IT Asset Data Processing Method and System Based on Intelligent Recognition

Abstract

This invention discloses an IT asset data processing method and system based on intelligent identification. The method includes: acquiring information on multiple IT devices within a target network area through multiple gateway devices; identifying multiple IT asset device information from the multiple IT device information based on a neural network algorithm; determining a mathematical model of association between any two pieces of IT asset device information according to preset device information association rules; and establishing an IT asset device topology model corresponding to the target network area based on the mathematical model of association corresponding to all the IT asset device information. Therefore, this invention can more efficiently and accurately identify and associate IT asset devices within a region, improving the efficiency and effectiveness of asset management and reducing labor costs.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for processing IT asset data based on intelligent recognition. Background Technology

[0002] With the increasing demand for big data computing and the widespread improvement in data processing capabilities, the number of IT assets within large enterprises is also increasing significantly. How to help enterprises efficiently manage these large amounts of IT equipment and resources has become a crucial technical issue. Current technologies for managing IT assets still largely rely on manual inventory methods to obtain asset data and simple data processing rules, failing to consider combining equipment intranet information scanning and topology algorithms to improve the efficiency and accuracy of asset management. Clearly, existing technologies have shortcomings that urgently need to be addressed. 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] To address the aforementioned technical problems, the first aspect of this invention discloses a method for processing IT asset data based on intelligent identification, the method comprising:

[0005] Information about multiple IT devices within the target network area is obtained through multiple gateway devices;

[0006] Based on neural network algorithms, multiple IT asset equipment information is identified from the multiple IT equipment information;

[0007] Based on the preset device information association rules, determine the association mathematical model between any two IT asset device information;

[0008] Based on the associated mathematical model corresponding to all the IT asset equipment information, a topological relationship model of IT asset equipment corresponding to the target network area is established.

[0009] As an optional implementation, in the first aspect of the present invention, the step of identifying multiple IT asset equipment information from the multiple IT equipment information based on a neural network algorithm includes:

[0010] The information of each IT device is input into the trained asset prediction algorithm model to obtain the predicted probability that each IT device belongs to an IT asset.

[0011] Based on historical IT asset management records, determine the historical similarity of each IT device information;

[0012] Calculate the product of the predicted probability and the historical similarity to obtain the device priority parameter corresponding to each piece of IT device information;

[0013] The information of all IT equipment whose priority parameters are greater than the first parameter threshold is filtered out to obtain multiple IT asset equipment information.

[0014] As an optional implementation, in the first aspect of the present invention, the asset prediction algorithm model is a CNN neural network, which is trained using a training dataset that includes multiple training IT device information and corresponding labels indicating whether they are assets.

[0015] As an optional implementation, in the first aspect of the present invention, determining the historical similarity of each piece of IT equipment information based on historical IT asset management records includes:

[0016] For each IT device information, the weighted summation average of the information similarity between the device information of all historical IT asset devices in the historical IT asset management record and the IT device information is calculated to obtain the historical similarity corresponding to the IT device information; wherein, the weighted calculation weight corresponding to the information similarity of each historical IT asset device is proportional to the number of times the historical IT asset device appears in the historical IT asset management record.

[0017] 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 preset equipment information association rules includes:

[0018] For any two IT asset device information, obtain the historical communication records corresponding to the two IT asset device information;

[0019] Based on the historical communication records corresponding to the two IT asset equipment information, the correlation parameter corresponding to the two IT asset equipment information is determined;

[0020] When the correlation parameter is greater than the second parameter threshold, a correlation mathematical model is established between the two IT asset equipment information, and the correlation parameter is determined as the correlation coefficient in the correlation mathematical model.

[0021] As an optional implementation, in the first aspect of the present invention, determining the correlation parameter corresponding to the two IT asset device information based on the historical communication records corresponding to the two IT asset device information includes:

[0022] Calculate the percentage of records in the historical communication records corresponding to the two IT asset equipment information that belong to mutual communication;

[0023] Calculate the record similarity between the historical communication records corresponding to the two IT asset device information entries;

[0024] The correlation parameter corresponding to the two IT asset equipment information is obtained by calculating the product of the record similarity and the record proportion.

