Power service network slice resource allocation method and system, storage medium and device

Abstract

The invention relates to a power service network slice resource allocation method, system and device and a medium. The method comprises the following steps: constructing a two-dimensional evaluation system comprising a service feature dimension and a network performance dimension; generating a priority decision sub-tree based on the service feature dimension, and generating a network performance decision sub-tree comprising a bandwidth decision sub-tree and a time delay decision sub-tree based on the network performance dimension; constructing a hierarchical decision-making model by taking the priority decision-making sub-tree as a root decision-making tree and the network performance decision-making sub-tree as a sub decision-making tree, and establishing a dynamic mapping relationship between service characteristic parameters and network performance parameters; and after receiving an access request in real time, executing a hierarchical decision through the hierarchical decision model. According to the invention, by constructing the two-dimensional evaluation system and the hierarchical decision model, the power service network slice resources can be efficiently and reasonably allocated, and the situation of resource waste or insufficiency is avoided.

Description

Technical Field

[0001] The present invention belongs to the field of smart grid technology, and specifically relates to a method, system, storage medium and device for allocating resources in a power business network slice, and in particular to a method, system, storage medium and device for allocating resources in a power business network slice based on a decision tree model. Background Art

[0002] With the ever-increasing demand for energy and electricity, the intelligentization of power grids has become an inevitable trend in modern society. Smart grids are built on an integrated, high-speed, two-way communication network. By applying advanced sensing and measurement technologies, equipment technologies, control methods, and decision support system technologies, they aim to achieve a reliable, secure, economical, efficient, environmentally friendly, and safe grid. my country's power grid has transitioned from a traditional power grid to a smart grid era, with network slicing technology being a key component of its implementation. Network slicing can separate multiple virtual end-to-end networks on a unified infrastructure. Each network slice is logically isolated from the radio access network to the bearer network and then to the core network to accommodate a variety of different applications.

[0003] However, existing resource allocation schemes for power service network slices have shortcomings. On the one hand, they fail to fully consider the impact of service priorities and network performance on resource allocation, resulting in irrational resource allocation and an inability to meet the needs of different services. On the other hand, they fail to dynamically and timely adjust network slice resource allocation strategies based on actual conditions, impacting the utilization of power service network slice resources. Therefore, a method for efficiently and rationally allocating power service network slice resources is urgently needed to improve the operational efficiency and service quality of smart grids. Summary of the Invention

[0004] In response to the above problems, the present invention provides a method, system, storage medium and device for allocating resources of power business network slices based on a decision tree model, aiming to solve the technical problems existing in the prior art.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for allocating power service network slice resources based on a decision tree model includes the following steps:

[0007] Step S1: Constructing a dual-dimensional evaluation system including a service feature dimension and a network performance dimension, wherein the service feature dimension includes service priority and user priority parameters, and the network performance dimension includes bandwidth performance parameters and latency performance parameters;

[0008] Step S2: generating a priority decision subtree based on the service feature dimension, and generating a network performance decision subtree including a bandwidth decision subtree and a delay decision subtree based on the network performance dimension, wherein the priority decision subtree includes a service type judgment node and a user credit evaluation node;

[0009] Step S3: constructing a hierarchical decision model with the priority decision subtree as the root decision tree and the network performance decision subtree as the sub-decision tree, and establishing a dynamic mapping relationship between service feature parameters and network performance parameters;

[0010] Step S4: After receiving the access request in real time, hierarchical decision making is performed through the hierarchical decision model: first, the basic resource quota is determined based on the service type judgment node, then the quota weight is adjusted through the user credit evaluation node, and finally, the optimal slice configuration plan is generated based on the bandwidth decision subtree and the delay decision subtree.

[0011] In one embodiment, the method for determining the service priority in step S1 is as follows: establish an electricity service type priority mapping table, map smart meter data collection to the first priority, distribution automation control to the second priority, and user value-added services to the third priority.

