CN114943575A - Early warning method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The disclosure provides an early warning method, an early warning device, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as cloud service, big data and deep learning. The early warning method comprises the following steps: determining a target cloud product, and acquiring the predicted resource consumption of the target cloud product at a future target time; acquiring target available resources of the target cloud product at the future target time according to the product information of the target cloud product and the future target time; and generating an early warning result corresponding to the target cloud product according to the target available resource and the predicted resource consumption. The cloud product early warning method and the cloud product early warning system achieve the purpose of automatically early warning the cloud product used by the user, and improve the accuracy when early warning is carried out on the cloud product.
Description
Technical Field
The disclosure relates to the technical field of data processing, in particular to the technical field of artificial intelligence such as cloud computing, big data and deep learning, and provides an early warning method, an early warning device, electronic equipment and a readable storage medium.
Background
Cloud products typically have two delivery modes, one is prepaid and one is postpaid. When the payment method of paying after using is used for paying the use cost of the cloud product, the asset balance of the user has great influence on whether the cloud product can be used normally all the time. If the asset balance of the user is less, fee deduction failure can be caused, and normal use of the cloud product by the user is seriously influenced; if the user's balance of assets is high, the user's use of funds or fund planning may be affected.
Disclosure of Invention
According to a first aspect of the present disclosure, there is provided an early warning method, including: determining a target cloud product, and acquiring the predicted resource consumption of the target cloud product at a future target time; acquiring target available resources of the target cloud product at the future target time according to the product information of the target cloud product and the future target time; and generating an early warning result corresponding to the target cloud product according to the target available resource and the predicted resource consumption.
According to a second aspect of the present disclosure, there is provided an early warning device, comprising: the prediction unit is used for determining a target cloud product and acquiring the predicted resource consumption of the target cloud product at the future target time; the processing unit is used for acquiring target available resources of the target cloud product at the future target time according to the product information of the target cloud product and the future target time; and the early warning unit is used for generating an early warning result corresponding to the target cloud product according to the target available resource and the predicted resource consumption.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method as described above.
According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as described above.
According to the technical scheme, the cloud product used by the user can be automatically early warned, the available target resources can be more accurately acquired by combining the product information of the target cloud product and the future target time, and the early warning accuracy of the cloud product is further improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;
FIG. 2 is a schematic diagram according to a second embodiment of the present disclosure;
FIG. 3 is a schematic diagram according to a third embodiment of the present disclosure;
FIG. 4 is a schematic diagram according to a fourth embodiment of the present disclosure;
FIG. 5 is a schematic diagram according to a fifth embodiment of the present disclosure;
FIG. 6 is a schematic diagram according to a sixth embodiment of the present disclosure;
fig. 7 is a block diagram of an electronic device for implementing an early warning method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure. As shown in fig. 1, the early warning method of the embodiment specifically includes the following steps:
s101, determining a target cloud product, and acquiring the predicted resource consumption of the target cloud product at a future target time;
s102, acquiring target available resources of the target cloud product at the future target time according to the product information of the target cloud product and the future target time;
s103, generating an early warning result corresponding to the target cloud product according to the target available resource and the predicted resource consumption.
According to the early warning method, the target cloud product is determined, the predicted resource consumption of the target cloud product at the future target time is obtained, the target available resources of the target cloud product at the future target time are obtained according to the product information and the future target time of the target cloud product, and the early warning result corresponding to the target cloud product is generated according to the target available resources and the predicted resource consumption.
In the embodiment, when S101 is executed to determine the target cloud product, an optional implementation manner that may be adopted is as follows: acquiring at least one cloud product corresponding to the target account; acquiring the priority of at least one cloud product; and taking the cloud product with the priority meeting the requirement as a target cloud product.
In this embodiment, at least one cloud product corresponding to the target account acquired in S101 is executed, and the cloud product is paid for use on the cloud platform for the target user corresponding to the target account.
It is to be understood that the resource in this embodiment may be money, or may be other specific real resource or virtual resource, and this embodiment is not limited thereto. For example, the resource consumption in this embodiment may be a consumption amount required to use a cloud product, and correspondingly, the available resource may be an asset in the target account.
In this embodiment, when executing S101 to obtain the priority of at least one cloud product, the optional implementation manner that may be adopted is: acquiring historical resource data liveness corresponding to each cloud product; and determining the cloud product with higher activity corresponding to the historical resource data as the corresponding higher priority.
