Service node recommendation method, system, device and storage medium

Abstract

The present invention provides a service node recommendation method, system, device and storage medium, wherein the method includes: receiving user input, responding to the user input, obtaining the service class required by the user, and extracting the node relationship data of the service class from the node resource pool, obtaining the service demander node and the service provider node that provides service services to the service demander node from the node relationship data, and constructing a node relationship network by setting the connection edge of the service provider node pointing to the service demander node, and setting the capacity value for each node according to the number of service services provided to the adjacent nodes, and outputting service node recommendation information according to the capacity value of each node in the node relationship network. The node relationship network of the present invention does not simply classify nodes by service class, but accurately recommends nodes by capacity value, accurately responds to user needs, and improves user service experience.

Description

Background Art

[0002] In the context of business services, node is an important concept. From the perspective of function, nodes are divided into business demander nodes and business provider nodes. Business provider nodes can provide business services to business demander nodes.

[0003] By using computer technology processing methods, nodes can be classified according to the node attributes of business nodes, so as to provide accurate node matching for business demanders.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] In view of the problems in the prior art, the purpose of the present invention is to provide a service node recommendation method, system, device and storage medium, which overcomes the difficulties of the prior art, can accurately respond to user needs and improve user service experience.

[0006] An embodiment of the present invention provides a service node recommendation method, the method comprising:

[0007] Receive user input;

[0008] In response to user input, obtain the business class required by the user, and extract node relationship data of the business class from the node resource pool;

[0009] Obtain the business demander node and the business provider node that provides business services to the business demander node from the node relationship data, and build a node relationship network by setting the connection edge from the business provider node to the business demander node, and set the capacity value of each node according to the number of business services provided to the adjacent nodes;

[0010] Output business node recommendation information based on the capability value of each node in the node relationship network.

[0011] Optionally, the capability value of each node is set according to the number of business services provided to adjacent nodes, specifically including the following steps:

[0012] A network adjacency matrix is ​​constructed for each node, and each value in the network adjacency matrix is ​​the number of business services provided to adjacent nodes;

[0013] The network adjacency matrix of each node is normalized to obtain the capacity value of each node in its initial state.

[0014] Optionally, before outputting the service node recommendation information according to the capability value of each node in the node relationship network, the service node recommendation method further includes:

[0015] In the node relationship network, the capability value set for each node is evenly distributed to the adjacent nodes pointed to according to a first set probability, and the capability value set for itself is updated by using the distributed capability value until the capability value converges, and the sum of the capability values ​​distributed from the adjacent nodes to each node plus the set capability value is used as the updated capability value;

[0016] Output business node recommendation information based on the capability value of each node in the node relationship network, including:

[0017] Output business node recommendation information based on the updated capability value of each node in the node relationship network.

[0018] Optionally, before outputting the service node recommendation information according to the updated capability value of each node in the node relationship network, the service node recommendation method further includes:

[0019] The set capability value of each node is evenly distributed to each node in the node relationship network according to a second set probability, and the set capability value is updated according to the distributed capability value until the capability value converges, and the capability value of each node is updated by using the sum of the capability values ​​distributed from each node;

[0020] The sum of the first set probability and the second set probability is equal to 1.

[0021] Optionally, the PageRank algorithm is used to iteratively perform the following based on the node pointing information in the node relationship network:

[0022] The capability value set for each node is evenly distributed to the adjacent nodes pointed to according to a first set probability, and the capability value set for itself is updated using the distributed capability value; and

[0023] Evenly distributing the set capability value of each node to each node in the node relationship network according to a second set probability, and updating the set capability value according to the distributed capability value;

[0024] Until the capability values ​​of each node converge.

[0025] The embodiment of the present invention further provides a service node recommendation system, which is used to implement a service node recommendation method, specifically comprising:

[0026] A receiving module receives user input;

[0027] The response module, in response to the user input, obtains the business class required by the user and extracts the node relationship data of the business class from the node resource pool;

[0028] The capability value assignment module obtains the business demander node and the business provider node that provides business services to the business demander node from the node relationship data, and builds a node relationship network by setting the connection edge from the business provider node to the business demander node, and sets the capability value for each node according to the number of business services provided to the adjacent nodes;

[0029] The recommendation module outputs the service node recommendation information according to the capability value of each node in the node relationship network.

