IT asset data processing method and system based on intelligent identification

Abstract

The invention discloses an IT asset data processing method and system based on intelligent identification. The method comprises the following steps: acquiring information of a plurality of IT devices in a target network area through a plurality of gateway devices; identifying multiple pieces of IT asset equipment information from the multiple pieces of IT equipment information based on a neural network algorithm; according to a preset equipment information association rule, determining an association mathematical model between any two pieces of IT asset equipment information; and establishing an IT asset equipment topological relation model corresponding to the target network area according to the association mathematical models corresponding to all the IT asset equipment information. Therefore, the IT asset equipment in the area can be identified and associated more efficiently and accurately, the asset management efficiency and effect are improved, and the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an IT asset data processing method and system based on intelligent identification. Background Art

[0002] With the increasing demand for big data computing and the widespread improvement of data processing capabilities, the number of IT assets in large enterprises has also increased significantly. How to help enterprises efficiently manage a large number of IT equipment and resources and other assets has become an important technical issue. When implementing the management needs of IT assets, existing technologies still mostly rely on manual inventory methods to obtain asset data, and rely on simple data sorting rules to implement asset data processing, without considering combining equipment intranet information scanning and topology algorithm technology to improve the efficiency and accuracy of asset management. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an IT asset data processing method and system based on intelligent identification, which can more efficiently and accurately identify and associate IT asset equipment in a region, improve the efficiency and effectiveness of asset management, and reduce labor costs.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses an IT asset data processing method based on intelligent identification, the method comprising: Obtain information on multiple IT devices in the target network area through multiple gateway devices; Based on a neural network algorithm, identifying a plurality of IT asset equipment information from the plurality of IT equipment information; Determine the association mathematical model between any two pieces of IT asset equipment information according to the preset equipment information association rule; According to the associated mathematical model corresponding to all the IT asset equipment information, an IT asset equipment topology relationship model corresponding to the target network area is established.

[0005] As an optional implementation, in the first aspect of the present invention, the identifying multiple IT asset equipment information from the multiple IT equipment information based on the neural network algorithm includes: Input each piece of IT equipment information into a trained asset prediction algorithm model to obtain a predicted probability that each piece of IT equipment information belongs to an IT asset; Determine the historical similarity corresponding to each IT equipment information according to the historical IT asset management records; Calculate the product of the predicted probability and the historical similarity to obtain a device priority parameter corresponding to each piece of IT device information; All the IT equipment information whose equipment priority parameters are greater than the first parameter threshold are screened out to obtain a plurality of IT asset equipment information.

[0006] As an optional embodiment, in the first aspect of the present invention, the asset prediction algorithm model is a CNN neural network, which is trained by a training data set including a plurality of training IT equipment information and corresponding annotations of whether they are assets.

[0007] As an optional implementation, in the first aspect of the present invention, determining the historical similarity corresponding to each piece of IT equipment information according to the historical IT asset management record includes: For each piece of IT equipment information, calculate the weighted sum average of the information similarity between the equipment information of all historical IT asset equipment in the historical IT asset management records and the IT equipment information to obtain the historical similarity corresponding to the IT equipment information; wherein the weighted calculation weight corresponding to the information similarity corresponding to each piece of historical IT asset equipment is proportional to the number of times the historical IT asset equipment appears in the historical IT asset management records.

[0008] As an optional implementation, in the first aspect of the present invention, determining the association mathematical model between any two pieces of IT asset equipment information according to a preset equipment information association rule includes: For any two pieces of IT asset equipment information, obtaining historical communication records corresponding to the two pieces of IT asset equipment information; Determine, according to the historical communication records corresponding to the two IT asset equipment information, the correlation parameters corresponding to the two IT asset equipment information; When the correlation parameter is greater than a second parameter threshold, a correlation mathematical model between the two IT asset equipment information is established, and the correlation parameter is determined as a correlation coefficient in the correlation mathematical model.

[0009] As an optional implementation manner, in the first aspect of the present invention, determining the correlation parameter corresponding to the two IT asset equipment information according to the historical communication records corresponding to the two IT asset equipment information includes: Calculate the proportion of mutual communication records in the historical communication records corresponding to the two IT asset device information; Calculating the record similarity between the historical communication records corresponding to the two IT asset device information; The product of the record similarity and the record proportion is calculated to obtain the correlation parameter corresponding to the two pieces of IT asset equipment information.

