CN114240007A - Resource allocation method and device for digital asset cloud service system - Google Patents
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Abstract
The embodiment of the disclosure provides a resource configuration method, device and equipment for a digital asset cloud service system and a computer readable storage medium. Calculating the evaluation value of each digital asset in a digital asset cloud service system; inputting the evaluation value and the current value of each type of digital assets into a resource model obtained by pre-training to respectively obtain the resource requirements of each type of digital assets; and according to the existing resources of the digital asset cloud service system and the resource requirements of each digital asset, performing resource allocation on each digital asset. In this way, load balancing and stability of the digital asset cloud service system can be improved.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of computer technology, and more particularly, to a method, an apparatus, a device, and a computer-readable storage medium for resource configuration of a digital asset cloud service system.
Background
In order to attract customers and increase customer liveness, various platforms and merchants issue own virtual digital assets. The informationized virtual digital assets of all the merchants are based on cloud services. However, the current cloud service equipment resource allocation of the virtual digital assets lacks guidance, data bearing is unbalanced, and the QoS of digital asset data is reduced.
The dynamic resource scheduling method based on Load Balance (LB) is currently most widely applied, and virtual machines loaded on hosts in a migration domain can be migrated in the migration domain. The method mainly comprises the following steps: and monitoring and obtaining load indexes of all hosts and virtual machines in the migration domain, judging whether a migration triggering condition is met, if the migration triggering condition is met, performing online migration of the virtual machines, selecting the virtual machines from a high-load source host, and migrating the virtual machines to a low-load target host, thereby achieving the purpose of load balancing in the migration domain.
However, the dynamic resource scheduling method based on load balancing only considers the load condition at the current moment, and does not consider passenger flow change caused by future digital asset value fluctuation, so that load conflict is formed, and the QoS of the virtual machine is reduced; meanwhile, the existing dynamic resource scheduling only considers the load balance at the current moment and is easy to repeatedly migrate along with the change of the load.
Disclosure of Invention
According to the embodiment of the disclosure, a resource configuration scheme of a digital asset cloud service system is provided.
In a first aspect of the disclosure, a method for configuring resources of a digital asset cloud service system is provided. The method comprises the following steps:
calculating the evaluation value of each digital asset in the digital asset cloud service system;
inputting the evaluation value and the current value of each type of digital assets into a resource model obtained by pre-training to respectively obtain the resource requirements of each type of digital assets;
and according to the existing resources of the digital asset cloud service system and the resource requirements of each digital asset, performing resource allocation on each digital asset.
Further, calculating the evaluation value of each digital asset in the digital asset cloud service system comprises:
calculating a growth parameter for the digital asset from data relating to its operation;
calculating static parameters representing the current state of the digital assets according to the data related to the commodities exchanged by the digital assets;
and carrying out weighted summation on the growth parameters and the static parameters, and calculating an estimation coefficient of the digital asset.
Further, calculating the growth parameter of the digital asset from data related to the operation of the digital asset comprises:
inputting collected issuing organization operation data and future expected economic index data into a pre-trained prediction model to obtain index data related to future digital asset issuing and active consumption of the issuing organization, and taking the ratio of the active consumption index data to the issuing index data as a growth parameter of the issuing organization;
the growth parameter is calculated by the following formula:
wherein, R ispvThe current value of active consumption;
Opvis the issue volume present value.
Further, the air conditioner is provided with a fan,
calculating the static parameters according to the goods price, the transaction amount and the third party price of the exchange mall of the digital assets;
the static parameters are calculated by the following formula:
the static parameter is Pr/Pb*βc/β*L；
Wherein, PrAn average physical price for the digital asset;
Pban average book price for the digital asset;
βca competitor mean coefficient for the digital asset issuer;
β coefficients of the digital asset issuing authority.
Further, the air conditioner is provided with a fan,
the resource model is a deep neural network model and is obtained by generating training samples through digital asset evaluation values, digital asset current values and corresponding resource requirements at different periods and training;
the corresponding resource requirement is a resource requirement after a preset time, or an average value of the resource requirements within the preset time.
Further, the air conditioner is provided with a fan,
and inputting the evaluation value and the current value of the digital assets, the remaining valid periods of the digital assets issued in different periods and the number of the remaining valid periods into a resource model obtained by pre-training to obtain the resource requirements of the digital assets.
Further, according to the existing resources of the digital asset cloud service system and the resource requirements of each digital asset, performing resource allocation for each digital asset includes:
if the sum of the resource requirements of the digital assets is smaller than a preset threshold value, distributing resources for the digital assets according to the resource requirements;
and if the sum of the resource requirements of the digital assets is greater than or equal to a preset threshold value, distributing the existing resources in proportion according to the proportion of the resource requirements of the digital assets according to the existing resources.