Optionally, for any cloud product, obtaining an average historical resource data activity of all accounts corresponding to the cloud product, and taking the average historical resource data activity as the historical resource data activity, where the average historical resource data activity is the sum of the historical resource data activities of the cloud product corresponding to each account divided by the number of accounts, and all the accounts are accounts of the cloud product corresponding to historical existence.
Or, for any cloud product, obtaining the historical resource data activity of the cloud product corresponding to the account, and taking the historical resource data activity of the cloud product corresponding to the account as the historical resource data activity corresponding to the cloud product.
In this embodiment, the activity of the historical resource data corresponding to each cloud product obtained by executing S101 may be preset, that is, the priority of each cloud product may be preset in this embodiment.
In this embodiment, when S101 is executed, the historical resource data activity of each cloud product may also be obtained according to the information of the resource consumption speed, the resource replenishment frequency, the resource replenishment amount, and the like of each cloud product in the historical time, for example, the faster the resource consumption speed is, the higher the historical resource data activity is, the higher the resource replenishment frequency is, the higher the historical resource data activity is, the larger the resource replenishment value is, the higher the historical resource data activity is.
When executing S101 to use cloud products with priorities meeting the requirements as target cloud products, the embodiment can use each cloud product as the target cloud product in sequence according to the sequence of priorities from high to low, so that early warning is performed only on one cloud product in one early warning process, the higher the priority of the target cloud product is, the higher the importance degree of the target cloud product is, and then early warning is preferentially performed on the target cloud product with the higher importance degree.
Accordingly, after the early warning of the current target cloud product is completed, the next target cloud product is determined in the same manner and the early warning of the target cloud product is performed, so that the early warning results corresponding to the target cloud products are sequentially generated.
In addition, in this embodiment, when S101 is executed to determine a target cloud product, one cloud product randomly selected from the cloud products may be used as the target cloud product, so that after the early warning of the current target cloud product is completed, another cloud product is randomly selected to perform the early warning.
After the target cloud product is determined in S101, the present embodiment obtains the predicted resource consumption of the target cloud product at the future target time.
In the embodiment, when S101 is executed, a future target time is determined first, and then predicted resource consumption of a target cloud product at the future target time is obtained; the future target time in this embodiment may be a future time point or a future time period; the number of the future target time may be one or more.
In the present embodiment, when S101 is executed to determine the future target time, the time input by the input terminal or the time selected by the input terminal may be used as the future target time; the future target time may also be a time that is a preset time interval from the current time, for example, the current time is 3 months and 28 days, and the preset time interval is 1 day, and then the present embodiment takes 3 months and 29 days as the future target time.
When S101 is executed to obtain the predicted resource consumption of the target cloud product at the future target time, the present embodiment may use an average value of historical resource consumption of the target cloud product in a preset number of days before the future target time as the predicted resource consumption.
It can be understood that, if the present embodiment determines a plurality of future target times, for example, a week in the future, the present embodiment respectively obtains the predicted resource consumption of the target cloud product corresponding to each future target time when executing S101.
After executing S101 to acquire the predicted resource consumption of the target cloud product at the future target time, executing S102 to acquire the target available resource of the target cloud product at the future target time according to the product information of the target cloud product and the future target time.
Because the target account of the target user includes multiple types of resources, such as cash, voucher, return points, and the like, and different types of resources generally correspond to different use conditions, for example, the voucher has a limitation on product type or use time, the embodiment needs to filter the resources in the target account according to the product information of the target cloud product and the future target time, so as to improve the accuracy of the acquired target available resources.
Specifically, when executing S102 to obtain the target available resources of the target cloud product at the future target time according to the product information and the future target time of the target cloud product, the embodiment may adopt an optional implementation manner as follows: acquiring current available resources of a target account; selecting resources meeting preset conditions from the current available resources as target available resources, wherein the resources meeting the preset conditions comprise: the product information applicable to the resource comprises product information of a target cloud product, and the resource meets the availability requirement at the future target time; in this embodiment, the product information of the target cloud product may be information such as a name of the target cloud product and a type of the target cloud product.
For example, if the resource list R of the currently available resource acquired in this embodiment is { cash 1, voucher 2, return point 1}, if the target cloud product is cloud product 1, the future target time is tuesday, if the product type limited to be used by voucher 1 is cloud product 2, and the available time of return point 1 is weekend, the resource list R' of the target available resource acquired in S102 in this embodiment is { cash 1, voucher 2 }.