[0010] As an optional implementation, in the first aspect of the present invention, establishing the IT asset equipment topology relationship model corresponding to the target network area according to the association mathematical model corresponding to all the IT asset equipment information includes: Filter out all the IT asset equipment information corresponding to at least one of the associated mathematical models to obtain a plurality of associated IT asset equipment; Taking each of the associated IT asset devices as a topological subject and the associated mathematical models corresponding to any two of the associated IT asset devices as the relationship between the corresponding topological subjects, a topological relationship model of the IT asset devices corresponding to the target network area is established.

[0011] As an optional embodiment, in the first aspect of the present invention, the method further comprises: Accept IT asset inventory instructions sent by users; Determine the corresponding target inventory object device according to the IT asset inventory instruction; Determine at least one primary associated object device corresponding to the target inventory object device according to the IT asset device topology relationship model; Determine at least one secondary associated object device corresponding to each of the primary associated object devices according to the IT asset device topology relationship model; The primary associated object device and the secondary associated object device and the corresponding associated degree parameters between the primary associated object device and the target inventory object device are pushed to the user to determine whether to perform inventory as associated assets.

[0012] A second aspect of an embodiment of the present invention discloses an IT asset data processing system based on intelligent identification, the system comprising: An acquisition module, used to acquire information of multiple IT devices in a target network area through multiple gateway devices; An identification module, used for identifying a plurality of IT asset equipment information from the plurality of IT equipment information based on a neural network algorithm; A determination module, used to determine the association mathematical model between any two pieces of IT asset equipment information according to a preset equipment information association rule; The modeling module is used to establish an IT asset equipment topology relationship model corresponding to the target network area according to the associated mathematical model corresponding to all the IT asset equipment information.

[0013] As an optional implementation, in the second aspect of the present invention, the specific manner in which the identification module identifies multiple IT asset device information from the multiple IT device information based on a neural network algorithm includes: Input each piece of IT equipment information into a trained asset prediction algorithm model to obtain a predicted probability that each piece of IT equipment information belongs to an IT asset; Determine the historical similarity corresponding to each IT equipment information according to the historical IT asset management records; Calculate the product of the predicted probability and the historical similarity to obtain a device priority parameter corresponding to each piece of IT device information; All the IT equipment information whose equipment priority parameters are greater than the first parameter threshold are screened out to obtain a plurality of IT asset equipment information.

[0014] As an optional embodiment, in the second aspect of the present invention, the asset prediction algorithm model is a CNN neural network, which is trained by a training data set including a plurality of training IT equipment information and corresponding annotations of whether they are assets.

[0015] As an optional implementation, in the second aspect of the present invention, the specific manner in which the identification module determines the historical similarity corresponding to each piece of IT equipment information according to the historical IT asset management record includes: For each piece of IT equipment information, calculate the weighted sum average of the information similarity between the equipment information of all historical IT asset equipment in the historical IT asset management records and the IT equipment information to obtain the historical similarity corresponding to the IT equipment information; wherein the weighted calculation weight corresponding to the information similarity corresponding to each piece of historical IT asset equipment is proportional to the number of times the historical IT asset equipment appears in the historical IT asset management records.

[0016] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the association mathematical model between any two pieces of IT asset equipment information according to a preset equipment information association rule includes: For any two pieces of IT asset equipment information, obtaining historical communication records corresponding to the two pieces of IT asset equipment information; Determine, according to the historical communication records corresponding to the two IT asset equipment information, the correlation parameters corresponding to the two IT asset equipment information; When the correlation parameter is greater than a second parameter threshold, a correlation mathematical model between the two IT asset equipment information is established, and the correlation parameter is determined as a correlation coefficient in the correlation mathematical model.

[0017] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the correlation parameter corresponding to the two IT asset equipment information according to the historical communication records corresponding to the two IT asset equipment information includes: Calculate the proportion of mutual communication records in the historical communication records corresponding to the two IT asset device information; Calculating the record similarity between the historical communication records corresponding to the two IT asset device information; The product of the record similarity and the record proportion is calculated to obtain the correlation parameter corresponding to the two pieces of IT asset equipment information.

