Resource allocation method, device, electronic device and computer-readable storage medium

Abstract

The present disclosure relates to a resource allocation method, device, electronic device and computer-readable storage medium. In at least one embodiment of the present disclosure, by acquiring the state information and historical information of resources, as well as the demand information of objects, it is possible to perceive the changes in the resource state and the dynamic fluctuations of the object demand in real time; further, objects of the same object type are processed uniformly so as to allocate resources more centrally and efficiently, reduce resource fragmentation and unreasonable allocation; by determining the multi-dimensional weights for resource allocation, including the multi-dimensional weights of resources and the multi-dimensional weights of object types, it is possible to more accurately measure the allocation priorities of different resources, avoid the problem of unreasonable allocation or low allocation efficiency caused by a single factor dominating resource allocation, make resource allocation more reasonable, improve the efficiency of resource allocation, and enable important and urgent objects to obtain appropriate resources first.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a resource allocation method, device, electronic device, and computer-readable storage medium. Background Art

[0002] With the development of logistics technology, when goods are transported to warehouses by logistics vehicles, it is necessary to allocate warehouse platform resources for the goods. The platform refers to the place where vehicles load and unload goods in the warehouse. Based on the differences in goods and logistics needs, warehouse operators usually set up different types of platforms according to actual business needs, so as to ensure the smoothness of the entire logistics chain and improve logistics efficiency.

[0003] However, in actual warehouse operations, the allocation of warehouse platform resources only considers certain single factors, such as the number of goods or the idle time of the platform. This may cause some platforms to be overloaded and cause congestion, while other platforms are idle, resulting in reduced logistics efficiency of the entire warehouse and affecting supplier satisfaction. Summary of the invention

[0004] At least one embodiment of the present disclosure provides a resource allocation method, device, electronic device, and computer-readable storage medium, which make resource allocation more reasonable and improve the efficiency of resource allocation.

[0005] In a first aspect, an embodiment of the present disclosure provides a resource allocation method, the method comprising:

[0006] Acquire status information and history information of at least one resource, and demand information of at least one object, where the demand information is information of the object that needs to be processed using the resource;

[0007] Determining a multi-dimensional weight of at least one resource based on state information and historical information of at least one resource, and determining a multi-dimensional weight of at least one object type based on demand information of at least one object, the at least one object having the same or different object types;

[0008] Determining an allocation priority of at least one resource to at least one object type based on a multi-dimensional weight of at least one resource and a multi-dimensional weight of at least one object type;

[0009] Based on the allocation priority of at least one resource to at least one object type, resources are allocated to at least one object.

[0010] In some embodiments, the status information includes at least one of the following:

[0011] Resource idle time, resource idle area, and resource adaptation score;

[0012] And / or, the historical information includes at least one of the following:

[0013] Historical average processing time and historical processing stability statistics;

[0014] And / or, the requirement information includes at least one of the following:

[0015] Object attributes, object quantity, waiting time; the object attributes include at least one of the following: object type, object complexity.

[0016] In some embodiments, determining a multi-dimensional weight of at least one resource based on state information and historical information of at least one resource includes:

[0017] Determining a resource idle time weight of at least one resource based on the resource idle time of at least one resource; and / or,

[0018] Determining a resource idle area weight of at least one resource based on the resource idle area of ​​at least one resource; and / or,

[0019] determining a resource adaptation weight of at least one resource based on the resource adaptation score of at least one resource; and / or,

[0020] Determining a historical average processing time weight of at least one resource based on a historical average processing time of at least one resource; and / or,

[0021] Based on the historical processing stability statistics of the at least one resource, a historical processing stability weight of the at least one resource is determined.

[0022] In some embodiments, determining a multi-dimensional weight of at least one object type based on demand information of at least one object includes:

[0023] Determining an object property weight of at least one object type based on an object property of at least one object; and / or,

[0024] determining an object quantity weight of at least one object type based on the object quantity of at least one object; and / or,

[0025] Based on the latency of the at least one object, a latency weight for at least one object type is determined.

[0026] In some embodiments, determining an object property weight of at least one object type based on an object property of at least one object includes:

[0027] Obtaining an object type weight of at least one object type; and / or,

[0028] Based on the object complexity of the at least one object, an object complexity weight of at least one object type is determined.

[0029] In some embodiments, determining a resource adaptation weight of at least one resource based on a resource adaptation score of at least one resource includes:

[0030] For any resource, the resource adaptation weight of any resource is determined based on the adaptation score of each resource item included in any resource and the preset adaptation coefficient of each resource item included in any resource.

[0031] In some embodiments, determining the allocation priority of at least one resource to at least one object type based on the multi-dimensional weight of at least one resource and the multi-dimensional weight of at least one object type includes:

[0032] Assignment priority to any object type for any resource:

[0033] Determining a first value based on an object type weight, an object quantity weight, an object complexity weight, and an object quantity of any object type;

[0034] Determining a second value based on a resource idle time weight, a resource idle area weight, and a resource adaptation weight of any resource;

[0035] Determining a third value based on a waiting time weight of any object type, a historical average processing time weight of any resource, and a historical processing stability weight of any resource;

[0036] Based on the first value, the second value and the third value, an allocation priority of any resource to any object type is determined.

[0037] In some embodiments, the first value is: the product of the object type weight, the object quantity weight, the object complexity weight and the object quantity of any object type;

[0038] And / or, the second value is: the product of a resource idle time weight, a resource idle area weight and a resource adaptation weight of any resource;

[0039] And / or, the third value is: the product of the historical average processing time weight and the historical processing stability weight of any resource and the sum of the waiting time weight of any object type.

[0040] In some embodiments, determining the allocation priority of any resource to any object type based on the first value, the second value, and the third value includes:

[0041] The allocation priority of any resource to any object type is: the sum of the first value and the second value multiplied by the third value.

[0042] In some embodiments, allocating resources to at least one object based on an allocation priority of at least one resource to at least one object type includes:

[0043] For any object, the object type of the object is determined, and resources with a higher allocation priority for the object type are selected and allocated to the object.

[0044] In a second aspect, an embodiment of the present disclosure further provides a resource allocation device, the device comprising:

[0045] An acquisition unit, used to acquire status information and history information of at least one resource, and demand information of at least one object, where the demand information is information of the object that needs to be processed using the resource;

[0046] A first determination unit, configured to determine a multi-dimensional weight of at least one resource based on state information and history information of at least one resource, and to determine a multi-dimensional weight of at least one object type based on demand information of at least one object, the at least one object having the same or different object types;

[0047] A second determining unit, configured to determine an allocation priority of at least one resource to at least one object type based on a multi-dimensional weight of at least one resource and a multi-dimensional weight of at least one object type;

[0048] The allocation unit is used to allocate resources to at least one object based on the allocation priority of at least one resource to at least one object type.