[0025] As an optional implementation, in the first aspect of the present invention, establishing an IT asset device topology model corresponding to the target network area based on the association mathematical model corresponding to all the IT asset device information includes:

[0026] Filter out all IT asset equipment information corresponding to at least one of the aforementioned mathematical models to obtain multiple associated IT asset equipment;

[0027] Using each of the associated IT asset devices as a topology subject, and the associated mathematical model between any two associated IT asset devices as the relationship between the corresponding topology subjects, an IT asset device topology relationship model corresponding to the target network area is established.

[0028] As an optional implementation, in the first aspect of the present invention, the method further includes:

[0029] Accept IT asset inventory instructions sent by users;

[0030] The corresponding target inventory device is determined according to the IT asset inventory instruction;

[0031] Based on the IT asset equipment topology model, at least one primary associated object device corresponding to the target inventory object device is determined;

[0032] Based on the IT asset equipment topology model, at least one secondary associated object device is determined for each primary associated object device;

[0033] The association parameters between the primary associated object device, the secondary associated object device, and the corresponding target inventory object device are pushed to the user to determine whether to inventory them as associated assets.

[0034] A second aspect of this invention discloses an IT asset data processing system based on intelligent identification, the system comprising:

[0035] The acquisition module is used to acquire information about multiple IT devices within a target network area through multiple gateway devices;

[0036] The identification module is used to identify multiple IT asset equipment information from the multiple IT equipment information based on a neural network algorithm;

[0037] The determination module is used to determine the association mathematical model between any two pieces of IT asset equipment information according to preset equipment information association rules;

[0038] The modeling module is used to establish a topological relationship model of IT asset devices corresponding to the target network area based on the associated mathematical model corresponding to all the IT asset device information.

[0039] As an optional implementation, in a second aspect of the present invention, the specific method by which the identification module identifies multiple IT asset equipment information from the multiple IT equipment information based on a neural network algorithm includes:

[0040] The information of each IT device is input into the trained asset prediction algorithm model to obtain the predicted probability that each IT device belongs to an IT asset.

[0041] Based on historical IT asset management records, determine the historical similarity of each IT device information;

[0042] Calculate the product of the predicted probability and the historical similarity to obtain the device priority parameter corresponding to each piece of IT device information;

[0043] The information of all IT equipment whose priority parameters are greater than the first parameter threshold is filtered out to obtain multiple IT asset equipment information.

[0044] As an optional implementation, in the second aspect of the present invention, the asset prediction algorithm model is a CNN neural network, which is trained using a training dataset that includes multiple training IT device information and corresponding labels indicating whether they are assets.

[0045] As an optional implementation, in a second aspect of the invention, the identification module determines the specific method for determining the historical similarity of each piece of IT equipment information based on historical IT asset management records, including:

[0046] For each IT device information, the weighted summation average of the information similarity between the device information of all historical IT asset devices in the historical IT asset management record and the IT device information is calculated to obtain the historical similarity corresponding to the IT device information; wherein, the weighted calculation weight corresponding to the information similarity of each historical IT asset device is proportional to the number of times the historical IT asset device appears in the historical IT asset management record.

[0047] As an optional implementation, in a second aspect of the invention, the determining module determines the specific method by which it determines the mathematical model of the association between any two pieces of IT asset equipment information according to preset equipment information association rules, including:

[0048] For any two IT asset device information, obtain the historical communication records corresponding to the two IT asset device information;

[0049] Based on the historical communication records corresponding to the two IT asset equipment information, the correlation parameter corresponding to the two IT asset equipment information is determined;

[0050] When the correlation parameter is greater than the second parameter threshold, a correlation mathematical model is established between the two IT asset equipment information, and the correlation parameter is determined as the correlation coefficient in the correlation mathematical model.

[0051] As an optional implementation, in the second aspect of the present invention, the specific method by which the determining module determines the correlation parameter corresponding to the two IT asset device information based on the historical communication records corresponding to the two IT asset device information includes:

[0052] Calculate the percentage of records in the historical communication records corresponding to the two IT asset equipment information that belong to mutual communication;

[0053] Calculate the record similarity between the historical communication records corresponding to the two IT asset device information entries;

[0054] The correlation parameter corresponding to the two IT asset equipment information is obtained by calculating the product of the record similarity and the record proportion.

