Information matching method and device, computer equipment and readable storage medium

Abstract

The invention relates to the technical field of computing power, in particular to an information matching method and device, computer equipment and a readable storage medium. Comprising the steps of obtaining entity demand information corresponding to a demand side, resource entities capable of being provided by a provider and entity feature information of the resource entities; the entity demand information and the entity feature information both comprise multi-dimension attribute information: carrying out information matching on the entity demand information and the entity feature information of each resource entity according to each piece of attribute information to obtain an information matching result; and selecting a target entity meeting the entity demand information from the resource entities according to an information matching result.

Description

Technical Field

[0001] The present application relates to the field of computing power technology, and in particular to an information matching method, apparatus, computer device and readable storage medium. Background Art

[0002] In the computing network, resource entities mainly refer to various physical or virtual computing resources, storage resources, and network resources that participate in the allocation, scheduling, and use of computing power. These resource entities together constitute the basic architecture of the computing network and provide users with the required computing services.

[0003] At present, the core of computing power network technology is to manage, allocate and schedule computing power resources on demand, and to optimize recommendations and allocations based on "user needs".

[0004] However, the current computing power network platform mainly relies on manually set rules for matching in terms of computing power resource allocation, which does have the problem of insufficient accuracy and is therefore in urgent need of improvement. Summary of the invention

[0005] Based on this, it is necessary to provide an information matching method, device, computer equipment and readable storage medium that can improve the accuracy of information matching in response to the above technical problems.

[0006] In a first aspect, the present application provides an information matching method, comprising:

[0007] Obtain the entity demand information corresponding to the demander, the resource entities that the provider can provide, and the entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information:

[0008] The entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result;

[0009] According to the information matching results, a target entity that meets the entity requirement information is selected from each resource entity.

[0010] In one embodiment, the entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain information matching results, including:

[0011] Get the sorting results between multiple dimension attribute information;

[0012] According to the sorting results, the entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain the information matching result.

[0013] In one embodiment, according to the sorting result, the entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain the information matching result, including:

[0014] For any attribute information, according to the sorting result, determine the distance correlation coefficient between the attribute information in the entity demand information and the attribute information corresponding to each resource entity, and obtain the attribute matching result corresponding to the attribute information;

[0015] The information matching result is determined according to the attribute matching result corresponding to each attribute information.

[0016] In one embodiment, the multiple dimensional attribute information includes entity attribute information computing power attribute, network attribute and location attribute; obtaining the sorting result between the multiple dimensional attribute information includes:

[0017] According to the multi-level matching order, the computing power attributes, network attributes and location attributes are sorted to obtain the sorting results among the attribute information of multiple dimensions.

[0018] In one embodiment, according to the sorting result, the entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain the information matching result, including:

[0019] Match the computing power attribute in the entity demand information with the computing power attribute of each resource entity to obtain an attribute matching result of the computing power attribute dimension; the attribute matching result of the computing power attribute dimension includes at least one computing power entity;

[0020] Match the network attributes in the entity demand information with the network attributes of each computing power entity to obtain an attribute matching result in the network attribute dimension; the attribute matching result in the network attribute dimension includes at least one network entity;

[0021] The location attributes in the entity demand information are matched with the location attributes of each network entity to obtain an attribute matching result of the location attributes.

[0022] In one embodiment, according to the information matching result, a target entity that meets the entity requirement information is selected from each resource entity, including:

[0023] According to the information matching result, select candidate entities that meet the entity requirement information from various resource entities;

[0024] Determine the resource weight of each candidate entity;

[0025] A target entity is selected from among the candidate entities according to the resource weights of the candidate entities.

[0026] In one embodiment, selecting a target entity from each candidate entity according to the resource weight of each candidate entity includes:

[0027] Sort the candidate entities in ascending order of their resource weights to obtain a sorted queue;

[0028] The first target number of candidate entities in the sorted queue are used as target entities.

[0029] In a second aspect, the present application also provides an information matching device, including:

[0030] The acquisition module is used to obtain the entity demand information corresponding to the demander, the resource entities that the provider can provide, and the entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information:

[0031] A matching module is used to match entity demand information and entity feature information of each resource entity according to each attribute information to obtain an information matching result;

[0032] The entity selection module is used to select a target entity that meets entity requirement information from various resource entities based on the information matching results.

