Asset information verification method, device, equipment, medium and program product

Abstract

The invention provides an asset information verification method which can be applied to the technical field of artificial intelligence and can also be applied to the field of financial science and technology or other fields. The asset information verification method comprises the steps that a received asset verification request is responded, the request is input into an intelligent model, an asset verification instruction is obtained, and the accuracy of the instruction about asset verification information is higher than the accuracy of the request about the asset verification information; in response to a received asset verification instruction, obtaining real-time asset information and reference asset information of the target area assets; and comparing the real-time asset information with the reference asset information to obtain a verification result. The invention further provides an asset information verification device and equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, and specifically to an asset information verification method, device, equipment, medium and program product. Background Art

[0002] Due to differences in corporate organizational structure and departmental responsibilities, the asset management system and network management platform were used by different departments. This often led to the following issues: Due to the asset inventory cycle, the asset management system's information lags behind. During the inventory phase, offline reconciliation combined with on-site inspections was inefficient, often leading to missing equipment and incorrect equipment information. This required significant effort to locate and inquire about the current status of assets. Changes to the business data on the network management platform often required rewriting by the responsible person, leading to frequent forgetfulness and errors in updated data entry. Summary of the Invention

[0003] In view of the above problems, the present disclosure provides an asset information verification method, apparatus, device, medium and program product.

[0004] According to the first aspect of the present disclosure, an asset information verification method is provided, comprising: in response to receiving an asset verification request, inputting the request into an intelligent model to obtain an asset verification instruction, wherein the accuracy of the asset verification information in the instruction is higher than the accuracy of the asset verification information in the request; in response to receiving the asset verification instruction, obtaining real-time asset information and reference asset information of the assets in the target area; and comparing the real-time asset information with the reference asset information to obtain a verification result.

[0005] According to an embodiment of the present disclosure, in response to receiving an asset verification request, the request is input into an intelligent model to obtain an asset verification instruction, including: extracting at least one original keyword in the request; in the case where the original keyword does not match the standard keyword, based on a mapping table, the original keyword is replaced with a standard keyword to obtain an instruction, and the mapping table is the correspondence between standard keywords and commonly used keywords, and the commonly used keywords include the original keyword.

[0006] According to an embodiment of the present disclosure, in response to receiving an asset verification request, the request is input into an intelligent model to obtain an asset verification instruction, including: extracting at least one fuzzy word in the request; based on the fuzzy word, determining the accurate word corresponding to the fuzzy word to obtain an instruction.

[0007] According to an embodiment of the present disclosure, in response to receiving an asset verification request, the request is input into an intelligent model to obtain an asset verification instruction, including: generating prompt information for at least one word according to the request; obtaining supplementary information for at least one word according to the prompt information; and generating an instruction according to the supplementary information and the request.

[0008] According to an embodiment of the present disclosure, real-time asset information and reference asset information are compared to obtain a verification result, including: determining the initial similarity of target features of multiple dimensions in the real-time asset information and the reference asset information respectively; obtaining the weight values ​​of the target features of multiple dimensions; and determining the target similarity based on the initial similarity and the weight value to obtain a verification result.

[0009] According to an embodiment of the present disclosure, the method also includes: obtaining the historical accuracy of the target similarity of the target feature of each dimension, the historical accuracy representing the accuracy of the historical asset information verification of the target feature; updating the weight value according to the historical accuracy; and determining the target similarity according to the updated weight value.

[0010] According to an embodiment of the present disclosure, the method further includes: when the target similarity is less than a preset threshold, determining that there is an abnormality in the asset configuration and generating an alarm message.

[0011] According to an embodiment of the present disclosure, the target features of multiple dimensions include: IP address, MAC address, device type, and topological location of the device.

[0012] The second aspect of the present disclosure provides an asset information verification device, including: a processing module for inputting an asset verification request into an intelligent model in response to receiving an asset verification request, and obtaining an asset verification instruction, wherein the accuracy of the asset verification information in the instruction is higher than the accuracy of the asset verification information in the request; an acquisition module for acquiring real-time asset information and reference asset information of assets in a target area in response to receiving the asset verification instruction; and a comparison module for comparing the real-time asset information and the reference asset information to obtain a verification result.

