CN116886633A - Transaction current limiting method and device, storage medium and electronic equipment - Google Patents

Transaction current limiting method and device, storage medium and electronic equipment Download PDF

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CN116886633A
CN116886633A CN202310678788.0A CN202310678788A CN116886633A CN 116886633 A CN116886633 A CN 116886633A CN 202310678788 A CN202310678788 A CN 202310678788A CN 116886633 A CN116886633 A CN 116886633A
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data
cluster
current limiting
task
current
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祁明
张俊羿
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Alipay Hangzhou Information Technology Co Ltd
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Alipay Hangzhou Information Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H04L47/2441—Traffic characterised by specific attributes, e.g. priority or QoS relying on flow classification, e.g. using integrated services [IntServ]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/466—Transaction processing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/20—Traffic policing

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

The specification discloses a transaction current limiting method, a device, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring task data aiming at service equipment clusters and equipment proportion of service equipment in the service equipment clusters, acquiring cluster task data in the task data based on transaction types, acquiring cluster adjustment data based on the task data and the equipment proportion, calculating the cluster adjustment data by adopting deviation proportion to obtain first cluster current limiting data, acquiring cluster historical data of historical duration, acquiring second cluster current limiting data based on the cluster historical data and the cluster task data, determining single-machine current limiting data corresponding to the transaction types based on the first cluster current limiting data, the second cluster current limiting data and the equipment proportion, and adopting the embodiment of the specification, the current limiting data of the service equipment clusters can meet the requirements of the task data by controlling the current limiting data output by the service equipment, so that dynamic adjustment of the current limiting data is realized, and unified and stable current limiting under multiple scenes is controlled in a self-adaptive mode.

Description

Transaction current limiting method and device, storage medium and electronic equipment
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a transaction current limiting method, a transaction current limiting device, a storage medium, and an electronic device.
Background
At present, when transactions are carried out between different institutions, transaction data are created according to the requirements of the transactions, but the transaction data of opposite institutions need to be limited in order to ensure the normal transaction of the opposite institutions because the different institutions have different processing capacities for different transactions.
Disclosure of Invention
The embodiment of the specification provides a transaction current limiting method, a transaction current limiting device, a storage medium and electronic equipment, which can enable current limiting data of a service equipment cluster to meet the requirement of task data by controlling the current limiting data output by the service equipment, realize dynamic adjustment of the current limiting data and further adaptively control unified and stable current limiting under multiple scenes.
In a first aspect, embodiments of the present disclosure provide a transaction flow restriction method, the method including:
task data aiming at a service equipment cluster at a first moment and equipment proportion of service equipment in a working state in the service equipment cluster are obtained;
acquiring cluster task data from the task data based on the transaction type, and acquiring cluster adjustment data based on the task data and the equipment proportion;
Calculating the cluster adjustment data by adopting a preset deviation proportion to obtain first cluster current limiting data;
acquiring cluster historical data of historical time length, and acquiring second cluster current limiting data based on the cluster historical data and the cluster task data, wherein the historical time length is adjacent time length before the first time;
and determining single machine current limiting data corresponding to the transaction type based on the first cluster current limiting data, the second cluster current limiting data and the equipment proportion.
In a second aspect, embodiments of the present disclosure provide a transaction flow restrictor device, including:
the task acquisition unit is used for acquiring task data aiming at the service equipment cluster at a first moment and equipment proportion of service equipment in a working state in the service equipment cluster;
the adjustment data acquisition unit is used for acquiring cluster task data from the task data based on the transaction type and acquiring cluster adjustment data based on the task data and the equipment proportion;
the first data acquisition unit is used for calculating the cluster adjustment data by adopting a preset deviation proportion to obtain first cluster current limiting data;
the second data acquisition unit is used for acquiring cluster historical data of historical time length, and acquiring second cluster current limiting data based on the cluster historical data and the cluster task data, wherein the historical time length is adjacent time length before the first time;
And the single machine data acquisition unit is used for determining single machine current limiting data corresponding to the transaction type based on the first cluster current limiting data, the second cluster current limiting data and the equipment proportion.
In a third aspect, the present description embodiments provide a computer program product storing at least one instruction adapted to be loaded by a processor and to perform the above-described method steps.
In a fourth aspect, the present description provides a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the method described above.
In a fifth aspect, embodiments of the present disclosure provide an electronic device, including: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the steps of the method described above.
In the embodiment of the specification, task data and equipment proportion are acquired, then the task data is analyzed, calculation is performed in a cluster angle, and single-machine current limiting data corresponding to each transaction type of each service equipment in a service equipment cluster corresponding to the cluster current limiting data is obtained according to the equipment proportion, so that adjustment of the current limiting data is realized, the current limiting data of the service equipment cluster can meet the requirement of the task data by controlling the current limiting data output by the service equipment, dynamic adjustment of the current limiting data is realized, and unified and stable current limiting under multiple scenes is controlled in a self-adaptive mode.
Drawings
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, it being obvious that the drawings in the following description are only some embodiments of the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system architecture diagram of a transaction flow restriction method according to an embodiment of the present disclosure;
fig. 2 is a flow chart of a transaction flow limiting method according to an embodiment of the present disclosure;
FIG. 3 is an exemplary schematic diagram of an apparatus scale provided in an embodiment of the present disclosure;
FIG. 4 is an exemplary diagram of clustered task data provided in an embodiment of the present disclosure;
FIG. 5 is a schematic flow chart of a transaction flow limiting method according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a transaction current limiting device according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an adjustment data obtaining unit according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of a second data acquisition unit according to an embodiment of the present disclosure;
Fig. 9 is a schematic structural diagram of a stand-alone data acquisition unit according to an embodiment of the present disclosure;
fig. 10 is a schematic structural diagram of a transaction current limiting device according to an embodiment of the present disclosure;
fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order to make the features and advantages of the present specification more comprehensible, the following description refers to the accompanying drawings in which embodiments of the present specification are described in detail, and it is apparent that the described embodiments are only some, but not all embodiments of the present specification. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present disclosure.
