Resource allocation method and device and electronic equipment

Abstract

The invention discloses a resource allocation method and device and electronic equipment. The method relates to the technical field of data processing, and comprises the following steps: acquiring flow distribution data and resource allocation data corresponding to a target object in a preset time period; inputting the resource allocation data and the flow distribution data into a mode generation model, and generating a plurality of resource allocation modes by using the mode generation model; determining a target resource allocation mode in the plurality of resource allocation modes; and allocating resources in a preset time period based on the target resource allocation mode to obtain an allocation result. According to the method and the device, the technical problem of relatively low resource allocation efficiency of the resources in related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a resource allocation method, device and electronic equipment. Background Art

[0002] With the development of science and technology, many industries need a lot of resources to maintain the development and operation of the industry. As one of the important parts of industry development, the reasonable allocation of resources is one of the important steps to ensure that resources can be fully utilized. Therefore, more and more industries begin to pay attention to the reasonable allocation and utilization of resources to ensure the utilization rate of resources and improve the efficiency of the industry. However, the efficiency of resource allocation in related technologies is low.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a resource allocation method, device and electronic device to at least solve the technical problem of low efficiency in resource allocation in the related art.

[0005] According to one aspect of an embodiment of the present invention, a resource allocation method is provided, comprising: obtaining traffic distribution data and resource allocation data corresponding to a target object in a preset time period, wherein the target object is used to represent an object of resources to be used in the preset time period, and the resource allocation data is used to represent data associated with resource allocation; inputting the resource allocation data and traffic distribution data into a pattern generation model, and using the pattern generation model to generate a plurality of resource allocation patterns, wherein different resource allocation patterns satisfy the resources required by the traffic distribution data in different ways; determining a target resource allocation pattern among the plurality of resource allocation patterns, wherein the target resource allocation pattern is the resource allocation pattern with the lowest resource consumption cost among the plurality of resource allocation patterns; and allocating resources within the preset time period based on the target resource allocation pattern to obtain an allocation result.

[0006] Furthermore, the resource allocation data and traffic distribution data are input into a pattern generation model, and a plurality of resource allocation patterns are generated using the pattern generation model, including: determining a plurality of initial resource allocation patterns corresponding to the resource allocation data; and screening the plurality of initial resource allocation patterns based on the traffic distribution data to obtain a plurality of resource allocation patterns.

[0007] Further, determining multiple initial resource allocation patterns corresponding to the resource allocation data includes: determining constraints corresponding to the resource allocation data; and screening multiple initial resource allocation patterns that meet the constraints from a resource pattern library, wherein the resource pattern library includes pre-configured resource allocation patterns.

[0008] Furthermore, determining a target resource allocation pattern among multiple resource allocation patterns includes: determining constituent elements of resource consumption costs, wherein the constituent elements include at least one of the following: direct cost of resource use, indirect cost of resource use, and excess idle cost of resources; performing cost evaluation on multiple resource allocation patterns based on the constituent elements to determine the resource consumption costs of the multiple resource allocation patterns; sorting the multiple resource allocation patterns based on the resource consumption costs to obtain sorting results; and determining a target resource allocation pattern among the multiple resource allocation patterns based on the sorting results.

[0009] Furthermore, the method also includes: acquiring resource preference parameters, resource configuration parameters and resource rule parameters; and generating resource allocation data based on the resource preference parameters, resource configuration parameters and resource rule parameters.

[0010] Furthermore, resources within a preset time period are allocated based on the target resource allocation pattern to obtain an allocation result, including: outputting the target resource allocation pattern; in response to a feedback instruction of the target resource allocation pattern received within the preset time period, adjusting the target resource allocation pattern based on the feedback instruction to obtain an adjusted resource allocation pattern; and allocating resources within the preset time period based on the adjusted resource allocation pattern to obtain an allocation result.

[0011] Furthermore, the method also includes: monitoring the resource demand status within a preset time period; in response to changes in the resource demand status, updating the target resource allocation pattern based on the resource demand status to obtain an updated resource allocation pattern; adjusting the resources within the preset time period based on the updated resource allocation pattern to obtain an adjustment result.

[0012] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present invention is executed when the program is running.