[0033] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] Obtain the entity demand information corresponding to the demander, the resource entities that the provider can provide, and the entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information:

[0035] The entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result;

[0036] According to the information matching results, a target entity that meets the entity requirement information is selected from each resource entity.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0038] Obtain the entity demand information corresponding to the demander, the resource entities that the provider can provide, and the entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information:

[0039] The entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result;

[0040] According to the information matching results, a target entity that meets the entity requirement information is selected from each resource entity.

[0041] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0042] Obtain the entity demand information corresponding to the demander, the resource entities that the provider can provide, and the entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information:

[0043] The entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result;

[0044] According to the information matching results, a target entity that meets the entity requirement information is selected from each resource entity.

[0045] The above-mentioned information matching method, apparatus, computer equipment and readable storage medium match entity demand information and entity feature information of each resource entity according to each attribute information, and improve the comprehensiveness and accuracy of entity information matching through multi-dimensional matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 An application environment diagram of an information matching method in an embodiment;

[0048] Figure 2 is a schematic flow chart of an information matching method in one embodiment;

[0049] Figure 3 A schematic diagram of a flow chart of steps for obtaining information matching results in one embodiment;

[0050] Figure 4 It is a flowchart of performing information matching steps according to each attribute information in one embodiment;

[0051] Figure 5A schematic flow chart of the step of obtaining information matching results in another embodiment;

[0052] Figure 6 A flowchart of a step of selecting a target entity that meets entity requirement information in one embodiment;

[0053] Figure 7 is a structural block diagram of an information matching device in an embodiment;

[0054] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] The information matching method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, it includes a resource recommendation platform, a resource management platform and various resource entities, wherein the resource entity is a network device, wherein the resource management platform communicates with the resource recommendation platform and various network devices; the resource recommendation platform includes a user demand feature extraction module, a computing power information matching module, a network information matching module, other information matching modules and a resource attribute information extraction module.

[0057] In an exemplary embodiment, Figure 2 As shown, an information matching method is provided, which is applied to Figure 1 The resource management platform in the example is used to illustrate, including the following S201 to S203. Among them:

[0058] S201, obtaining entity demand information corresponding to the demander, resource entities that can be provided by the provider, and entity feature information of each resource entity.

[0059] Among them, the resource entity is the computing power entity; the demand side is Figure 1 Users in , provided by Figure 1 The resource recommendation platform in . The user demand feature extraction module is used to obtain entity demand information. Entity demand information in the computing power network refers to the demand description and requirements of users or applications for specific computing power resources; the resource attribute information extraction module is used to obtain the entity feature information of each resource entity and each resource entity that can be provided by the provider.

[0060] Among them, both entity demand information and entity feature information include multiple dimensional attribute information.

[0061] Exemplarily, the multiple dimensional attribute information may include the following dimensions:

[0062] 1) Hardware dimensions, such as processor type, specifications, and storage configuration;

[0063] 2) Performance dimension, e.g., network performance;

[0064] 3) Benefit dimension, for example, distance communication cost and resource procurement cost.

[0065] S202, matching the entity demand information and the entity characteristic information of each resource entity according to each attribute information to obtain an information matching result.

[0066] Specifically, for each attribute information, a preset algorithm matching rule can be used to match the entity demand information with the entity feature information of each resource entity, and the attribute matching result of matching the entity demand information with the entity feature information of each resource entity in each attribute dimension can be obtained. Further, the information matching result is determined according to the attribute matching result corresponding to each attribute information.

[0067] Optionally, the computing power matching rule may be determined according to a string matching algorithm, a fuzzy matching algorithm or a rule matching algorithm.

[0068] The information matching result may include an entity matching degree value between each resource entity and the entity demand information; further, according to the entity matching degree value corresponding to any resource entity, it can be determined whether the resource entity is matched successfully or failed.

[0069] For example, for any resource entity, when the entity matching value corresponding to the resource entity is greater than the matching threshold, it is determined that the entity matching value corresponding to the entity represents a successful match. Specifically, a successful match may also include the following two situations:

[0070] 1) The entity matching degree between any resource entity and entity demand information can represent all conditions for the resource entity to fully meet the resource demand, including performance, quantity, type, benefit and other aspects.

[0071] 2) The entity matching degree between any resource entity and entity requirement information can also represent that although the resource entity cannot fully meet all the conditions of resource requirements, it reaches the entity matching degree threshold in key aspects (such as performance and quantity).