In the prior art, in order to limit the current transaction data, the current transaction is limited in a unified current limiting association mode, but the problems of resource waste, poor current limiting effect and the like exist when the current transaction is actually limited, so that the current transaction is insufficient to achieve the purpose of fully utilizing cluster resources.
Based on this, the embodiment of the specification provides a transaction current limiting method, by adopting the embodiment of the specification, analyzing task data and equipment proportion, calculating the task data in a cluster angle, and obtaining single-machine current limiting data corresponding to each transaction type of each service equipment in a service equipment cluster corresponding to the cluster current limiting data according to the equipment proportion, thereby realizing adjustment of the current limiting data, so that the current limiting data of the service equipment cluster can meet the requirement of the task data by controlling the current limiting data output by the service equipment, realizing dynamic adjustment of the current limiting data, and further realizing uniform and stable current limiting under self-adaptive control of multiple scenes.
Referring to fig. 1, a system architecture diagram of transaction flow limiting is provided for an embodiment of the present disclosure. As shown in fig. 1, the transaction flow restriction method provided in the embodiment of the present disclosure may be applied to a service device to implement a transaction flow restriction process for adjusting flow restriction data, and the system structure provided in the embodiment of the present disclosure mainly includes a service device 10 and a transaction device 20. The service device 10 may be one of large cluster servers used by an enterprise; the transaction device 20 may be a large-scale integrated server used by an enterprise, or may be a micro-computer, such as a personal computer.
In this embodiment of the present disclosure, the service device 10 obtains task data sent by the transaction device 20 at a first time, obtains a device proportion in a working state in a service device cluster where the service device 10 is located, obtains cluster task data and cluster adjustment data corresponding to a transaction type based on the task data and the device proportion, obtains first cluster current limiting data based on a preset deviation proportion and the cluster adjustment data, obtains cluster history data, obtains second cluster current limiting data based on the cluster history data and the cluster task data, and obtains stand-alone current limiting data corresponding to the transaction type based on the first cluster current limiting data, the second cluster current limiting data and the device proportion.
In the embodiment of the specification, task data and equipment proportion are acquired, then the task data is analyzed, calculation is performed in a cluster angle, and single-machine current limiting data corresponding to each transaction type of each service equipment in a service equipment cluster corresponding to the cluster current limiting data is obtained according to the equipment proportion, so that adjustment of the current limiting data is realized, the current limiting data of the service equipment cluster can meet the requirement of the task data by controlling the current limiting data output by the service equipment, dynamic adjustment of the current limiting data is realized, and unified and stable current limiting under multiple scenes is controlled in a self-adaptive mode.
Based on the system architecture shown in fig. 1, the transaction flow limiting method provided in the embodiment of the present disclosure will be described in detail below with reference to fig. 2 to 4.
Referring to fig. 2, a flow chart of a transaction flow limiting method is provided in an embodiment of the present disclosure. As shown in fig. 2, the method may include the following steps S102 to S110.
S102, task data aiming at a service equipment cluster at a first moment and equipment proportion of service equipment in a working state in the service equipment cluster are obtained;
in one embodiment, each service device in the service device cluster obtains task data for the service device cluster at a first time, and obtains a device proportion of the service devices in the service device cluster that are in an operational state at the first time.
The service device cluster may be a service device cluster for sending the transaction data to the transaction partner, and the service device cluster may include at least one service device, and the transaction data is sent to the transaction partner according to each service device.
The first time may be the current time when the service device receives the task data, for example, when the service device receives the task data at the time of 12:00, the time of "12:00" is taken as the current time when the service device receives the task data, that is, the first time.
The task data may include specific task information that needs to be executed by the terminal device cluster.
The device proportion may be the proportion of service devices capable of working and in a working state in the service device cluster to the service device cluster, for example, 10 machines exist in the service device cluster, and the proportion of service devices capable of working and in a working state is 10, and the device proportion is 1:10. For example, as shown in fig. 3, 9 service devices exist in the service device cluster in fig. 3, and if the 9 service devices are all in a working state, the device ratio is 1:9.
In the embodiment of the present disclosure, any service device is selected as an object of description to describe when each service device in the service device cluster obtains task data, and the data processing process of other service devices may refer to the data processing method of the service device.
S104, acquiring cluster task data from the task data based on the transaction type, and acquiring cluster adjustment data based on the task data and the equipment proportion;
in one embodiment, after task data is acquired, the task data is parsed, a transaction type and current cluster current limit data in the task data are acquired, cluster task data are acquired in the task data based on the transaction type, and cluster adjustment data are acquired based on equipment proportion and the current cluster current limit data.
It can be understood that in order to ensure the processing efficiency of the transaction, more than one task often exists in the task data, so that the task data is analyzed to obtain the transaction types of each task, and the cluster task data corresponding to the transaction types is obtained from the task data. It should be noted that, because the cluster task data corresponding to different task types are not mutually interfered when performing calculation and execution, for convenience of explanation, in the embodiment of the present disclosure, any one of the task types is described, and other task types are not described in detail.
The current cluster current limiting data may be current limiting data output by a service device cluster of a terminal device at a time adjacent to a previous time of the first time, so as to adjust the current limiting data of the first time according to the current cluster current limiting data. It can be understood that the current cluster current-limiting data can be data carried in the task data, or can be data obtained by searching in a database of the service equipment cluster or a storage unit of the service equipment according to the task identifier in the task data.
Furthermore, the cluster task data can be the data quantity which can be accepted and is set by the transaction partner according to the actual condition of the system of the transaction partner. It should be noted that the size of the cluster task data may also be different for different transaction types. For example, a transaction partner can accept 100 transactions per minute for cluster task data of transaction type A and 200 transactions per minute for cluster task data of transaction type B. For example, as shown in fig. 4, the task data after the task data is parsed is shown in fig. 4, and specifically includes 100 tasks per minute for the task data with a transaction type a, 200 tasks per minute for the task data with a transaction type B, and so on.