[0013] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0015] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0017] Figure 1 Schematically illustrates an application scenario diagram of the asset information verification method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;

[0018] Figure 2 The following schematically shows a flow chart of an asset information verification method according to an embodiment of the present disclosure;

[0019] Figure 3 Schematically illustrates one of the flow charts of the method for processing asset verification requests by an intelligent model according to an embodiment of the present disclosure;

[0020] Figure 4 Schematically shows a second flow chart of the method for processing asset verification requests using an intelligent model according to an embodiment of the present disclosure;

[0021] Figure 5 Schematically shows a third flow chart of the method for processing asset verification requests using an intelligent model according to an embodiment of the present disclosure;

[0022] Figure 6 Schematically illustrates one of the flow charts of a method for comparing real-time asset information with reference asset information according to an embodiment of the present disclosure;

[0023] Figure 7 Schematically illustrates a second flow chart of a method for comparing real-time asset information with reference asset information according to an embodiment of the present disclosure;

[0024] Figure 8 Schematically shows a structural block diagram of an asset information verification device according to an embodiment of the present disclosure; and

[0025] Figure 9 A block diagram of an electronic device suitable for implementing the asset information verification method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0026] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0027] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0029] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0030] In the technical solutions disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0031] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure all provide users with corresponding operation portals for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge, and skills, and have reached a certain level of professionalism.

[0032] The embodiment of the present disclosure provides an asset information verification method. Before introducing the technical solution provided by the embodiment of the present disclosure, the relevant technologies involved in the present disclosure are first described.

[0033] The asset management system is independently maintained and managed by the asset manager. The company's procurement data is directly imported into the asset management system, and basic information such as asset transfers, inbound and outbound transactions, asset location, and usage is registered.

[0034] The network management platform is used by the operations and maintenance managers of each discipline. By accessing information from the monitoring system, it displays the availability and performance indicators of each business. Used asset information is stored independently as business ledger data for each discipline. Work order processes are integrated to bring some work online and electronically.

[0035] If there are any changes to the information of currently used assets, the operations manager will manually modify the relevant information and update it in the network management platform system to ensure the accuracy of the business ledger data. Asset managers conduct quarterly / semi-annual asset inventories, verifying asset information with each responsible department using offline forms, and even conducting physical inventory checks of asset equipment to confirm asset status.

[0036] Due to differences in corporate organizational structure and departmental responsibilities, the asset management system and network management platform were used by different departments. This often led to the following issues: Due to the asset inventory cycle, the asset management system's information lags behind. During the inventory phase, offline reconciliation combined with on-site inspections was inefficient, often leading to missing equipment and incorrect equipment information. This required significant effort to locate and inquire about the current status of assets. Changes to the business data on the network management platform often required rewriting by the responsible person, leading to frequent forgetfulness and errors in updated data entry.

[0037] Due to differences in corporate organizational structure and departmental responsibilities, the asset management system and network management platform were used by different departments. This often led to the following issues: Due to the asset inventory cycle, the asset management system's information lags behind. During the inventory phase, offline reconciliation combined with on-site inspections was inefficient, often leading to missing equipment and incorrect equipment information. This required significant effort to locate and inquire about the current status of assets. Changes to the business data on the network management platform often required rewriting by the responsible person, leading to frequent forgetfulness and errors in updated data entry.

[0038] Before further describing the embodiments of the present disclosure in detail, the nouns and terms involved in the embodiments of the present disclosure are explained. The nouns and terms involved in the embodiments of the present disclosure are subject to the following interpretations.

[0039] Monitoring system: It can monitor the applications of business systems, server systems, and network systems, and provide a flexible notification mechanism for administrators to quickly locate and solve various existing problems.

[0040] Asset Management System: This system stores and manages data center assets, including fixed assets such as office computers, video conferencing equipment, network devices, security equipment, and servers, as well as non-fixed assets such as various software licenses. CMDBs are commonly used to store this data.

[0041] Network management platform system: also known as the operation and maintenance management platform, usually carries out work functions related to enterprise operation and maintenance, basically including the display of the entire network business status, overall performance indicator display, electronic information integration, work order integration, etc.

[0042] Data center big model: This model aggregates data from the network management platform and the asset management system. It uses a pre-trained Chinese table model combined with table question-answering technologies (such as TableQA and TabPedia) to integrate existing data relationships and output them to various work scenarios.

[0043] Asset data: Data generated for the first time by the asset management system is defined as asset data. Once generated, asset data cannot be revised and can only be deleted or added.