[0055] As an optional implementation, in the second aspect of the present invention, the specific method by which the modeling module establishes the IT asset equipment topology model corresponding to the target network area based on the association mathematical model corresponding to all the IT asset equipment information includes:

[0056] Filter out all IT asset equipment information corresponding to at least one of the aforementioned mathematical models to obtain multiple associated IT asset equipment;

[0057] Using each of the associated IT asset devices as a topology subject, and the associated mathematical model between any two associated IT asset devices as the relationship between the corresponding topology subjects, an IT asset device topology relationship model corresponding to the target network area is established.

[0058] As an optional implementation, in a second aspect of the invention, the system is further configured to perform the following steps:

[0059] Accept IT asset inventory instructions sent by users;

[0060] The corresponding target inventory device is determined according to the IT asset inventory instruction;

[0061] Based on the IT asset equipment topology model, at least one primary associated object device corresponding to the target inventory object device is determined;

[0062] Based on the IT asset equipment topology model, at least one secondary associated object device is determined for each primary associated object device;

[0063] The association parameters between the primary associated object device, the secondary associated object device, and the corresponding target inventory object device are pushed to the user to determine whether to inventory them as associated assets.

[0064] A third aspect of this invention discloses another IT asset data processing system based on intelligent identification, the system comprising:

[0065] Memory containing executable program code;

[0066] A processor coupled to the memory;

[0067] The processor calls the executable program code stored in the memory to execute some 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.

[0068] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some 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.

[0069] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0070] This invention can identify multiple IT asset equipment information from multiple IT equipment information within a region based on neural networks. Then, based on the equipment information association rules, it determines the mathematical model of the association between equipment and establishes a device topology model for the entire region for subsequent IT asset management. This enables more efficient and accurate identification and association of IT asset equipment within the region, improving the efficiency and effectiveness of asset management and reducing labor costs. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a flowchart illustrating an IT asset data processing method based on intelligent recognition disclosed in an embodiment of the present invention.

[0073] Figure 2 This is a schematic diagram of the structure of an IT asset data processing system based on intelligent recognition disclosed in an embodiment of the present invention.

[0074] Figure 3 This is a schematic diagram of another IT asset data processing system based on intelligent recognition disclosed in an embodiment of the present invention. Detailed Implementation

[0075] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0077] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0078] This invention discloses an IT asset data processing method and system based on intelligent identification. It can identify multiple IT asset device information from multiple IT device information within a region based on neural networks, and then establish a device topology model for the entire region after determining the mathematical model of the association between devices based on device information association rules. This model is used for subsequent IT asset management, thereby enabling more efficient and accurate identification and association of IT asset devices within the region, improving the efficiency and effectiveness of asset management, and reducing labor costs. Detailed descriptions follow.

[0079] Example 1

[0080] Please see Figure 1 , Figure 1 This is a flowchart illustrating an IT asset data processing method based on intelligent recognition, as disclosed in an embodiment of the present invention. Wherein, Figure 1 The described intelligent identification-based IT asset data processing method can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 1 As shown, this IT asset data processing method based on intelligent recognition may include the following operations:

[0081] 101. Obtain information on multiple IT devices within the target network area through multiple gateway devices.

[0082] 102. Based on neural network algorithms, identify multiple IT asset equipment information from multiple IT equipment information.

[0083] 103. Based on the preset equipment information association rules, determine the association mathematical model between any two IT asset equipment information.

[0084] 104. Based on the mathematical model corresponding to all IT asset and equipment information, establish a topological relationship model of IT asset and equipment corresponding to the target network area.

[0085] As can be seen, the above-described embodiments of the invention can identify multiple IT asset equipment information from multiple IT equipment information within a region based on neural networks, and then establish a device topology model for the entire region after determining the mathematical model of the association between devices based on the device information association rules, so as to use it for subsequent IT asset management. This enables more efficient and accurate identification and association of IT asset equipment within the region, improving the efficiency and effectiveness of asset management and reducing labor costs.