In a second aspect of the present disclosure, a digital asset cloud service system resource configuration apparatus is provided. The device includes:
the evaluation module is used for calculating the evaluation value of each digital asset in the digital asset cloud service system;
the resource demand calculation module is used for inputting the evaluation value and the current value of each type of digital assets into a resource model obtained by pre-training to respectively obtain the resource demands of each type of digital assets;
and the resource allocation module is used for allocating resources for each digital asset according to the existing resources of the digital asset cloud service system and the resource requirements of each digital asset.
In a third aspect of the disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor implementing the method as described above when executing the program.
In a fourth aspect of the present disclosure, a computer readable storage medium is provided, having stored thereon a computer program, which when executed by a processor, implements a method as in accordance with the first aspect of the present disclosure.
According to the resource allocation method of the digital asset cloud service system, the evaluation value of each digital asset in the digital asset cloud service system is calculated; inputting the evaluation value and the current value of each type of digital assets into a resource model obtained by pre-training to respectively obtain the resource requirements of each type of digital assets; and according to the existing resources of the digital asset cloud service system and the resource requirements of each digital asset, performing resource allocation on each digital asset. The future transaction amount of the digital assets can be effectively predicted, a basis is provided for resource allocation of the digital asset cloud service system, and load balance and stability of the digital asset cloud service system are improved.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily apparent from the following description.
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The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters denote like or similar elements, and wherein:
fig. 1 illustrates a flow diagram of a digital asset cloud service system resource configuration method according to an embodiment of the present disclosure;
FIG. 2 illustrates a flow diagram of a digital asset cloud service system value assessment method according to an embodiment of the present disclosure;
fig. 3 illustrates a block diagram of a digital asset cloud service system resource configuration apparatus according to an embodiment of the present disclosure;
FIG. 4 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
In some embodiments, as shown in fig. 1, a digital asset cloud service system resource configuration method according to an embodiment of the present disclosure is illustrated, the method comprising:
at block 110, calculating an evaluation value of each digital asset in the digital asset cloud service system;
in a digital asset cloud service system, there are different types of digital assets, i.e., digital assets issued by different issuers. Wherein the digital assets can be points, priority purchases, discount coupons, premium coupons, voucher, group purchase coupons, lottery coupons, consumer coupons, etc. Digital asset issuing and transaction of different issuing organizations are realized by virtual machines based on cloud services.
In some embodiments, the evaluation value of each type of digital assets supported by the digital asset cloud service system is calculated respectively; the method comprises the following substeps:
at block 210, computing valuation coefficients for the digital assets;
in particular, a growth parameter of the digital asset is calculated from data relating to its operation; calculating static parameters representing the current state of the digital assets according to the data related to the commodities exchanged by the digital assets; and calculating an estimation coefficient of the digital asset by combining the growth parameter and the static parameter.
In some embodiments, calculating the growth parameter of the digital asset from data related to the operation of the digital asset comprises:
obtaining economic data, wherein the economic data is economic indicator data related to the digital asset operation; including expected economic indicator data for the industry in which the digital asset is located. Obtaining the digital asset's issuer business data by means including, but not limited to, retrieving relevant information from an issuer server or crawling relevant information from a network. And according to the economic data and the issuing mechanism operation data of the digital assets, predicting to obtain index data related to future digital asset issuing and active consumption of the issuing mechanism of the digital assets.
In some embodiments, obtaining economic data may be predicting a macro economic indicator based on past economic indicator states and future development expectations: including a nominal domestic production total (GDP), a resident Consumption Price Index (CPI), and an actual domestic production total (Real GDP).
In some embodiments, obtaining operational data for the issuer of the digital asset may be based on past and current issuer operations in combination with macro-economic development forecasts to predict future key operational indicators for the issuer: including core indicators of transaction amount, sales amount, marketing fees, cash flow, etc. associated with the issuance of digital assets.
In some embodiments, the collected issuer operational data, future expected economic indicator data, are input into a pre-trained predictive model to obtain indicator data relevant to future digital asset issuances and active consumptions by the issuer. The prediction model is a neural network model, and training is carried out on the neural network model by marking index data related to future digital asset issuing and active consumption by acquiring issuing agency operation data taking the time point as a reference and future expected economic index data taking the time point as a reference from different historical event points and taking the time point as a reference.
In some embodiments, the predicted values of future digital asset issuance and active consumption are discounted according to the Markov portfolio theory, the Goden growth model and the capital asset pricing model to obtain the present value of issuance and active consumption.