[0049] In a third aspect, an embodiment of the present disclosure further proposes an electronic device, which includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the resource allocation method as in any embodiment of the first aspect.

[0050] In a fourth aspect, an embodiment of the present disclosure further proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instructions, and the program or instructions enable a computer to execute the steps of the resource allocation method as in any embodiment of the first aspect.

[0051] In the fifth aspect, the embodiments of the present disclosure also provide a computer program product, wherein the computer program product includes a computer program, the computer program is stored in a computer-readable storage medium, and at least one processor of a computer reads and executes the computer program from the computer-readable storage medium, so that the computer performs the steps of the resource allocation method of any embodiment of the first aspect.

[0052] It can be seen that in at least one embodiment of the present disclosure, by acquiring the status information and historical information of resources, as well as the demand information of objects, it is possible to perceive the changes in resource status and the dynamic fluctuations of object demand in real time; then, objects of the same object type are processed uniformly to allocate resources more centrally and efficiently, and reduce resource fragmentation and unreasonable allocation; by determining the multi-dimensional weights for resource allocation, including the multi-dimensional weights of resources and the multi-dimensional weights of object types, it is possible to more accurately measure the allocation priority of different resources, avoid the problem of unreasonable allocation or low allocation efficiency caused by a single factor dominating resource allocation, make resource allocation more reasonable, improve the efficiency of resource allocation, and enable important and urgent objects to obtain appropriate resources first. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure, and a person skilled in the art can also obtain other drawings based on these drawings.

[0054] Figure 1 A schematic diagram of a flow chart of a resource allocation method provided in an embodiment of the present disclosure;

[0055] Figure 2 A schematic diagram of a resource allocation device provided in an embodiment of the present disclosure;

[0056] Figure 3 An exemplary block diagram of an electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0057] In order to more clearly understand the above-mentioned purposes, features and advantages of the present disclosure, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. The specific embodiments described herein are only used to explain the present disclosure, rather than to limit the present disclosure. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in the field belong to the scope of protection of the present disclosure.

[0058] It should be noted that, in this document, relational terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0059] In the Internet decoration industry, business is highly diverse and complex.

[0060] Customer demands cover a variety of customized packaged goods (referred to as packaged goods) with specific specifications and requirements, as well as relatively standardized general goods, namely standard products.

[0061] When goods are transported to the warehouse by logistics vehicles, three processes are required: quality inspection (i.e. checking whether the goods are damaged), receiving goods (i.e. sorting them into piles according to the renovation project site and checking the quantity of goods), and batch attribute entry (i.e. entering the attribute information on the packaging).

[0062] For parcel items, the receiving and handling process is often more cumbersome and time-consuming, and the requirements for resources such as platform facilities are more special; for standard products, the receiving and handling process is relatively fixed.

[0063] In actual warehouse operations, platform resources are limited and valuable. If there is a lack of a scientific and reasonable allocation algorithm, some platforms will be overwhelmed with tasks, while other platforms will be idle, which will seriously affect the operation of the entire warehouse, waste human resources, and affect supplier satisfaction.

[0064] In addition, the idle time of the platform will change dynamically due to various business operations, and the idle area of ​​the platform will also change according to the occupancy of the allocated resources. These dynamic data need to be monitored in real time and accurately and used effectively. At the same time, the facility configurations of different platforms are different, and the compatibility with the goods has become a key factor affecting business operations. In addition, by analyzing the waiting time of goods and historical processing data, the bottleneck links and optimization directions of the business can be discovered, and the overall operation level and quality can be improved.

[0065] Figure 1 A flow chart of a resource allocation method provided in an embodiment of the present disclosure is provided. The resource allocation method can be applied to the allocation of platform resources, shelf resources, and other resources used to carry or process goods. The execution subject of the resource allocation method is an electronic device, and the electronic device includes but is not limited to vehicle-mounted devices, smart phones, PDAs, tablet computers, wearable devices with display screens, desktop computers, laptop computers, all-in-one computers, smart home devices, servers, etc., wherein the server can be an independent server or a cluster of multiple servers, including servers built locally and servers built in the cloud.

[0066] like Figure 1 As shown, the resource allocation method may include but is not limited to steps 101 to 104:

[0067] In step 101, state information and history information of at least one resource and demand information of at least one object are obtained.

[0068] Among them, a resource is, for example, a platform of a logistics warehouse, and one platform is recorded as one resource. If the warehouse has multiple platforms, multiple platforms are recorded as multiple resources. The status information of a resource can be understood as the relevant information about the usage of the resource. The historical information of a resource can be understood as the relevant information about the processing of the object after the resource is allocated to the object. An object is, for example, a logistics commodity, and one object is recorded as one commodity, and multiple objects are recorded as multiple commodities. The demand information of an object is the information of an object that needs to be processed using resources. For example, the demand information of an object can be understood as the information of commodities that are transported to the warehouse by logistics vehicles and need to be loaded and unloaded at the warehouse platform.

[0069] It can be seen that in this embodiment, by acquiring the status information and historical information of the resources, as well as the demand information of the object, it is possible to perceive the changes in the resource status (i.e., platform status) and the dynamic fluctuations of the object demand (i.e., commodity demand) in real time, so as to adaptively adjust the resource allocation, make the resource allocation more reasonable, and improve the efficiency of resource allocation.

[0070] In step 102, a multi-dimensional weight of at least one resource is determined based on state information and historical information of at least one resource, and a multi-dimensional weight of at least one object type is determined based on demand information of at least one object.

[0071] Each resource in at least one resource has a multi-dimensional weight, at least one object has the same or different object types, and each object type in at least one object type has a multi-dimensional weight. Specifically, it involves the following (i) to (iii):

[0072] (i) Based on the status information of at least one resource (including resource idle time, resource idle area and / or resource adaptation score), a status information weight of at least one resource (including resource idle time weight, resource idle area weight and / or resource adaptation weight) may be determined.

[0073] (ii) Based on the historical information of at least one resource (including the historical average processing time and / or the historical processing stability statistical information), the historical information weight of at least one resource (including the historical average processing time weight and / or the historical processing stability weight) can be determined.

[0074] (iii) Determining a multi-dimensional weight (including object attribute weight, object quantity weight and / or waiting time) of at least one object type based on demand information of at least one object (including object attributes, object quantity and / or waiting time).

[0075] It can be seen that in this embodiment, by determining the multi-dimensional weights for resource allocation (including the multi-dimensional weights of resources and the multi-dimensional weights of object types), the allocation priorities of different resources can be measured more accurately, making resource allocation more reasonable, improving the efficiency of resource allocation, and avoiding the problem of unreasonable allocation or low allocation efficiency caused by a single factor dominating resource allocation, effectively reducing errors and rework caused by improper resource allocation, and reducing operating costs.