[0086] As an optional embodiment, the step described above, identifying multiple IT asset equipment information from multiple IT equipment information based on a neural network algorithm, includes:

[0087] The information of each IT device is input into the trained asset prediction algorithm model to obtain the predicted probability that each IT device belongs to an IT asset.

[0088] Based on historical IT asset management records, determine the historical similarity of information for each IT device.

[0089] Calculate the product of the predicted probability and the historical similarity to obtain the device priority parameters corresponding to each IT device information;

[0090] The system filters out IT equipment information whose priority parameters are greater than the first parameter threshold, thus obtaining multiple IT asset equipment information.

[0091] As can be seen, through the above 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. In order to comprehensively calculate the characteristic parameters of each device belonging to assets, IT asset equipment information can be accurately screened and identified, so as to facilitate subsequent device association and topology modeling. This helps to achieve more efficient and accurate identification and association of IT asset equipment in the region, improve the efficiency and effectiveness of asset management, and reduce labor costs.

[0092] As an optional embodiment, the asset prediction algorithm model in the above steps is a CNN neural network, which is trained using a training dataset that includes information on multiple training IT devices and corresponding labels indicating whether they are assets.

[0093] As can be seen, the network details and training details of the asset prediction algorithm model are clarified through the above optional embodiments. It can be used to accurately predict the probability that IT equipment belongs to assets, so as to facilitate subsequent equipment association and topology modeling, and help 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.

[0094] As an optional embodiment, the step above, determining the historical similarity of each IT device information based on historical IT asset management records, includes:

[0095] For each IT device information, the weighted summation average of the information similarity between the device information of all historical IT asset devices in the historical IT asset management records and the information of this IT device is calculated to obtain the historical similarity corresponding to the IT device information; wherein, the weighted calculation weight corresponding to the information similarity of each historical IT asset device is proportional to the number of times the historical IT asset device appears in the historical IT asset management records.

[0096] As can be seen, through the above optional embodiments, the historical similarity of IT equipment information can be accurately determined by weighted summation and average calculation of the information similarity between the equipment information of all historical IT asset devices in the historical IT asset management records and the IT equipment information. This facilitates subsequent device association and topology modeling, helps to achieve more efficient and accurate identification and association of IT asset devices in the region, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0097] As an optional embodiment, the step above, determining the association mathematical model between any two IT asset device information according to preset device information association rules, includes:

[0098] For any two IT asset device information, retrieve the historical communication records corresponding to those two IT asset device information;

[0099] Based on the historical communication records corresponding to the two IT asset device information, the correlation parameters corresponding to the two IT asset device information are determined;

[0100] When the correlation parameter is greater than the second parameter threshold, a correlation mathematical model is established between the two IT asset equipment information, and the correlation parameter is determined as the correlation coefficient in the correlation mathematical model.

[0101] As can be seen, through the above optional embodiments, the corresponding correlation parameters can be determined based on 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 facilitates subsequent topology modeling, helps to achieve more efficient and accurate identification and correlation of IT asset devices in the region, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0102] As an optional embodiment, the step above, determining the correlation parameter corresponding to the two IT asset device information based on the historical communication records corresponding to the two IT asset device information, includes:

[0103] Calculate the percentage of records in the historical communication records corresponding to the two IT asset devices that are mutual communications;

[0104] Calculate the record similarity between the historical communication records corresponding to the two IT asset device information;

[0105] The correlation parameter between the two IT asset device information is obtained by multiplying the record similarity and the record proportion.

[0106] As can be seen, through the above optional embodiments, the corresponding correlation parameters can be determined based on the mutual recording ratio and record similarity calculation between 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 facilitates subsequent topology modeling, helps to achieve more efficient and accurate identification and correlation of IT asset devices in the region, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0107] As an optional embodiment, the step above, establishing an IT asset device topology model corresponding to the target network area based on the associated mathematical model corresponding to all IT asset device information, includes:

[0108] Filter out all IT asset equipment information that corresponds to at least one associated mathematical model to obtain multiple associated IT asset equipment;

[0109] Using each associated IT asset device as the topology subject, and the corresponding mathematical model of any two associated IT asset devices as the relationship between the corresponding topology subjects, a topology relationship model of IT asset devices corresponding to the target network area is established.