[0012] In one embodiment, the method for determining the user priority in step S1 is as follows: a user credit evaluation model is constructed, and a credit score Y is calculated based on three dimensions: the on-time rate of historical slice usage, the timely rate of resource release, and the number of QoS violations:

[0013] Y=α·C+β(1-D0 / D)-γ*T

[0014] Among them, C is the historical slice usage punctuality rate, D0 is the violation threshold, D is the actual number of violations, T is the resource delay release duration, α, β, γ are weight coefficients, α + β + γ = 1.

[0015] In one embodiment, the bandwidth decision subtree in step S2 adopts an LSTM prediction model, and input parameters include historical bandwidth usage fluctuation rate, current network load factor and service bandwidth baseline requirement.

[0016] In one embodiment, the method for constructing the delay decision subtree in step S2 is as follows:

[0017] Step S2.1: Establish a delay-sensitive service classification library, classify power services into delay-sensitive levels based on control instruction, status monitoring, and file transfer, and configure differentiated delay threshold sets;

[0018] Step S2.2: Monitor the network slice delay indicator in real time. When it is detected that the measured delay value of the current service flow exceeds the delay threshold of the corresponding service type, generate a delay compensation trigger instruction;

[0019] Step S2.3: In response to the delay compensation trigger instruction, activate the pre-deployed backup low-latency channel and dynamically migrate the excess service flow to the backup low-latency channel;

[0020] Step S2.4: Continuously monitor the delay status of the original network slice. When the measured delay value falls back to the safe range and remains stable, perform the service flow switching operation.

[0021] In one embodiment, the dynamic migration in step S2.3 includes: determining the migration priority according to the service sensitivity level, and controlling the instruction-type service to enjoy the highest preemption authority; adopting a lossless migration strategy, establishing a dual-channel parallel transmission buffer period, and the buffer time is positively correlated with the service flow size, and the buffer time ∈ [100ms, 500ms].

[0022] In one embodiment, the dynamic mapping relationship in step S3 is as follows:

[0023]

[0024] Among them, W is the resource weight factor, P1 is the service priority coefficient, Y is the user credit score, E is the actual delay measurement value, E0 is the delay sensitivity threshold (the maximum delay upper limit that the service can tolerate, which can be set according to the service type), ω1, ω2, ω3 are weight coefficients, ω1+ω2+ω3=1.

[0025] In order to achieve the above objectives, the present invention also provides a power business network slice resource allocation system based on a decision tree model, comprising:

[0026] A feature parameter preset module is used to build a two-dimensional evaluation system including service feature dimensions and network performance dimensions. The service feature dimensions include service priority and user priority parameters, and the network performance dimensions include bandwidth performance parameters and latency performance parameters.

[0027] A decision subtree construction module is used to generate a priority decision subtree based on the service feature dimension, and to generate a network performance decision subtree including a bandwidth decision subtree and a delay decision subtree based on the network performance dimension, wherein the priority decision subtree includes a service type judgment node and a user credit evaluation node;

[0028] A decision tree model construction module is used to construct a hierarchical decision model using the priority decision subtree as a root decision tree and the network performance decision subtree as a sub-decision tree, and to establish a dynamic mapping relationship between service feature parameters and network performance parameters;

[0029] The decision-making scheme and execution module is used to receive access requests in real time and then perform hierarchical decisions through the hierarchical decision model: first, the basic resource quota is determined based on the business type judgment node, then the quota weight is adjusted through the user credit evaluation node, and finally, the optimal slice configuration scheme is generated based on the bandwidth decision subtree and the delay decision subtree.

[0030] In order to achieve the above-mentioned objectives, the present invention also provides a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor to implement the power business network slice resource allocation method based on the decision tree model as described above.

[0031] In order to achieve the above-mentioned objectives, the present invention also provides an electric power business network slice resource allocation device based on a decision tree model, comprising a processor and a memory, wherein the memory is used to store a computer program; the processor is connected to the memory, and is used to execute the computer program stored in the memory, so that the electric power business network slice resource allocation device based on the decision tree model executes the electric power business network slice resource allocation method based on the decision tree model as described above.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] (1) By constructing a two-dimensional evaluation system and a hierarchical decision-making model, the present invention can efficiently and reasonably allocate power business network slice resources to avoid resource waste or shortage.