[0030] Optionally, the capability value assignment module is further used to:

[0031] Before outputting the service node recommendation information according to the capability value of each node in the node relationship network, in the node relationship network, the capability value set for each node is evenly distributed to the adjacent nodes pointed to according to the first set probability, and the capability value set for itself is updated by using the distributed capability value until the capability value converges, and the sum of the capability values ​​allocated by each node from the adjacent nodes plus the set capability value is used as the updated capability value;

[0032] Recommended modules are specifically used for:

[0033] Output business node recommendation information based on the updated capability value of each node in the node relationship network.

[0034] Optionally, the capability value assignment module is further used to:

[0035] Before outputting the service node recommendation information according to the updated capability value of each node in the node relationship network, the set capability value of each node is evenly distributed to each node in the node relationship network according to a second set probability, and the set capability value is updated according to the distributed capability value until the capability value converges, and the capability value of each node is updated by using the sum of the capability values ​​distributed from each node;

[0036] The sum of the first set probability and the second set probability is equal to 1.

[0037] An embodiment of the present invention further provides a service node recommendation device, comprising:

[0038] processor;

[0039] a memory in which executable instructions for the processor are stored;

[0040] The processor is configured to execute the steps of the above-mentioned service node recommendation method by executing executable instructions.

[0041] An embodiment of the present invention further provides a computer-readable storage medium for storing a program, which implements the steps of the above-mentioned service node recommendation method when executed.

[0042] The purpose of the present invention is to provide a service node recommendation method, system, device and storage medium, which can construct a node relationship network based on a directed weighted complex network through the pointing relationship between the service provider node and the service demander node. In this way, the service provider node is automatically assigned a capability value based on the number of service services provided by the service provider node to the adjacent nodes, and the capability value can reflect the ability of each node to provide service services. Therefore, the node relationship network in the embodiment of the present invention does not simply classify nodes by service type, but accurately recommends nodes by capability value, accurately responds to user needs, and improves user service experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Other features, objectives and advantages of the present invention will become more apparent from a reading of the detailed description of non-limiting embodiments made with reference to the following accompanying drawings.

[0044] Figure 1 is a flow chart of one embodiment of the service node recommendation method of the present invention;

[0045] Figure 2 is a flow chart of a second embodiment of the service node recommendation method of the present invention;

[0046] Figure 3 is a timing diagram of the third embodiment of the service node recommendation method of the present invention;

[0047] Figure 4 It is a module diagram of an embodiment of a service node recommendation system of the present invention;

[0048] Figure 5 It is a schematic diagram of the operation of the service node recommendation system of the present invention. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein. Instead, these embodiments are provided to make the present invention comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art.

[0050] The accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware forwarding modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0051] In addition, the processes shown in the accompanying drawings are only exemplary and do not necessarily include all the steps. For example, some steps can be decomposed, some steps can be combined or partially combined, and the actual execution order may change according to the actual situation. The words "first", "second" and similar words used in the specific description do not indicate any order, quantity or importance, but are only used to distinguish different components. It should be noted that the embodiments of the present invention and the features in different embodiments can be combined with each other without conflict.

[0052] The inventors have encountered in practice that, although nodes of corresponding business classes can be matched and provided to users by virtue of node attributes, nodes that meet the business class matching conditions may not necessarily provide matching business services, and sometimes the business services provided by these nodes are of low quality.

[0053] Therefore, how to respond to user needs and make node recommendations more accurately is a technical problem to be solved by the embodiments of the present invention.