Because the embodiment relates to early warning of a plurality of cloud products, and in consideration of the fact that user resources in the target account are at least partially occupied after one early warning, the embodiment proposes that available resources in the target account are acquired when corresponding different times are acquired according to the times of the current generated early warning result, so as to improve the accuracy of acquiring the current available resources.
In this embodiment, when executing S102 to acquire the current available resource of the target account, an optional implementation manner that may be adopted is: acquiring the times of the current generated early warning result; and acquiring the current available resources of the target account according to the times of generating the early warning result.
For example, if the current warning is the first warning, the present embodiment executes the current available assets acquired in S102, which are all resources of the target account; if the current early warning is not the first early warning, the present embodiment executes the current available resource obtained in S102, which is a subtraction result between the current available asset obtained in the previous early warning and the predicted resource consumption obtained in the previous early warning.
That is to say, in the embodiment, the target available resources are obtained by screening from the current available resources of the target account according to the product information of the target cloud product and the future target time, so that an error is avoided when the target available resources are obtained, and the accuracy of the obtained target available resources is improved.
In this embodiment, when S102 is executed to acquire the current available resources of the target account according to the number of times that the warning result has been generated, the acquired current available resources may be further sorted according to at least one of the priority of the resource category, the available time of the resources, the numerical value of the resources, and the like; the priority of the resource category may be that the voucher is the first priority, the return point is the second priority, and the cash is the third priority, and the higher the priority of the asset, the higher the asset is, the lower the asset is.
In this embodiment, when S102 is executed to select a resource satisfying a preset condition from the current available resources, and the resource that does not exceed the limit in the resources that can be used by the target cloud product at the future target time may be used as the target available resource in combination with the limit of resource use, such as a single-day limit, a single-week limit, or a single-product limit, when the target available resource is used as the target available resource, so as to further improve the accuracy of the obtained target available resource.
For example, if the current warning is the first warning, if the amount of the voucher 2 in the resources available for the target cloud product in the future target time is 100 yuan, and if the unit product limit of the voucher 2 is 40 yuan, the resource list R' of the target available resources obtained by executing S102 in this embodiment is { cash 1, 40 yuan in voucher 2 }.
In this embodiment, after the target available resources of the target cloud product at the future target time are acquired in step S102, step S103 is performed to generate an early warning result corresponding to the target cloud product according to the target available resources and the predicted resource consumption.
In this embodiment, when S103 is executed to generate an early warning result corresponding to a target cloud product according to the target available resource and the predicted resource consumption, an optional implementation manner that may be adopted is as follows: and generating an early warning result corresponding to the target cloud product under the condition that the target available resource is determined to be less than or equal to the predicted resource consumption.
In this embodiment, if it is determined that the target available resource is greater than the predicted resource consumption and indicates that the assets in the target account of the target user are abundant, the early warning result does not need to be generated in step S103.
In this embodiment, after the early warning result corresponding to the target cloud product is generated in S103, an operation of outputting the early warning result may be further performed, that is, the early warning result is sent to the target user.
In this embodiment, when the S103 is executed to output the warning result, the optional implementation manner that may be adopted is: acquiring a notification mode corresponding to the priority of the target cloud product; and outputting an early warning result by using a notification mode corresponding to the priority, so that the intelligence of the notification is improved.
For example, if the priority of the target cloud product obtained in this embodiment is priority 1, if the sending method corresponding to priority 1 is a telephone notification or uses a high notification frequency (for example, once per minute or many times per hour), the sending method is used to output the warning result.
The embodiment may further set a termination condition, for example, stop outputting the warning result after the target user completes resource replenishment or the target user clicks a specific button.
After the early warning result corresponding to the target cloud product is generated by executing the step S103, the method may shift to executing the step S101 to determine the target cloud product, and this continues until all cloud products corresponding to the target account are early warned.
Fig. 2 is a schematic diagram according to a second embodiment of the present disclosure. As shown in fig. 2, when executing S101 "obtaining predicted resource consumption of the target cloud product at the future target time", the embodiment specifically includes the following steps:
s201, acquiring first characteristic data of the target cloud product corresponding to historical time;
s202, training a neural network model according to the first characteristic data to obtain a resource consumption prediction model corresponding to the target cloud product;
s203, inputting second characteristic data of the target cloud product corresponding to the future target time into the resource consumption prediction model, and taking an output result of the resource consumption prediction model as the predicted resource consumption.