[0034] (2) The present invention includes multiple dimensions of business characteristics and network performance, and can perform differentiated resource allocation according to business type and user credit status, thereby optimizing resource utilization efficiency and making full use of limited network resources. At the same time, it can better meet the needs of various businesses, ensure the stable operation of power business, and thus improve the overall service quality.

[0035] (3) Through the design of the delay decision subtree, the present invention can promptly handle the delay limit problem, ensure the stable operation of power services with high real-time requirements, and avoid business interruptions or anomalies caused by delay problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic flow diagram of Example 1;

[0037] Figure 2 This is a structural diagram of Example 4. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to have a clearer understanding and knowledge of the present invention, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described below are only used to explain the present invention and facilitate understanding. The technical solutions provided by the present invention are not limited to the technical solutions provided by the following embodiments, and the technical solutions provided by the embodiments should not limit the scope of protection of the present invention.

[0039] Example 1

[0040] like Figure 1 As shown, this embodiment provides a power business network slice resource allocation method based on a decision tree model. The resource allocation method is based on the decision tree model, comprehensively considers the access user situation, network performance, etc., dynamically and timely adjusts the network slice resource allocation strategy to obtain the optimal allocation strategy.

[0041] In this embodiment, a method for allocating power service network slice resources based on a decision tree model includes the following steps:

[0042] S1. Build a two-dimensional evaluation system that includes business characteristics and network performance.

[0043] The service feature dimension includes service priority and user priority parameters, and the network performance dimension includes bandwidth performance parameters and delay performance parameters.

[0044] Based on the network performance requirements of network slicing, this embodiment uses bandwidth and latency requirements as network performance indicators. Bandwidth refers to the amount of data that a network can transmit per unit time, usually measured in bits per second (bps). Common units include kilobits per second (Kbps), megabits per second (Mbps), and gigabits per second (Gbps). Bandwidth reflects the network's ability to transmit data. Latency refers to the time required for data to be transmitted from the sender to the receiver, usually measured in milliseconds (ms). Latency includes transmission delay, propagation delay, processing delay, and queuing delay. Processing delay is the time required for a network device to process a message, queuing delay is the time a data packet waits in a queue for transmission, transmission delay is the length of the data packet divided by the link transmission rate, and propagation delay is the time required for a message to propagate on a physical link.

[0045] The bandwidth performance parameter reflects the network's transmission capacity and is used to evaluate the bandwidth resources that the network can provide for services. The latency performance parameter reflects the transmission delay of service data in the network and is crucial for power services with high real-time requirements.

[0046] The method for determining service priority is as follows: establish a power service type priority mapping table, map smart meter data collection to the first priority, distribution automation control to the second priority, and user value-added services to the third priority.

[0047] The method for determining user priority is as follows: a user credit evaluation model is constructed, and a credit score Y is calculated based on three dimensions: the on-time rate of historical slice usage, the timely rate of resource release, and the number of QoS violations:

[0048] Y=α·C+β(1-D0 / D)-γ*T

[0049] Among them, C is the on-time rate of historical slice usage, D0 is the violation threshold, D is the actual number of violations, T is the duration of resource delay release, α, β, γ are weight coefficients, which can be set, α + β + γ = 1.

[0050] For example, a user with a high historical slice usage on-time rate, few QoS violations, and timely resource release will have a higher credit score and will receive more priority in resource allocation.

[0051] S2. Generate a priority decision subtree based on the service feature dimension, and generate a network performance decision subtree including a bandwidth decision subtree and a delay decision subtree based on the network performance dimension.

[0052] The priority decision subtree includes a service type determination node and a user credit assessment node. The service type determination node determines the basic resource quota based on the type of service being connected, such as smart meter data collection, distribution automation control, or user value-added services. The user credit assessment node adjusts the weight of the basic resource quota based on the user's credit score.