[0054] Figure 1 This is a flowchart of a node recommendation method provided by an embodiment of the present invention. The execution subject of this method is a node recommendation system, which is not a computer device and can run a node recommendation program. Figure 1 ,The node recommendation method includes the following steps:

[0055] Step 110: receiving user input;

[0056] Step 120: in response to the user input, obtaining the service class required by the user, and extracting the node relationship data of the service class from the node resource pool;

[0057] Step 130: Obtain the business demander node and the business provider node that provides business services to the business demander node from the node relationship data, and build a node relationship network by setting the connection edge from the business provider node to the business demander node, and set the capacity value of each node according to the number of business services provided to the adjacent nodes;

[0058] Step 140: Output service node recommendation information according to the capability value of each node in the node relationship network.

[0059] In the embodiment of the present invention, the service demander node and the service provider node do not refer to a specific node, but refer to the business transaction relationship between the two nodes, and the node relationship data refers to a collection of a large amount of data of such business transaction relationship.

[0060] Each node may be a service demander node or a service provider node, or may be both a service demander node and a service provider node.

[0061] In the embodiment of the present invention, the connection edge between two adjacent nodes has a clear pointing relationship, that is, the service provider node points to the service demander node, and the node relationship network constructed in this way is a directed weighted complex network. In this way, when the node is assigned a capability value later, the capability value can be assigned to the service provider node according to the pointing determination.

[0062] Using the embodiment of the present invention, the service provider node is automatically assigned a capability value based on the number of service services provided by the service provider node to the adjacent nodes, and the capability value can reflect the ability of each node to provide service services. Therefore, the node relationship network in the embodiment of the present invention does not simply classify nodes by service type, but accurately recommends nodes by capability value, accurately responds to user needs, and improves user service experience.

[0063] In an optional embodiment, the capability value of each node is set according to the number of business services provided to adjacent nodes, specifically including the following steps:

[0064] A network adjacency matrix is ​​constructed for each node, and each value in the network adjacency matrix is ​​the number of business services provided to adjacent nodes;

[0065] The network adjacency matrix of each node is normalized to obtain the capacity value of each node in its initial state.

[0066] In an optional embodiment, a user interaction interface may be provided to receive user input, which may be in the form of text input, control triggering or clicking, etc., which is not specifically limited here.

[0067] In this case, the user can input specific business requirement information such as the required business class through the user interaction interface.

[0068] In an optional embodiment, outputting the service node recommendation information may specifically include displaying the recommended node information for the user to confirm or select.

[0069] In an alternative embodiment, reference Figure 2 The node recommendation method shown includes the following steps:

[0070] Step 210: receiving user input;

[0071] Step 220: in response to the user input, obtaining the service class required by the user, and extracting the node relationship data of the service class from the node resource pool;

[0072] Step 230: Obtain the business demander node and the business provider node that provides business services to the business demander node from the node relationship data, and build a node relationship network by setting the connection edge from the business provider node to the business demander node, and set the capacity value of each node according to the number of business services provided to the adjacent nodes;

[0073] Step 240: In the node relationship network, the capability value set for each node is evenly distributed to the adjacent nodes pointed to according to the first set probability, and the capability value set for itself is updated by using the distributed capability value until the capability value converges. The sum of the capability values ​​distributed from the adjacent nodes to each node plus the set capability value is used as the updated capability value;

[0074] Step 250: Output service node recommendation information according to the updated capability value of each node in the node relationship network.

[0075] In this embodiment, each node distributes the initial capacity value obtained in its initial state to the adjacent node it points to and obtains the capacity value allocated by the adjacent node, which represents the influence of the node on the adjacent node it points to, that is, the capacity value of the business service provided by the node. Therefore, using this embodiment can further accurately and comprehensively evaluate the capacity value of the node, more accurately respond to user needs, and provide users with more accurate node recommendation services.

[0076] In an optional embodiment, before outputting the service node recommendation information according to the capability value of each node in the node relationship network, the capability value set for each node may be evenly distributed to each node in the node relationship network according to a second set probability, and the set capability value may be updated according to the distributed capability value until the capability value converges, and the capability value of each node may be updated using the sum of the capability values ​​distributed from each node;

[0077] The sum of the first set probability and the second set probability is equal to 1.