[0044] Operation and maintenance data: Data created by the network management platform system is defined as operation and maintenance data. The network management platform system has the authority to add, delete, and modify operation and maintenance data. The network management platform does not have the authority to delete or modify non-operation and maintenance data.

[0045] An embodiment of the present disclosure provides an asset information verification method, comprising: in response to receiving an asset verification request, inputting the request into an intelligent model to obtain an asset verification instruction, wherein the accuracy of the asset verification information in the instruction is higher than the accuracy of the asset verification information in the request; in response to receiving the asset verification instruction, obtaining real-time asset information and reference asset information of the target area assets; and comparing the real-time asset information and the reference asset information to obtain a verification result.

[0046] Figure 1 The system architecture of the asset information verification method and device according to the embodiment of the present disclosure is schematically shown. It should be noted that, Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0047] like Figure 1 As shown, system architecture 100 may include a monitoring platform 101, a network management platform 102, an asset management platform 103, and a network 104. Network 104 is the medium used to provide communication links between monitoring platform 101, network management platform 102, and asset management platform 103. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables. Monitoring platform 101, network management platform 102, and asset management platform 103 may be independent physical servers, or a server cluster or distributed system composed of multiple physical servers. They may also be cloud servers that provide basic cloud computing services such as cloud services, cloud computing, network services, and middleware services.

[0048] First, in response to a received asset verification request, network management platform 102 invokes monitoring platform 101 to obtain real-time asset information for data center assets and reference asset information from asset management platform 103. Next, network management platform 102 inputs the asset verification request into the intelligent model to obtain an asset verification instruction. In response to receiving the asset verification instruction, network management platform 102 obtains real-time and reference asset information for assets in the target area. The real-time and reference asset information are compared to obtain a verification result. Finally, asset management platform 103 generates asset change information based on the verification result and updates the reference asset information based on the asset change information.

[0049] It should be noted that the asset information verification method and device of the embodiment of the present disclosure can be used in the financial field, and can also be used in any field other than the financial field. The present disclosure will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] The following will be based on Figure 1 The scene described by Figures 2 to 7 The asset information verification method of the disclosed embodiment is described in detail.

[0051] Figure 2 The flowchart of the asset information verification method according to the embodiment of the present disclosure is schematically shown.

[0052] like Figure 2 As shown, the asset information verification method of this embodiment includes operations S210 to S230.

[0053] In operation S210 , in response to receiving an asset verification request, the request is input into an intelligent model to obtain an asset verification instruction, where the accuracy of the asset verification information in the instruction is higher than the accuracy of the asset verification information in the request.

[0054] In operation S220 , in response to receiving the asset verification instruction, real-time asset information and reference asset information of the target area assets are acquired.

[0055] In operation S230 , the real-time asset information is compared with the reference asset information to obtain a verification result.

[0056] For example, the asset verification request may be information about asset verification input by a user into the network management platform, which may be input by voice or text.

[0057] The intelligent model is used to optimize the accuracy of asset verification requests to make the information contained in the asset verification requests more standardized and accurate.

[0058] An asset verification instruction can be a new request generated by the intelligent model after optimizing an asset verification request. Optimization can involve modifying or supplementing the content of an asset verification request to make it more standardized and accurate. It's important to note that optimization involves more precisely qualifying the original content of the asset verification request without changing it.

[0059] In response to the received asset verification instruction, the network management platform invokes the monitoring platform to obtain real-time asset information for the data center's assets. For example, the monitoring platform's collection module is invoked to collect network-wide ARP (Address Resolution Protocol) data to obtain real-time asset information for the data center's assets. Furthermore, the network management platform can retrieve reference asset information from the asset management platform as a verification target. This reference asset information can specifically be the asset registration information stored in the asset management platform.

[0060] Data center assets can include fixed assets and non-fixed assets. Fixed assets include, for example, office computers, video conferencing equipment, network equipment, security equipment, and IT equipment. Network equipment includes, for example, switches and firewalls, and IT equipment includes, for example, servers and storage devices. Non-fixed assets include, for example, various software licenses.

[0061] Asset information may include, for example, asset number, asset type, asset location, asset serial number, asset owner, MAC address (Media Access Control Address), IP address (Internet Protocol Address), uplink switch IP, uplink switch name, uplink switch port, data center name, cabinet number, cabinet U position, and other information.

[0062] It is understandable that the intelligent model is used to analyze the user's asset verification request and output asset verification instructions with higher accuracy, so that in subsequent asset verification, more complete and accurate verification results can be obtained based on the more accurate verification instructions.