Optionally, one possible method for obtaining cluster adjustment data may be to obtain current single machine current limiting data based on the current limiting data and the device proportion, obtain single machine deviation data at the first moment, perform difference calculation based on the single machine deviation data and the current single machine current limiting data, obtain single machine adjustment data, and obtain cluster adjustment data corresponding to the single machine adjustment data based on the device proportion.
Alternatively, a possible single machine adjustment data calculation method may be to subtract single machine deviation data from current single machine current limit data to obtain single machine adjustment data. For example, when the current single machine current limit data is 5 and the single machine deviation data is 2.1, the single machine adjustment data is 2.9 (5-2.1), i.e. compared with the current single machine current limit data, the current limit data of the service equipment needs to be adjusted downwards by 2.9; if the current single machine current limit data is-1 and the single machine deviation data is-1.2, the single machine adjustment data is 0.2 (-1- (-1.2)), i.e. compared with the current single machine current limit data, the current limit data of the service equipment needs to be adjusted down by 0.2.
After the single machine adjustment data is obtained, cluster adjustment data is obtained based on the single machine adjustment data and the equipment proportion, for example, the single machine current limit data is 2.9, the equipment proportion is 1:10, and the cluster current limit data is 29 (2.9x10).
S106, calculating the cluster adjustment data by adopting a preset deviation ratio to obtain first cluster current limiting data;
in one embodiment, a preset deviation ratio is obtained, and the deviation ratio is used to calculate the cluster adjustment data, so as to obtain first cluster current limiting data.
Optionally, one possible method for calculating the first cluster current limit data may be to multiply the cluster adjustment data and the deviation ratio to obtain the first cluster current limit data.
The deviation ratio may be a preset value, and the deviation ratio may be a value for adjusting the change amplitude of the cluster adjustment data. For example, if the deviation ratio is 0.5 and the cluster adjustment data is 29, the first cluster current limit data is 14.5 (29×0.5).
S108, acquiring cluster historical data of historical time length, and acquiring second cluster current limiting data based on the cluster historical data and the cluster task data;
in one embodiment, cluster history data of a history duration is obtained, and second cluster current limit data is obtained after calculation is performed based on the cluster history data and the cluster task data.
The historical time length is adjacent time length before the first time, and cluster historical data of the service equipment in the historical time length are obtained from a database of the service equipment cluster or a storage unit of the service equipment. For example, the historical time period may be an adjacent time period of 5 minutes before the current time, and if the first time is 12:00, the historical time period includes 11:55, 11:56, 11:57, 11:58, and 11:59.
After the cluster historical data is obtained, cluster time period time of each adjacent time period in the cluster historical data is obtained, and each cluster time period data and cluster task data are respectively calculated to obtain a cluster deviation average value; and respectively calculating cluster time period data of each adjacent time period to obtain a cluster change rate value, and adding the cluster deviation mean value and the cluster change mean value to obtain second cluster current limiting data.
Optionally, in one possible cluster deviation average value calculating method, difference value calculation is performed on the data of each cluster period and the cluster task data to obtain first difference values, and average values are obtained after summation is performed on the first difference values to obtain a cluster deviation average value.
Optionally, one possible cluster change rate value calculating method may be to calculate a difference value of cluster time periods of each adjacent time period to obtain a second difference value, and sum up each second search and then obtain an average value to obtain a cluster change rate value.
S110, determining single machine current limiting data corresponding to the transaction type based on the first cluster current limiting data, the second cluster current limiting data and the equipment proportion;
in one embodiment, after the first cluster current limiting data, the second cluster current limiting data and the equipment proportion are obtained, summing the first cluster current limiting data and the second cluster current limiting data to obtain target cluster current limiting data, and calculating the target cluster current limiting data based on the equipment proportion to obtain single machine current limiting data corresponding to the transaction type.
When the target cluster current limiting data can be the first moment, the service equipment cluster determines the data to be adjusted after acquiring the task data aiming at the service equipment cluster. It can be understood that, because the target cluster current limit data is obtained by calculation, and each service device in the service device cluster is calculated and actually executed, the current single-machine current limit data and the current single-machine current limit data of the service device are needed to be distributed according to the device proportion, and then the actual output current limit data when the service device executes the task data is obtained, and the service device executes the task data by adopting the actual output current limit data.
The single machine current limit data can be data which is used by the service equipment for determining that the current single machine current limit data is required to be adjusted according to the transaction type.
It should be noted that, the service device includes a trained transaction current limiting model, where the transaction current limiting model may be an algorithm model for calculating single machine current limiting data, and the execution process in the embodiment of the present disclosure is a specific execution process in which the service device adopts the transaction current limiting model, inputs the obtained task data and the device proportion to the transaction current limiting model, and finally outputs the single machine current limiting data in combination with the cluster history data.
Optionally, a feasible training method of the transaction current limiting model may be that sample task data, sample equipment proportion and sample single machine current limiting data are obtained, the transaction current limiting model is trained based on the sample task data, the sample equipment proportion and the sample single machine current limiting data, and various parameters corresponding to the transaction current limiting model are obtained, so that training of the transaction current limiting model is completed.
Furthermore, another possible training method of the transaction current limiting model may be to input the obtained sample task data, the sample equipment proportion and the sample single machine current limiting data into the initialized transaction current limiting model, and improve the output rate of the single machine current limiting data output by the transaction current limiting model and the sample motor current limiting data which are the same or have the difference larger than a preset threshold value through adjusting the deviation proportion. And when the output rate reaches a preset output rate range, the transaction current limiting model is considered to be trained. It can be appreciated that when the deviation ratio is larger, the cluster current limiting data will also increase, so the adjustment amplitude will also increase accordingly, thereby affecting the output efficiency.