[0110] As can be seen, through the above optional embodiments, the IT asset equipment information corresponding to at least one associated mathematical model can be used as the topological subject and the associated mathematical model between them as the topological relationship to establish the IT asset equipment topological relationship model corresponding to the target network area. This enables more efficient and accurate identification and association of IT asset equipment in the area, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0111] As an optional embodiment, the method further includes the following steps:

[0112] Accept IT asset inventory instructions sent by users;

[0113] Determine the corresponding target equipment to be inventoried based on the IT asset inventory instructions;

[0114] Based on the IT asset equipment topology model, identify at least one primary associated object device corresponding to the target inventory object device;

[0115] Based on the IT asset equipment topology model, at least one secondary associated object device is identified for each primary associated object device.

[0116] The primary associated object device, the secondary associated object device, and the associated degree parameters between them and the target inventory object device are pushed to the user to determine whether to count them as associated assets.

[0117] As can be seen, through the above 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 topology model and pushed to the user for confirmation. This enables more efficient and accurate identification and association of IT asset equipment in the area, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0118] Example 2

[0119] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of an IT asset data processing system based on intelligent recognition, as disclosed in an embodiment of the present invention. Figure 2 The described intelligent identification-based IT asset data processing system can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 2 As shown, the IT asset data processing system based on intelligent recognition may include:

[0120] The acquisition module 201 is used to acquire information about multiple IT devices within a target network area through multiple gateway devices.

[0121] The identification module 202 is used to identify multiple IT asset equipment information from multiple IT equipment information based on a neural network algorithm.

[0122] The determination module 203 is used to determine the mathematical model of the association between any two IT asset equipment information according to the preset equipment information association rules.

[0123] Modeling module 204 is used to establish a topological relationship model of IT asset devices corresponding to the target network area based on the associated mathematical model corresponding to all IT asset device information.

[0124] As can be seen, the above-described embodiments of the invention can identify multiple IT asset equipment information from multiple IT equipment information within a region based on neural networks, and then establish a device topology model for the entire region after determining the mathematical model of the association between devices based on the device information association rules, so as to use it for subsequent IT asset management. This enables more efficient and accurate identification and association of IT asset equipment within the region, improving the efficiency and effectiveness of asset management and reducing labor costs.

[0125] As an optional embodiment, the identification module, based on a neural network algorithm, identifies multiple IT asset equipment information from multiple IT equipment information in the following specific ways:

[0126] The information of each IT device is input into the trained asset prediction algorithm model to obtain the predicted probability that each IT device belongs to an IT asset.

[0127] Based on historical IT asset management records, determine the historical similarity of information for each IT device.

[0128] Calculate the product of the predicted probability and the historical similarity to obtain the device priority parameters corresponding to each IT device information;

[0129] The system filters out IT equipment information whose priority parameters are greater than the first parameter threshold, thus obtaining multiple IT asset equipment information.

[0130] As can be seen, through the above 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. In order to comprehensively calculate the characteristic parameters of each device belonging to assets, IT asset equipment information can be accurately screened and identified, so as to facilitate subsequent device association and topology modeling. This helps to achieve more efficient and accurate identification and association of IT asset equipment in the region, improve the efficiency and effectiveness of asset management, and reduce labor costs.

[0131] As an optional embodiment, the asset prediction algorithm model is a CNN neural network, which is trained on a training dataset that includes information on multiple training IT devices and corresponding labels indicating whether they are assets.

[0132] As can be seen, the network details and training details of the asset prediction algorithm model are clarified through the above optional embodiments. It can be used to accurately predict the probability that IT equipment belongs to assets, so as to facilitate subsequent equipment association and topology modeling, and help 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.

[0133] As an optional embodiment, the identification module determines the specific method for determining the historical similarity of each IT device information based on historical IT asset management records, including:

[0134] For each IT device information, the weighted summation average of the information similarity between the device information of all historical IT asset devices in the historical IT asset management records and the information of this IT device is calculated to obtain the historical similarity corresponding to the IT device information; wherein, the weighted calculation weight corresponding to the information similarity of each historical IT asset device is proportional to the number of times the historical IT asset device appears in the historical IT asset management records.