[0063] In some embodiments, the intelligent model can implement single-form, round-trip natural language instruction output. For example, a simple natural statement, SQL, can be modeled: "Tell me which servers were purchased in 2023 (select asset number where table A and purchase date = 2023 and asset type = server).

[0064] The intelligent model can also output natural language commands across multiple tables and multiple rounds. For example, for example, "I need to verify all server assets purchased between 2020 and 2023": This pushes a task to the monitoring system's data collection module to collect real-time asset information. Real-time asset information is matched with reference asset information, and if a complete match is achieved, the online device is marked as having passed verification. The verification conclusion is then matched and integrated with data for server-type devices purchased between 2020 and 2023, providing feedback on the matching conclusion. Fields are added to indicate the verification result (passed, failed) and verification notes (in stock, offline, pending O&M confirmation). The verification conclusion fusion form is then output, along with O&M asset verification process work order information (number, responsible person, status). The O&M asset verification process is enabled, work order monitoring begins, and an updated verification conclusion form is output again after the work order is completed.

[0065] Figure 3 One of the flowcharts of the method for processing asset verification requests by an intelligent model according to an embodiment of the present disclosure is schematically shown.

[0066] As described above, in operation S210, in response to receiving an asset verification request, the request is input into the intelligent model to obtain an asset verification instruction. Figure 3 As shown, the operation may further include operations S310 to S340.

[0067] In operation S310 , at least one original keyword in the request is extracted.

[0068] In operation S320 , it is determined whether the original keyword matches the standard keyword.

[0069] In operation S330, when the original keyword does not match the standard keyword, the original keyword is replaced with the standard keyword based on the mapping table to obtain an instruction. The mapping table is a correspondence between the standard keyword and the common keywords, and the common keywords include the original keyword.

[0070] In operation S340 , when the original keyword matches the standard keyword, an instruction is obtained.

[0071] For example, the original keywords may be words related to the asset verification operation in the asset verification request, such as "FW," "Core SW," etc. The original keywords may be idiomatic expressions of the user, which may be standard expressions or spoken expressions.

[0072] A knowledge base is established in advance, which stores various professional terms in the field of asset verification.

[0073] The mapping table can be a mapping relationship between common keywords and standard keywords. Common keywords can be user-generated expressions, while standard keywords can be a knowledge base containing various specialized terms in the asset verification field. For example, "core SW" refers to core switch, "FW" refers to firewall, and "ARP check" refers to ARP table verification.

[0074] In one example, a user voice-enters an asset verification request: "Immediately verify the ARP of the core SW." The original keywords are recognized as: "immediately," "core SW," and "ARP." These three original keywords are matched against the professional terms in the knowledge base. If "immediately" and "core SW" do not match, then, according to the mapping table, "core SW" is replaced with "core switch," and a new request (command) is generated: "Immediately verify the ARP of the core switch."

[0075] It is understandable that converting unstructured input requests into a standardized form that the model can process can achieve efficient conversion from fuzzy requests to precise instructions, which can not only improve the accuracy of asset verification request expression, but also lower the user operation threshold and improve user experience.

[0076] Figure 4 The second flowchart of the method for processing asset verification requests using an intelligent model according to an embodiment of the present disclosure is schematically shown.

[0077] As described above, in operation S210, in response to receiving the asset verification request, the request is input into the intelligent model to obtain the asset verification instruction. Figure 4 As shown, the operation may further include operations S410 to S420.

[0078] In operation S410, at least one ambiguous word in the request is extracted.

[0079] In operation S420, based on the ambiguous word, an accurate word corresponding to the ambiguous word is determined to obtain an instruction.

[0080] For example, a fuzzy word can be a word that expresses multiple options, such as "recently", "immediately", "all servers", etc.

[0081] Precise terms can be used to define the representation of fuzzy terms. For example, "recent" - within 24 hours (configurable), "immediately" - highest priority, "all servers" - ["10.1.1.1-10.1.1.254"], and "core network" - [vlan10,vlan20]. This allows for converting time descriptions into specific values, expanding device types, and performing fuzzy IP matching.

[0082] In one example, a user voice-enters an asset verification request: "Immediately verify the ARP of the core SW." The ambiguous word "immediately" is recognized. "Immediately" is converted to the precise word "within 24 hours," and a new request (instruction) is generated: "Priority = Highest" to verify the ARP of the core switch.