In the embodiment of the specification, task data and equipment proportion are acquired, then the task data is analyzed, calculation is performed in a cluster angle, and single-machine current limiting data corresponding to each transaction type of each service equipment in a service equipment cluster corresponding to the cluster current limiting data is obtained according to the equipment proportion, so that adjustment of the current limiting data is realized, the current limiting data of the service equipment cluster can meet the requirement of the task data by controlling the current limiting data output by the service equipment, dynamic adjustment of the current limiting data is realized, and unified and stable current limiting under multiple scenes is controlled in a self-adaptive mode.
Referring to fig. 5, a flow chart of a transaction flow limiting method is provided in an embodiment of the present disclosure. As shown in fig. 5, the method may include the following steps S202 to S232.
S202, task data aiming at a service equipment cluster at a first moment and equipment proportion of service equipment in a working state in the service equipment cluster are obtained;
in one embodiment, each service device in the service device cluster obtains task data for the service device cluster at a first time, and obtains a device proportion of the service devices in the service device cluster that are in an operational state at the first time.
The service device cluster may be a service device cluster for sending the transaction data to the transaction partner, and the service device cluster may include at least one service device, and the transaction data is sent to the transaction partner according to each service device.
The first time may be the current time when the service device receives the task data, for example, when the service device receives the task data at the time of 12:00, the time of "12:00" is taken as the current time when the service device receives the task data, that is, the first time.
The task data may include specific task information that needs to be executed by the terminal device cluster.
The device proportion may be the proportion of service devices capable of working and in a working state in the service device cluster to the service device cluster, for example, 10 machines exist in the service device cluster, and the proportion of service devices capable of working and in a working state is 10, and the device proportion is 1:10. For example, as shown in fig. 3, 9 service devices exist in the service device cluster in fig. 3, and if the 9 service devices are all in a working state, the device ratio is 1:9.
In the embodiment of the present disclosure, any service device is selected as an object of description to describe when each service device in the service device cluster obtains task data, and the data processing process of other service devices may refer to the data processing method of the service device.
S204, analyzing the task data to obtain the transaction type corresponding to the task data and current cluster current-limiting data;
in one embodiment, in order to ensure the processing efficiency of the transaction, more than one task often exists in the task data, so that the task data is parsed to obtain the transaction types of each task. It should be noted that, because the cluster task data corresponding to different task types are not mutually interfered when performing calculation and execution, for convenience of explanation, in the embodiment of the present disclosure, any one of the task types is described, and other task types are not described in detail.
The current cluster current-limiting data may be current-limiting data output by a service device cluster corresponding to a transaction type of a terminal device adjacent to a last time at the first time, so as to adjust the current-limiting data at the first time according to the current cluster current-limiting data. It can be understood that the current cluster current-limiting data can be data carried in the task data, or can be data obtained by searching in a database of the service equipment cluster or a storage unit of the service equipment according to the task identifier in the task data.
S206, acquiring cluster task data from the task data based on the transaction type;
in one embodiment, after the transaction type in the task data is acquired, searching is performed in the task data based on the transaction type, and cluster task data corresponding to the transaction type in the task data is acquired.
The cluster task data can be the data quantity which can be accepted and is set by the transaction partner according to the actual condition of the system. It should be noted that the size of the cluster task data may also be different for different transaction types. For example, a transaction partner can accept 100 transactions per minute for cluster task data of transaction type A and 200 transactions per minute for cluster task data of transaction type B. For example, as shown in fig. 4, the task data after the task data is parsed is shown in fig. 4, and specifically includes 100 tasks per minute for the task data with a transaction type a, 200 tasks per minute for the task data with a transaction type B, and so on. S208, current single machine current limiting data is obtained based on the current cluster current limiting data and the equipment proportion;
in one embodiment, based on the obtained current cluster current limit data and the device proportion, the current single machine current limit data is obtained through calculation between the current cluster current limit data and the device proportion.
For example, the current cluster current limit data is 110, the device ratio is 1:10, and the current single machine current limit data is 11 (110×0.1).
The stand-alone current limit data may be current limit data corresponding to a transaction type output by the service device at a time immediately preceding the first time.
S210, obtaining single machine deviation data at the first moment, and obtaining single machine adjustment data based on the single machine deviation data and the current single machine current limiting data;
in one embodiment, single machine deviation data at a first moment is obtained, and difference value calculation is performed based on the single machine deviation data and the single machine current limiting data obtained through calculation, so that single machine adjustment data of the service equipment at the first moment is obtained.
Alternatively, a possible single machine adjustment data calculation method may be to subtract single machine deviation data from current single machine current limit data to obtain single machine adjustment data. For example, when the current single machine current limit data is 11 and the single machine deviation data is 2.1, the single machine adjustment data is 8.9 (11-2.1)), i.e. compared with the current single machine current limit data, the current limit data of the service equipment needs to be adjusted down by 8.9; if the current single machine current limit data is-1 and the single machine deviation data is-1.2, the single machine adjustment data is 0.2 (-1- (-1.2)), i.e. compared with the current single machine current limit data, the current limit data of the service equipment needs to be adjusted down by 0.2.
S212, acquiring cluster adjustment data based on the single machine adjustment data and the equipment proportion;
in one embodiment, the cluster current limit data may be data obtained by performing calculation based on the stand-alone adjustment data and the device proportion, and the obtained data is primarily used for adjusting the cluster current limit data.
S214, calculating the cluster adjustment data by adopting a preset deviation ratio to obtain first cluster current limiting data;
in one embodiment, a preset deviation ratio is obtained, and the deviation ratio is used to calculate the cluster adjustment data, so as to obtain first cluster current limiting data.
Optionally, one possible method for calculating the first cluster current limit data may be to multiply the cluster adjustment data and the deviation ratio to obtain the first cluster current limit data.
The deviation ratio may be a preset value, and the deviation ratio may be a value for adjusting the change amplitude of the cluster adjustment data. For example, if the deviation ratio is 0.5 and the cluster adjustment data is 29, the first cluster current limit data is 14.5 (29×0.5).