[0078] In this embodiment, each node distributes the initial capacity value obtained in its initial state to all nodes and obtains the capacity value allocated by other nodes, which represents the depth and breadth of the node's influence on other nodes, and this part is also converted into the capacity value of the business service that the node can provide. Therefore, using this embodiment can further accurately and comprehensively evaluate the capacity value of the node, more accurately respond to user needs, and provide users with more accurate node recommendation services.

[0079] This embodiment adopts technical means to abstract the strength and depth of influence on nodes into capability values ​​and characterize them through the allocation and being allocated of capability values, which can achieve accurate evaluation of nodes and bring unexpected technical effects.

[0080] In an optional embodiment, the PageRank algorithm is used to iteratively perform the following based on the node pointing information in the node relationship network:

[0081] The capability value set for each node is evenly distributed to the adjacent nodes pointed to according to a first set probability, and the capability value set for itself is updated using the distributed capability value; and

[0082] Evenly distributing the set capability value of each node to each node in the node relationship network according to a second set probability, and updating the set capability value according to the distributed capability value;

[0083] Until the capability values ​​of each node converge.

[0084] In this embodiment, the basic idea of ​​the PageRank algorithm is to define a random walk model on a directed graph, namely a first-order Markov chain, to describe the behavior of a random walker randomly visiting each node along the directed graph. Under certain conditions, the probability of visiting each node in the extreme case converges to a stable distribution. At this time, the stable probability value of each node is its PageRank value, which indicates the importance of the node. PageRank is defined recursively, and the calculation of PageRank can be performed through an iterative algorithm.

[0085] The value of the PageRank algorithm depends on the topological structure of the network. Once the topology (connection relationship) of the network is determined, the PageRank value is determined, and the corresponding node capability value in this embodiment is determined.

[0086] Applied to the embodiment of the present invention, a node relationship network is first constructed, and the PageRank values ​​of all nodes, that is, the capability values, are continuously calculated through iteration until convergence.

[0087] As an implementable method, the present invention provides a method for identifying the influence of an enterprise based on an industrial ecological network, referring to Figure 3 , specifically including the following steps:

[0088] Step 310: Acquire enterprise ecological cooperation data, and perform structural processing on the acquired enterprise ecological cooperation data to construct a cooperation relationship network, wherein the enterprise is an example of a node, and the cooperation relationship network is a node relationship network of the above node relationship network;

[0089] Step 320: Calculate the ecological cooperation capability (ST value) of each enterprise in the cooperative relationship network according to the network adjacency matrix W and the algorithm. The larger the ST value, the stronger the ecological cooperation capability of the enterprise. The ST value corresponds to the capability value above.

[0090] Step 330: Based on the ecological cooperation capability (ST value) of each enterprise in the cooperative relationship network, cooperative enterprises with industry influence can be found in the industrial ecology and recommended to users. It can also be used as an ecological cooperation capability indicator in evaluating the influence of enterprises, and can realize the enterprise ecological value evaluation method.

[0091] As an optional embodiment, obtaining enterprise ecological cooperation data, and performing structured processing on the obtained enterprise ecological cooperation data to construct a cooperation relationship network (directed weighted complex network) includes the following steps:

[0092] 1. According to the current status of industrial ecological development, collect information on relevant industrial alliances (such as the Industrial Internet Alliance) or associations through online and offline channels, and obtain a list of companies in the industrial alliance / association as nodes of the industrial ecological cooperation network;

[0093] 2. Obtain the bidding information of enterprises through bidding websites, sort out and form the cooperative relationship between enterprises in the industrial ecology, and serve as the edge of the industrial ecological cooperative relationship network;

[0094] 3. Build a network for the processed data to form a corporate partnership network.

[0095] As an implementable method, the enterprise cooperation network is a directed weighted complex network, represented by G(V, E), where the node V is the enterprise node, the connecting edge E is the bidding information, and the network adjacency matrix is ​​recorded as W n×n =(w ij ), w ij is the number of projects won by enterprise node i, j is the number of projects released, otherwise w ij =0.