[0018] As an optional implementation, in the second aspect of the present invention, the specific manner in which the modeling module establishes the IT asset equipment topology relationship model corresponding to the target network area according to the associated mathematical model corresponding to all the IT asset equipment information includes: Filter out all the IT asset equipment information corresponding to at least one of the associated mathematical models to obtain a plurality of associated IT asset equipment; Taking each of the associated IT asset devices as a topological subject and the associated mathematical models corresponding to any two of the associated IT asset devices as the relationship between the corresponding topological subjects, a topological relationship model of the IT asset devices corresponding to the target network area is established.

[0019] As an optional embodiment, in the second aspect of the present invention, the system is further configured to perform the following steps: Accept IT asset inventory instructions sent by users; Determine the corresponding target inventory object device according to the IT asset inventory instruction; Determine at least one primary associated object device corresponding to the target inventory object device according to the IT asset device topology relationship model; Determine at least one secondary associated object device corresponding to each of the primary associated object devices according to the IT asset device topology relationship model; The primary associated object device and the secondary associated object device and the corresponding associated degree parameters between the primary associated object device and the target inventory object device are pushed to the user to determine whether to perform inventory as associated assets.

[0020] The third aspect of the present invention discloses another IT asset data processing system based on intelligent identification, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the IT asset data processing method based on intelligent identification disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the IT asset data processing method based on intelligent identification disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can identify multiple IT asset equipment information from multiple IT equipment information in a region based on a neural network, and then establish a device topology relationship model for the entire region after determining the association mathematical model between devices based on the device information association rules for subsequent IT asset management, thereby being able to more efficiently and accurately identify and associate IT asset equipment in the region, thereby improving the efficiency and effectiveness of asset management and reducing labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of an IT asset data processing method based on intelligent identification disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural diagram of an IT asset data processing system based on intelligent identification disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural diagram of another IT asset data processing system based on intelligent identification disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses an IT asset data processing method and system based on intelligent identification, which can identify multiple IT asset equipment information from multiple IT equipment information in a region based on a neural network, and then establish a device topology relationship model for the entire region after determining the association mathematical model between the equipment based on the equipment information association rule, so as to be used for subsequent IT asset management, thereby being able to more efficiently and accurately identify and associate IT asset equipment in the region, improve the efficiency and effect of asset management, and reduce labor costs. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 This is a flow chart of an IT asset data processing method based on intelligent identification disclosed in an embodiment of the present invention. Figure 1 The described IT asset data processing method based on intelligent identification can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the IT asset data processing method based on intelligent identification may include the following operations: 101. Obtain information of multiple IT devices in the target network area through multiple gateway devices.

[0032] 102. Based on the neural network algorithm, multiple IT asset equipment information is identified from multiple IT equipment information. 103. According to the preset equipment information association rules, determine the association mathematical model between any two IT asset equipment information. 104. Based on the associated mathematical model corresponding to all IT asset equipment information, establish an IT asset equipment topology relationship model corresponding to the target network area.

[0033] It can be seen that the above-mentioned embodiment of the invention can identify multiple IT asset equipment information from multiple IT equipment information in the area based on the neural network, and then establish a device topology relationship model for the entire area after determining the association mathematical model between the devices based on the equipment information association rules for subsequent IT asset management, thereby being able to more efficiently and accurately identify and associate IT asset equipment in the area, improve the efficiency and effectiveness of asset management, and reduce labor costs.

[0034] As an optional embodiment, in the above step, identifying multiple IT asset equipment information from multiple IT equipment information based on a neural network algorithm includes: Input each IT equipment information into the trained asset prediction algorithm model to obtain the predicted probability that each IT equipment information belongs to an IT asset; Determine the historical similarity of each IT equipment information based on historical IT asset management records; Calculate the product of the predicted probability and the historical similarity to obtain the device priority parameter corresponding to each IT device information; All IT equipment information whose equipment priority parameters are greater than the first parameter threshold is screened out to obtain multiple IT asset equipment information.

[0035] It can be seen that through the above-mentioned optional embodiments, the probability of IT equipment belonging to assets can be predicted based on the prediction algorithm and its historical similarity can be determined through historical asset management records, so as to comprehensively calculate the characterization parameters of each device belonging to assets, so as to accurately screen and identify IT asset equipment information, so as to facilitate subsequent equipment association and topology modeling, and assist in more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0036] As an optional embodiment, in the above steps, the asset prediction algorithm model is a CNN neural network, which is trained by a training data set including a plurality of training IT equipment information and corresponding annotations of whether they are assets.