[0053] The bandwidth decision subtree uses an LSTM prediction model. Input parameters include historical bandwidth usage fluctuations, the current network load factor, and the baseline service bandwidth requirement. The output is a forecast of bandwidth requirements for the future time period. For example, the following formula is used: input parameters (historical fluctuations, current load, baseline requirement) → LSTM model → output of the bandwidth requirement for the next time slice (e.g., 150 Mbps). This multi-dimensional input parameter approach effectively improves bandwidth prediction accuracy.

[0054] The method for constructing the delay decision subtree is as follows:

[0055] Step S2.1: Establish a delay-sensitive service classification library, classify power services into delay-sensitive levels based on control instruction, status monitoring, and file transfer, and configure differentiated delay threshold sets. The delay threshold sets include: a first-level delay threshold range of [15ms, 20ms] for control instruction services; a second-level delay threshold range of [40ms, 50ms] for status monitoring services; and a third-level delay threshold range of [180ms, 200ms] for file transfer services. The delay threshold ranges are negatively correlated with service criticality.

[0056] Step S2.2: Monitor the network slice delay indicator in real time. When it is detected that the measured delay value of the current service flow exceeds the delay threshold of the corresponding service type, generate a delay compensation trigger instruction;

[0057] Step S2.3: In response to the delay compensation trigger instruction, activate the pre-deployed backup low-latency channel and dynamically migrate the excess service flow to the backup low-latency channel; the backup low-latency channel includes at least two technical implementation methods: (1) an independent transmission channel based on the power-dedicated 5G slice, which is physically isolated from the main service channel; (2) a virtual isolation channel dynamically divided by the SDN controller, configured with a QoS guarantee level higher than the main channel; dynamic migration includes: determining the migration priority according to the service sensitivity level, and the control instruction type service enjoys the highest preemption authority; adopting a lossless migration strategy, establishing a dual-channel parallel transmission buffer period, and the buffer time is positively correlated with the service flow size, and the buffer time ∈ [100ms, 500ms].

[0058] Step S2.4: Continuously monitor the delay status of the original network slice. When the measured delay value falls back to the safe range and remains stable, perform the service flow switching operation.

[0059] S3. Build a hierarchical decision tree model

[0060] A hierarchical decision model is constructed with the priority decision subtree as the root decision tree and the network performance decision subtree as the child decision tree. A dynamic mapping relationship between service feature parameters and network performance parameters is established. The dynamic mapping relationship is as follows:

[0061]

[0062] Among them, W is the resource weight factor (determines the proportion of resources ultimately allocated to the service flow, with a value range of 0-1), P1 is the service priority coefficient (reflects the inherent importance level of the service type, with a value range of 0.6-1), Y is the user credit score (characterizes the credibility of resource allocation based on the user's historical behavior), E is the actual delay measurement value (reflects the current network performance status), E0 is the delay sensitivity threshold (the maximum delay that the service can tolerate, which can be set according to the service type), ω1, ω2, ω3 are weight coefficients, ω1+ω2+ω3=1.

[0063] Through this dynamic mapping relationship, the resource allocation ratio can be dynamically adjusted according to real-time changes in service characteristics and network performance.

[0064] In machine learning, a decision tree is a predictive model that represents a mapping between object attributes and object values. Each node in the tree represents an object, each branching path represents a possible attribute value, and each leaf node corresponds to the value of the object represented by the path from the root node to the leaf node.

[0065] For the training and evaluation of decision tree models, data preparation is performed, and a data set containing features and labels is collected. Then, data cleaning is performed to handle missing values, outliers, and duplicate values ​​to ensure data quality. The prepared data is divided into training sets and test sets to evaluate the performance of the model. Generally, the ratio of training data to test data can be 8:2, 7:3, etc. The decision tree model is trained based on the training set and evaluated based on the test volume. If the decision tree model is overfitting, adjustments are made.

[0066] S4. Execution of hierarchical decision making

[0067] After receiving access requests in real time, hierarchical decisions are made through a hierarchical decision model:

[0068] First, determine the basic resource quota based on the service type. For example, for the smart meter data collection service, relatively more basic resources are allocated based on its first priority.