[0096] As an implementable method, the calculation method of the ST value of ecological cooperation capability integrates the core idea of ​​the PageRank algorithm. The calculation process is as follows:

[0097] 1. At the initial moment, the adjacency matrix W (n×n) is vector normalized to give node i the initial ecological cooperation capability (ST value):

[0098] 2. Then iterate and update the node ST value. The ST value of each node at time t-1 in the network is evenly distributed to all nodes it points to with a probability of 1-c, and evenly distributed to all nodes in the network with a probability of c. The ST value of each node at time t is the sum of its ST values. The ST value of node i at the t-step iteration is:

[0099] 3. Iterate until the ST value of each node is basically stable, and use the result as the enterprise's ecological cooperation capability (ST value). The larger the ST value, the stronger the enterprise's ecological cooperation capability.

[0100] By utilizing the enterprise influence identification method based on the industrial ecological network provided by the present invention, a cooperative relationship network is constructed based on the enterprise cooperation ability factor in the industry influence, and the PageRank network node sorting algorithm is optimized. Combined with the industrial ecological cooperation relationship, enterprises with outstanding cooperation ability in the industrial ecology can be identified more quickly and accurately, which is helpful to the needs of industrial ecological development cooperation.

[0101] The method proposed in the embodiment of the present invention can construct an industrial ecological network, find cooperative enterprises with industry influence in the industrial ecology, and can also be used as an indicator of ecological cooperation ability in evaluating corporate influence. It can more quickly and accurately identify enterprises with outstanding cooperation capabilities in the industrial ecology, which is helpful to meet the needs of industrial ecological development cooperation.

[0102] The embodiment of the present invention combines public information on the Internet, creatively proposes to use industry alliances, organizational members and bidding information, and combines complex network technology to build an industrial ecological network, providing a data basis for network node influence identification. Based on traditional PageRank, the embodiment of the present invention normalizes the input node vector to adapt to the multi-attribute characteristics of industrial ecological enterprises.

[0103] Figure 4 FIG. 1 is a schematic diagram of a module of an embodiment of the service node recommendation system of the present invention. Figure 4 As shown, the service node recommendation system of the present invention includes but is not limited to:

[0104] Receiving module 410, receiving user input;

[0105] The response module 420 obtains the service class required by the user in response to the user input, and extracts the node relationship data of the service class from the node resource pool;

[0106] The capability value assignment module 430 obtains the business demander node and the business provider node that provides business services to the business demander node from the node relationship data, and builds a node relationship network by setting the connection edge from the business provider node to the business demander node, and sets the capability value for each node according to the number of business services provided to the adjacent nodes;

[0107] The recommendation module 440 outputs service node recommendation information according to the capability value of each node in the node relationship network.

[0108] The implementation principle of the above modules can be found in the relevant introduction of the business node recommendation method, which will not be repeated here.

[0109] In the embodiment of the present invention, the connection edge between two adjacent nodes has a clear pointing relationship, that is, the service provider node points to the service demander node, and the node relationship network constructed in this way is a directed weighted complex network. In this way, when the node is assigned a capability value later, the capability value can be assigned to the service provider node according to the pointing determination.

[0110] Using the embodiment of the present invention, the service provider node is automatically assigned a capability value based on the number of service services provided by the service provider node to the adjacent nodes, and the capability value can reflect the ability of each node to provide service services. Therefore, the node relationship network in the embodiment of the present invention does not simply classify nodes by service type, but accurately recommends nodes by capability value, accurately responds to user needs, and improves user service experience.

[0111] Optionally, the capability value assignment module 430 is specifically used for:

[0112] A network adjacency matrix is ​​constructed for each node, and each value in the network adjacency matrix is ​​the number of business services provided to adjacent nodes;

[0113] The network adjacency matrix of each node is normalized to obtain the capacity value of each node in its initial state.

[0114] Optionally, the capability value assignment module 430 is further configured to:

[0115] Before outputting the service node recommendation information according to the capability value of each node in the node relationship network, in the node relationship network, the capability value set for each node is evenly distributed to the adjacent nodes pointed to according to the first set probability, and the capability value set for itself is updated by using the distributed capability value until the capability value converges, and the sum of the capability values ​​allocated by each node from the adjacent nodes plus the set capability value is used as the updated capability value;

[0116] The recommendation module 440 is specifically used for:

[0117] Output business node recommendation information based on the updated capability value of each node in the node relationship network.