[0037] It can be seen that through the above optional embodiments, the network details and training details of the asset prediction algorithm model are clarified, which can be used to accurately predict the probability that IT equipment belongs to an asset, so as to facilitate subsequent equipment association and topology modeling, and assist in more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0038] As an optional embodiment, in the above step, determining the historical similarity corresponding to each IT device information according to the historical IT asset management record includes: For each piece of IT equipment information, calculate the weighted sum average of the information similarity between the equipment information of all historical IT asset equipment in the historical IT asset management records and the IT equipment information to obtain the historical similarity corresponding to the IT equipment information; wherein the weighted calculation weight corresponding to the information similarity corresponding to each historical IT asset equipment is proportional to the number of times the historical IT asset equipment appears in the historical IT asset management records.

[0039] It can be seen that through the above-mentioned optional embodiments, the historical similarity corresponding to the IT equipment information can be accurately determined by weighted summing and averaging the information similarity between the equipment information of all historical IT asset equipment in the historical IT asset management records and the IT equipment information, so as to facilitate subsequent equipment association and topology modeling, and assist in achieving more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0040] As an optional embodiment, in the above step, determining the association mathematical model between any two pieces of IT asset equipment information according to a preset equipment information association rule includes: For any two pieces of IT asset equipment information, obtain the historical communication records corresponding to the two pieces of IT asset equipment information; Determine, according to the historical communication records corresponding to the two IT asset equipment information, the correlation parameter corresponding to the two IT asset equipment information; When the correlation parameter is greater than the second parameter threshold, a correlation mathematical model between the two pieces of IT asset equipment information is established, and the correlation parameter is determined as a correlation coefficient in the correlation mathematical model.

[0041] It can be seen that through the above-mentioned optional embodiments, the corresponding correlation parameters can be determined according to the historical communication records corresponding to the information of two IT asset devices, so as to establish a correlation mathematical model and correlation coefficient, which is convenient for subsequent topological modeling, and assists in realizing more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0042] As an optional embodiment, in the above step, determining the correlation parameter corresponding to the two IT asset equipment information according to the historical communication records corresponding to the two IT asset equipment information includes: Calculate the proportion of mutual communication records in the historical communication records corresponding to the two IT asset device information; Calculate the record similarity between the historical communication records corresponding to the two IT asset device information; The product of the record similarity and the record proportion is calculated to obtain the correlation parameter corresponding to the two IT asset equipment information.

[0043] It can be seen that through the above-mentioned optional embodiments, the corresponding correlation parameters can be determined according to the mutual record ratio and record similarity calculation between the historical communication records corresponding to the information of two IT asset equipment, so as to establish a correlation mathematical model and correlation coefficient, which is convenient for subsequent topological modeling, and assists in realizing more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0044] As an optional embodiment, in the above steps, establishing an IT asset equipment topology relationship model corresponding to the target network area according to the associated mathematical model corresponding to all IT asset equipment information includes: Filter out all IT asset equipment information corresponding to at least one associated mathematical model to obtain a plurality of associated IT asset equipment; Taking each associated IT asset device as a topological subject and the corresponding associated mathematical model between any two associated IT asset devices as the relationship between the corresponding topological subjects, a topological relationship model of IT asset devices corresponding to the target network area is established.

[0045] It can be seen that through the above-mentioned optional embodiments, it is possible to establish an IT asset equipment topological relationship model corresponding to the target network area based on the IT asset equipment information corresponding to at least one associated mathematical model as the topological subject and the associated mathematical models between each other as the topological relationship, so as to achieve more efficient and accurate identification and association of IT asset equipment in the area, improve the efficiency and effectiveness of asset management, and reduce labor costs.

[0046] As an optional embodiment, in the above steps, the method further includes: Accept IT asset inventory instructions sent by users; Determine the corresponding target inventory object equipment according to the IT asset inventory instruction; Determine at least one primary associated object device corresponding to the target inventory object device according to the IT asset device topology relationship model; Determine at least one secondary associated object device corresponding to each primary associated object device according to the IT asset device topology relationship model; The primary associated object device and the secondary associated object device and the corresponding association degree parameters between the primary associated object device and the target inventory object device are pushed to the user to determine whether to be counted as associated assets.