[0013] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0014] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0015] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0016] In an embodiment of the present invention, traffic distribution data and resource allocation data corresponding to a target object in a preset time period are obtained; the resource allocation data and traffic distribution data are input into a pattern generation model, and a plurality of resource allocation patterns are generated using the pattern generation model; a target resource allocation pattern among the plurality of resource allocation patterns is determined; and resources within the preset time period are allocated based on the target resource allocation pattern to obtain an allocation result. In the present application, traffic distribution data and resource distribution data corresponding to a target object are obtained to comprehensively determine the object corresponding to the resource and data related to resource allocation, and a plurality of resource allocation patterns are obtained by generating a resource allocation pattern through a pattern generation model, and a resource allocation pattern with the least resource consumption cost is obtained from the plurality of resource allocation patterns to improve the rationality of resource allocation, thereby achieving the purpose of effectively improving the efficiency of resource allocation and solving the technical problem of low efficiency of resource allocation in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of an optional resource allocation method according to an embodiment of the present invention;

[0019] Figure 2 is a schematic diagram of an optional idea of ​​establishing a pattern generation model according to an embodiment of the present invention;

[0020] Figure 3 is a data flow diagram of an optional check-in counter shift planning algorithm according to an embodiment of the present invention;

[0021] Figure 4 It is a schematic diagram of the structure of an optional resource allocation device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to an embodiment of the present invention, an embodiment of a resource allocation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 is a flow chart of an optional resource allocation method according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0026] Step S102, obtaining traffic distribution data and resource allocation data corresponding to a target object in a preset time period, wherein the target object is used to represent an object of resources to be used in the preset time period, and the resource allocation data is used to represent data associated with resource allocation.

[0027] The above-mentioned preset time period refers to a pre-set time period for obtaining the traffic distribution data and resource allocation data corresponding to the target object. In the present application, the traffic distribution data and resource allocation data corresponding to the target object in the target time period are determined to represent the traffic distribution data and resource allocation data of the resource as a whole. The above-mentioned preset time period can be set according to the status of the resources, the above-mentioned preset time period can also be set manually according to experience and needs, and the above-mentioned preset time period can also be set according to the actual application scenario.

[0028] The target object refers to the object to be used within the preset period. The target object can use the resource for production or life activities within the preset period. The target object can be a person, equipment or system. In this application, it is necessary to obtain the flow distribution data and resource allocation data corresponding to the target object in order to reasonably plan the resource allocation.

[0029] The above resources refer to resources that can be used in different systems. In this application, the target object uses resources to carry out production or life activities. Among them, the above resources include but are not limited to: natural resources, human resources, economic resources, social resources or information resources.

[0030] The above-mentioned traffic distribution data refers to the traffic distribution data corresponding to the above-mentioned target object. The above-mentioned traffic distribution data is used to characterize the frequency of use of the above-mentioned resources, etc. Through the above-mentioned traffic distribution data, users can determine the usage of the resources and then reasonably allocate the resources. Each target object has corresponding traffic distribution data corresponding to it.

[0031] The above-mentioned resource allocation data refers to the resource allocation data corresponding to the above-mentioned target object. The above-mentioned resource allocation data is used to characterize the allocation of the above-mentioned resources at the above-mentioned target object. According to the above-mentioned resource allocation data, the user can determine the resource allocation status of the target object and then reasonably allocate the resources. Each target object has corresponding resource allocation data corresponding to it.

[0032] In an optional embodiment, when resource allocation is required, the user determines a preset time period. The user can manually determine the object for which resources are to be used within the preset time period, i.e., the target object. After determining the target object, the traffic distribution data and resource allocation data corresponding to the target object in the preset time period can be obtained by retrieving them from a server or the cloud.

[0033] In another optional embodiment, an object determination model can be established in advance. When the user needs to allocate resources, he can determine a preset time period and the above-mentioned resources to be used can be input into the above-mentioned object determination model to output the object of the resources to be used within the preset time period, that is, the target object. After determining the target object, the traffic distribution data and resource allocation data corresponding to the target object in the preset time period can be obtained by retrieving them from the server or the cloud.

[0034] In the present application, by obtaining the traffic distribution data and resource allocation data corresponding to the target object within a preset time period, a comprehensive analysis is performed on the object using the resource, which effectively improves the comprehensiveness of obtaining the traffic distribution data and resource allocation data, and then effectively improves the comprehensiveness of resource allocation, thereby effectively improving the user experience.

[0035] Step S104, inputting the resource allocation data and the traffic distribution data into the pattern generation model, and using the pattern generation model to generate multiple resource allocation patterns, wherein different resource allocation patterns satisfy the resources required by the traffic distribution data in different ways.

[0036] The above-mentioned pattern generation model refers to a model for obtaining a resource allocation pattern. In the present application, through the above-mentioned pattern generation model, multiple resource allocation patterns can be obtained, and then the resource allocation pattern required by the user can be selected from multiple resource allocation patterns. Among them, the above-mentioned pattern generation model can be: a large model, a set partitioning model, a set covering model, a set packaging model or a set segmentation model, etc.