[0076] In step 103, based on the multi-dimensional weight of at least one resource and the multi-dimensional weight of at least one object type, an allocation priority of at least one resource to at least one object type is determined.

[0077] In this embodiment, by determining the multi-dimensional weights for resource allocation, an intelligent resource allocation priority sorting mechanism is constructed, breaking the traditional fixed resource allocation model, so that important and urgent objects (ie, goods) can obtain appropriate resources first.

[0078] In step 104, resources are allocated to at least one object based on the allocation priority of at least one resource to at least one object type.

[0079] In this embodiment, for any object, the object type of the object is determined, and resources with a higher allocation priority for the object type are selected and allocated to the object.

[0080] It can be seen that in the above embodiments, by obtaining the status information and historical information of resources, as well as the demand information of objects, it is possible to perceive the changes in resource status and the dynamic fluctuations of object demand in real time; further, objects of the same object type are processed uniformly to allocate resources more centrally and efficiently, reducing resource fragmentation and unreasonable allocation; by determining the multi-dimensional weights for resource allocation, including the multi-dimensional weights of resources and the multi-dimensional weights of object types, it is possible to more accurately measure the allocation priority of different resources, avoid the problem of unreasonable allocation or low allocation efficiency caused by a single factor dominating resource allocation, make resource allocation more reasonable, improve the efficiency of resource allocation, and enable important and urgent objects to obtain appropriate resources first. The fluency and consistency of the overall business process are enhanced, the waiting time for objects is shortened, and customer satisfaction is improved.

[0081] In some embodiments, in step 101, the state information of the resource includes at least one of the following:

[0082] Resource idle time, resource idle area, and resource adaptation score.

[0083] The resource idle time is the time when all or part of the platform is idle. The resource idle area is the area of ​​the idle area of ​​the platform. The resource adaptation score is the degree of adaptation between the resource and the object.

[0084] In some embodiments, the historical information of a resource includes at least one of the following:

[0085] Historical average processing time and historical processing stability statistics.

[0086] The historical average processing time is the average time of processing objects in history. It should be noted that the historical average processing time of objects of different object types is different. The historical processing stability statistical information can reflect the stability of the historical processing process. For example, the historical processing stability statistical information is a statistic such as the standard deviation of the historical processing time.

[0087] In some embodiments, the requirement information of the object includes at least one of the following:

[0088] Object attributes, object quantity.

[0089] The object attributes include at least one of the following:

[0090] Object type, object complexity.

[0091] Object types include: packaged items and standard items.

[0092] Object complexity is an expression of the complexity of an object in terms of structure, process, etc. Object complexity can be configured for each stock keeping unit (SKU) on the warehouse object, where SKU is the unit of measurement for inventory in and out (buyers purchase, merchants purchase, suppliers prepare, and factories produce based on SKU), and is most commonly used in objects such as clothing, footwear, and daily chemicals.

[0093] It can be seen that in this embodiment, by acquiring the status information and historical information of the resources, as well as the demand information of the object, it is possible to perceive the changes in the resource status (i.e., platform status) and the dynamic fluctuations of the object demand (i.e., commodity demand) in real time, so as to adaptively adjust the resource allocation, make the resource allocation more reasonable, and improve the efficiency of resource allocation.

[0094] In some embodiments, in step 102, each resource in the at least one resource has a multi-dimensional weight, and each object type in the at least one object type has a multi-dimensional weight. Specifically, it involves the following (i) to (iii):

[0095] (i) Based on the state information of at least one resource (including resource idle time, resource idle area and / or resource adaptation score), the state information weight of at least one resource (including resource idle time weight, resource idle area weight and / or resource adaptation weight) may be determined. Specifically, at least one of the following (1) to (3) may be included:

[0096] (1) Based on the resource idle time of at least one resource, determine the resource idle time weight of at least one resource.

[0097] In this embodiment, the ratio of the resource idle time to the preset reference time can be used as the resource idle time weight. For example, if the preset reference time is 24 hours and the resource idle time is 3 hours, the resource idle time weight is 0.125 (ie, 3 hours / 24 hours).

[0098] (2) Based on the resource idle area of ​​the at least one resource, determine the resource idle area weight of the at least one resource.

[0099] In this embodiment, the ratio of the resource idle area to the preset reference area can be used as the resource idle area weight. For example, the preset reference area is the total resource area (assuming it is 100 square meters), if the resource idle area is 20 square meters, then the resource idle area weight is 0.2 (i.e., 20 square meters / 100 square meters).

[0100] In some embodiments, a value-taking rule for the resource idle area weight can also be set. For example, if the ratio of the resource idle area to the preset baseline area is less than or equal to 0.1, the resource idle area weight is 0.1; if the ratio of the resource idle area to the preset baseline area is greater than 0.1 and less than or equal to 0.3, the resource idle area weight is 0.3; if the ratio of the resource idle area to the preset baseline area is greater than 0.3, the resource idle area weight is 0.5.

[0101] In some embodiments, the resource idle area weight may be determined based on the ratio of the resource idle area to the preset reference area. For example, the resource idle area weight is positively correlated with the ratio. If the ratio of the resource idle area to the preset reference area is 0.2, the resource idle area weight is 0.3; if the ratio of the resource idle area to the preset reference area is 0.4, the resource idle area weight is 0.6.

[0102] (3) Determine a resource adaptation weight of at least one resource based on the resource adaptation score of at least one resource.

[0103] In this embodiment, for any resource, the resource adaptation weight of any resource is determined based on the adaptation score of each resource item included in any resource and the preset adaptation coefficient of each resource item included in any resource.

[0104] For example, for any platform, the platform includes three resource items: crane, conveyor belt and storage space.

[0105] The preset adaptation coefficients of each resource item are: the preset adaptation coefficient of the crane is 0.3, the preset adaptation coefficient of the conveyor belt is 0.4, and the preset adaptation coefficient of the storage space is 0.3. If the upper limit of the adaptation score of each resource item is 5 points, the sum of the preset adaptation scores of each resource item is: 5×0.3+5×0.4+5×0.3=5 points.

[0106] The adaptation scores of each resource item are as follows: the resource adaptation score of the crane is 4 points, the resource adaptation score of the conveyor belt is 3 points, and the resource adaptation score of the storage space is 3 points.

[0107] Based on the preset adaptation coefficient of each resource item, the adaptation score of each resource item is weighted averaged to obtain a weighted score of: 4×0.3+3×0.4+3×0.3=3.3 points.

[0108] The resource adaptation weight of the platform is: the ratio of the weighted score of the adaptation score of each resource item of the platform (3.3 points) to the sum of the preset adaptation scores of each resource item (5 points) (ie 3.3 points / 5 points=0.66).