In some embodiments, the collected issuer operational data, future expected economic indicator data, are input into a pre-trained predictive model to obtain indicator data relevant to future digital asset issuances and active consumptions by the issuer. The prediction model is a neural network model, issuing organization operation data which are collected at different historical event points and are based on the time point, and future expected economic index data which are based on the time point are labeled by an issuing quantity current value and an active consumption current value which are based on the event point to generate a training sample, and the neural network model is trained.
And calculating the growth parameter of the digital asset, wherein the growth parameter is the ratio of the current value of the active consumption to the current value of the issue quantity.
Wherein, R ispvThe current value of active consumption;
Opvis the issue volume present value.
Further, said RpvCan be calculated by the following model:
wherein n is the number of years used to describe the total number of teenagers;
the t is the current year;
the r is the discount rate;
the R istIs the digital asset consumption of the t year.
Said O ispvCan be calculated by the following model:
wherein n is the number of years used to describe the total number of teenagers;
the t is the current year;
the r is the discount rate;
said O istIs the digital asset release volume of the t year.
In some embodiments, calculating the static parameters representing the current status of the digital asset as a function of the data associated with the item redeemed for the digital asset comprises:
obtaining an average account price P of the digital assets according to the goods price of the exchange mall of the digital assets and the price of the money replaced by the digital assetsb；
Obtaining the average physical price P of the digital assets according to the third party price of the goods of the exchange mall of the digital assetsr；
Obtaining beta of the digital asset issuing organization and/or the average beta of competitors of the digital asset issuing organization according to the Mackowitz investment portfolio theory and the capital asset pricing modelc；
Grading the liquidity of the goods in the exchange mall of the digital assets, and assigning 1 to the goods with the best liquidity; the value of the worst-flowing goods is assigned 0; calculating the average liquidity of the goods, and then obtaining the liquidity parameter L of the exchanged goods of the digital asset, wherein the value range of the L is between 0 and 1;
calculating a static parameter P for the digital assetr/Pb*βc/β*L。
In some embodiments, the static parameters of the digital assets are calculated according to the goods price and the transaction amount of the exchange mall of the digital assets and the goods third party price of the exchange mall of the digital assets. This is because, the goods bid price of the redeeming mall for the digital asset is for different goods, and some goods bid prices are higher and some goods bid prices are lower, so for the transaction amount of different goods, that is, liquidity, the sum of the value of each goods bid price multiplied by each goods transaction amount/the sum of the value of each goods third party price multiplied by each goods transaction amount of the redeeming mall for the digital asset is calculated as the static parameter representing the current state thereof.
In some embodiments, computing an valuation factor for the digital asset in combination with the growth parameters and the static parameters comprises:
and according to the operation condition of the digital asset issuing organization, giving a first weighting coefficient a of the growth parameter and a second weighting coefficient b of the static parameter, and adding the product of the growth parameter and the first weighting coefficient and the product of the static parameter and the second weighting coefficient to obtain an estimation coefficient. In some embodiments, the first and second weighting factors are assigned values of 0.8 and 0.2, respectively.
Calculating the estimation coefficient P/C ═ a Rpv/Opv+b*Pr/Pb*βc/β*L。
Wherein a is a first weighting coefficient;
Rpv/Opvthe value of (a) is a growth parameter;
b is a second weighting coefficient;
Pr/Pb*βcthe value of/β × L is a static parameter.
Calculating a digital asset valuation value using the valuation coefficients of the digital asset and the price data of the digital asset at block 220;
and the price data is the average physical price Pr of the digital assets according to the third price of the goods of the exchange mall of the digital assets and the currency price replaced by the digital assets.
In some embodiments, the validity period of the digital asset is used for calculating the evaluation value of the digital asset by multiplying the valuation coefficient, the ratio of the remaining validity period of the digital asset to the total validity period and price data obtained according to the price of the digital asset and the exchanged commodity. In some embodiments, the remaining validity periods of the digital assets issued by the issuing authority at different times are different, and the product of the remaining validity periods of the digital assets at different times and the issuing quantity thereof is ratioed to the product of the total quantity of the digital assets and the total validity period.
Calculating the digital asset valuation value P of the validity periodb*P/C*D/T；
Wherein, the PbPrice data obtained according to prices of the digital assets and the exchanged commodities; the value of the P/C is an estimation coefficient; d is total effective period; and the T is the residual effective period.
In some embodiments, if the digital asset does not have a validity period, the evaluation value of the digital asset is calculated by using the valuation coefficient and price data obtained by the price of the digital asset and the commodity exchanged by the digital asset.