[0047] It can be seen that through the above-mentioned optional embodiments, when an inventory instruction is received, the associated devices and the degree of association can be determined based on the IT asset equipment topological relationship model and pushed to the user for confirmation, thereby achieving more efficient and accurate identification and association of IT asset equipment in the area, improving the efficiency and effectiveness of asset management, and reducing labor costs.

[0048] Embodiment 2 See also Figure 2 , Figure 2 : is a schematic diagram of the structure of an IT asset data processing system based on intelligent identification disclosed in an embodiment of the present invention. Figure 2 The described IT asset data processing system based on intelligent identification can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the IT asset data processing system based on intelligent identification may include: The acquisition module 201 is used to acquire information of multiple IT devices in a target network area through multiple gateway devices.

[0049] The identification module 202 is used to identify multiple IT asset equipment information from multiple IT equipment information based on a neural network algorithm. The determination module 203 is used to determine the association mathematical model between any two pieces of IT asset equipment information according to a preset equipment information association rule. The modeling module 204 is used to establish an IT asset equipment topology relationship model corresponding to the target network area according to the associated mathematical model corresponding to all IT asset equipment information.

[0050] It can be seen that the above-mentioned embodiment of the invention can identify multiple IT asset equipment information from multiple IT equipment information in the area based on the neural network, and then establish a device topology relationship model for the entire area after determining the association mathematical model between the devices based on the equipment information association rules for subsequent IT asset management, thereby being able to more efficiently and accurately identify and associate IT asset equipment in the area, improve the efficiency and effectiveness of asset management, and reduce labor costs.

[0051] As an optional embodiment, the specific manner in which the identification module identifies multiple IT asset device information from multiple IT device information based on a neural network algorithm includes: Input each IT equipment information into the trained asset prediction algorithm model to obtain the predicted probability that each IT equipment information belongs to an IT asset; Determine the historical similarity of each IT equipment information based on historical IT asset management records; Calculate the product of the predicted probability and the historical similarity to obtain the device priority parameter corresponding to each IT device information; All IT equipment information whose equipment priority parameters are greater than the first parameter threshold is screened out to obtain multiple IT asset equipment information.

[0052] It can be seen that through the above-mentioned optional embodiments, the probability of IT equipment belonging to assets can be predicted based on the prediction algorithm and its historical similarity can be determined through historical asset management records, so as to comprehensively calculate the characterization parameters of each device belonging to assets, so as to accurately screen and identify IT asset equipment information, so as to facilitate subsequent equipment association and topology modeling, and assist in more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0053] As an optional embodiment, the asset prediction algorithm model is a CNN neural network, which is trained by a training data set including a plurality of training IT equipment information and corresponding annotations of whether they are assets.

[0054] It can be seen that through the above optional embodiments, the network details and training details of the asset prediction algorithm model are clarified, which can be used to accurately predict the probability that IT equipment belongs to an asset, so as to facilitate subsequent equipment association and topology modeling, and assist in more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0055] As an optional embodiment, the identification module determines the specific manner of the historical similarity corresponding to each IT device information according to the historical IT asset management record, including: For each piece of IT equipment information, calculate the weighted sum average of the information similarity between the equipment information of all historical IT asset equipment in the historical IT asset management records and the IT equipment information to obtain the historical similarity corresponding to the IT equipment information; wherein the weighted calculation weight corresponding to the information similarity corresponding to each historical IT asset equipment is proportional to the number of times the historical IT asset equipment appears in the historical IT asset management records.

[0056] It can be seen that through the above-mentioned optional embodiments, the historical similarity corresponding to the IT equipment information can be accurately determined by weighted summing and averaging the information similarity between the equipment information of all historical IT asset equipment in the historical IT asset management records and the IT equipment information, so as to facilitate subsequent equipment association and topology modeling, and assist in achieving more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0057] As an optional embodiment, the specific manner in which the determination module determines the association mathematical model between any two pieces of IT asset equipment information according to a preset equipment information association rule includes: For any two pieces of IT asset equipment information, obtain the historical communication records corresponding to the two pieces of IT asset equipment information; Determine, according to the historical communication records corresponding to the two IT asset equipment information, the correlation parameter corresponding to the two IT asset equipment information; When the correlation parameter is greater than the second parameter threshold, a correlation mathematical model between the two pieces of IT asset equipment information is established, and the correlation parameter is determined as a correlation coefficient in the correlation mathematical model.