S216, cluster history data of history duration is obtained;
in one embodiment, cluster history data of the service device within a history duration is obtained from a database of the service device cluster or a storage unit of the service device itself.
Further, the historical time period may be an adjacent time period before the first time, and the specific value of the historical time period may be set according to actual needs, for example, the historical time period may be an adjacent time period 5 minutes before the current time, and each time interval is 1 minute, and if the first time period is 12:00, the historical time period includes 11:55, 11:56, 11:57, 11:58, and 11:59.
It should be noted that, the cluster history data may be cluster current-limiting data of each history period in the history duration, where the cluster history data may be stored in a database of the service device cluster or a storage unit of the service device itself, or may be set by a user in a storage device set by itself, and may specifically be set according to actual needs.
S218, acquiring cluster time period data of each adjacent time period in the cluster historical data, and respectively calculating each cluster time period data and the cluster task data to obtain a cluster deviation average value;
in one embodiment, cluster time period time of each adjacent time period in cluster historical data is obtained, difference value calculation is carried out on each cluster time period data and cluster task data to obtain first difference values of each adjacent time period, and average value is obtained after summation is carried out on each first difference value to obtain cluster deviation average value.
For example, if the cluster task data is 100 and each cluster period data is 132, 120, 105, 97 and 102, respectively, the difference between each cluster period data and the cluster task data is calculated to obtain each first difference value which is 32, 20, 5, -3 and 2, respectively, and the first difference values are summed and then averaged to obtain the cluster deviation average value 11.2.
S220, respectively calculating the cluster time period data of each adjacent time period to obtain a cluster change rate value;
in one embodiment, difference value calculation is performed on cluster time period data of each adjacent time period respectively to obtain second difference values corresponding to each adjacent time period, and average value is obtained after summation is performed on each second difference value to obtain a cluster change rate value.
For example, if the data of each cluster period is 132, 120, 105, 97 and 102 respectively, the obtained second differences are 12, 15, 8 and-5 respectively, and the average value is obtained after summing the second differences, so as to obtain the scenario change rate value of 7.5. Alternatively, the second difference may be calculated by subtracting the cluster period data of the next time adjacent to the previous time from the cluster period data of the previous time.
It should be noted that, the numbers used for examples in the embodiments of the present disclosure are only for illustrating the calculation method, and do not have a practical effect.
S222, obtaining second cluster current limiting data based on the cluster deviation average value and the cluster change rate value;
in one embodiment, after the cluster deviation average value and the cluster change rate value are calculated, the cluster deviation average value and the cluster change rate value are summed to obtain the second cluster current limiting data.
S224, obtaining target cluster current limiting data based on the first cluster current limiting data and the second cluster current limiting data;
in one embodiment, summation calculation is performed based on the obtained first cluster current limit data and the second cluster current limit data to obtain target cluster current limit data.
The target cluster current limit data may be current limit data to be adopted corresponding to a transaction type used by the service device cluster to execute the task data at the first moment.
S226, determining single machine current limiting data corresponding to the transaction type based on the equipment proportion and the target cluster current limiting data;
in one embodiment, the target cluster current limit data is calculated based on the device proportions to determine the stand-alone current limit data corresponding to the transaction type of the serving device.
The single machine current limit data can be data which is used by the service equipment for determining that the current single machine current limit data is required to be adjusted according to the transaction type.
Further, based on the current single machine current limiting data and the single machine current limiting data of the service equipment, the actual output current limiting data when the service equipment executes the task data is obtained, and the service equipment executes the task data by adopting the actual output current limiting data.
S228, acquiring actual cluster current limiting data at the first moment;
in one embodiment, after the target cluster current limit data is obtained, the actual cluster current limit data of the service device cluster at the first moment is further obtained.
The actual cluster current limit data may be current limit data corresponding to a transaction type actually transmitted by the service device cluster to the transaction partner in the first moment.
Optionally, a feasible actual cluster current-limiting data obtaining method may be that the service device obtains data corresponding to a transaction type transmitted by the service device cluster to the transaction partner when the first moment is over, so as to obtain actual cluster current-limiting data; in another possible method for obtaining actual cluster current-limiting data, if all service devices in the service device cluster stop transmitting data corresponding to the transaction type of the transaction partner in the first moment, the actual cluster current-limiting data of the service device cluster in the first moment is obtained.
S230, obtaining cluster deviation data based on the actual cluster current limit data and the target cluster current limit data;
in one embodiment, the cluster current limit deviation data may be data obtained by performing difference calculation according to the actual cluster current limit data and the target cluster current limit data, and indicates a gap between the actual cluster current limit data and the target cluster current limit data based on the cluster current limit deviation data, so as to provide data support when performing stand-alone current limit data calculation at a next time of the first moment.
S232, obtaining single machine deviation data at the second moment based on the cluster deviation data and the equipment proportion, and transferring to the step of obtaining task data aiming at a service equipment cluster at the first moment and the equipment proportion of the service equipment in a working state in the service equipment cluster;
in one embodiment, the calculation is performed based on the cluster deviation data and the device proportion, so as to obtain stand-alone deviation data at the second moment, and the step S202 is executed, and the data processing is started on the task data acquired at the second moment.
The second time is the next time adjacent to the first time, for example, the first time is 12:00, and the interval duration between each adjacent time is one minute, then the second time is 12:01. The interval time between each adjacent time can be set according to the actual situation.
It should be noted that, the service device includes a trained transaction current limiting model, where the transaction current limiting model may be an algorithm model for calculating single machine current limiting data, and the execution process in the embodiment of the present disclosure is a specific execution process in which the service device adopts the transaction current limiting model, inputs the obtained task data and the device proportion to the transaction current limiting model, and finally outputs the single machine current limiting data in combination with the cluster history data.