[0109] It can be seen that the various preset parameters mentioned in the above embodiments (for example, preset reference time, preset reference area, preset adaptation coefficient) can be flexibly adjusted and optimized according to actual business needs and characteristics to adapt to different scenarios and changes, with stronger adaptability and flexibility. For example, it can adapt to Internet decoration business scenarios of different scales and complexities.

[0110] (ii) Based on the historical information of at least one resource (including historical average processing time and / or historical processing stability statistics), the historical information weight of at least one resource (including historical average processing time weight and / or historical processing stability weight) may be determined. Specifically, at least one of the following (1) and (2):

[0111] (1) Based on the historical average processing time of at least one resource, determine the historical average processing time weight of at least one resource.

[0112] In this embodiment, for any resource, the average time for processing objects in the history of the resource can be normalized to obtain the weight of the historical average processing time. For example, the average time for processing parcels in the history of the resource is 30 minutes, and the average time for processing standard products is 20 minutes. The weight of the historical average processing time for processing parcels in the history of the resource is 0.6, that is, 30 minutes / (30 minutes+20 minutes)=0.6. The weight of the historical average processing time for processing standard products in the history of the resource is 0.4, that is, 20 minutes / (30 minutes+20 minutes)=0.4. It can be seen that the weights of the historical average processing time of objects of different object types for the same resource are different.

[0113] (2) Based on the historical processing stability statistical information of the at least one resource, determine the historical processing stability weight of the at least one resource.

[0114] In this embodiment, for any resource, the standard deviation of the historical processing time of the resource can be converted into a value between 0 and 1 as the historical processing stability weight of the resource.

[0115] (III) Determining a multi-dimensional weight (including object attribute weight, object quantity weight and / or waiting time) of at least one object type based on demand information of at least one object (including object attributes, object quantity and / or waiting time). Specifically, the method includes at least one of the following (1) to (3):

[0116] (1) Based on object properties of at least one object (including object type and / or object complexity), determine an object property weight of at least one object type (including object type weight and / or object complexity weight).

[0117] In this embodiment, an object type weight can be pre-configured for each object type. After obtaining the requirement information of at least one object, at least one object type can be obtained from the requirement information, and then based on the pre-configured object type weight, the object type weight of at least one object type in the requirement information can be directly obtained.

[0118] In this embodiment, based on the object complexity of each object in the requirement information, the object complexity weight of at least one object type in the requirement information is determined.

[0119] For example, object complexity can be configured for each stock keeping unit (SKU) on the warehouse object, and then the object complexity of each object in the demand information can be determined, so that the number of objects and object complexity of the same object type in the demand information can be determined, and the average value of the object complexity of the same object type is used as the object complexity weight of the object type.

[0120] It can be seen that in this embodiment, objects of the same object type (ie, commodities) are processed uniformly so as to allocate resources more centrally and efficiently and reduce resource fragmentation and unreasonable allocation.

[0121] (2) Based on the object quantity of at least one object, determine an object quantity weight of at least one object type.

[0122] In this embodiment, by counting the number of objects of each object type in the demand information, the ratio of the number of objects of each object type to the total number of objects in the demand information is used as the object number weight of the corresponding object type.

[0123] (3) Determine a waiting time weight of at least one object type based on the waiting time of at least one object.

[0124] In this embodiment, the ratio or difference between the waiting time of at least one object and the preset reference waiting time is first determined, and then the ratio or difference is converted into a waiting time weight. The longer the waiting time, the greater the waiting time weight.

[0125] For example, the preset benchmark waiting time is 2 hours, the object's waiting time is 3 hours, and the calculated ratio is 1.5 (3 hours / 2 hours). The pre-configured conversion rule is: when the waiting time exceeds 1 times the benchmark waiting time, the waiting time weight is 0.3; when it exceeds 2 times, the waiting time weight is 0.5. In the example, the waiting time exceeds 1 times the benchmark waiting time, so the waiting time coefficient is 0.3. It should be noted that if the object's waiting time starts from the acquisition of the demand information, the waiting time of each object in the demand information is the same.

[0126] It can be seen that in this embodiment, by determining the multi-dimensional weights for resource allocation (including the multi-dimensional weights of resources and the multi-dimensional weights of object types), the allocation priorities of different resources can be measured more accurately, making resource allocation more reasonable, improving the efficiency of resource allocation, and avoiding the problem of unreasonable allocation or low allocation efficiency caused by a single factor dominating resource allocation, effectively reducing errors and rework caused by improper resource allocation, and reducing operating costs.

[0127] In some embodiments, in step 103, the allocation priority of any resource to any object type is determined by the following steps 1 to 4:

[0128] Step 1: Determine a first value based on an object type weight, an object quantity weight, an object complexity weight and the object quantity of any object type.

[0129] For example, the first value is: the product of the object type weight, the object quantity weight, the object complexity weight and the object quantity of any object type.

[0130] That is, object type weight×object quantity weight×object complexity weight×object quantity=first value.

[0131] Step 2: Determine a second value based on the resource idle time weight, resource idle area weight, and resource adaptation weight of any resource.

[0132] For example, the second value is: the product of the resource idle time weight, the resource idle area weight, and the resource adaptation weight of any resource.

[0133] That is, resource idle time weight×resource idle area weight×resource adaptation weight=second value.

[0134] Step 3: Determine a third value based on the waiting time weight of any object type, the historical average processing time weight of any resource, and the historical processing stability weight of any resource.

[0135] For example, the third value is: the product of the historical average processing time weight and the historical processing stability weight of any resource and the sum of the waiting time weight of any object type.

[0136] That is, waiting time weight+(historical average processing time weight×historical processing stability weight)=third value.

[0137] Step 4: Based on the first value, the second value and the third value, determine the allocation priority of any resource to any object type.

[0138] For example, the allocation priority of any resource to any object type is: the sum of the first value and the second value multiplied by the third value.

[0139] That is, (first value+second value)×third value=allocation priority.

[0140] It can be seen that in this embodiment, by determining the multi-dimensional weights for resource allocation, an intelligent resource allocation priority sorting mechanism is constructed, breaking the traditional fixed resource allocation model, so that important and urgent objects (i.e., goods) can obtain appropriate resources first.

[0141] Embodiment 1

[0142] This embodiment discloses a platform resource allocation method, including steps 1 to 4:

[0143] Step 1: Obtain status information and history information of at least one platform resource and demand information of at least one commodity.

[0144] The status information of the platform resources can be understood as the information related to the use of the platform. The historical information of the platform resources can be understood as the information related to the processing of the goods after the platform resources are allocated to the goods. The demand information of the goods can be understood as the information of the goods transported to the warehouse by logistics vehicles and needing to be loaded and unloaded at the warehouse platform.

[0145] The status information of platform resources includes: platform idle time, platform idle area, and platform adaptation score. The platform idle time refers to the time when all or part of the platform is idle. The platform idle area refers to the area of ​​the idle area of ​​the platform. The platform adaptation score refers to the degree of adaptation between the platform and the product.