[0072] For example, for any resource entity, when the entity matching value corresponding to the resource entity is greater than the matching threshold, it is determined that the entity matching value corresponding to the entity indicates a matching failure. Specifically, the matching failure may also include the following three situations:

[0073] 1) Insufficient resources: The available resource entities in the resource pool cannot meet the quantity or performance requirements of the resource demand; for example, the number of processors required by a parallel computing task exceeds the number of currently available resource entities in the resource recommendation platform.

[0074] 2) Type mismatch: There is a significant difference in type between the resource entity and the resource requirement; for example, a deep learning training task that requires GPU acceleration cannot be matched with computing resources that only have CPUs.

[0075] 3) Low service efficiency: The cost of resource entities exceeds the budget limit of resource requirements.

[0076] S203, selecting a target entity that meets the entity requirement information from each resource entity according to the information matching result.

[0077] Optionally, the entity matching values ​​of the resource entities in the information matching results may be sorted, and resource entities with higher rankings may be used as target entities.

[0078] The above information matching method matches the entity demand information and the entity feature information of each resource entity according to each attribute information, and improves the comprehensiveness and accuracy of entity information matching through multi-dimensional matching.

[0079] In an exemplary embodiment, Figure 3 As shown, the entity demand information and the entity feature information of each resource entity are matched according to each attribute information to obtain information matching results, including:

[0080] S301, obtaining the sorting results among multiple dimensional attribute information.

[0081] Optionally, weight values ​​between multiple dimensional attribute information may be obtained, and the multiple dimensional attribute information may be sorted to obtain a sorting result between the multiple dimensional attribute information.

[0082] S302, according to the sorting result, the entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result.

[0083] Optional,

[0084] According to the sorting results, the entity demand information and the entity feature information of each resource entity are matched according to each attribute information one by one to obtain the information matching result.

[0085] Specifically, Figure 4 As shown, according to the sorting results, the entity demand information and the entity feature information of each resource entity are matched according to each attribute information to obtain the information matching results, including:

[0086] S401, for any attribute information, determine the distance correlation coefficient between the attribute information in the entity demand information and the attribute information corresponding to each resource entity according to the sorting result, and obtain the attribute matching result corresponding to the attribute information.

[0087] Optionally, the distance correlation coefficient may be a Pearson correlation coefficient.

[0088] For example, for any attribute information, when determining the attribute information in the entity demand information and the attribute information corresponding to any resource entity, the distance between the two entity attributes (i.e., the distance correlation coefficient) can be measured by the Pearson correlation coefficient. The higher the correlation strength, the smaller the distance, the higher the similarity, and the higher the matching degree.

[0089] It can be understood that the correlation coefficient is a statistical indicator that reflects the closeness of the relationship between variables. The indicator for measuring the degree of linear correlation between two random variables is the simple correlation coefficient, which was proposed by statistician Karl Pearson. It is also called the Pearson correlation coefficient and is represented by r. The value range is [-1,1]. 1 means that the two variables are completely linearly correlated, -1 means that the two variables are completely negatively correlated, and 0 means that the two variables are unrelated. The closer the correlation coefficient is to 0, the weaker the correlation. When analyzing the correlation of variables, hypothesis testing is also required to observe whether the correlation is significant. Generally, the significance level p<0.05 is required, indicating that there is a significant correlation between the variables.

[0090] The Pearson correlation coefficient is calculated as follows:

[0091] Two vectors The Pearson correlation coefficient r is:

[0092]

[0093] The correlation coefficient value and the correlation strength generally correspond to the following:

[0094] If |r|>0.95: it is determined to be significantly correlated;

[0095] If 0.8≤|r|<0.95, it is determined to be highly correlated;

[0096] If 0.5≤|r|<0.8, it is determined to be moderately correlated;

[0097] If 0.3≤|r|<0.5, it is determined to be low correlation;

[0098] If |r|<0.3, it is determined to be a weak correlation.

[0099] Therefore, the larger the Pearson correlation coefficient, the stronger the correlation between the two entities, the closer the distance, and the higher the match.

[0100] S402: Determine information matching results according to the attribute matching results corresponding to each attribute information.

[0101] Optionally, the information matching result is determined according to the attribute matching result corresponding to each attribute information and the attribute weight corresponding to each attribute information.

[0102] Exemplarily, the multiple dimensional attribute information includes entity attribute information computing power attributes, network attributes and location attributes.

[0103] 1) Hashrate attributes can include:

[0104] Computing power type: Determine the required computing power type based on task requirements, such as CPU, GPU, FPGA, etc., and select resources with the corresponding computing power type.