Optionally, a feasible training method of the transaction current limiting model may be that sample task data, sample equipment proportion and sample single machine current limiting data are obtained, the transaction current limiting model is trained based on the sample task data, the sample equipment proportion and the sample single machine current limiting data, and various parameters corresponding to the transaction current limiting model are obtained, so that training of the transaction current limiting model is completed.
Furthermore, another possible training method of the transaction current limiting model may be to input the obtained sample task data, the sample equipment proportion and the sample single machine current limiting data into the initialized transaction current limiting model, and improve the output rate of the single machine current limiting data output by the transaction current limiting model and the sample motor current limiting data which are the same or have the difference larger than a preset threshold value through adjusting the deviation proportion. And when the output rate reaches a preset output rate range, the transaction current limiting model is considered to be trained. It can be appreciated that when the deviation ratio is larger, the cluster current limiting data will also increase, so the adjustment amplitude will also increase accordingly, thereby affecting the output efficiency.
In the embodiment of the specification, task data and equipment proportion are acquired, then the task data are analyzed, calculation is performed in a cluster angle, single-machine current-limiting data corresponding to each transaction type of each service equipment in a service equipment cluster corresponding to the cluster current-limiting data are obtained according to the equipment proportion, single-machine deviation data required for calculation at the next moment are obtained through calculation, current-limiting data calculation at the next moment is started, adjustment of the current-limiting data is achieved, data are provided for current-limiting data calculation at the next moment, the current-limiting data output by the service equipment are adjusted, the current-limiting data of the service equipment cluster can meet the requirement of the task data, dynamic adjustment of the current-limiting data along with the time is achieved, and unified and stable current limiting under multiple scenes is controlled in a self-adaptive mode.
Based on the system architecture shown in fig. 1, the transaction flow limiting device provided in the embodiment of the present disclosure will be described in detail below with reference to fig. 6 to 10. It should be noted that, the transaction flow restrictor device in fig. 6-10 is used to execute the method of the embodiment shown in fig. 2-5 of the present specification, and for convenience of explanation, only the portion relevant to the embodiment of the present specification is shown, and specific technical details are not disclosed, please refer to the embodiment shown in fig. 2-5 of the present specification.
Referring to fig. 6, a schematic structural diagram of a transaction current limiting device is provided in an embodiment of the present disclosure. As shown in fig. 6, the transaction current limiting device 1 of the embodiment of the present specification may include: a task acquisition unit 11, an adjustment data acquisition unit 12, a first data acquisition unit 13, a second data acquisition unit 14, and a stand-alone data acquisition unit 15.
A task obtaining unit 11, configured to obtain task data for a service device cluster at a first moment and a device proportion of a service device in a working state in the service device cluster;
an adjustment data obtaining unit 12, configured to obtain cluster task data from the task data based on a transaction type, and obtain cluster adjustment data based on the task data and the device proportion;
a first data obtaining unit 13, configured to calculate the cluster adjustment data by using a preset deviation ratio to obtain first cluster current limiting data;
a second data obtaining unit 14, configured to obtain cluster history data of a history duration, and obtain second cluster current limit data based on the cluster history data and the cluster task data, where the history duration is an adjacent duration before the first time;
And the single machine data acquisition unit 15 is configured to determine single machine current limiting data corresponding to the transaction type based on the first cluster current limiting data, the second cluster current limiting data and the device proportion.
Alternatively, as shown in fig. 7, the adjustment data acquisition unit 12 includes:
a data parsing subunit 121, configured to parse the task data, and obtain a transaction type corresponding to the task data and current cluster current-limiting data;
a clustered task data acquisition subunit 122, configured to acquire clustered task data from the task data based on the transaction type;
a current single machine data obtaining subunit 123, configured to obtain current single machine current limiting data based on the current cluster current limiting data and the device proportion;
a single machine adjustment data obtaining subunit 124, configured to obtain single machine deviation data at the first moment, and obtain single machine adjustment data based on the single machine deviation data and the current single machine current limiting data;
an adjustment data obtaining subunit 125, configured to obtain cluster adjustment data based on the stand-alone adjustment data and the device proportion.
Alternatively, as shown in fig. 8, the second data acquisition unit 14 includes:
A history data obtaining subunit 141, configured to obtain cluster history data of a history duration;
the average value obtaining subunit 142 is configured to obtain cluster period data of each adjacent period in the cluster history data, and calculate each of the cluster period data and the cluster task data respectively to obtain a cluster deviation average value;
a rate obtaining subunit 143, configured to calculate the cluster period data of each adjacent period to obtain a cluster change rate value;
and a second data obtaining subunit 144, configured to obtain second cluster current limiting data based on the cluster deviation average value and the cluster change rate value.
Optionally, the mean value obtaining subunit 142 is further configured to:
acquiring cluster time period data of each adjacent time period in the cluster historical data, and performing difference value calculation on each cluster time period data and the cluster task data to obtain a first difference value corresponding to each adjacent time period;
and carrying out average value calculation on each first difference value to obtain a cluster deviation average value.
Optionally, the rate acquisition subunit 143 is further configured to:
respectively carrying out difference value calculation on the cluster time period data of each adjacent time period to obtain a second difference value;
And carrying out average value calculation on each second difference value to obtain a cluster change rate value.
Alternatively, as shown in fig. 9, the stand-alone data acquisition unit 15 includes:
a target data obtaining subunit 151, configured to obtain target cluster current limiting data based on the first cluster current limiting data and the second cluster current limiting data;
and the single machine data obtaining subunit 152 is configured to determine single machine current limiting data corresponding to the transaction type based on the device proportion and the target cluster current limiting data.
Optionally, as shown in fig. 10, the transaction current limiting device 1 further includes:
an actual data obtaining subunit 16, configured to obtain actual cluster current limiting data at the first moment;
a cluster deviation data obtaining subunit 17, configured to obtain cluster deviation data based on the actual cluster current limit data and the target cluster current limit data;
and a stand-alone deviation data obtaining subunit 18, configured to obtain stand-alone deviation data at a second moment based on the cluster deviation data and the device proportion, and transfer to perform the step of obtaining task data for a service device cluster at a first moment and the device proportion of a service device in a working state in the service device cluster, where the second moment is a next moment adjacent to the first moment.