[0037] The above-mentioned resource allocation mode refers to a mode for allocating the above-mentioned resources. The above-mentioned mode generation model is used to generate multiple resource allocation modes according to resource allocation data and traffic distribution data. The above-mentioned resource allocation mode can allocate the above-mentioned resources to meet the resources required by the traffic distribution data in different ways. Users can select the required resource allocation mode according to their needs and then allocate the above-mentioned resources.

[0038] In an optional embodiment, after obtaining the above-mentioned resource allocation data and the above-mentioned traffic distribution data, the above-mentioned resource allocation data and the above-mentioned traffic distribution data are used as inputs of the above-mentioned pattern generation model, input into the above-mentioned pattern generation model, and output multiple resource allocation patterns to allocate resources in different ways, but the allocated resources all meet the resources required by the traffic distribution data.

[0039] Further, Figure 2 is a schematic diagram of an optional idea of ​​establishing a pattern generation model according to an embodiment of the present invention, such as Figure 2 As shown, when establishing the above-mentioned pattern generation model, first initialize the search tree, generate the initial feasible solution, and add the root node; after adding the root node, select the node in the search tree, and generate the initial problem. At this time, when the search tree is empty, generate the solution; when the search tree is not empty, solve the relaxed main problem, use the dynamic programming method to solve the path planning problem, and judge whether the path value is negative. When the path value is negative, add columns to the auxiliary problem and re-solve the relaxed main problem until the path value is not negative. When the path value is not negative, determine whether the path value is lower than the lower bound. When the path value is not lower than the lower bound, perform pruning, select nodes in the search tree, and regenerate the initial problem until the search tree is empty and a solution is generated; when the path value is lower than the lower bound, determine the integer nature of the path value. When the path value is not an integer, connect the branches with tasks, add branch nodes, and select nodes in the search tree to regenerate the initial problem until the search tree is empty and a solution is generated; when the path value is an integer, update the search tree, select nodes in the search tree, and regenerate the initial problem until the search tree is empty and a solution is generated.

[0040] Furthermore, the above-mentioned multiple resource allocation patterns can be obtained by genetic algorithm in the above-mentioned pattern generation model. When the pattern generation model is a set partitioning model, the calculation formula for solving the multiple resource allocation patterns is as follows:

[0041]

[0042] in,

[0043] The algorithm in the above set partitioning model is a genetic algorithm mathematical model, and the expression of the chromosome structure of the above genetic algorithm mathematical model is as follows:

[0044] [C] m×N×ps ,

[0045] Among them, the expressions of the main inequalities of the above genetic algorithm mathematical model are as follows:

[0046]

[0047] in,

[0048] Furthermore, in the case where resource allocation is check-in counter shift planning, Figure 3 is a data flow diagram of an optional check-in counter shift planning algorithm according to an embodiment of the present invention, such as Figure 3 As shown, when shift planning is required, preference parameter setting, configuration parameter setting, rule parameter setting and data processing are performed, the cost function coefficient is determined through the above preference parameter setting, the algorithm configuration parameters are determined through the configuration parameter setting, the data processing generates the working days, time granularity and processed work wave, the shift template position and length range are obtained through the rule parameter setting, the working days, time granularity, the processed work wave shift template position and length range are input into the shift set generator, the shift template set is generated, the shift template set, the cost function coefficient and the algorithm configuration parameters are input into the optimization core algorithm, and the active shift set Gantt and quantity are output.

[0049] The maximum passenger capacity of aircraft a is L a , the passenger load factor is p a , the take-off time is t a , where a∈A is the flight number, and A is the set of all flight numbers on that day. For each flight a, the arrival time window is T a , that is, the earliest passenger of this flight a -T a Arrive at the airport at the latest on t a a arrives at the airport. We consider a discrete time scale, namely t a , T a If all are integers, the number of passengers arriving at the airport on flight a at time t is I a The expression of (t) is as follows:

[0050] I a (t) = L a p a C(Ta , t)q t (1-q) t ,

[0051] Where q is the distribution probability, which can be used as a parameter to adjust based on experience or fit historical data to accurately estimate the passenger arrival distribution. The expression for the number of people arriving at the terminal or counter at any time t in a day is as follows:

[0052] I(t)=∑ a∈A I a (t),

[0053] Therefore, the mathematical expression of the queue model constraint is:

[0054] Q(t+1)=Q(t)+I(t)-x(t)r,

[0055] Among them, Q(t) is the queue length at time t, Q(t+1) is the queue length at time t+1, r is the service rate, and x(t) is the decision variable referring to the number of open counters at time t.