[0105] Computing power: Evaluate the specific computing power requirements of the task, such as processing speed, concurrency, etc., and select resources with sufficient computing power.

[0106] Computing power type: Consider the special requirements of the task for the type of computing power, such as general computing power, dedicated computing power, etc., and select resources that meet the requirements.

[0107] Computing power form: Determine the required computing power form, such as bare metal servers, cloud instances, GPU rentals, etc., and select the appropriate computing power form.

[0108] 2) Network attributes can include:

[0109] Bandwidth: Based on the task's requirements for network bandwidth, select resources with sufficient bandwidth to ensure the speed and stability of data transmission.

[0110] Latency: Consider the task’s sensitivity to network latency and select resources with lower latency to reduce data transmission delays.

[0111] Packet loss rate: Evaluate the task's tolerance to network packet loss rate and select resources with a lower packet loss rate to ensure the reliability of data transmission.

[0112] Reliability: Consider the task's requirements for network reliability and select resources with high reliability to reduce the impact of network failures on the task.

[0113] 3) Location attribute matching can include:

[0114] Location information: Select resources located in appropriate locations based on the task's geographical requirements to reduce network latency and data transmission costs.

[0115] Furthermore, the attribute matching results are standardized to the same scale, and the corresponding weights are assigned according to the importance of each attribute. Each attribute matching result is multiplied by the corresponding attribute weight, and the results are added to obtain the weighted sum. The computing power resources are sorted according to the size of the weighted sum, and the resource with the highest weighted sum is selected as the best match.

[0116] Correspondingly, the sorting results among the attribute information of multiple dimensions are obtained, including: sorting the computing power attributes, network attributes and location attributes according to the multi-level matching order to obtain the sorting results among the attribute information of multiple dimensions.

[0117] Specifically, Figure 5 As shown, according to the sorting results, the entity demand information and the entity feature information of each resource entity are matched according to each attribute information to obtain the information matching results, including:

[0118] S501, matching the computing power attribute in the entity demand information with the computing power attribute of each resource entity to obtain an attribute matching result in the computing power attribute dimension.

[0119] Among them, the attribute matching result of the computing power attribute dimension includes at least one computing power entity.

[0120] Specifically, computing power attributes such as computing power type, computing power size, computing power type, and computing power form are extracted from the entity demand information. The extracted computing power attributes are compared with the computing power attributes of each resource entity to find the computing power entity that meets the demand. The computing power entity that meets the demand is used as the attribute matching result of the computing power attribute dimension.

[0121] S502, matching the network attributes in the entity demand information with the network attributes of each computing power entity to obtain an attribute matching result in the network attribute dimension.

[0122] Among them, the attribute matching result of the network attribute dimension includes at least one network entity.

[0123] Specifically, network attributes such as bandwidth, latency, packet loss rate, and reliability are extracted from the entity demand information. The extracted network attributes are compared with the network attributes of each computing power entity to find the network entity that meets the requirements. The network entity that meets the requirements is used as the attribute matching result of the network attribute dimension.

[0124] S503: Perform information matching on the location attribute in the entity demand information and the location attribute of each network entity to obtain an attribute matching result of the location attribute.

[0125] Specifically, location attributes, such as geographic location requirements, are extracted from entity requirement information. The extracted location attributes are compared with location information in location attributes of each network entity to find a location entity that meets the requirements. The location entity that meets the requirements is used as an attribute matching result of the location attribute.

[0126] For example, after obtaining the attribute matching results of computing power attributes, network attributes and location attributes, further comprehensive matching can be performed according to actual needs. For example, a target entity that satisfies the requirements of computing power attributes, network attributes and location attributes can be found as the final information matching result.

[0127] In an exemplary embodiment, Figure 6 As shown, according to the information matching result, a target entity that meets the entity requirement information is selected from each resource entity, including:

[0128] S601, selecting candidate entities that meet entity requirement information from resource entities according to the information matching result.

[0129] Among them, according to the previous information matching results, candidate entities that meet the entity requirement information are screened from all resource entities. These candidate entities fully meet or basically meet all the key attributes specified in the entity requirement information, including computing power attributes, network attributes, and location attributes.

[0130] S602: Determine the resource weight of each candidate entity.

[0131] Among them, the resource weight can be resource income, and the resource income is determined based on the difference between resource income and resource cost.

[0132] Specifically, for each candidate entity, its resource weight needs to be calculated, which usually involves a detailed analysis and quantification of resource costs and resource income.

[0133] S603: Select a target entity from the candidate entities according to the resource weights of the candidate entities.