[0146] The historical information of platform resources includes: historical average processing time and historical processing stability statistics. The historical average processing time is the average time for processing commodities in history. It should be noted that the historical average processing time of commodities of different commodity types (packages and standard commodities) is different. The historical processing stability statistics can reflect the stability of the historical processing process. For example, the historical processing stability statistics are statistics such as the standard deviation of the historical processing time.

[0147] The demand information of goods includes: goods attributes and goods quantity. Among them, goods attributes include: goods type and goods complexity. Goods types include: packaged goods and standard goods.

[0148] Product complexity is an expression of the complexity of a product in terms of structure, process, etc. Product complexity can be configured for each stock keeping unit (SKU) on warehouse products.

[0149] It can be seen that in this embodiment, by acquiring the status information and historical information of platform resources, as well as the demand information of commodities, it is possible to perceive the changes in the status of platform resources and the dynamic fluctuations in commodity demand in real time, so as to adaptively adjust the platform resource allocation, make the platform resource allocation more reasonable, and improve the efficiency of platform resource allocation.

[0150] Step 2: Based on the status information and historical information of at least one platform resource, determine the multi-dimensional weight of at least one platform resource, and based on the demand information of at least one commodity, determine the multi-dimensional weight of at least one commodity type.

[0151] Each platform resource in at least one platform resource has a multi-dimensional weight, and each commodity type in at least one commodity type has a multi-dimensional weight. Specifically, it involves the following (i) to (iii):

[0152] (I) Based on the status information of at least one platform resource (including platform idle time, platform idle area and platform adaptation score), the status information weight of at least one platform resource (including platform idle time weight, platform idle area weight and platform adaptation weight) can be determined. Specifically, it includes the following (1) to (3):

[0153] (1) Based on the platform idle time of at least one platform resource, determine the platform idle time weight of at least one platform resource.

[0154] In this embodiment, the ratio of the platform idle time to the preset reference time can be used as the platform idle time weight. For example, if the preset reference time is 24 hours and the platform idle time is 3 hours, the platform idle time weight is 0.125 (ie, 3 hours / 24 hours).

[0155] (2) Based on the platform free area of ​​at least one platform resource, determine the platform free area weight of at least one platform resource.

[0156] In this embodiment, the ratio of the platform free area to the preset reference area can be used as the platform free area weight. For example, the preset reference area is the total resource area (assuming it is 100 square meters), if the platform free area is 20 square meters, then the platform free area weight is 0.2 (i.e. 20 square meters / 100 square meters).

[0157] In some embodiments, a value-taking rule for the platform idle area weight may also be set. For example, if the ratio of the platform idle area to the preset reference area is less than or equal to 0.1, the platform idle area weight is 0.1; if the ratio of the platform idle area to the preset reference area is greater than 0.1 and less than or equal to 0.3, the platform idle area weight is 0.3; if the ratio of the platform idle area to the preset reference area is greater than 0.3, the platform idle area weight is 0.5.

[0158] In some embodiments, the platform free area weight may be determined based on the ratio of the platform free area to the preset reference area. For example, the platform free area weight is positively correlated with the ratio. If the ratio of the platform free area to the preset reference area is 0.2, the platform free area weight is 0.3; if the ratio of the platform free area to the preset reference area is 0.4, the platform free area weight is 0.6.

[0159] (3) Determine a platform adaptation weight of at least one platform resource based on the platform adaptation score of at least one platform resource.

[0160] In this embodiment, for any platform, the platform adaptation weight of any platform is determined based on the adaptation score of each resource item included in any platform resource and the preset adaptation coefficient of each resource item included in any platform resource.

[0161] For example, for any platform, the platform includes three resource items: crane, conveyor belt and storage space.

[0162] The preset adaptation coefficients of each resource item are: the preset adaptation coefficient of the crane is 0.3, the preset adaptation coefficient of the conveyor belt is 0.4, and the preset adaptation coefficient of the storage space is 0.3. If the upper limit of the adaptation score of each resource item is 5 points, the sum of the preset adaptation scores of each resource item is: 5×0.3+5×0.4+5×0.3=5 points.

[0163] The adaptability scores of each resource item are as follows: the adaptability score of the crane is 4 points, the adaptability score of the conveyor belt is 3 points, and the adaptability score of the storage space is 3 points.

[0164] Based on the preset adaptation coefficient of each resource item, the adaptation score of each resource item is weighted averaged to obtain a weighted score of: 4×0.3+3×0.4+3×0.3=3.3 points.

[0165] The platform adaptation weight of the platform resources is: the ratio of the weighted score of the adaptation score of each resource item of the platform (3.3 points) to the sum of the preset adaptation scores of each resource item (5 points) (ie 3.3 points / 5 points=0.66).

[0166] It can be seen that the various preset parameters mentioned in the above embodiments (for example, preset reference time, preset reference area, preset adaptation coefficient) can be flexibly adjusted and optimized according to actual business needs and characteristics to adapt to different scenarios and changes, with stronger adaptability and flexibility. For example, it can adapt to Internet decoration business scenarios of different scales and complexities.

[0167] (ii) Based on the historical information of at least one platform resource (including historical average processing time and historical processing stability statistics), the historical information weight of at least one platform resource (including historical average processing time weight and historical processing stability weight) can be determined. Specifically, it includes the following (1) and (2):

[0168] (1) Based on the historical average processing time of at least one platform resource, determine the historical average processing time weight of at least one platform resource.

[0169] In this embodiment, for any platform resource, the average time for processing goods in the platform resource's history can be normalized to obtain the weight of the historical average processing time. For example, the average time for the platform resource to process parcels in history is 30 minutes, and the average time for processing standard products is 20 minutes. Then the weight of the historical average processing time for the platform resource to process parcels in history is 0.6, that is, 30 minutes / (30 minutes+20 minutes)=0.6. The weight of the historical average processing time for the platform resource to process standard products in history is 0.4, that is, 20 minutes / (30 minutes+20 minutes)=0.4. It can be seen that the weights of the historical average processing time for different types of goods are different for the same platform resource.

[0170] (2) Based on the historical processing stability statistical information of at least one platform resource, determine the historical processing stability weight of at least one platform resource.

[0171] In this embodiment, for any platform resource, the standard deviation of the historical processing time of the platform resource can be converted into a value between 0 and 1 as the historical processing stability weight of the platform resource.

[0172] (III) Based on the demand information of at least one commodity (including commodity attributes, commodity quantity and waiting time), determine the multi-dimensional weight of at least one commodity type (including commodity attribute weight, commodity quantity weight and waiting time). Specifically including the following (1) to (3):

[0173] (1) Based on the commodity attributes (including commodity type and commodity complexity) of at least one commodity, determine the commodity attribute weight (including commodity type weight and commodity complexity weight) of at least one commodity type.