In the embodiment of the specification, task data and equipment proportion are acquired, then the task data are analyzed, calculation is performed in a cluster angle, single-machine current-limiting data corresponding to each transaction type of each service equipment in a service equipment cluster corresponding to the cluster current-limiting data are obtained according to the equipment proportion, single-machine deviation data required for calculation at the next moment are obtained through calculation, current-limiting data calculation at the next moment is started, adjustment of the current-limiting data is achieved, data are provided for current-limiting data calculation at the next moment, the current-limiting data output by the service equipment are adjusted, the current-limiting data of the service equipment cluster can meet the requirement of the task data, dynamic adjustment of the current-limiting data along with the time is achieved, and unified and stable current limiting under multiple scenes is controlled in a self-adaptive mode.
The embodiments of the present disclosure further provide a computer storage medium, where a plurality of program instructions may be stored, where the program instructions are adapted to be loaded by a processor and execute the steps of the method according to the embodiments shown in fig. 1 to 5, and the specific execution process may refer to the specific description of the embodiments shown in fig. 1 to 5, which is not repeated herein.
The present disclosure further provides a computer program product, where at least one instruction is stored, where the at least one instruction is loaded by the processor and executed by the processor to perform the transaction flow restriction method as described in the embodiments shown in fig. 1 to 5, and the specific execution process may refer to the specific description of the embodiments shown in fig. 1 to 5, which is not repeated herein.
Referring to fig. 11, a schematic structural diagram of an electronic device is provided in an embodiment of the present disclosure. As shown in fig. 11, the electronic device 1000 may include: at least one processor 1001, such as a CPU, at least one network interface 1004, an input output interface 1003, a memory 1005, at least one communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 1005 may also optionally be at least one storage device located remotely from the processor 1001. As shown in fig. 11, an operating system, a network communication module, an input-output interface module, and a transaction flow limiting application may be included in a memory 1005, which is a type of computer storage medium.
In the electronic device 1000 shown in fig. 11, the input/output interface 1003 is mainly used as an interface for providing input for a user, and acquires data input by the user.
In one embodiment, the processor 1001 may be configured to invoke a transaction flow limiting application stored in the memory 1005 and specifically perform the following operations:
task data aiming at a service equipment cluster at a first moment and equipment proportion of service equipment in a working state in the service equipment cluster are obtained;
acquiring cluster task data from the task data based on the transaction type, and acquiring cluster adjustment data based on the task data and the equipment proportion;
calculating the cluster adjustment data by adopting a preset deviation proportion to obtain first cluster current limiting data;
acquiring cluster historical data of historical time length, and acquiring second cluster current limiting data based on the cluster historical data and the cluster task data, wherein the historical time length is adjacent time length before the first time;
and determining single machine current limiting data corresponding to the transaction type based on the first cluster current limiting data, the second cluster current limiting data and the equipment proportion.
Optionally, when executing the obtaining cluster task data in the task data based on the transaction type and obtaining cluster adjustment data based on the task data and the device proportion, the processor 1001 specifically performs the following operations:
Analyzing the task data to obtain the transaction type corresponding to the task data and current cluster current-limiting data;
acquiring cluster task data from the task data based on the transaction type;
acquiring current single machine current limiting data based on the current cluster current limiting data and the equipment proportion;
obtaining single machine deviation data at the first moment, and obtaining single machine adjustment data based on the single machine deviation data and the current single machine current limiting data;
and acquiring cluster adjustment data based on the single machine adjustment data and the equipment proportion.
Optionally, when executing the cluster history data of the acquired history duration and obtaining the second cluster current limit data based on the cluster history data and the cluster task data, the processor 1001 specifically performs the following operations:
acquiring cluster history data of history duration;
acquiring cluster time period data of each adjacent time period in the cluster historical data, and respectively calculating each cluster time period data and the cluster task data to obtain a cluster deviation average value;
respectively calculating the cluster time period data of each adjacent time period to obtain a cluster change rate value;
and obtaining second cluster current limiting data based on the cluster deviation average value and the cluster change rate value.
Optionally, when executing to obtain cluster period data of each adjacent period in the cluster history data, the processor 1001 calculates each of the cluster period data and the cluster task data to obtain a cluster deviation average value, specifically executes the following operations:
acquiring cluster time period data of each adjacent time period in the cluster historical data, and performing difference value calculation on each cluster time period data and the cluster task data to obtain a first difference value corresponding to each adjacent time period;
and carrying out average value calculation on each first difference value to obtain a cluster deviation average value.
Optionally, the processor 1001 performs the following operations when performing calculation between the cluster period data of each adjacent period to obtain a cluster change rate value:
respectively carrying out difference value calculation on the cluster time period data of each adjacent time period to obtain a second difference value;
and carrying out average value calculation on each second difference value to obtain a cluster change rate value.
Optionally, when executing the determination of the single machine current limit data corresponding to the transaction type based on the first cluster current limit data, the second cluster current limit data, and the device proportion, the processor 1001 specifically performs the following operations:
Obtaining target cluster current limiting data based on the first cluster current limiting data and the second cluster current limiting data;
and determining single machine current limiting data corresponding to the transaction type based on the equipment proportion and the target cluster current limiting data.
Optionally, after determining the single machine current limit data corresponding to the transaction type based on the first cluster current limit data, the second cluster current limit data, and the device proportion, the processor 1001 further performs the following operations:
acquiring actual cluster current limiting data at the first moment;
obtaining cluster deviation data based on the actual cluster current limiting data and the target cluster current limiting data;
and obtaining single machine deviation data at a second moment based on the cluster deviation data and the equipment proportion, and switching to the step of obtaining task data aiming at a service equipment cluster at a first moment and the equipment proportion of the service equipment in a working state in the service equipment cluster, wherein the second moment is the next moment adjacent to the first moment.