That is to say, in this embodiment, a resource consumption prediction model is obtained according to the historical data of the corresponding target cloud product through training, and then the resource consumption prediction model is used to obtain the predicted resource consumption of the target cloud product at the future target time, and different target cloud products correspond to different resource consumption prediction models, so that the accuracy of the obtained predicted resource consumption can be improved.
In the embodiment, when S201 is executed, firstly, historical time is determined, and then first feature data of the target cloud product corresponding to the historical time is acquired, where the first feature data includes historical resource consumption of the target cloud product at the historical time; the historical time in this embodiment may be a time point or a time period.
In the embodiment, the first feature data of the target cloud product corresponding to the historical time, which is obtained in S201, includes at least one of user feature data, product feature data and consumption feature data corresponding to the historical time; in this embodiment, the server may store the feature data corresponding to different times in advance, that is, the feature data may be obtained according to the determined historical time, where the obtained feature data is a numerical value corresponding to different features.
The user characteristic data corresponding to the historical time obtained by executing S201 in this embodiment may include at least one of data of whether the user is a real name (e.g., 1, or not 0), a real name authentication type of the user (e.g., unauthenticated to 0, personal authentication to 1, organization authentication to 2, enterprise authentication to 3, and the like), a first-level industry classification of the user (numerical mapping with the code according to the national economic industry classification), a second-level industry classification of the user (numerical mapping with the code according to the national economic industry classification), a VIP level (e.g., a common user is 0, a VIP is 1, a VVIP is 2, and the like), a user type (e.g., self-registration is 0, a business mining is 1, a project union is 2, a group relationship is 3, and the like), an account validation duration of the user, and the like.
The product characteristic data corresponding to the historical time acquired by the embodiment executing S201 may include at least one of data such as a basic configuration price of the product corresponding to the minimum time unit, whether the product is promoted (for example, 1, 0), a price difference of the product (for example, a unit price difference on the same day or between the same dates), a number of days of use of the product, a total consumed amount of the product, a difference between a price set by the user and the basic configuration price, and the like.
The consumption characteristic data corresponding to the history time acquired in S201 executed by the present embodiment may include resource consumption of the user at the history time, yesterday, daily resource consumption of the user at the history time, total consumption of the user, the history time is day of week (for example, monday to sunday are 1 to 7 in order), the history time is several months (for example, 1 to 12 in order from 1 to 12 months), and the history time is number of several (for example, 1 to 31 in order from 1 to 31).
If the first feature data obtained by executing S201 in this embodiment is different features, in this embodiment, when executing S201, the numerical value corresponding to the feature obtained by conversion may be used as the first feature data according to the different features and the corresponding numerical value conversion manners.
In this embodiment, when S202 is executed to train the neural network model according to the first feature data to obtain the resource consumption prediction model corresponding to the target cloud product, an optional implementation manner that may be adopted is as follows: inputting the first characteristic data into a neural network model to obtain the predicted resource consumption output by the neural network model; calculating a loss function value according to the historical resource consumption and the predicted resource consumption; and adjusting parameters of the neural network model according to the calculated loss function value until the neural network model converges to obtain a resource consumption prediction model.
It can be understood that the neural network model in this embodiment may be a regression model, and different feature data correspond to different parameters in the regression model, so the training process for the regression model is a process of adjusting parameters corresponding to different feature data.
In this embodiment, when S203 is executed, first, second feature data corresponding to a future target time of the target cloud product is obtained (the obtaining manner of the second feature data is the same as that of the first feature data, except that the second feature data does not include resource consumption of the future target time), and then the obtained second feature data is input into the resource consumption prediction model, which can output predicted resource consumption corresponding to the future target time.
Fig. 3 is a schematic diagram according to a third embodiment of the present disclosure. Fig. 3 shows a flowchart of the resource consumption prediction model obtained by the embodiment during training, which corresponds to different target cloud products: in fig. 3, different feature modules are used to obtain different feature data; if the acquired feature data are features, the data preprocessing module converts the acquired features into numerical values, and the conversion results are used as first feature data, namely data sets, and different cloud products correspond to different data sets; and respectively training on a machine learning regression model training platform according to data sets corresponding to different cloud products to obtain resource consumption prediction models corresponding to the different cloud products, wherein a neural network model used for training is a regression model.