[0069] The quota weight is then adjusted through the user credit evaluation node. For users with high credit scores, their resource quota weight is increased;

[0070] Finally, the optimal slice configuration plan is generated based on the bandwidth decision subtree and the delay decision subtree, taking into account the bandwidth requirements and delay requirements to ensure that the service can obtain network slice resources that meet its performance requirements.

[0071] Through the above steps, based on the construction of a two-dimensional evaluation system and a hierarchical decision-making model, efficient and reasonable allocation of power business network slice resources can be achieved. At the same time, resource allocation can be dynamically adjusted according to different business types and user credit status, thereby improving network resource utilization and business service quality.

[0072] Example 2

[0073] This embodiment provides a power service network slice resource allocation system based on a decision tree model, including:

[0074] A feature parameter preset module is used to build a two-dimensional evaluation system including service feature dimensions and network performance dimensions. The service feature dimensions include service priority and user priority parameters, and the network performance dimensions include bandwidth performance parameters and latency performance parameters.

[0075] A decision subtree construction module is used to generate a priority decision subtree based on the service feature dimension, and to generate a network performance decision subtree including a bandwidth decision subtree and a delay decision subtree based on the network performance dimension, wherein the priority decision subtree includes a service type judgment node and a user credit evaluation node;

[0076] A decision tree model construction module is used to construct a hierarchical decision model using the priority decision subtree as a root decision tree and the network performance decision subtree as a sub-decision tree, and to establish a dynamic mapping relationship between service feature parameters and network performance parameters;

[0077] The decision-making scheme and execution module is used to receive access requests in real time and then perform hierarchical decisions through the hierarchical decision model: first, the basic resource quota is determined based on the business type judgment node, then the quota weight is adjusted through the user credit evaluation node, and finally, the optimal slice configuration scheme is generated based on the bandwidth decision subtree and the delay decision subtree.

[0078] It should be noted that the structures and / or principles of the above modules correspond one-to-one to the steps in the power business network slice resource allocation method based on the decision tree model described in Example 1, so they will not be repeated here.

[0079] Example 3

[0080] This embodiment provides a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor to implement the power business network slice resource allocation method based on the decision tree model as described above.

[0081] A person skilled in the art can understand that all or part of the steps of the method provided in Example 1 can be implemented by hardware related to the computer program, and the above-mentioned computer program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the method provided in Example 1; and the above-mentioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc. Various media that can store program codes.

[0082] Example 4

[0083] like Figure 2As shown, this embodiment provides an electric power business network slice resource allocation device based on a decision tree model, including a processor and a memory, wherein the memory is used to store a computer program; the processor is connected to the memory, and is used to execute the computer program stored in the memory, so that the electric power business network slice resource allocation device based on the decision tree model executes the electric power business network slice resource allocation method based on the decision tree model as described above.

[0084] Specifically, the memory includes various media that can store program codes, such as ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk.

[0085] Preferably, the processor can be a general-purpose processor, including a central processing unit, a network processor, etc.; it can also be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0086] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A method for allocating resources in a power service network slice, characterized in that: The following steps are involved: Step S1: Constructing a dual-dimensional evaluation system including a service feature dimension and a network performance dimension, wherein the service feature dimension includes service priority and user priority parameters, and the network performance dimension includes bandwidth performance parameters and latency performance parameters; Step S2: generating a priority decision subtree based on the service feature dimension, and generating a network performance decision subtree including a bandwidth decision subtree and a delay decision subtree based on the network performance dimension, wherein the priority decision subtree includes a service type judgment node and a user credit evaluation node; Step S3: constructing a hierarchical decision model with the priority decision subtree as the root decision tree and the network performance decision subtree as the sub-decision tree, and establishing a dynamic mapping relationship between service feature parameters and network performance parameters; Step S4: After receiving the access request in real time, hierarchical decision making is performed through the hierarchical decision model: first, the basic resource quota is determined based on the service type judgment node, then the quota weight is adjusted through the user credit evaluation node, and finally, the optimal slice configuration plan is generated based on the bandwidth decision subtree and the delay decision subtree.