[0118] Optionally, the capability value assignment module 430 is further configured to:

[0119] Before outputting the service node recommendation information according to the updated capability value of each node in the node relationship network, the set capability value of each node is evenly distributed to each node in the node relationship network according to a second set probability, and the set capability value is updated according to the distributed capability value until the capability value converges, and the capability value of each node is updated by using the sum of the capability values ​​distributed from each node;

[0120] The sum of the first set probability and the second set probability is equal to 1.

[0121] Optionally, the capability value assignment module 430 is further configured to:

[0122] The PageRank algorithm is used to iteratively execute based on the node pointing information in the node relationship network:

[0123] The capability value set for each node is evenly distributed to the adjacent nodes pointed to according to a first set probability, and the capability value set for itself is updated using the distributed capability value; and

[0124] Evenly distributing the set capability value of each node to each node in the node relationship network according to a second set probability, and updating the set capability value according to the distributed capability value;

[0125] Until the capability values ​​of each node converge.

[0126] The embodiment of the present invention further provides a service node recommendation device, comprising a processor and a memory, wherein executable instructions of the processor are stored, wherein the processor is configured to execute the steps of the service node recommendation method by executing the executable instructions.

[0127] As shown above, the service node recommendation device of this embodiment can construct a node relationship network based on a directed weighted complex network through the pointing relationship between the service provider node and the service demander node. In this way, the service provider node is automatically assigned a capability value based on the number of service services provided by the service provider node to the adjacent nodes, and the capability value can reflect the ability of each node to provide service services. Therefore, the node relationship network in the embodiment of the present invention does not simply classify nodes by service type, but accurately recommends nodes through capability values, accurately responds to user needs, and improves user service experience.

[0128] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0129] Figure 5 This is a schematic diagram of the structure of the service node recommendation device of the present invention. Figure 5 The electronic device 500 according to this embodiment of the present invention is described. Figure 5 The electronic device 500 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0130] like Figure 5 As shown, the electronic device 500 is in the form of a general computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different platform components (including the storage unit 520 and the processing unit 510), a display unit 540, etc.

[0131] The storage unit stores program codes, which can be executed by the processing unit 510, so that the processing unit 510 executes the steps of various exemplary embodiments of the present invention described in the service node recommendation method section of the present specification. For example, the processing unit 510 can execute the following steps: Figure 1-3 Follow the steps shown in .

[0132] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 521 and / or a cache memory unit 522 , and may further include a read-only memory unit (ROM) 523 .

[0133] The storage unit 520 may also include a program / utility 524 having a set (at least one) of program modules 525, such program modules 525 including but not limited to: a processing system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0134] Bus 530 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0135] The electronic device 500 may also communicate with one or more external devices 570 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 500, and / or any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 550.

[0136] Furthermore, the electronic device 500 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 560. The network adapter 560 may communicate with other modules of the electronic device 500 via the bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0137] The embodiment of the present invention also provides a computer-readable storage medium for storing a program, and the steps of the service node recommendation method are implemented when the program is executed. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of various exemplary embodiments of the present invention described in the above service node recommendation method section of this specification.

[0138] The program product for implementing the above method according to an embodiment of the present invention may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0139] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0140] Computer readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program codes contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0141] Program code for performing the processes of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0142] In summary, the purpose of the present invention is to provide a service node recommendation method, system, device and storage medium, which can construct a node relationship network based on a directed weighted complex network through the pointing relationship between the service provider node and the service demander node. In this way, the service provider node is automatically assigned a capability value based on the number of service services provided by the service provider node to the adjacent nodes, and the capability value can reflect the ability of each node to provide service services. Therefore, the node relationship network in the embodiment of the present invention does not simply classify nodes by service type, but accurately recommends nodes through capability values, accurately responds to user needs, and improves user service experience.