Fig. 4 is a schematic diagram according to a fourth embodiment of the present disclosure. Fig. 4 shows a flowchart of the embodiment when early warning is performed on a product x: the early warning configuration module is used for acquiring a resource consumption prediction model N corresponding to the product x from the machine learning model training platform; the machine learning model training platform is used for training to obtain a resource consumption model N corresponding to the product x, the resource consumption model N is a regression model, and the obtained resource consumption prediction model N is used for obtaining the predicted resource consumption of the product x at the future target time; the user resource module is used for acquiring the current available resources of the target account, acquiring the target available resources of the product x, performing resource matching on the target available resources and the predicted resource consumption, and acquiring an early warning result according to the matching result; and the message notification module is used for acquiring the early warning notification configuration from the early warning configuration module to perform message notification, and particularly notifying the target user according to a pre-configured notification frequency and a notification mechanism.
Fig. 5 is a schematic diagram according to a fifth embodiment of the present disclosure. Fig. 5 shows a flowchart of the embodiment when early warning is performed on a plurality of target cloud products, and early warning is performed on one target cloud product in a multi-dimensional matching manner; the first dimension is a product, and the dimension determines a target cloud product; the second dimension is a resource category, and the dimension is used for acquiring current available resources R of different resource categories corresponding to the target account; the third dimension is resource verification, and the dimension is used for acquiring target available resources R' of the target cloud product at the future target time; the fourth dimension is resource matching, and the dimension is used for matching the target available resource with the predicted resource consumption; and after the early warning of the target cloud product x is finished, continuing to early warn the next target cloud product y.
Fig. 6 is a schematic diagram according to a sixth embodiment of the present disclosure. As shown in fig. 6, the warning device 600 of the present embodiment includes:
the prediction unit 601 is used for determining a target cloud product and acquiring the predicted resource consumption of the target cloud product at a future target time;
the processing unit 602 is configured to obtain, according to the product information of the target cloud product and the future target time, a target available resource of the target cloud product at the future target time;
the early warning unit 603 is configured to generate an early warning result corresponding to the target cloud product according to the target available resource and the predicted resource consumption.
When determining the target cloud product, the prediction unit 601 may adopt the following optional implementation manners: acquiring at least one cloud product corresponding to the target account; acquiring the priority of at least one cloud product; and taking the cloud product with the priority meeting the requirement as a target cloud product.
The at least one cloud product corresponding to the target account acquired by the prediction unit 601 is a cloud product paid for use on the cloud platform by a target user corresponding to the target account.
When the prediction unit 601 obtains the priority of at least one cloud product, the optional implementation manners that may be adopted are: acquiring historical resource data liveness corresponding to each cloud product; and determining the cloud product with higher activity corresponding to the historical resource data as the corresponding higher priority.
Optionally, for any cloud product, the prediction unit 601 obtains an average historical resource data activity of all accounts corresponding to the cloud product, and takes the average historical resource data activity as the historical resource data activity, where the average historical resource data activity is the sum of the historical resource data activities of the cloud product corresponding to each account divided by the number of the accounts, and all the accounts are accounts corresponding to the cloud product for which the history exists.
Alternatively, for any cloud product, the prediction unit 601 obtains the historical resource data activity of the cloud product corresponding to the account, and takes the historical resource data activity of the cloud product corresponding to the account as the historical resource data activity corresponding to the cloud product.
The historical resource data activity corresponding to each cloud product acquired by the prediction unit 601 may be preset, that is, the priority of each cloud product is preset in this embodiment.
The prediction unit 601 may further obtain the historical resource data activity of each cloud product according to the resource consumption speed, the resource supplement frequency, the resource supplement amount and other information of each cloud product in the historical time, for example, the faster the resource consumption speed is, the higher the historical resource data activity is, the higher the resource supplement frequency is, the higher the historical resource data activity is, and the larger the resource supplement value is, the higher the historical resource data activity is.
When cloud products with priorities meeting the requirements are taken as target cloud products, the prediction unit 601 may sequentially take the cloud products as the target cloud products according to the sequence from high priorities to low priorities, so that early warning is performed on only one cloud product in one early warning process, the higher the priority of the target cloud product is, the higher the importance degree of the target cloud product is, and early warning is preferentially performed on the target cloud product with the higher importance degree.
Accordingly, after the early warning of the current target cloud product is completed, the next target cloud product is determined in the same manner and the early warning of the target cloud product is performed, so that the early warning results corresponding to the target cloud products are sequentially generated.