In the embodiment of the specification, task data and equipment proportion are acquired, then the task data are analyzed, calculation is performed in a cluster angle, single-machine current-limiting data corresponding to each transaction type of each service equipment in a service equipment cluster corresponding to the cluster current-limiting data are obtained according to the equipment proportion, single-machine deviation data required for calculation at the next moment are obtained through calculation, current-limiting data calculation at the next moment is started, adjustment of the current-limiting data is achieved, data are provided for current-limiting data calculation at the next moment, the current-limiting data output by the service equipment are adjusted, the current-limiting data of the service equipment cluster can meet the requirement of the task data, dynamic adjustment of the current-limiting data along with the time is achieved, and unified and stable current limiting under multiple scenes is controlled in a self-adaptive mode.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
The foregoing disclosure is only illustrative of the preferred embodiments of the present invention and is not to be construed as limiting the scope of the claims, which follow the meaning of the claims of the present invention.

Claims (11)

1. A transaction flow limiting method, the method comprising:
task data aiming at a service equipment cluster at a first moment and equipment proportion of service equipment in a working state in the service equipment cluster are obtained;
acquiring cluster task data from the task data based on the transaction type, and acquiring cluster adjustment data based on the task data and the equipment proportion;
calculating the cluster adjustment data by adopting a preset deviation proportion to obtain first cluster current limiting data;
Acquiring cluster historical data of historical time length, and acquiring second cluster current limiting data based on the cluster historical data and the cluster task data, wherein the historical time length is adjacent time length before the first time;
and determining single machine current limiting data corresponding to the transaction type based on the first cluster current limiting data, the second cluster current limiting data and the equipment proportion.
2. The method of claim 1, the obtaining cluster task data in the task data based on a transaction type, obtaining cluster adjustment data based on the task data and the device proportion, comprising:
analyzing the task data to obtain the transaction type corresponding to the task data and current cluster current-limiting data;
acquiring cluster task data from the task data based on the transaction type;
acquiring current single machine current limiting data based on the current cluster current limiting data and the equipment proportion;
obtaining single machine deviation data at the first moment, and obtaining single machine adjustment data based on the single machine deviation data and the current single machine current limiting data;
and acquiring cluster adjustment data based on the single machine adjustment data and the equipment proportion.
3. The method of claim 1, wherein the obtaining cluster history data for a history duration, and obtaining second cluster current limit data based on the cluster history data and the cluster task data, comprises:
acquiring cluster history data of history duration;
acquiring cluster time period data of each adjacent time period in the cluster historical data, and respectively calculating each cluster time period data and the cluster task data to obtain a cluster deviation average value;
respectively calculating the cluster time period data of each adjacent time period to obtain a cluster change rate value;
and obtaining second cluster current limiting data based on the cluster deviation average value and the cluster change rate value.
4. The method of claim 3, wherein the obtaining cluster period data of each adjacent period in the cluster history data, and calculating each cluster period data and the cluster task data respectively to obtain a cluster deviation average value, includes:
acquiring cluster time period data of each adjacent time period in the cluster historical data, and performing difference value calculation on each cluster time period data and the cluster task data to obtain a first difference value corresponding to each adjacent time period;
And carrying out average value calculation on each first difference value to obtain a cluster deviation average value.
5. A method according to claim 3, wherein the calculating the cluster change rate value between the cluster period data of each adjacent period includes:
respectively carrying out difference value calculation on the cluster time period data of each adjacent time period to obtain a second difference value;
and carrying out average value calculation on each second difference value to obtain a cluster change rate value.
6. The method of claim 1, wherein the determining the single machine current limit data corresponding to the transaction type based on the first cluster current limit data, the second cluster current limit data, and the device proportion comprises:
obtaining target cluster current limiting data based on the first cluster current limiting data and the second cluster current limiting data;
and determining single machine current limiting data corresponding to the transaction type based on the equipment proportion and the target cluster current limiting data.
7. The method of claim 1, wherein after determining the single machine current limit data corresponding to the transaction type based on the first cluster current limit data, the second cluster current limit data, and the device proportion, further comprises:
Acquiring actual cluster current limiting data at the first moment;
obtaining cluster deviation data based on the actual cluster current limiting data and the target cluster current limiting data;
and obtaining single machine deviation data at a second moment based on the cluster deviation data and the equipment proportion, and switching to the step of obtaining task data aiming at a service equipment cluster at a first moment and the equipment proportion of the service equipment in a working state in the service equipment cluster, wherein the second moment is the next moment adjacent to the first moment.
8. A transaction flow-limiting device, the device comprising:
the task acquisition unit is used for acquiring task data aiming at the service equipment cluster at a first moment and equipment proportion of service equipment in a working state in the service equipment cluster;
the adjustment data acquisition unit is used for acquiring cluster task data from the task data based on the transaction type and acquiring cluster adjustment data based on the task data and the equipment proportion;
the first data acquisition unit is used for calculating the cluster adjustment data by adopting a preset deviation proportion to obtain first cluster current limiting data;
the second data acquisition unit is used for acquiring cluster historical data of historical time length, and acquiring second cluster current limiting data based on the cluster historical data and the cluster task data, wherein the historical time length is adjacent time length before the first time;
And the single machine data acquisition unit is used for determining single machine current limiting data corresponding to the transaction type based on the first cluster current limiting data, the second cluster current limiting data and the equipment proportion.
9. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the method according to any one of claims 1 to 7.
10. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the steps of the method according to any one of claims 1-7.
11. A computer program product having stored thereon at least one instruction which when executed by a processor implements the steps of the method of any of claims 1 to 7.
CN202310678788.0A 2023-06-08 2023-06-08 Transaction current limiting method and device, storage medium and electronic equipment Pending CN116886633A (en)

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