[0135] As can be seen, through the above optional embodiments, the historical similarity of IT equipment information can be accurately determined by weighted summation and average calculation of the information similarity between the equipment information of all historical IT asset devices in the historical IT asset management records and the IT equipment information. This facilitates subsequent device association and topology modeling, helps to achieve more efficient and accurate identification and association of IT asset devices in the region, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0136] As an optional embodiment, the determining module determines the specific method of the association mathematical model between any two IT asset device information according to preset device information association rules, including:

[0137] For any two IT asset device information, retrieve the historical communication records corresponding to those two IT asset device information;

[0138] Based on the historical communication records corresponding to the two IT asset device information, the correlation parameters corresponding to the two IT asset device information are determined;

[0139] When the correlation parameter is greater than the second parameter threshold, a correlation mathematical model is established between the two IT asset equipment information, and the correlation parameter is determined as the correlation coefficient in the correlation mathematical model.

[0140] As can be seen, through the above optional embodiments, the corresponding correlation parameters can be determined based on 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 facilitates subsequent topology modeling, helps to achieve more efficient and accurate identification and correlation of IT asset devices in the region, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0141] As an optional embodiment, the determining module determines the specific method for determining the correlation parameter corresponding to the two IT asset device information based on the historical communication records corresponding to the two IT asset device information, including:

[0142] Calculate the percentage of records in the historical communication records corresponding to the two IT asset devices that are mutual communications;

[0143] Calculate the record similarity between the historical communication records corresponding to the two IT asset device information;

[0144] The correlation parameter between the two IT asset device information is obtained by multiplying the record similarity and the record proportion.

[0145] As can be seen, through the above optional embodiments, the corresponding correlation parameters can be determined based on the mutual recording ratio and record similarity calculation between 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 facilitates subsequent topology modeling, helps to achieve more efficient and accurate identification and correlation of IT asset devices in the region, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0146] As an optional implementation, the modeling module establishes a topological relationship model of IT asset devices corresponding to the target network area based on the associated mathematical model of all IT asset device information in the following specific ways:

[0147] Filter out all IT asset equipment information that corresponds to at least one associated mathematical model to obtain multiple associated IT asset equipment;

[0148] Using each associated IT asset device as the topology subject, and the corresponding mathematical model of any two associated IT asset devices as the relationship between the corresponding topology subjects, a topology relationship model of IT asset devices corresponding to the target network area is established.

[0149] As can be seen, through the above optional embodiments, the IT asset equipment information corresponding to at least one associated mathematical model can be used as the topological subject and the associated mathematical model between them as the topological relationship to establish the IT asset equipment topological relationship model corresponding to the target network area. This enables more efficient and accurate identification and association of IT asset equipment in the area, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0150] As an optional embodiment, the system is also used to perform the following steps:

[0151] Accept IT asset inventory instructions sent by users;

[0152] Determine the corresponding target equipment to be inventoried based on the IT asset inventory instructions;

[0153] Based on the IT asset equipment topology model, identify at least one primary associated object device corresponding to the target inventory object device;

[0154] Based on the IT asset equipment topology model, at least one secondary associated object device is identified for each primary associated object device.

[0155] The primary associated object device, the secondary associated object device, and the associated degree parameters between them and the target inventory object device are pushed to the user to determine whether to count them as associated assets.

[0156] As can be seen, through the above 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 topology model and pushed to the user for confirmation. This enables more efficient and accurate identification and association of IT asset equipment in the area, improves the efficiency and effectiveness of asset management, and reduces labor costs.

[0157] Example 3

[0158] Please see Figure 3 , Figure 3 This is another IT asset data processing system based on intelligent recognition disclosed in the embodiments of the present invention. Figure 3 The described intelligent identification-based IT asset data processing system is applied in data processing systems / data processing equipment / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the IT asset data processing system based on intelligent recognition may include:

[0159] Memory 301 storing executable program code;

[0160] Processor 302 coupled to memory 301;

[0161] 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 Embodiment 1.

[0162] Example 4

[0163] This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the IT asset data processing method based on intelligent identification described in Embodiment 1.

[0164] Example 5

[0165] This 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 cause a computer to perform the steps of the IT asset data processing method based on intelligent identification described in Embodiment 1.