[0174] In this embodiment, a product type weight can be pre-configured for each product type. After obtaining demand information of at least one product, at least one product type can be obtained from the demand information, and then based on the pre-configured product type weight, the product type weight of at least one product type in the demand information can be directly obtained.

[0175] In this embodiment, based on the commodity complexity of each commodity in the demand information, the commodity complexity weight of at least one commodity type in the demand information is determined.

[0176] For example, product complexity can be configured for each stock keeping unit (SKU) on the warehouse goods, and then the product complexity of each product in the demand information can be determined, so that the number of products and product complexity of the same product type in the demand information can be determined, and the average product complexity of the same product type is used as the product complexity weight of the product type.

[0177] It can be seen that in this embodiment, commodities of the same commodity type are processed uniformly so as to allocate platform resources more centrally and efficiently, thereby reducing the fragmentation and unreasonable allocation of platform resources.

[0178] (2) Based on the commodity quantity of at least one commodity, determine a commodity quantity weight of at least one commodity type.

[0179] In this embodiment, by counting the number of commodities of each commodity type in the demand information, the ratio of the number of commodities of each commodity type to the total number of commodities in the demand information is used as the commodity quantity weight of the corresponding commodity type.

[0180] (3) Determine a waiting time weight of at least one commodity type based on the waiting time of at least one commodity.

[0181] In this embodiment, the ratio or difference between the waiting time of at least one commodity and the preset reference waiting time is first determined, and then the ratio or difference is converted into a waiting time weight. The longer the waiting time, the greater the waiting time weight.

[0182] For example, the preset benchmark waiting time is 2 hours, and the waiting time for the product is 3 hours, and the calculated ratio is 1.5 (3 hours / 2 hours). The pre-configured conversion rule is: when the waiting time exceeds 1 times the benchmark waiting time, the waiting time weight is 0.3; when it exceeds 2 times, the waiting time weight is 0.5. In the example, the waiting time exceeds 1 times the benchmark waiting time, so the waiting time coefficient is 0.3. It should be noted that if the waiting time for the product starts from the acquisition of the demand information, the waiting time for each product in the demand information is the same.

[0183] It can be seen that in this embodiment, by determining the multi-dimensional weights for platform resource allocation (including the multi-dimensional weights of platform resources and the multi-dimensional weights of commodity types), the allocation priorities of different platform resources can be measured more accurately, making platform resource allocation more reasonable, improving the efficiency of platform resource allocation, and avoiding the problem of unreasonable allocation or low allocation efficiency caused by a single factor dominating platform resource allocation, effectively reducing errors and rework caused by improper platform resource allocation, and reducing warehouse operating costs.

[0184] Step 3: Based on the multi-dimensional weight of at least one platform resource and the multi-dimensional weight of at least one commodity type, determine the allocation priority of at least one platform resource to at least one commodity type.

[0185] In this embodiment, the allocation priority of any platform resource to any commodity type is determined through the following steps 1 to 4:

[0186] Step 1: Determine a first value based on a product type weight, a product quantity weight, a product complexity weight, and a product quantity of any product type.

[0187] For example, the first value is: the product of the commodity type weight, commodity quantity weight, commodity complexity weight and commodity quantity of any commodity type.

[0188] That is, product type weight×product quantity weight×product complexity weight×product quantity=first value.

[0189] Step 2: Determine the second value based on the platform idle time weight, platform idle area weight and platform adaptation weight of any platform resource.

[0190] For example, the second value is: the product of the platform idle time weight, the platform idle area weight and the platform adaptation weight of any platform resource.

[0191] That is, platform idle time weight × platform idle area weight × platform adaptation weight = second value.

[0192] Step 3: Determine a third value based on the waiting time weight of any commodity type, the historical average processing time weight of any platform resource, and the historical processing stability weight of any platform resource.

[0193] For example, the third value is: the product of the historical average processing time weight and the historical processing stability weight of any platform resource and the sum of the waiting time weight of any commodity type.

[0194] That is, waiting time weight+(historical average processing time weight×historical processing stability weight)=third value.

[0195] Step 4: Based on the first value, the second value, and the third value, determine the allocation priority of any platform resource to any commodity type.

[0196] For example, the allocation priority of any platform resource to any type of product is: the sum of the first value and the second value multiplied by the third value.

[0197] That is, (first value+second value)×third value=allocation priority.

[0198] It can be seen that in this embodiment, by determining the multi-dimensional weights for platform resource allocation, an intelligent platform resource allocation priority sorting mechanism is constructed, breaking the traditional fixed platform resource allocation model, so that important and urgent goods can get priority to obtain appropriate platform resources.

[0199] Step 4: Allocate resources to at least one commodity based on the allocation priority of at least one platform resource to at least one commodity type.

[0200] In this embodiment, for any commodity, the commodity type of the commodity is determined, and a platform resource with a higher allocation priority for the commodity type is selected and allocated to the commodity.

[0201] It can be seen that in the embodiments of the present disclosure, by acquiring the status information and historical information of the platform, as well as the demand information of the goods, the changes in the platform status and the dynamic fluctuations in the demand for goods can be perceived in real time; then, the goods of the same type are processed uniformly, so as to allocate platform resources more centrally and efficiently, and reduce the fragmentation and unreasonable allocation of platform resources; by determining the multi-dimensional weights for platform resource allocation, including the multi-dimensional weights of platform resources and the multi-dimensional weights of commodity types, the allocation priority of different platform resources can be measured more accurately, and the problem of unreasonable allocation or low allocation efficiency caused by a single factor dominating resource allocation can be avoided, so that the platform resource allocation is more reasonable, the utilization rate of platform resources is improved, the idle area of ​​the platform is reduced, the congestion situation is improved, and the efficiency of platform resource allocation is improved, so that important and urgent goods can get appropriate platform resources first, thereby improving the logistics efficiency of the entire warehouse, improving supplier satisfaction, shortening the waiting time of goods, and improving customer satisfaction.

[0202] Embodiment 2

[0203] Based on the first embodiment, this embodiment provides a specific example of platform resource allocation.

[0204] 1. Set the commodity type weight C1 for packaged products and C2 for standard products.

[0205] 2. Set the quantity weight of packaged products to N1 and the quantity weight of standard products to N2.

[0206] 3. Set the product complexity weight K1 for packaged products and K2 for standard products.

[0207] 4. Calculate the platform idle time weight Ti, platform idle area weight Si and platform adaptation weight Fi for each platform.

[0208] 5. Determine the waiting time weight W, the historical average processing time weight H, and the historical processing stability weight SH.

[0209] 6. Assign priority formula:

[0210] Allocation priority = (commodity type weight × commodity quantity weight × commodity complexity weight × commodity quantity + platform idle time weight × platform idle area weight × platform adaptation weight) × (waiting time weight + historical average processing time weight × historical processing stability weight).