In addition, when determining a target cloud product, the prediction unit 601 may further use one cloud product randomly selected from the cloud products as the target cloud product, so that after the early warning of the current target cloud product is completed, another cloud product is randomly selected to perform the early warning.
The prediction unit 601 acquires the predicted resource consumption of the target cloud product at the future target time after determining the target cloud product.
The prediction unit 601 firstly determines the future target time, and then obtains the predicted resource consumption of the target cloud product at the future target time; the future target time in this embodiment may be a time point or a time period; the number of the future target time may be one or more.
The prediction unit 601 may take the time input by the input terminal or the time selected by the input terminal as the future target time when determining the future target time; the time that is a preset time interval from the current time may also be taken as the future target time.
The prediction unit 601 may use an average value of historical resource consumption of the target cloud product within a preset number of days before the future target time as the predicted resource consumption when obtaining the predicted resource consumption of the target cloud product at the future target time.
When obtaining the predicted resource consumption of the target cloud product at the future target time, the prediction unit 601 may further adopt the following manner: acquiring first characteristic data of a target cloud product corresponding to historical time; training a neural network model according to the first characteristic data to obtain a resource consumption prediction model corresponding to the target cloud product; and inputting second characteristic data of the target cloud product corresponding to future target time into the resource consumption prediction model, and taking an output result of the resource consumption prediction model as predicted resource consumption.
That is to say, the prediction unit 601 obtains a resource consumption prediction model according to the historical data of the corresponding target cloud product, and then obtains the predicted resource consumption of the target cloud product at the future target time by using the resource consumption prediction model, and different target cloud products correspond to different resource consumption prediction models, so that the accuracy of the obtained predicted resource consumption can be improved.
The prediction unit 601 firstly determines the historical time, and then acquires first feature data of the target cloud product corresponding to the historical time, wherein the first feature data comprises the historical resource consumption of the target cloud product at the historical time; the historical time in this embodiment may be a time point or a time period.
The first feature data of the target cloud product corresponding to the historical time, which is acquired by the prediction unit 601, includes at least one of user feature data, product feature data and consumption feature data corresponding to the historical time; in this embodiment, the server may store the feature data corresponding to different times in advance, that is, the feature data may be obtained according to the determined historical time, where the obtained feature data is a numerical value corresponding to different features.
If the first feature data acquired by the prediction unit 601 is different features, the prediction unit 601 may use the converted numerical value corresponding to the feature as the first feature data according to the different features and the corresponding numerical conversion manners.
When the prediction unit 601 trains the neural network model according to the first feature data to obtain the resource consumption prediction model corresponding to the target cloud product, the optional implementation manner that can be adopted is as follows: inputting the first characteristic data into a neural network model to obtain the predicted resource consumption output by the neural network model; calculating a loss function value according to the historical resource consumption and the predicted resource consumption; and adjusting parameters of the neural network model according to the calculated loss function value until the neural network model converges to obtain a resource consumption prediction model.
The prediction unit 601 first obtains second feature data corresponding to a future target time of the target cloud product (the second feature data is obtained in the same manner as the first feature data, except that the second feature data does not include resource consumption of the future target time), and then inputs the obtained second feature data into the resource consumption prediction model, which can output predicted resource consumption corresponding to the future target time.
It can be understood that, if the prediction unit 601 determines a plurality of future target times, for example, a week in the future, the predicted resource consumption of the target cloud product corresponding to each future target time is obtained.
In the embodiment, after the prediction unit 601 obtains the predicted resource consumption of the target cloud product at the future target time, the processing unit 602 obtains the target available resource of the target cloud product at the future target time according to the product information of the target cloud product and the future target time.
When the processing unit 602 acquires the target available resource of the target cloud product at the future target time according to the product information of the target cloud product and the future target time, the optional implementation manner that can be adopted is as follows: acquiring current available resources of a target account; selecting resources meeting preset conditions from the current available resources as target available resources, wherein the resources meeting the preset conditions comprise: the product information applicable to the resource comprises product information of a target cloud product, and the resource meets the availability requirement at the future target time; in this embodiment, the product information of the target cloud product may be information such as a name of the target cloud product and a type of the target cloud product.
Since the embodiment may involve performing early warning on a plurality of cloud products, and after one early warning, user resources in the target account may be at least partially occupied, the processing unit 602 needs to obtain available resources in the target account corresponding to different times according to the number of times that the early warning result has been currently generated.