[0056] The mathematical expression of the counter quantity constraint is as follows:

[0057] x(t)≤M,

[0058] Where x(t) is the number of counters and M is the maximum number of configurable counters.

[0059] c. The mathematical expression of the counter opening frequency constraint is as follows:

[0060]

[0061] Among them, x(t j ) is the counter opening frequency, N is the unit opening time of the counter, and T is the total length of time in a day.

[0062] The formula for calculating the passenger waiting time is as follows:

[0063] f1(t)=cmax{0, Q(t)-x(t)rW},

[0064] Among them, f1(t) is the waiting time of the passenger, W is the expected maximum waiting time, and c is the maximum value.

[0065] The calculation formula for open counter resources is as follows:

[0066]

[0067] Among them, f2(t) is the open counter resource and x(t) is the number of counters.

[0068] Therefore, the target mathematical expression of the pattern generation model is obtained as follows:

[0069] min x(t) f1(t)+f2(t),

[0070] Among them, f1(t) is the waiting time of passengers, and f2(t) is the open counter resources.

[0071] The mathematical expression of the constraint conditions of the obtained pattern generation model is:

[0072]

[0073] Where x(t) is an integer.

[0074] Furthermore, in the case where resource allocation is check-in counter shift planning, the mathematical modeling expression is as follows:

[0075]

[0076] Among them, S refers to the shift set, and the corresponding quantity demand of each shift is q s ∈Q s , scheduling template group g∈G,m g Refers to the maximum value of template group g, x r is the number of the rth shift schedule, a sr is whether shift s appears in the rth shift table (0 for appearance, 1 for non-appearance), a gr Refers to whether the rth shift schedule belongs to mode g, c r Refers to the cost value of the rth shift schedule. The result of mathematical modeling is expressed as:

[0077] min r∈R (c r -∑ s∈S a sr π s -∑ g∈G a g r g -γ),

[0078] in,

[0079] In the present application, the above-mentioned pattern generation model is used to generate multiple resource allocation patterns that meet the resources required for traffic distribution data in different ways. Users can select the required resource allocation mode according to their needs, which effectively increases the various possibilities for resource allocation, thereby effectively improving the flexibility of resource allocation, improving the efficiency of resource allocation, and thus improving the user experience.

[0080] Step S106, determining a target resource allocation mode among the multiple resource allocation modes, wherein the target resource allocation mode is the resource allocation mode with the lowest resource consumption cost among the multiple resource allocation modes.

[0081] The above-mentioned target resource allocation mode refers to the resource allocation mode with the lowest resource consumption cost among multiple resource allocation modes. Due to the limited nature of resources, it is necessary to minimize resource consumption while ensuring the normal operation of the system. Therefore, after obtaining the above-mentioned multiple resource allocation modes, it is necessary to select the resource allocation mode with the lowest consumption cost from the above-mentioned multiple resource allocation modes to determine the above-mentioned target resource allocation mode.

[0082] In an optional embodiment, after obtaining multiple resource allocation modes, the user may manually select the resource allocation mode with the lowest resource consumption cost among the multiple resource allocation modes, and determine the resource allocation mode with the lowest resource consumption cost as the target resource allocation mode.

[0083] In another optional embodiment, a determination model can be established in advance. After obtaining multiple resource allocation modes, the above-mentioned multiple resource allocation modes can be used as inputs of the above-mentioned determination model, input into the above-mentioned determination model, output the resource allocation mode with the least resource consumption cost among the multiple resource allocation modes, and determine the resource allocation mode with the least resource consumption cost as the above-mentioned target resource allocation mode.

[0084] In this application, by selecting the resource allocation mode with the lowest resource consumption cost from multiple resource allocation modes as the target resource allocation mode, the resource utilization rate is effectively improved, thereby effectively improving the rationality of resource allocation, improving the efficiency of resource allocation, and improving the user experience.

[0085] Step S108: Allocate resources within a preset time period based on the target resource allocation mode to obtain an allocation result.

[0086] The above allocation result refers to the result of allocating the above resources. The above resource allocation result is used to characterize whether the resource allocation through the above target resource allocation mode is successfully applied. Among them, the above allocation result can be: the target resource allocation mode is successfully applied, or the target resource allocation mode is successfully applied.

[0087] In an optional embodiment, after obtaining the target resource allocation mode, the user may manually allocate the above resources according to the above target resource allocation mode to obtain the target resource allocation mode is successfully applied, or the allocation result of the target resource allocation mode is successfully applied.

[0088] In the present application, the above-mentioned allocation results are obtained to determine whether the above-mentioned target resource allocation mode is successfully applied, so that the user can adjust the resource allocation method in time when the above-mentioned target resource allocation mode is not successfully applied, thereby effectively improving the controllability of the allocation of the above-mentioned resources, improving the rationality of resource allocation, and improving the user experience.