[0134] Specifically, according to the resource weight of each candidate entity, a target entity is selected from each candidate entity, including: sorting each candidate entity in order from small to large according to the resource weight of each candidate entity to obtain a sorted queue; and taking the first target number of candidate entities in the sorted queue as the target entity.

[0135] Optionally, the candidate entities are sorted from small to large according to their resource weights to form a sorting queue; from the sorting queue, a target number of candidate entities at the front are selected as the final target entities; wherein the target number is a preset value. For example, if the target number is 3, the first 3 entities with the smallest resource weight (or the largest according to business logic) are selected.

[0136] In an exemplary embodiment, this embodiment provides an information matching method, including:

[0137] Match the entity demand information with each resource entity in the resource recommendation platform according to the attribute information of the three dimensions, and select the target entity from each resource entity;

[0138] The process of matching entity demand information with any resource entity in the resource recommendation platform according to the attribute information of the three dimensions is as follows:

[0139] a) The computing power information matching module matches the entity attribute information with each resource entity according to the computing power attribute dimension, and obtains the attribute matching result of the computing power dimension:

[0140] Exemplarily, the computing power attribute CD in the entity attribute information is matched with the computing power attribute RD of any resource entity. The computing power attributes of each resource The correlation coefficient between ,in , N is the number of computing resources. Sort all computing resources from large to small, select the first m computing entities, and proceed to step b) to continue screening;

[0141] b) The network information matching module matches the entity attribute information with each resource entity according to the network attribute dimension to obtain the attribute matching result of the network dimension:

[0142] Exemplarily, the network attribute CN in the entity attribute information is matched with the computing resource network information RN in any resource entity. Network information about each resource The correlation coefficient between ,in .according to Sort the m computing resources from largest to smallest, select the first n network entities, and proceed to step c) to continue screening, where n is less than or equal to m;

[0143] c) The location information matching module matches the entity attribute information with each resource entity according to the location attribute dimension to obtain the attribute matching result of the location dimension:

[0144] Exemplarily, the information matching module matches the location attribute CO in the entity attribute information with the location attribute RO in any resource entity according to the location attribute matching. Information about each resource The correlation coefficient between ,in ,according to Sort the n network entities from large to small and get q location entities.

[0145] Determine the resource management platform's entity recommendation list consisting of location entities, reorder the list from small to large according to the resource weight attribute RP, and select the target number of target entities from the q location entities.

[0146] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0147] Based on the same inventive concept, the embodiment of the present application also provides an information matching device for implementing the information matching method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more information matching device embodiments provided below can refer to the limitations on the information matching method above, and will not be repeated here.

[0148] In an exemplary embodiment, Figure 7 As shown, an information matching device is provided, comprising: an acquisition module 11, a matching module 12 and an entity selection module 13, wherein:

[0149] The acquisition module 11 is used to acquire the entity demand information corresponding to the demander, each resource entity that the provider can provide, and the entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information:

[0150] The matching module 12 is used to match the entity demand information and the entity characteristic information of each resource entity according to each attribute information to obtain an information matching result;

[0151] The entity selection module 13 is used to select a target entity that meets the entity requirement information from various resource entities according to the information matching result.

[0152] In one embodiment, the matching module 12 is further used to: obtain a ranking result between multiple dimensional attribute information;

[0153] According to the sorting results, the entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain the information matching result.

[0154] In one embodiment, the matching module 12 is further used to: for any attribute information, determine the distance correlation coefficient between the attribute information in the entity demand information and the attribute information corresponding to each resource entity according to the sorting result, and obtain the attribute matching result corresponding to the attribute information;

[0155] The information matching result is determined according to the attribute matching result corresponding to each attribute information.

[0156] In one of the embodiments, the matching module 12 is further used to sort the computing power attributes, network attributes and location attributes according to the multi-level matching order to obtain the sorting results among the attribute information of multiple dimensions.

[0157] In one embodiment, the matching module 12 is further configured to:

[0158] Match the computing power attribute in the entity demand information with the computing power attribute of each resource entity to obtain an attribute matching result of the computing power attribute dimension; the attribute matching result of the computing power attribute dimension includes at least one computing power entity;

[0159] Match the network attributes in the entity demand information with the network attributes of each computing power entity to obtain an attribute matching result in the network attribute dimension; the attribute matching result in the network attribute dimension includes at least one network entity;

[0160] The location attributes in the entity demand information are matched with the location attributes of each network entity to obtain an attribute matching result of the location attributes.