[0211] Assumptions:

[0212] The commodity type weight of the packaged product is C1 = 0.75, and the commodity type weight of the standard product is C2 = 0.25.

[0213] The quantity weight of the packaged product is N1=0.4, and the quantity weight of the standard product is N2=0.15.

[0214] The commodity complexity weight of packaged products is K1=0.6, and the commodity complexity weight of standard products is K2=0.3.

[0215] The waiting time weight W=0.3, the historical average processing time weight H=0.4, and the historical processing stability weight SH=0.8.

[0216] Suppose there are two platforms A and B:

[0217] The idle time weight of platform A is TA=0.85, the idle area weight is SA=0.7, and the adaptation weight of platform A is FA=0.9.

[0218] The idle time weight TB of platform B is 0.9, the idle area weight SB is 0.65, and the adaptation weight FB of platform B is 0.85.

[0219] Assume that the demand information for goods includes: 8 parcels and 150 standard goods to be received. According to the above allocation priority formula:

[0220] For Platform A:

[0221] The priority of package allocation is:

[0222] (0.75×0.4×0.6×8+0.85×0.7×0.9×1)×(0.3+0.4×0.8)=1.22481.

[0223] The priority for allocating standards is:

[0224] (0.25×0.15×0.3×150+0.85×0.7×0.9×1)×(0.3+0.4×0.8)=1.37826.

[0225] For Platform B:

[0226] The priority of package allocation is:

[0227] (0.75×0.4×0.6×8+0.9×0.65×0.85×1)×(0.3+0.4×0.8)=1.39005.

[0228] The priority for allocating standards is:

[0229] (0.25×0.15×0.3×150+0.9×0.65×0.85×1)×(0.3+0.4×0.8)=1.354545.

[0230] It can be seen that for packaged goods, the allocation priority of platform B is higher than that of platform A, so the packaged goods are allocated to platform B. For standard goods, the allocation priority of platform A is higher than that of platform B, so the standard goods are allocated to platform A.

[0231] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all described as a series of action combinations, but those skilled in the art can understand that the embodiments of the present disclosure are not limited by the described action sequence, because according to the embodiments of the present disclosure, some steps can be performed in other sequences or simultaneously. In addition, those skilled in the art can understand that the embodiments described in the specification are all optional embodiments.

[0232] Figure 2 A schematic diagram of a resource allocation device provided in an embodiment of the present disclosure, which can be applied to electronic devices, including but not limited to vehicle-mounted devices, smart phones, PDAs, tablet computers, wearable devices with display screens, desktop computers, laptop computers, all-in-one machines, smart home devices, servers, etc., wherein the server can be an independent server or a cluster of multiple servers, including servers built locally and servers set up in the cloud.

[0233] The resource allocation device provided in the embodiments of the present disclosure can execute the processing flow provided in each embodiment of the resource allocation method, such as Figure 2 As shown, the resource allocation device includes but is not limited to: an acquisition unit 21, a first determination unit 22, a second determination unit 23 and an allocation unit 24. The functions of each unit are described as follows:

[0234] An acquisition unit 21 is used to acquire state information and history information of at least one resource and demand information of at least one object, where the demand information is information of the object that needs to be processed using the resource;

[0235] A first determination unit 22, configured to determine a multi-dimensional weight of at least one resource based on state information and history information of at least one resource, and to determine a multi-dimensional weight of at least one object type based on demand information of at least one object, the at least one object having the same or different object types;

[0236] A second determining unit 23, configured to determine an allocation priority of at least one resource to at least one object type based on a multi-dimensional weight of at least one resource and a multi-dimensional weight of at least one object type;

[0237] The allocation unit 24 is configured to allocate resources to at least one object based on an allocation priority of at least one resource to at least one object type.

[0238] In some embodiments, the status information includes at least one of the following:

[0239] Resource idle time, resource idle area, and resource adaptation score;

[0240] And / or, the historical information includes at least one of the following:

[0241] Historical average processing time and historical processing stability statistics;

[0242] And / or, the requirement information includes at least one of the following:

[0243] Object attributes, object quantity, waiting time; object attributes include at least one of the following: object type, object complexity.

[0244] In some embodiments, the first determining unit 22 determines the multi-dimensional weight of at least one resource based on the state information and historical information of at least one resource, including:

[0245] Determining a resource idle time weight of at least one resource based on the resource idle time of at least one resource; and / or,

[0246] Determining a resource idle area weight of at least one resource based on the resource idle area of ​​at least one resource; and / or,

[0247] determining a resource adaptation weight of at least one resource based on the resource adaptation score of at least one resource; and / or,

[0248] Determining a historical average processing time weight of at least one resource based on a historical average processing time of at least one resource; and / or,

[0249] Based on the historical processing stability statistics of the at least one resource, a historical processing stability weight of the at least one resource is determined.

[0250] In some embodiments, the first determining unit 22 determines the multi-dimensional weight of at least one object type based on the demand information of at least one object, including:

[0251] Determining an object property weight of at least one object type based on an object property of at least one object; and / or,

[0252] determining an object quantity weight of at least one object type based on the object quantity of at least one object; and / or,

[0253] Based on the latency of the at least one object, a latency weight for at least one object type is determined.

[0254] In some embodiments, the first determining unit 22 determines the object attribute weight of at least one object type based on the object attribute of at least one object, including:

[0255] Obtaining an object type weight of at least one object type; and / or,

[0256] Based on the object complexity of the at least one object, an object complexity weight of at least one object type is determined.

[0257] In some embodiments, the first determining unit 22 determines the resource adaptation weight of at least one resource based on the resource adaptation score of at least one resource, including:

[0258] For any resource, the resource adaptation weight of any resource is determined based on the adaptation score of each resource item included in any resource and the preset adaptation coefficient of each resource item included in any resource.

[0259] In some embodiments, the second determining unit 23 is configured to:

[0260] Assignment priority to any object type for any resource:

[0261] Determining a first value based on an object type weight, an object quantity weight, an object complexity weight, and an object quantity of any object type;

[0262] Determining a second value based on a resource idle time weight, a resource idle area weight, and a resource adaptation weight of any resource;

[0263] Determining a third value based on a waiting time weight of any object type, a historical average processing time weight of any resource, and a historical processing stability weight of any resource;

[0264] Based on the first value, the second value and the third value, an allocation priority of any resource to any object type is determined.

[0265] In some embodiments, the first value is: the product of the object type weight, the object quantity weight, the object complexity weight and the object quantity of any object type;

[0266] And / or, the second value is: the product of a resource idle time weight, a resource idle area weight and a resource adaptation weight of any resource;

[0267] And / or, the third value is: the product of the historical average processing time weight and the historical processing stability weight of any resource and the sum of the waiting time weight of any object type.