[0058] It can be seen that through the above-mentioned optional embodiments, the corresponding correlation parameters can be determined according to the historical communication records corresponding to the information of two IT asset devices, so as to establish a correlation mathematical model and correlation coefficient, which is convenient for subsequent topological modeling, and assists in realizing more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0059] As an optional embodiment, the specific manner in which the determination module determines the correlation parameter corresponding to the two IT asset equipment information according to the historical communication records corresponding to the two IT asset equipment information includes: Calculate the proportion of mutual communication records in the historical communication records corresponding to the two IT asset device information; Calculate the record similarity between the historical communication records corresponding to the two IT asset device information; The product of the record similarity and the record proportion is calculated to obtain the correlation parameter corresponding to the two IT asset equipment information.

[0060] It can be seen that through the above-mentioned optional embodiments, the corresponding correlation parameters can be determined according to the mutual record ratio and record similarity calculation between the historical communication records corresponding to the information of two IT asset equipment, so as to establish a correlation mathematical model and correlation coefficient, which is convenient for subsequent topological modeling, and assists in realizing more efficient and accurate identification and association of IT asset equipment in the area, thereby improving the efficiency and effectiveness of asset management and reducing labor costs.

[0061] As an optional embodiment, the modeling module establishes a specific manner of the IT asset equipment topology relationship model corresponding to the target network area according to the associated mathematical model corresponding to all IT asset equipment information, including: Filter out all IT asset equipment information corresponding to at least one associated mathematical model to obtain a plurality of associated IT asset equipment; Taking each associated IT asset device as a topological subject and the corresponding associated mathematical model between any two associated IT asset devices as the relationship between the corresponding topological subjects, a topological relationship model of IT asset devices corresponding to the target network area is established.

[0062] It can be seen that through the above-mentioned optional embodiments, it is possible to establish an IT asset equipment topological relationship model corresponding to the target network area based on the IT asset equipment information corresponding to at least one associated mathematical model as the topological subject and the associated mathematical models between each other as the topological relationship, so as to achieve more efficient and accurate identification and association of IT asset equipment in the area, improve the efficiency and effectiveness of asset management, and reduce labor costs.

[0063] As an optional embodiment, the system is further configured to perform the following steps: Accept IT asset inventory instructions sent by users; Determine the corresponding target inventory object equipment according to the IT asset inventory instruction; Determine at least one primary associated object device corresponding to the target inventory object device according to the IT asset device topology relationship model; Determine at least one secondary associated object device corresponding to each primary associated object device according to the IT asset device topology relationship model; The primary associated object device and the secondary associated object device and the corresponding association degree parameters between the primary associated object device and the target inventory object device are pushed to the user to determine whether to be counted as associated assets.

[0064] It can be seen that through the above-mentioned optional embodiments, when an inventory instruction is received, the associated devices and the degree of association can be determined based on the IT asset equipment topological relationship model and pushed to the user for confirmation, thereby achieving more efficient and accurate identification and association of IT asset equipment in the area, improving the efficiency and effectiveness of asset management, and reducing labor costs.

[0065] Embodiment 3 See also Figure 3 , Figure 3 This is another IT asset data processing system based on intelligent identification disclosed in an embodiment of the present invention. Figure 3 The described IT asset data processing system based on intelligent identification is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the IT asset data processing system based on intelligent identification may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the IT asset data processing method based on intelligent identification described in the first embodiment.

[0066] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the IT asset data processing method based on intelligent identification described in the first embodiment.

[0067] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the IT asset data processing method based on intelligent identification described in the first embodiment.

[0068] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0070] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0079] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0081] Finally, it should be noted that the IT asset data processing method and system based on intelligent identification disclosed in the embodiment of the present invention discloses only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for processing IT asset data based on intelligent identification, characterized in that: The method comprises: Obtain information on multiple IT devices in the target network area through multiple gateway devices; Based on a neural network algorithm, identifying a plurality of IT asset equipment information from the plurality of IT equipment information; Determine the association mathematical model between any two pieces of IT asset equipment information according to the preset equipment information association rule; According to the associated mathematical model corresponding to all the IT asset equipment information, an IT asset equipment topology relationship model corresponding to the target network area is established.