[0161] In one embodiment, the entity selection module 13 is further used to: select candidate entities that meet entity requirement information from resource entities according to the information matching result;

[0162] Determine the resource weight of each candidate entity;

[0163] A target entity is selected from among the candidate entities according to the resource weights of the candidate entities.

[0164] In one embodiment, the entity selection module 13 is further used to: sort the candidate entities in order of resource weights from small to large to obtain a sorting queue;

[0165] The first target number of candidate entities in the sorted queue are used as target entities.

[0166] Each module in the above information matching device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0167] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store XX data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an information matching method is implemented.

[0168] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0169] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0170] Obtain the entity demand information corresponding to the demander, the resource entities that the provider can provide, and the entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information:

[0171] The entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result;

[0172] According to the information matching results, a target entity that meets the entity requirement information is selected from each resource entity.

[0173] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0174] Obtain the entity demand information corresponding to the demander, the resource entities that the provider can provide, and the entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information:

[0175] The entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result;

[0176] According to the information matching result, a target entity that meets the entity requirement information is selected from each resource entity. In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

[0177] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0178] Obtain the entity demand information corresponding to the demander, the resource entities that the provider can provide, and the entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information:

[0179] The entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result;

[0180] According to the information matching results, a target entity that meets the entity requirement information is selected from each resource entity.

[0181] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0182] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0183] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0184] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An information matching method, characterized in that: The method comprises: Obtain entity demand information corresponding to the demander, resource entities that the provider can provide, and entity feature information of each resource entity; both the entity demand information and the entity feature information include multiple dimensional attribute information: Matching the entity demand information and the entity characteristic information of each resource entity according to each attribute information to obtain an information matching result; According to the information matching result, a target entity that meets the entity requirement information is selected from each resource entity.

2. The method according to claim 1, characterized in that: The entity requirement information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain information matching results, including: Get the sorting results between multiple dimension attribute information; According to the ranking result, the entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result.

3. The method according to claim 2, characterized in that According to the sorting result, the entity demand information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain the information matching result, including: For any attribute information, according to the sorting result, determine the distance correlation coefficient between the attribute information in the entity demand information and the attribute information corresponding to each resource entity, and obtain the attribute matching result corresponding to the attribute information; The information matching result is determined according to the attribute matching result corresponding to each attribute information.

4. The method according to claim 2 or 3, characterized in that: The multiple dimensional attribute information includes entity attribute information computing power attribute, network attribute and location attribute; the obtaining of the sorting results among the multiple dimensional attribute information includes: According to the multi-level matching order, the computing power attributes, network attributes and location attributes are sorted to obtain the sorting results among the attribute information of multiple dimensions.

5. The method according to claim 4, characterized in that According to the sorting result, the entity requirement information and the entity characteristic information of each resource entity are matched according to each attribute information to obtain an information matching result, including: Matching the computing power attribute in the entity demand information with the computing power attribute of each resource entity to obtain an attribute matching result in a computing power attribute dimension; the attribute matching result in the computing power attribute dimension includes at least one computing power entity; Matching the network attributes in the entity demand information with the network attributes of each computing power entity to obtain an attribute matching result in a network attribute dimension; the attribute matching result in the network attribute dimension includes at least one network entity; The location attribute in the entity demand information is matched with the location attribute of each network entity to obtain an attribute matching result of the location attribute.

6. The method according to any one of claims 1 to 5, characterized in that The step of selecting, from each resource entity according to the information matching result, a target entity that meets the entity requirement information comprises: According to the information matching result, selecting candidate entities that meet the entity requirement information from the resource entities; Determine the resource weight of each candidate entity; A target entity is selected from among the candidate entities according to the resource weights of the candidate entities.

7. The method according to claim 6, characterized in that The step of selecting a target entity from each candidate entity according to the resource weight of each candidate entity includes: Sort the candidate entities in ascending order of their resource weights to obtain a sorted queue; The first target number of candidate entities in the sorted queue are used as target entities.

8. An information matching device, characterized in that: The device comprises: The acquisition module is used to acquire the entity demand information corresponding to the demander, each resource entity that the provider can provide, and the entity feature information of each resource entity; the entity demand information and the entity feature information both include multiple dimensional attribute information: A matching module, used for matching the entity demand information and the entity characteristic information of each resource entity according to each attribute information to obtain an information matching result; The entity selection module is used to select a target entity that meets the entity requirement information from various resource entities according to the information matching result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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