[0268] In some embodiments, the second determining unit 23 determines the allocation priority of any resource to any object type based on the first value, the second value, and the third value, including:

[0269] The allocation priority of any resource to any object type is: the sum of the first value and the second value multiplied by the third value.

[0270] In some embodiments, the allocation unit 24 is configured to:

[0271] For any object, the object type of the object is determined, and resources with a higher allocation priority for the object type are selected and allocated to the object.

[0272] Figure 2 For details of each embodiment of the resource allocation device shown in FIG. Figure 1 To avoid repetition, the details of each embodiment of the resource allocation method are not described again.

[0273] Figure 3 is an exemplary block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 As shown, the electronic device includes: a memory 31, a processor 32, and a computer program stored in the memory 31. It can be understood that the memory 31 in this embodiment can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0274] In some implementations, the memory 31 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and application programs.

[0275] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic tasks and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application tasks. The program that implements the resource allocation method provided in the embodiment of the present disclosure can be included in the application.

[0276] In the embodiment of the present disclosure, at least one processor 32 calls a program or instruction stored in at least one memory 31, specifically, a program or instruction stored in an application, and at least one processor 32 is used to execute the steps of each embodiment of the resource allocation method provided in the embodiment of the present disclosure.

[0277] The resource allocation method provided in the embodiment of the present disclosure can be applied to the processor 32, or implemented by the processor 32. The processor 32 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 32 or an instruction in the form of software. The above-mentioned processor 32 can be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0278] The steps of the resource allocation method provided in the embodiment of the present disclosure can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 31, and the processor 32 reads the information in the memory 31 and completes the steps of the method in combination with its hardware.

[0279] The embodiment of the present disclosure also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, wherein the program or instruction enables a computer to execute the steps of each embodiment of the resource allocation method, which will not be described here to avoid repeated description. The computer-readable storage medium may be a non-transitory computer-readable storage medium.

[0280] The embodiment of the present disclosure also proposes a computer program product, which includes a computer program, which is stored in a computer-readable storage medium, and the computer-readable storage medium may be a non-transitory computer-readable storage medium. At least one processor of a computer reads and executes the computer program from the computer-readable storage medium, so that the computer executes the steps of each embodiment of the resource allocation method, which will not be described here in detail to avoid repeated description.

[0281] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0282] Those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present disclosure and to form different embodiments.

[0283] Those skilled in the art will appreciate that the description of each embodiment has its own emphasis, and for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0284] Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A resource allocation method, the method comprising: Acquire status information and history information of at least one resource, and demand information of at least one object, wherein the demand information is information of the object that needs to be processed using the resource; Determining a multi-dimensional weight of the at least one resource based on the state information and the history information of the at least one resource, and determining a multi-dimensional weight of at least one object type based on the demand information of the at least one object, the at least one object having the same or different object types; Determining an allocation priority of the at least one resource to the at least one object type based on the multi-dimensional weight of the at least one resource and the multi-dimensional weight of the at least one object type; Based on the allocation priority of the at least one resource to the at least one object type, resources are allocated to the at least one object.

2. The method according to claim 1, wherein: The status information includes at least one of the following: Resource idle time, resource idle area, and resource adaptation score; And / or, the historical information includes at least one of the following: Historical average processing time and historical processing stability statistics; And / or, the demand information includes at least one of the following: Object attributes, object quantity, waiting time; the object attributes include at least one of the following: object type, object complexity.

3. The method according to claim 2, wherein: The determining the multi-dimensional weight of the at least one resource based on the state information and the history information of the at least one resource includes: determining a resource idle time weight of the at least one resource based on the resource idle time of the at least one resource; and / or, Determining a resource idle area weight of the at least one resource based on the resource idle area of ​​the at least one resource; and / or, determining a resource adaptation weight of the at least one resource based on the resource adaptation score of the at least one resource; and / or, Determining a historical average processing time weight of the at least one resource based on the historical average processing time of the at least one resource; and / or, Based on the historical processing stability statistics of the at least one resource, a historical processing stability weight of the at least one resource is determined.

4. The method according to any one of claims 2 to 3, wherein: The determining the multi-dimensional weight of the at least one object type based on the demand information of the at least one object includes: determining an object attribute weight of the at least one object type based on the object attribute of the at least one object; and / or, determining an object quantity weight of the at least one object type based on the object quantity of the at least one object; and / or, Based on the latency of the at least one object, a latency weight of the at least one object type is determined.

5. The method according to claim 4, wherein: The determining the object attribute weight of the at least one object type based on the object attribute of the at least one object includes: obtaining an object type weight of the at least one object type; and / or, Based on the object complexity of the at least one object, an object complexity weight of the at least one object type is determined.

6. The method according to claim 3, wherein: The determining the resource adaptation weight of the at least one resource based on the resource adaptation score of the at least one resource includes: For any resource, a resource adaptation weight of the any resource is determined based on the adaptation score of each resource item included in the any resource and the preset adaptation coefficient of each resource item included in the any resource.

7. The method according to claim 5, wherein: The determining, based on the multi-dimensional weight of the at least one resource and the multi-dimensional weight of the at least one object type, an allocation priority of the at least one resource to the at least one object type comprises: Assignment priority to any object type for any resource: Determining a first value based on an object type weight, an object quantity weight, an object complexity weight, and an object quantity of any one of the object types; Determine a second value based on a resource idle time weight, a resource idle area weight, and a resource adaptation weight of any one of the resources; Determining a third value based on the waiting time weight of any object type, the historical average processing time weight of any resource, and the historical processing stability weight of any resource; Based on the first value, the second value, and the third value, an allocation priority of any one resource to any one object type is determined.

8. The method according to claim 7, wherein: The first value is: the product of the object type weight, the object quantity weight, the object complexity weight and the object quantity of any object type; And / or, the second value is: the product of the resource idle time weight, the resource idle area weight and the resource adaptation weight of any resource; And / or, the third value is: the product of the historical average processing time weight and the historical processing stability weight of any resource and the sum of the waiting time weight of any object type.

9. The method according to claim 7 or 8, wherein: The determining, based on the first value, the second value, and the third value, the allocation priority of any one of the resources to any one of the object types comprises: The allocation priority of any resource to any object type is: the sum of the first value and the second value multiplied by the third value.

10. The method according to claim 1, wherein: The allocating resources to the at least one object based on the allocation priority of the at least one resource to the at least one object type comprises: For any object, the object type of the object is determined, and resources with a higher allocation priority for the object type are selected and allocated to the object.

11. An electronic device, wherein: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the resource allocation method according to any one of claims 1 to 10.

12. A computer-readable storage medium, wherein: The computer-readable storage medium stores a program or an instruction, and the program or the instruction enables a computer to execute the steps of the resource allocation method according to any one of claims 1 to 10.

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