2. The method for processing IT asset data based on intelligent identification according to claim 1, characterized in that: The identifying of multiple IT asset equipment information from the multiple IT equipment information based on the neural network algorithm includes: Input each piece of IT equipment information into a trained asset prediction algorithm model to obtain a predicted probability that each piece of IT equipment information belongs to an IT asset; Determine the historical similarity corresponding to each IT equipment information according to the historical IT asset management records; Calculate the product of the predicted probability and the historical similarity to obtain a device priority parameter corresponding to each IT device information; All the IT equipment information whose equipment priority parameters are greater than the first parameter threshold are screened out to obtain a plurality of IT asset equipment information.

3. The method for processing IT asset data based on intelligent identification according to claim 2, characterized in that: The asset prediction algorithm model is a CNN neural network, which is trained by a training data set including a plurality of training IT equipment information and corresponding labels of whether they are assets.

4. The method for processing IT asset data based on intelligent identification according to claim 2, characterized in that: The determining, based on the historical IT asset management records, the historical similarity corresponding to each piece of IT equipment information includes: For each piece of IT equipment information, calculate the weighted sum average of the information similarity between the equipment information of all historical IT asset equipment in the historical IT asset management records and the IT equipment information to obtain the historical similarity corresponding to the IT equipment information; wherein the weighted calculation weight corresponding to the information similarity corresponding to each piece of historical IT asset equipment is proportional to the number of times the historical IT asset equipment appears in the historical IT asset management records.

5. The method for processing IT asset data based on intelligent identification according to claim 1, characterized in that: Determining the association mathematical model between any two pieces of IT asset equipment information according to the preset equipment information association rule includes: For any two pieces of IT asset equipment information, obtaining historical communication records corresponding to the two pieces of IT asset equipment information; Determining, according to the historical communication records corresponding to the two IT asset equipment information, the correlation parameters corresponding to the two IT asset equipment information; When the correlation parameter is greater than a second parameter threshold, a correlation mathematical model between the two IT asset equipment information is established, and the correlation parameter is determined as a correlation coefficient in the correlation mathematical model.

6. The method for processing IT asset data based on intelligent identification according to claim 5, characterized in that: Determining the correlation parameter corresponding to the two IT asset equipment information according to the historical communication records corresponding to the two IT asset equipment information includes: Calculate the proportion of mutual communication records in the historical communication records corresponding to the two IT asset device information; Calculating the record similarity between the historical communication records corresponding to the two IT asset device information; The product of the record similarity and the record proportion is calculated to obtain a correlation parameter corresponding to the two pieces of IT asset equipment information.

7. The method for processing IT asset data based on intelligent identification according to claim 5, characterized in that: The step of establishing an IT asset equipment topology relationship model corresponding to the target network area according to the associated mathematical model corresponding to all the IT asset equipment information includes: Filter out all the IT asset equipment information corresponding to at least one of the associated mathematical models to obtain a plurality of associated IT asset equipment; Taking each of the associated IT asset devices as a topological subject and the associated mathematical models corresponding to any two of the associated IT asset devices as the relationship between the corresponding topological subjects, a topological relationship model of the IT asset devices corresponding to the target network area is established.

8. The method for processing IT asset data based on intelligent identification according to claim 7, characterized in that: The method further comprises: Accept IT asset inventory instructions sent by users; Determine the corresponding target inventory object device according to the IT asset inventory instruction; Determine at least one primary associated object device corresponding to the target inventory object device according to the IT asset device topology relationship model; Determine at least one secondary associated object device corresponding to each of the primary associated object devices according to the IT asset device topology relationship model; The primary associated object device and the secondary associated object device and the corresponding associated degree parameters between the primary associated object device and the target inventory object device are pushed to the user to determine whether to perform inventory as associated assets.

9. An IT asset data processing system based on intelligent identification, characterized in that: The system comprises: An acquisition module, used to acquire information of multiple IT devices in a target network area through multiple gateway devices; An identification module, used for identifying a plurality of IT asset equipment information from the plurality of IT equipment information based on a neural network algorithm; A determination module, used to determine the association mathematical model between any two pieces of IT asset equipment information according to a preset equipment information association rule; The modeling module is used to establish an IT asset equipment topology relationship model corresponding to the target network area according to the associated mathematical model corresponding to all the IT asset equipment information.

10. An IT asset data processing system based on intelligent identification, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the IT asset data processing method based on intelligent identification as described in any one of claims 1-8.

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