[0089] Through the above steps, the traffic distribution data and resource allocation data corresponding to the target object in the preset time period are obtained; the resource allocation data and traffic distribution data are input into the pattern generation model, and multiple resource allocation patterns are generated using the pattern generation model; the target resource allocation pattern among the multiple resource allocation patterns is determined; the resources within the preset time period are allocated based on the target resource allocation pattern to obtain the allocation result. In the present application, the traffic distribution data and resource distribution data corresponding to the target object are obtained to comprehensively determine the object corresponding to the resource and the data related to the resource allocation, and the resource allocation pattern is generated by the pattern generation model to obtain multiple resource allocation patterns, and the resource allocation pattern with the least resource consumption cost is obtained from the multiple resource allocation patterns to improve the rationality of resource allocation, thereby achieving the purpose of effectively improving the efficiency of resource allocation and solving the technical problem of low efficiency of resource allocation in related technologies.

[0090] Optionally, the resource allocation data and traffic distribution data are input into a pattern generation model, and multiple resource allocation patterns are generated using the pattern generation model, including: determining multiple initial resource allocation patterns corresponding to the resource allocation data; and screening the multiple initial resource allocation patterns based on the traffic distribution data to obtain multiple resource allocation patterns.

[0091] The above-mentioned initial resource allocation pattern refers to a resource allocation pattern that does not necessarily satisfy the traffic distribution data. In the present application, multiple initial resource allocation patterns are obtained through the above-mentioned resource allocation data. The user needs to extract the resource allocation pattern that satisfies the traffic distribution data from the multiple initial resource allocation patterns based on the traffic distribution data.

[0092] In an optional embodiment, after obtaining the resource allocation data and the traffic distribution data, the above resource allocation data and the traffic distribution data are used as inputs of the above pattern generation model, and multiple initial resource allocation patterns corresponding to the above resource allocation data are output. After obtaining the multiple initial resource allocation patterns, the user can filter the multiple initial resource allocation patterns according to the traffic distribution data to obtain the resource allocation pattern that satisfies the traffic distribution data among the multiple initial resource allocation patterns.

[0093] In another optional embodiment, a screening model may be established in advance, and after obtaining resource allocation data and traffic distribution data, the resource allocation data and traffic distribution data are used as inputs of the pattern generation model, and a plurality of initial resource allocation patterns corresponding to the resource allocation data are output; after obtaining a plurality of initial resource allocation patterns, the plurality of initial resource allocation patterns are used as inputs of the screening model, and input into the screening model; the screening model screens the plurality of initial resource allocation patterns according to the traffic distribution data, and outputs a resource allocation pattern that satisfies the traffic distribution data among the plurality of initial resource allocation patterns.

[0094] In the present application, the above-mentioned multiple initial resource allocation modes are screened to obtain multiple more accurate resource allocation modes, so that the above-mentioned multiple resource allocation modes all meet the above-mentioned traffic distribution data, thereby effectively improving the rationality of resource allocation and further improving the user experience.

[0095] Optionally, determining multiple initial resource allocation patterns corresponding to the resource allocation data includes: determining constraints corresponding to the resource allocation data; and screening multiple initial resource allocation patterns that meet the constraints from a resource pattern library, wherein the resource pattern library includes pre-configured resource allocation patterns.

[0096] The above-mentioned constraint conditions refer to the conditions used to constrain the above-mentioned multiple initial resource allocation modes. The above-mentioned constraint conditions correspond to the above-mentioned resource allocation data, so as to constrain the above-mentioned initial resource allocation mode to satisfy the above-mentioned resource allocation data, thereby effectively improving the reliability of the obtained initial resource allocation mode.

[0097] The resource pattern library is a pattern library that stores initial resource allocation patterns. Users can store historical resource allocation patterns in the resource pattern library, and users can also pre-set the resource allocation patterns. When resources need to be allocated, the initial resource allocation pattern is extracted from the resource pattern library.

[0098] In an optional embodiment, after obtaining the above-mentioned resource allocation data, the user can manually calculate the constraints corresponding to the above-mentioned resource allocation data based on the above-mentioned resource allocation data. After obtaining the above-mentioned constraints, according to the above-mentioned constraints, multiple initial resource allocation patterns that meet the constraints are screened out from the resource pattern library.