When acquiring the current available resources of the target account, the processing unit 602 may adopt the following optional implementation manners: acquiring the times of the current generated early warning result; and acquiring the current available resources of the target account according to the times of generating the early warning result.
That is to say, the processing unit 602 obtains the target available resources by screening from the current available resources of the target account according to the product information of the target cloud product and the future target time, so as to avoid errors occurring when the target available resources are obtained, and improve the accuracy of the obtained target available resources.
When acquiring the current available resource of the target account according to the number of times of generating the early warning result, the processing unit 602 may further rank the acquired current available resource according to at least one of the priority of the resource category, the available time of the resource, the numerical value of the resource, and the like; the priority of the resource category may be that the voucher is the first priority, the return point is the second priority, and the cash is the third priority, and the higher the priority of the asset, the higher the asset is, the lower the asset is.
When the resources meeting the preset condition are selected from the current available resources and used as the target available resources, the processing unit 602 may further use, in combination with a limit of resource usage, such as a single-day limit, a single-week limit, or a single-product limit, resources that do not exceed the limit in the resources that can be used by the target cloud product at the future target time as the target available resources, so as to further improve the accuracy of the obtained target available resources.
In this embodiment, after the processing unit 602 acquires the target available resources of the target cloud product at the future target time, the early warning unit 603 generates an early warning result corresponding to the target cloud product according to the target available resources and the predicted resource consumption.
When the early warning unit 603 generates an early warning result corresponding to the target cloud product according to the target available resource and the predicted resource consumption, the optional implementation manner that can be adopted is as follows: and generating an early warning result corresponding to the target cloud product under the condition that the target available resource is determined to be less than or equal to the predicted resource consumption.
If the early warning unit 603 determines that the target available resource is greater than the predicted resource consumption and indicates that the assets in the target account of the target user are abundant, it is not necessary to generate an early warning result.
After the early warning unit 603 generates the early warning result corresponding to the target cloud product, it may further perform an operation of outputting the early warning result, that is, sending the early warning result to the target user.
When the early warning unit 603 outputs the early warning result, the optional implementation manner that can be adopted is: acquiring a notification mode corresponding to the priority of the target cloud product; and outputting an early warning result by using a notification mode corresponding to the priority, so that the intelligence of the notification is improved.
The early warning unit 603 may further set a termination condition, for example, stop outputting the early warning result after the target user completes resource replenishment or the target user clicks a specific button.
After the early warning unit 603 generates the early warning result of the corresponding target cloud product, the prediction unit 601 may be switched to determine the target cloud product, and the process continues until all cloud products corresponding to the target account are early warned.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the customs of public sequences.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
As shown in fig. 7, is a block diagram of an electronic device of an early warning method according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 7, the device 700 comprises a computing unit 701 which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the device 700 can be stored. The computing unit 701, the ROM702, and the RAM703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM702 and/or communications unit 709. When the computer program is loaded into the RAM703 and executed by the computing unit 701, one or more steps of the above described warning method may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the early warning method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable warning device such that the program codes, when executed by the processor or controller, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a presentation device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for presenting information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service extensibility in a traditional physical host and VPS service ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (21)
1. An early warning method, comprising:
determining a target cloud product, and acquiring the predicted resource consumption of the target cloud product at a future target time;
acquiring target available resources of the target cloud product at the future target time according to the product information of the target cloud product and the future target time;
and generating an early warning result corresponding to the target cloud product according to the target available resource and the predicted resource consumption.
2. The method of claim 1, wherein the determining a target cloud product comprises:
acquiring at least one cloud product corresponding to the target account;
obtaining a priority of the at least one cloud product;
and taking the cloud product with the priority meeting the requirement as the target cloud product.
3. The method of any of claims 1-2, wherein the obtaining the predicted resource consumption of the target cloud product at a future target time comprises:
acquiring first characteristic data of the target cloud product corresponding to historical time;
training a neural network model according to the first characteristic data to obtain a resource consumption prediction model corresponding to the target cloud product;
and inputting second characteristic data of the target cloud product corresponding to the future target time into the resource consumption prediction model, and taking an output result of the resource consumption prediction model as the predicted resource consumption.
4. The method according to any one of claims 1-3, wherein the obtaining of the target available resources of the target cloud product at the future target time according to the product information of the target cloud product and the future target time comprises:
acquiring the current available resources of the target account;
selecting a resource meeting a preset condition from the current available resources as the target available resource, wherein the resource meeting the preset condition comprises: the product information applicable to the resource comprises the product information of the target cloud product, and the resource meets the availability requirement at the future target time.