[0143] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A service node recommendation method, characterized in that: include: Receive user input; In response to the user input, obtaining the service class required by the user, and extracting node relationship data of the service class from a node resource pool; Obtaining a business demander node and a business provider node that provides business services to the business demander node from the node relationship data, and building a node relationship network by setting a connection edge from the business provider node to the business demander node, and setting a capacity value for each node according to the number of business services provided to adjacent nodes; Outputting service node recommendation information according to the capability value of each node in the node relationship network; Before outputting the service node recommendation information according to the capability value of each node in the node relationship network, the service node recommendation method further includes: In the node relationship network, the capability value set for each node is evenly distributed to the adjacent nodes pointed to according to a first set probability, and the capability value set for itself is updated by using the distributed capability value until the capability value converges, and the sum of the capability values ​​distributed from the adjacent nodes to each node plus the set capability value is used as the updated capability value; The outputting service node recommendation information according to the capability value of each node in the node relationship network includes: The service node recommendation information is output according to the updated capability value of each node in the node relationship network.

2. The service node recommendation method according to claim 1, characterized in that: The capacity value of each node is set according to the number of business services provided to adjacent nodes, which specifically includes the following steps: Constructing a network adjacency matrix for each node, wherein each value in the network adjacency matrix is ​​the number of business services provided to adjacent nodes; The network adjacency matrix of each node is normalized to obtain the capacity value of each node in the initial state.

3. The service node recommendation method according to claim 1, characterized in that: Before outputting service node recommendation information according to the updated capability value of each node in the node relationship network, the service node recommendation method further includes: The set capability value of each node is evenly distributed to each node in the node relationship network according to a second set probability, and the set capability value is updated according to the distributed capability value until the capability value converges, and the updated capability value is updated by using the sum of the capability values ​​distributed to each node from each node; The sum of the first set probability and the second set probability is equal to 1.

4. The service node recommendation method according to claim 3, characterized in that: The PageRank algorithm is used to iteratively execute the following based on the node pointing information in the node relationship network: The capability value set for each node is evenly distributed to the adjacent nodes pointed to according to a first set probability, and the capability value set for itself is updated using the distributed capability value; and The capability value set for each node is evenly distributed to each node in the node relationship network according to a second set probability, and the set capability value is updated according to the distributed capability value; Until the capability values ​​of each node converge.

5. A service node recommendation system, characterized in that: include: A receiving module receives user input; A response module, in response to the user input, obtains the service class required by the user and extracts the node relationship data of the service class from the node resource pool; A capability value assignment module, which obtains the business demander node and the business provider node that provides business services to the business demander node from the node relationship data, and builds a node relationship network by setting a connection edge from the business provider node to the business demander node, and sets a capability value for each node according to the number of business services provided to adjacent nodes; A recommendation module, which outputs service node recommendation information according to the capability value of each node in the node relationship network; The capability value assignment module is also specifically used for: Before outputting the service node recommendation information according to the capability value of each node in the node relationship network, in the node relationship network, the capability value set for each node is evenly distributed to the adjacent nodes pointed to according to the first set probability, and the capability value set for itself is updated by using the distributed capability value until the capability value converges, and the sum of the capability values ​​allocated by each node from the adjacent nodes plus the set capability value is used as the updated capability value; The recommendation module is specifically used for: Output business node recommendation information based on the updated capability value of each node in the node relationship network.

6. The service node recommendation system according to claim 5, characterized in that: The capability value assignment module is also specifically used for: Before outputting the service node recommendation information according to the updated capability value of each node in the node relationship network, the set capability value of each node is evenly distributed to each node in the node relationship network according to a second set probability, and the set capability value is updated according to the distributed capability value until the capability value converges, and the updated capability value is updated by using the sum of the capability values ​​distributed to each node from each node; The sum of the first set probability and the second set probability is equal to 1.

7. A service node recommendation device, characterized in that: include: processor; a memory storing executable instructions of the processor; The processor is configured to execute the steps of the service node recommendation method according to any one of claims 1 to 4 by executing the executable instructions.

8. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the steps of the service node recommendation method according to any one of claims 1 to 4 are implemented.

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