[0099] In another optional embodiment, a constraint determination model can be established in advance. After obtaining the above-mentioned resource allocation data, the above-mentioned resource allocation data can be used as the input of the above-mentioned constraint determination model, input into the above-mentioned constraint determination model, and output the constraints corresponding to the above-mentioned resource allocation data. After obtaining the above-mentioned constraints, according to the above-mentioned constraints, multiple initial resource allocation patterns that meet the constraints are screened out from the resource pattern library.

[0100] In the present application, by setting constraints, users can filter out multiple initial resource allocation patterns that meet the constraints corresponding to the resource allocation data from the above-mentioned resource pattern library according to the constraints, thereby improving the accuracy of the initial resource allocation pattern, improving the accuracy of the resource allocation pattern, and thus improving the accuracy of resource allocation, thereby improving the user experience.

[0101] Optionally, determining a target resource allocation pattern among multiple resource allocation patterns includes: determining constituent elements of resource consumption costs, wherein the constituent elements include at least one of the following: direct cost of resource use, indirect cost of resource use, and excess idle cost of resources; performing cost evaluation on multiple resource allocation patterns based on the constituent elements to determine the resource consumption costs of the multiple resource allocation patterns; sorting the multiple resource allocation patterns based on the resource consumption costs to obtain sorting results; and determining a target resource allocation pattern among the multiple resource allocation patterns based on the sorting results.

[0102] The above-mentioned constituent elements refer to the elements constituting the above-mentioned resource consumption cost, through which the user can determine the resource consumption cost of the above-mentioned resource, wherein the above-mentioned constituent elements include at least one of the following: direct cost of resource use, indirect cost of resource use and excess idle cost of resources.

[0103] The above-mentioned sorting result refers to the result obtained after sorting multiple resource allocation modes. In the present application, the above-mentioned multiple resource allocation modes are sorted by the above-mentioned resource consumption cost to obtain the sorting result of resource consumption cost from small to large, or to obtain the sorting result of resource consumption cost from large to small.

[0104] In an optional embodiment, the constituent elements corresponding to the consumption costs corresponding to the above-mentioned resources can be retrieved from the server to determine the direct resource usage costs, indirect resource usage costs and excess idle resource costs corresponding to the above-mentioned resources, that is, to determine the constituent elements of the resource consumption costs corresponding to the above-mentioned resources. After obtaining the constituent elements corresponding to the above-mentioned resource consumption costs, a cost evaluation is performed on multiple resource allocation modes according to the constituent elements of the above-mentioned resource consumption costs to determine the resource consumption costs corresponding to the multiple resource allocation modes. After obtaining the resource consumption costs corresponding to the above-mentioned multiple resource allocation modes, the above-mentioned multiple resource allocation modes can be sorted according to the above-mentioned resource consumption costs to obtain a sorting result of the resource consumption costs from small to large, or to obtain a sorting result of the resource consumption costs from large to small. After obtaining the sorting result, the target resource allocation mode with the smallest resource consumption cost among the multiple resource allocation modes is determined according to the above-mentioned sorting result.

[0105] In an optional embodiment, an evaluation model can be established in advance, and the constituent elements corresponding to the consumption costs corresponding to the above-mentioned resources can be retrieved from the cloud to determine the direct resource usage costs, indirect resource usage costs and excess idle resource costs corresponding to the above-mentioned resources, that is, to determine the constituent elements of the resource consumption costs corresponding to the above-mentioned resources. After obtaining the constituent elements corresponding to the above-mentioned resource consumption costs, the above-mentioned constituent elements can be used as inputs to the above-mentioned evaluation model, and input into the above-mentioned evaluation model to output the resource consumption costs corresponding to multiple resource allocation modes. After obtaining the resource consumption costs corresponding to the above-mentioned multiple resource allocation modes, the above-mentioned multiple resource allocation modes can be sorted according to the above-mentioned resource consumption costs to obtain a sorting result of the resource consumption costs from small to large, or to obtain a sorting result of the resource consumption costs from large to small. After obtaining the sorting result, the target resource allocation mode with the smallest resource consumption cost among the multiple resource allocation modes is determined according to the above-mentioned sorting result.

[0106] In the present application, by determining the resource consumption costs of the above-mentioned multiple resource allocation modes, the resource allocation mode with the lowest resource consumption cost among the multiple resource allocation modes is determined, and then the above-mentioned target resource allocation mode is quickly and accurately determined, which effectively improves the accuracy and speed of obtaining the above-mentioned target resource allocation mode, improves the efficiency of resource allocation, and improves the user experience.

[0107] Optionally, the method further includes: acquiring resource preference parameters, resource configuration parameters and resource rule parameters; and generating resource allocation data based on the resource preference parameters, resource configuration parameters and resource rule parameters.

