the following steps: determining a first group with the largest sum of the included object members and a second group with the smallest sum of the included object members from the current groups; determining a target object member meeting a preset condition in the first group; the target object member is adapted from the first group to the second group.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
From the foregoing description of embodiments, it will be apparent to those skilled in the art that the present embodiments may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solutions of the embodiments of the present specification may be embodied in essence or what contributes to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present specification.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or a combination of any of these devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The apparatus embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the functions of the modules may be implemented in the same piece or pieces of software and/or hardware when implementing the embodiments of the present disclosure. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The foregoing is merely a specific implementation of the embodiments of this disclosure, and it should be noted that, for a person skilled in the art, several improvements and modifications may be made without departing from the principles of the embodiments of this disclosure, and these improvements and modifications should also be considered as protective scope of the embodiments of this disclosure.
Claims (11)
1. A method for grouping wind control tasks in a wind control business system, the method comprising:
grouping each wind control task to be executed by using a setting algorithm, wherein the obtained grouping result meets the following conditions: the sum of the memory resources required by the wind control task included in any obtained group does not exceed the memory capacity of the wind control service system, and the number of the obtained groups is the least in all the group results which can be exhausted;
performing iterative processing until a preset iteration stop condition is met by the following steps:
determining a first group with the largest sum of memory resources required by the wind control task and a second group with the smallest sum of memory resources required by the wind control task from the current groups;
determining a target wind control task meeting preset conditions in the first group;
the target wind control task is deployed from the first group to the second group.
2. The method of claim 1, each of the wind control tasks comprising:
the feature automatic unfolding task, the feature metadata extraction task or the feature source data filtering task.
3. The method of claim 1, the wind control business system to:
based on each group after the iterative process is completed, the wind control task in each group is executed.
4. The method of claim 1, the preset conditions comprising:
the memory resources required by the wind control tasks are smaller than a specified value, wherein the specified value is the difference value between the sum of the memory resources required by the wind control tasks included in the first group and the sum of the memory resources required by the wind control tasks included in the second group.
5. The method of claim 1, the determining, in the first group, a target wind control task that satisfies a preset condition, comprising:
determining wind control tasks meeting preset conditions in the first group;
if the number of the wind control tasks meeting the preset conditions is more than two, determining the minimum memory resource required in the wind control tasks meeting the preset conditions as a target wind control task.
6. The method of claim 1, wherein grouping each wind control task to be performed using a preset algorithm comprises:
grouping each wind control task to be executed by using a greedy algorithm under a preset measurement standard, wherein the measurement standard comprises the following steps: the sum of the memory resources required by the wind control task included in any obtained group does not exceed the memory capacity of the wind control service system, and the number of the obtained groups is the least in all the exhaustible grouping results.
7. The method of claim 1, the preset iteration stop condition comprising one of:
the iteration times reach a preset time threshold;
the grouping result after the current iteration processing is the same as the grouping result after the previous iteration processing.
8. A wind control task grouping apparatus in a wind control business system, the apparatus comprising:
the initial grouping module is used for grouping all wind control tasks to be executed by using a setting algorithm, wherein the obtained grouping result meets the following conditions: the sum of the memory resources required by the wind control task included in any obtained group does not exceed the memory capacity of the wind control service system, and the number of the obtained groups is the least in all the group results which can be exhausted;
the first determining module is used for determining a first group with the largest sum of memory resources required by the wind control task and a second group with the smallest sum of memory resources required by the wind control task from the current groups;
the second determining module is used for determining a target wind control task meeting a preset condition in the first group;
a re-allocation module for allocating the target wind control task from the first group to the second group;
the first determining module, the second determining module and the re-allocation module are mutually matched to realize iterative processing until a preset iteration stop condition is met.
9. The apparatus of claim 8, the initial grouping module is specifically configured to:
grouping each wind control task to be executed by using a greedy algorithm under a preset measurement standard, wherein the measurement standard comprises the following steps: the sum of the memory resources required by the wind control task included in any obtained group does not exceed the memory capacity of the wind control service system, and the number of the obtained groups is the least in all the exhaustible grouping results.
10. The apparatus of claim 8, the preset iteration stop condition comprising one of:
the iteration times reach a preset time threshold;
the grouping result after the current iteration processing is the same as the grouping result after the previous iteration processing.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-7 when the program is executed by the processor.
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