2. A method for allocating power service network slice resources according to claim 1, characterized in that: The method for determining the service priority in step S1 is as follows: establish a power service type priority mapping table, map smart meter data collection to the first priority, distribution automation control to the second priority, and user value-added services to the third priority.

3. The method for allocating power service network slice resources based on a decision tree model according to claim 2, characterized in that: The method for determining the user priority in step S1 is as follows: construct a user credit evaluation model and calculate the credit score Y based on the three dimensions of historical slice usage punctuality rate, resource release timeliness rate and QoS violation number: Y=α·C+β(1-D0 / D)-γ * T Where C is the punctuality rate of historical slice usage, D0 is the violation threshold, D is the actual number of violations, t is the duration of resource release delay, α, β, γ are weight coefficients, and α+β+γ=1.

4. The method for allocating power service network slice resources based on a decision tree model according to claim 3 is characterized in that: The bandwidth decision subtree in step S2 adopts an LSTM prediction model, and its input parameters include historical bandwidth usage fluctuation rate, current network load factor, and service bandwidth baseline requirement.

5. The method for allocating power service network slice resources based on a decision tree model according to claim 4 is characterized in that: The method for constructing the delay decision subtree in step S2 is as follows: Step S2.1: Establish a delay-sensitive service classification library, classify power services into delay-sensitive levels based on control instruction, status monitoring, and file transfer, and configure differentiated delay threshold sets; Step S2.2: Monitor the network slice delay indicator in real time. When it is detected that the measured delay value of the current service flow exceeds the delay threshold of the corresponding service type, generate a delay compensation trigger instruction; Step S2.3: In response to the delay compensation trigger instruction, activate the pre-deployed backup low-latency channel and dynamically migrate the excess service flow to the backup low-latency channel; Step S2.4: Continuously monitor the delay status of the original network slice. When the measured delay value falls back to the safe range and remains stable, perform the service flow switching operation.

6. The method for allocating power service network slice resources based on a decision tree model according to claim 5, characterized in that: The dynamic migration in step S2.3 includes: determining the migration priority according to the service sensitivity level, and the control instruction type service has the highest preemption authority; adopting a lossless migration strategy, establishing a dual-channel parallel transmission buffer period, and the buffer time is positively correlated with the size of the service flow, and the buffer time ∈ [100ms, 500ms].

7. The method for allocating power service network slice resources based on a decision tree model according to claim 6, characterized in that: The dynamic mapping relationship of step S3 is as follows: Where W is the resource weight factor, P1 is the service priority coefficient, Y is the user credit score, E is the actual delay measurement value, E0 is the delay sensitivity threshold, ω1, ω2, ω3 are weight coefficients, and ω1+ω2+ω3=1.

8. A power business network slice resource allocation system, characterized in that: include: A feature parameter preset module is used to build a two-dimensional evaluation system including service feature dimensions and network performance dimensions. The service feature dimensions include service priority and user priority parameters, and the network performance dimensions include bandwidth performance parameters and latency performance parameters. A decision subtree construction module is used to generate a priority decision subtree based on the service feature dimension, and to generate a network performance decision subtree including a bandwidth decision subtree and a delay decision subtree based on the network performance dimension, wherein the priority decision subtree includes a service type judgment node and a user credit evaluation node; A decision tree model construction module is used to construct a hierarchical decision model using the priority decision subtree as a root decision tree and the network performance decision subtree as a sub-decision tree, and to establish a dynamic mapping relationship between service feature parameters and network performance parameters; The decision-making scheme and execution module is used to receive access requests in real time and then perform hierarchical decisions through the hierarchical decision model: first, the basic resource quota is determined based on the business type judgment node, then the quota weight is adjusted through the user credit evaluation node, and finally, the optimal slice configuration scheme is generated based on the bandwidth decision subtree and the delay decision subtree.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the power business network slice resource allocation method as described in any one of claims 1 to 7.

10. A power service network slice resource allocation device, comprising a processor and a memory, characterized in that: The memory is used to store computer programs; the processor is connected to the memory and is used to execute the computer programs stored in the memory, so that the power business network slice resource allocation device executes the power business network slice resource allocation method as described in any one of claims 1 to 7.

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