[0108] In an optional embodiment, the resource preference parameters, the resource configuration parameters and the resource rule parameters may be retrieved from a server or a cloud; and the resource allocation data may be generated based on the resource preference parameters, the resource configuration parameters and the resource rule parameters.

[0109] In the present application, by acquiring resource preference parameters, resource configuration parameters and resource rule parameters to generate more accurate resource allocation data, the rationality and accuracy of acquiring the above resource allocation data are effectively improved, the accuracy of resource allocation is effectively improved, and the user experience is improved.

[0110] Optionally, resources within a preset time period are allocated based on a target resource allocation pattern to obtain an allocation result, including: outputting a target resource allocation pattern; in response to a feedback instruction of the target resource allocation pattern received within the preset time period, adjusting the target resource allocation pattern based on the feedback instruction to obtain an adjusted resource allocation pattern; and allocating resources within the preset time period based on the adjusted resource allocation pattern to obtain an allocation result.

[0111] The above-mentioned feedback instruction refers to an instruction issued to adjust the above-mentioned target resource allocation mode. The user can adjust the above-mentioned target resource allocation mode according to the above-mentioned feedback instruction to obtain a more accurate adjusted resource allocation mode, wherein the above-mentioned feedback instruction can be: a touch instruction, a voice instruction or an action instruction, etc.

[0112] In an optional embodiment, after obtaining the above-mentioned target resource allocation mode, the user can confirm the above-mentioned target resource allocation mode. When the user needs to adjust the above-mentioned target resource allocation mode manually, the user can issue a feedback instruction within a preset time period. After detecting the feedback instruction of the target resource allocation mode sent by the user within the preset time period, the target resource allocation mode is adjusted according to the feedback instruction to obtain the adjusted resource allocation mode. After obtaining the adjusted resource allocation mode, the resources within the preset time period can be allocated according to the adjusted resource allocation mode to obtain the above-mentioned allocation result.

[0113] In the present application, the above-mentioned target resource allocation mode is adjusted in real time through feedback instructions, so that the user can adjust the above-mentioned target resource allocation mode at any time within the preset time period according to user needs, which effectively improves the flexibility of resource allocation and improves the user experience.

[0114] Optionally, the method also includes: monitoring the resource demand status within a preset time period; in response to changes in the resource demand status, updating the target resource allocation pattern based on the resource demand status to obtain an updated resource allocation pattern; adjusting the resources within the preset time period based on the updated resource allocation pattern to obtain an adjustment result.

[0115] In an optional embodiment, after resources are allocated, the resource demand status is monitored within a preset time period. When a change in the resource demand status is detected, the target resource allocation pattern is updated according to the resource demand status to obtain an updated resource allocation pattern. After obtaining the updated resource allocation pattern, the resources within the preset time period are adjusted to obtain an adjusted result.

[0116] In the present application, the above-mentioned resource demand status is monitored to determine whether the resource demand status changes within a preset time period, and the above-mentioned target resource allocation mode is updated in time when the resource demand status changes to obtain an updated resource allocation mode, thereby improving the flexibility of resource allocation and improving the user experience.

[0117] According to another aspect of an embodiment of the present invention, a resource allocation device is further provided, which can execute the resource allocation method of the above embodiment. The specific implementation method and preferred application scenario are the same as those of the above embodiment and will not be described in detail here. Figure 4is a schematic diagram of the structure of an optional resource allocation device according to an embodiment of the present invention, such as Figure 4 As shown, the device includes: an acquisition module 40, used to obtain traffic distribution data and resource allocation data corresponding to a target object in a preset time period, wherein the target object is used to represent an object of resources to be used in the preset time period, and the resource allocation data is used to represent data associated with resource allocation; a generation module 42, used to input the resource allocation data and traffic distribution data into a pattern generation model, and use the pattern generation model to generate multiple resource allocation patterns, wherein different resource allocation patterns meet the resources required by the traffic distribution data in different ways; a determination module 44, used to determine a target resource allocation pattern among multiple resource allocation patterns, wherein the target resource allocation pattern is the resource allocation pattern with the lowest resource consumption cost among the multiple resource allocation patterns; an allocation module 46, used to allocate resources within the preset time period based on the target resource allocation pattern to obtain an allocation result.

[0118] Optionally, the generation module includes: a determination unit, used to determine multiple initial resource allocation patterns corresponding to the resource allocation data; and a screening unit, used to screen the multiple initial resource allocation patterns based on the traffic distribution data to obtain multiple resource allocation patterns.

[0119] Optionally, the determination unit includes: a determination subunit, used to determine the constraint conditions corresponding to the resource allocation data; and a screening subunit, used to screen multiple initial resource allocation patterns that meet the constraint conditions from a resource pattern library, wherein the resource pattern library contains pre-configured resource allocation patterns.