Calculating the digital asset valuation value P without validity periodb*P/C；
Wherein, the PbPrice data obtained according to prices of the digital assets and the exchanged commodities; the value of the P/C is an estimation coefficient.
At block 120, inputting the evaluation value and the current value of each type of digital assets into a resource model obtained by pre-training, and respectively obtaining resource requirements of each type of digital assets;
in some embodiments, the corresponding resource requirement is a resource requirement after a preset time, or an average value of resource requirements within a preset time. This is because, when there is a deviation between the evaluated value and the current value of each digital asset, it will gradually cause a change in the transaction amount and thus a change in the resource demand until the transaction is completed or the value returns, which is a gradual change process. In some embodiments, the preset time may be one day, one week.
In some embodiments, the resource model is a deep neural network model, and is trained by generating training samples from the evaluation value of the digital asset, the current value of the digital asset and the corresponding resource demand at different periods.
In some embodiments, for a digital asset with a validity period, although the remaining validity period may affect the evaluation value of the digital asset, there may also be a centralized selling or exchanging requirement before the validity period expires, which is prone to large fluctuation, and therefore, it is necessary to train corresponding resource models for the digital asset with the validity period and the digital asset without the validity period. Aiming at the digital assets with valid periods, the evaluation value of the digital assets, the current value of the digital assets, the remaining valid periods and the number of the digital assets issued in different periods and the corresponding resource requirements are generated into training samples. Correspondingly, when calculating the resource demand of the digital asset, the evaluation value, the current value, the remaining validity period of the digital asset issued in different periods and the number of the remaining validity periods of the digital asset need to be input into the resource model obtained by pre-training to obtain the resource demand of the digital asset.
In some embodiments, for a digital asset, if resource allocation is performed for the first time, the resource allocation is improved on the basis of average resource allocation according to judgment of an evaluation value and a current value of the digital asset, and if the current value is lower than the evaluation value, the possibility of sudden increase of traffic exists; otherwise, the resource allocation is reduced.
At block 130, a resource allocation is performed for each digital asset based on the existing resources of the digital asset cloud service system and the resource requirements of each digital asset.
In some embodiments, there are situations where data in the same resource pool is used for multiple digital assets, each being allocated resources according to its resource requirements.
In some embodiments, if the sum of the resource requirements of each digital asset is less than a preset threshold, allocating resources to the digital asset according to the resource requirements; if the sum of the resource requirements of the digital assets is larger than or equal to the preset threshold, allocating the allocable resources in the resource pool according to the proportion of the resource requirements of the digital assets according to the allocable resource condition in the resource pool. The preset threshold may be a certain proportion of the allocable resources (total amount of resources) in the resource pool, such as 75%.
In some embodiments, according to the allocable resource condition in the resource pool, according to the resource demand and the priority of each digital asset, the resource demand is multiplied by the coefficient of different priorities, and then the resources are allocated in proportion.
In some embodiments, the resource pool primarily includes a virtual computing resource pool, a virtual network resource pool, and a virtual storage resource pool. The virtual computing resource pool is formed by one or more physical hosts through a virtualization technology and mainly comprises resources such as a CPU (central processing unit), a memory and the like; the virtual network resource pool is formed by network equipment such as various routers, switches, firewalls, load balancers and the like through a virtualization technology and mainly comprises network bandwidth and other resources; the storage resource pool is formed by various storage devices through virtualization technology, and mainly comprises resources such as storage capacity, storage I/O and the like, and the storage devices can be local storage, IPSAN, NAS, object storage and the like. The resource pool comprises a plurality of hosts (Host), and the hosts are loaded with a plurality of VMs and are allocated with virtual resources. Hosts that can migrate VM to each other constitute a migration domain. VMs on a HOST share computing resources (CPU or memory, etc.), storage resources (local storage or storage I/O), and network resources (network I/O). When one HOST cannot meet the resources required by the VM to be loaded, the QoS of the VM is lowered, and MV migration is required to guarantee the QoS of the VM.
According to the embodiment of the disclosure, the following technical effects are achieved:
the future transaction amount of the digital assets can be effectively predicted, a basis is provided for resource allocation of the digital asset cloud service system, and load balance and stability of the digital asset cloud service system are improved.
It should be noted that for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present disclosure is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the disclosure. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules referred to are not necessarily required for the disclosure.
The above is a description of embodiments of the method, and the following is a further description of the embodiments of the apparatus.