5. The method of claim 4, wherein the obtaining currently available resources of the target account comprises:
acquiring the times of the current generated early warning result;
and acquiring the current available resources of the target account according to the times of generating the early warning result.
6. The method of any one of claims 1-5, wherein the generating an early warning result corresponding to the target cloud product as a function of the target available resources and the predicted resource consumption comprises:
and generating an early warning result corresponding to the target cloud product under the condition that the target available resource is determined to be less than or equal to the predicted resource consumption.
7. The method of any of claims 1-6, further comprising:
after an early warning result corresponding to the target cloud product is generated, acquiring a notification mode corresponding to the priority of the target cloud product;
and outputting the early warning result by using a notification mode corresponding to the priority.
8. The method of claim 2, the obtaining the priority of the at least one cloud product comprising:
acquiring historical resource data liveness corresponding to each cloud product;
and determining the cloud product with higher activity corresponding to the historical resource data as the corresponding higher priority.
9. The method of claim 2, wherein the prioritizing the eligible cloud product as the target cloud product comprises:
according to the sequence of the priority from high to low, sequentially taking each cloud product as a target cloud product;
the generating of the early warning result corresponding to the target cloud product comprises:
and sequentially generating early warning results corresponding to the target cloud products.
10. An early warning device comprising:
the prediction unit is used for determining a target cloud product and acquiring the predicted resource consumption of the target cloud product at the future target time;
the processing unit is used for acquiring target available resources of the target cloud product at the future target time according to the product information of the target cloud product and the future target time;
and the early warning unit is used for generating an early warning result corresponding to the target cloud product according to the target available resource and the predicted resource consumption.
11. The apparatus according to claim 10, wherein the prediction unit, when determining the target cloud product, specifically performs:
obtaining at least one cloud product corresponding to the target account;
obtaining a priority of the at least one cloud product;
and taking the cloud product with the priority meeting the requirement as the target cloud product.
12. The apparatus according to any one of claims 10 to 11, wherein the prediction unit, when obtaining the predicted resource consumption of the target cloud product at the future target time, specifically performs:
acquiring first characteristic data of the target cloud product corresponding to historical time;
training a neural network model according to the first characteristic data to obtain a resource consumption prediction model corresponding to the target cloud product;
and inputting second characteristic data of the current target cloud product corresponding to the future target time into the resource consumption prediction model, and taking an output result of the resource consumption prediction model as the predicted resource consumption.
13. The apparatus according to any one of claims 10 to 12, wherein the processing unit, when obtaining the target available resources of the target cloud product at the future target time according to the product information of the target cloud product and the future target time, specifically performs:
acquiring the current available resources of the target account;
selecting a resource meeting a preset condition from the current available resources as the target available resource, wherein the resource meeting the preset condition comprises: the product information applicable to the resource comprises the product information of the target cloud product, and the resource meets the availability requirement at the future target time.
14. The apparatus according to claim 13, wherein the processing unit, when acquiring the currently available resources of the target account, specifically performs:
acquiring the times of the current generated early warning result;
and acquiring the current available resources of the target account according to the times of generating the early warning result.
15. The apparatus according to any one of claims 10 to 13, wherein the early warning unit, when generating an early warning result corresponding to the target cloud product according to the target available resource and the predicted resource consumption, specifically performs:
and generating an early warning result corresponding to the target cloud product under the condition that the target available resource is determined to be less than or equal to the predicted resource consumption.
16. The apparatus of any one of claims 10-15, wherein the early warning unit is further configured to perform,
after an early warning result corresponding to the target cloud product is generated, acquiring a notification mode corresponding to the priority of the target cloud product;
and outputting the early warning result by using a notification mode corresponding to the priority.
17. The apparatus according to claim 11, wherein the prediction unit, when obtaining the priority of the at least one cloud product, specifically performs:
acquiring historical resource data activity corresponding to each cloud product;
and determining the cloud product with higher activity corresponding to the historical resource data as the corresponding higher priority.
18. The apparatus according to claim 11, wherein the prediction unit, when the cloud product with the priority meeting the requirement is taken as the target cloud product, specifically performs:
according to the sequence of the priority from high to low, sequentially taking each cloud product as a target cloud product;
the generating of the early warning result corresponding to the target cloud product comprises:
and sequentially generating early warning results corresponding to the target cloud products.
19. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-9.
21. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-9.
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