[0120] Optionally, the determination module includes: a constituent unit for determining constituent elements of resource consumption costs, wherein the constituent elements include at least one of the following: direct cost of resource usage, indirect cost of resource usage, and excess idle cost of resources; an evaluation unit for performing cost evaluation on multiple resource allocation modes based on the constituent elements to determine the resource consumption costs of the multiple resource allocation modes; a sorting unit for sorting the multiple resource allocation modes based on the resource consumption costs to obtain a sorting result; and an extraction unit for determining a target resource allocation mode among the multiple resource allocation modes based on the sorting result.

[0121] Optionally, the allocation module includes: an output unit for outputting a target resource allocation mode; an adjustment unit for responding to feedback instructions of the target resource allocation mode received within a preset time period, adjusting the target resource allocation mode based on the feedback instructions, and obtaining an adjusted resource allocation mode; and a result unit for allocating resources within a preset time period based on the adjusted resource allocation mode to obtain an allocation result.

[0122] Optionally, the device also includes: a monitoring module for monitoring the resource demand status within a preset time period; a change module for responding to changes in the resource demand status and updating the target resource allocation pattern based on the resource demand status to obtain an updated resource allocation pattern; and an adjustment module for adjusting the resources within the preset time period based on the updated resource allocation pattern to obtain an adjustment result.

[0123] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention when running.

[0124] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0125] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0126] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0127] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0128] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0130] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0131] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0132] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0133] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A resource allocation method, characterized in that: include: Obtaining traffic distribution data and resource allocation data corresponding to a target object in a preset time period, wherein the target object is used to represent an object of resources to be used in the preset time period, and the resource allocation data is used to represent data associated with resource allocation; Inputting the resource allocation data and the traffic distribution data into a pattern generation model, and using the pattern generation model to generate a plurality of resource allocation patterns, wherein different resource allocation patterns satisfy the resources required by the traffic distribution data in different ways; Determine a target resource allocation mode among the multiple resource allocation modes, wherein the target resource allocation mode is a resource allocation mode with the lowest resource consumption cost among the multiple resource allocation modes; The resources within the preset time period are allocated based on the target resource allocation mode to obtain an allocation result.

2. The resource allocation method according to claim 1, characterized in that: Inputting the resource allocation data and the traffic distribution data into a pattern generation model, and using the pattern generation model to generate a plurality of resource allocation patterns, including: Determining a plurality of initial resource allocation modes corresponding to the resource allocation data; The multiple initial resource allocation patterns are screened based on the traffic distribution data to obtain the multiple resource allocation patterns.

3. The resource allocation method according to claim 2, characterized in that: Determining a plurality of initial resource allocation modes corresponding to the resource allocation data includes: Determining constraint conditions corresponding to the resource allocation data; The multiple initial resource allocation patterns that meet the constraint conditions are screened from a resource pattern library, wherein the resource pattern library includes pre-configured resource allocation patterns.

4. The resource allocation method according to claim 1, characterized in that: Determining a target resource allocation mode among the multiple resource allocation modes includes: Determining the constituent elements of the resource consumption cost, wherein the constituent elements include at least one of the following: direct cost of resource use, indirect cost of resource use, and excess idle cost of resources; Performing cost evaluation on the multiple resource allocation modes based on the constituent elements to determine the resource consumption costs of the multiple resource allocation modes; Sorting the multiple resource allocation modes based on the resource consumption cost to obtain a sorting result; The target resource allocation mode among the plurality of resource allocation modes is determined based on the ranking result.

5. The resource allocation method according to claim 1, characterized in that: The method further comprises: Obtain resource preference parameters, resource configuration parameters, and resource rule parameters; The resource allocation data is generated based on the resource preference parameters, the resource configuration parameters and the resource rule parameters.

6. The resource allocation method according to claim 1, characterized in that: Allocating resources within the preset time period based on the target resource allocation mode to obtain an allocation result, including: outputting the target resource allocation mode; In response to receiving a feedback instruction of the target resource allocation mode within a preset time period, adjusting the target resource allocation mode based on the feedback instruction to obtain an adjusted resource allocation mode; The resources within the preset time period are allocated based on the adjusted resource allocation mode to obtain the allocation result.

7. The resource allocation method according to claim 1, characterized in that: The method further comprises: Monitoring resource demand status within the preset time period; In response to a change in the resource demand state, updating the target resource allocation mode based on the resource demand state to obtain an updated resource allocation mode; The resources within the preset time period are adjusted based on the updated resource allocation mode to obtain an adjustment result.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 7 when running.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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