Fig. 3 illustrates a block diagram of a digital asset cloud service system resource configuration apparatus 300 according to an embodiment of the present disclosure. As shown in fig. 3, the apparatus 300 includes:
the evaluation module 310 is used for calculating the evaluation value of each digital asset in the digital asset cloud service system;
the resource demand calculation module 320 is used for inputting the evaluation value and the current value of each type of digital assets into a resource model obtained by pre-training to respectively obtain the resource demands of each type of digital assets;
a resource allocation module 330, configured to perform resource allocation for each digital asset according to the existing resources of the digital asset cloud service system and the resource requirement of each digital asset
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
FIG. 4 shows a schematic block diagram of an electronic device 400 that may be used to implement embodiments of the present disclosure. The apparatus 400 may be used to implement the digital asset cloud service system resource configuration device 400 of fig. 3. As shown, device 400 includes a Central Processing Unit (CPU)401 that may perform various suitable actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)402 or loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processing unit 401 performs the various methods and processes described above, such as the methods 100, 200. For example, in some embodiments, the methods 100, 200 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 400 via the ROM 402 and/or the communication unit 409. When loaded into RAM 403 and executed by CPU 401, may perform one or more of the steps of methods 100, 200 described above. Alternatively, in other embodiments, the CPU 401 may be configured to perform the methods 100, 200 by any other suitable means (e.g., by way of firmware).
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), System On Chip (SOCs), load programmable logic devices (CPLDs), and the like.
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 data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/acts specified in the flowchart and/or block diagram 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A resource allocation method of a digital asset cloud service system is characterized by comprising the following steps:
calculating the evaluation value of each digital asset in the digital asset cloud service system;
inputting the evaluation value and the current value of each type of digital assets into a resource model obtained by pre-training to respectively obtain the resource requirements of each type of digital assets;
and according to the existing resources of the digital asset cloud service system and the resource requirements of each digital asset, performing resource allocation on each digital asset.
2. The method of claim 1, wherein computing the valuation of each digital asset in the digital asset cloud service system comprises:
calculating a growth parameter for the digital asset from data relating to its operation;
calculating static parameters representing the current state of the digital assets according to the data related to the commodities exchanged by the digital assets;
and carrying out weighted summation on the growth parameters and the static parameters, and calculating an estimation coefficient of the digital asset.
3. The method of claim 2, wherein calculating the growth parameters of the digital assets from data related to their operation comprises:
inputting collected issuing organization operation data and future expected economic index data into a pre-trained prediction model to obtain index data related to future digital asset issuing and active consumption of the issuing organization, and taking the ratio of the active consumption index data to the issuing index data as a growth parameter of the issuing organization;
the growth parameter is calculated by the following formula:
the above-mentioned
Wherein, R ispvThe current value of active consumption;
Opvis the issue volume present value.
4. The method of claim 2,
calculating the static parameters according to the goods price, the transaction amount and the third party price of the exchange mall of the digital assets;
the static parameters are calculated by the following formula:
the static parameter is Pr/Pb*βc/β*L；
Wherein, PrAn average physical price for the digital asset;
Pban average book price for the digital asset;
βca competitor mean coefficient for the digital asset issuer;
β coefficients of the digital asset issuing authority.
5. The method of claim 1,
the resource model is a deep neural network model and is obtained by generating training samples through the evaluation value of the digital assets, the current value of the digital assets and the corresponding resource requirements at different periods and training;
the corresponding resource requirement is a resource requirement after a preset time, or an average value of the resource requirements within the preset time.
6. The method of claim 5,
and inputting the evaluation value and the current value of the digital assets, the remaining valid periods of the digital assets issued in different periods and the number of the remaining valid periods into a resource model obtained by pre-training to obtain the resource requirements of the digital assets.
7. The method of claim 1, wherein performing resource allocation for each digital asset according to existing resources of the digital asset cloud service system and resource requirements of each digital asset comprises:
if the sum of the resource requirements of the digital assets is smaller than a preset threshold value, distributing resources for the digital assets according to the resource requirements;
and if the sum of the resource requirements of the digital assets is greater than or equal to a preset threshold value, distributing the existing resources in proportion according to the existing resources and the proportion of the resource requirements of the digital assets.
8. A resource allocation apparatus for a digital asset cloud service system, comprising:
the evaluation module is used for calculating the evaluation value of each digital asset in the digital asset cloud service system;
the resource demand calculation module is used for inputting the evaluation value and the current value of each type of digital assets into a resource model obtained by pre-training to respectively obtain the resource demands of each type of digital assets;
and the resource allocation module is used for allocating resources for each digital asset according to the existing resources of the digital asset cloud service system and the resource requirements of each digital asset.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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