[0166] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0167] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0168] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0169] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0171] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0173] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0174] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0175] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0176] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0177] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0178] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0179] Finally, it should be noted that the IT asset data processing method and system based on intelligent identification disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing IT asset data based on intelligent recognition, characterized in that, The method includes: Information on multiple IT devices within the target network area is obtained through multiple gateway devices; Based on a neural network algorithm, multiple IT asset equipment information is identified from the multiple IT equipment information, including: The information of each IT device is input into the trained asset prediction algorithm model to obtain the predicted probability that each IT device belongs to an IT asset. Based on historical IT asset management records, determine the historical similarity of each IT device information; Calculate the product of the predicted probability and the historical similarity to obtain the device priority parameter corresponding to each piece of IT device information; Filter out all IT equipment information whose priority parameters are greater than the first parameter threshold to obtain multiple IT asset equipment information; Based on preset device information association rules, determine the association mathematical model between any two pieces of IT asset device information, including: For any two IT asset device information, obtain the historical communication records corresponding to the two IT asset device information; Calculate the percentage of records in the historical communication records corresponding to the two IT asset equipment information that belong to mutual communication; Calculate the record similarity between the historical communication records corresponding to the two IT asset device information entries; Calculate the product of the record similarity and the record proportion to obtain 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 is established between the two IT asset equipment information, and the correlation parameter is determined as the correlation coefficient in the correlation mathematical model. Based on the associated mathematical model corresponding to all the IT asset equipment information, a topological relationship model of IT asset equipment corresponding to the target network area is established.

2. The IT asset data processing method based on intelligent recognition according to claim 1, characterized in that, The asset prediction algorithm model is a CNN neural network, which is trained using a training dataset that includes information on multiple training IT devices and corresponding labels indicating whether they are assets.

3. The IT asset data processing method based on intelligent recognition according to claim 1, characterized in that, The step of determining the historical similarity of each IT device information based on historical IT asset management records includes: For each IT device information, the weighted summation average of the information similarity between the device information of all historical IT asset devices in the historical IT asset management record and the IT device information is calculated to obtain the historical similarity corresponding to the IT device information; wherein, the weighted calculation weight corresponding to the information similarity of each historical IT asset device is proportional to the number of times the historical IT asset device appears in the historical IT asset management record.

4. The IT asset data processing method based on intelligent identification according to claim 1, characterized in that, The step of establishing an IT asset device topology model corresponding to the target network area based on the association mathematical model corresponding to all the IT asset device information includes: Filter out all IT asset equipment information corresponding to at least one of the aforementioned mathematical models to obtain multiple associated IT asset equipment; Using each of the associated IT asset devices as a topology subject, and the associated mathematical model between any two associated IT asset devices as the relationship between the corresponding topology subjects, an IT asset device topology relationship model corresponding to the target network area is established.

5. The IT asset data processing method based on intelligent identification according to claim 4, characterized in that, The method further includes: Accept IT asset inventory instructions sent by users; The corresponding target inventory device is determined according to the IT asset inventory instruction; Based on the IT asset equipment topology model, at least one primary associated object device corresponding to the target inventory object device is determined; Based on the IT asset equipment topology model, at least one secondary associated object device is determined for each primary associated object device; The association parameters between the primary associated object device, the secondary associated object device, and the corresponding target inventory object device are pushed to the user to determine whether to inventory them as associated assets.

6. An IT asset data processing system based on intelligent recognition, characterized in that, The system executes the IT asset data processing method based on intelligent identification as described in any one of claims 1-5, the system comprising: The acquisition module is used to acquire information about multiple IT devices within a target network area through multiple gateway devices; The identification module is used to identify multiple IT asset equipment information from the multiple IT equipment information based on a neural network algorithm; The determination module is used to determine the association mathematical model between any two pieces of IT asset equipment information according to preset equipment information association rules; The modeling module is used to establish a topological relationship model of IT asset devices corresponding to the target network area based on the associated mathematical model corresponding to all the IT asset device information.

7. An IT asset data processing system based on intelligent recognition, characterized in that, The system includes: Memory containing 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 1-5.

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