[0083] Figure 5 The third flowchart of the method for processing asset verification requests using an intelligent model according to an embodiment of the present disclosure is schematically shown.

[0084] As described above, in operation S210, in response to receiving the asset verification request, the request is input into the intelligent model to obtain the asset verification instruction. Figure 5 As shown, the operation may further include operations S510 to S530.

[0085] In operation S510 , prompt information for at least one word is generated according to a request.

[0086] In operation S520, supplementary information for at least one word is acquired according to the prompt information.

[0087] In operation S530 , an instruction is generated according to the supplementary information and the request.

[0088] For example, the prompt information can be associated information about keywords in the asset verification request. The prompt information is used to remind the user of the terms that should be clearly defined in the asset verification request. When the system cannot understand or execute the request, it provides remediation suggestions. For example, "The IP 10.1.1.256 entered is invalid. Please refer to the example: 10.1.1.1-10.1.1.254"; "'Core Router X' was not found in the CMDB. The nearest similar device is: Core Router A (10.1.1.1)"; "A backup task is currently executing. Please try again in 15 minutes or contact the administrator to force a shutdown."

[0089] The supplementary information may be content that is input or selected according to the prompt information to replace or supplement the keyword information.

[0090] In one example, a user initiates an asset verification request: Verify the ARP table. A prompt is generated: Please select the scope: 1. Core network 2. Finance department VLAN 3. Custom IP segment. The user enters: ARP table for the 10.1.1.0 / 24 network segment. This generates an asset verification instruction: Verify the ARP table for the 10.1.1.0 / 24 network segment.

[0091] In another example, a user initiates an asset verification request to check recently modified equipment. A prompt message is generated: "There have been three changes in the last 24 hours. Do you want to verify all?" (Yes / No / Specify a time range). If the user selects "Yes," an asset verification instruction is generated: "Check all equipment that has changed in the past 24 hours."

[0092] In some embodiments, the intelligent model can be obtained by:

[0093] A plurality of sample data are obtained, where the sample data includes historical asset verification requests and historical asset verification instructions, where the accuracy of the historical asset verification instructions is higher than the accuracy of the historical asset verification requests.

[0094] Input multiple sample data into the intelligent model to obtain the predicted asset verification instructions.

[0095] According to the difference between the predicted asset verification instructions and the historical asset verification instructions, the parameters of the intelligent model are adjusted until the model converges to obtain a trained intelligent model.

[0096] Figure 6 One of the flow charts of a method for comparing real-time asset information with reference asset information according to an embodiment of the present disclosure is schematically shown.

[0097] As described above, in operation S230, the real-time asset information is compared with the reference asset information to obtain a verification result. Figure 6 As shown, the operation may further include operations S610 to S630.

[0098] In operation S610 , initial similarities of target features in the real-time asset information and the reference asset information with respect to multiple dimensions are determined.

[0099] In operation S620 , weight values ​​of target features in multiple dimensions are obtained.

[0100] In operation S630 , a target similarity is determined based on the initial similarity and the weight value to obtain a verification result.

[0101] Among them, the target features of multiple dimensions include: IP address, MAC address, device type and topological location of the device.

[0102] In one example, the initial similarity of the IP addresses can be calculated using the following formula:

[0103] Initial similarity of IP address = 1-(different number of digits / total number of digits)

[0104] For example, in the real-time asset information, IP1 = 192.168.1.1, binary: 11000000.10101000.00000001.00000001, and in the reference asset information, IP2 = 192.168.1.2, binary: 11000000.10101000.00000001.00000010. The initial similarity between IP1 and IP2 is: 1 - (1 / 32) ≈ 0.97.

[0105] The initial similarity of MAC addresses can be calculated using the following formula:

[0106] MAC address initial similarity = manufacturer weight * manufacturer similarity + device weight * device similarity

[0107] Among them, manufacturer similarity and device similarity can be calculated based on the degree of byte repetition.

[0108] For example, MAC1 in the real-time asset information is 00:1A:2B:3C:4D:5E; MAC2 in the reference asset information is 00:1A:2B:AA:BB:CC. For the manufacturer comparison, if they are identical, they score 1 (60% weight for the manufacturer). For the device comparison, if they are 3C:4D:5E vs. AA:BB:CC, they score 0.2 (40% weight for the device). Therefore, the initial MAC address similarity is 0.6*1 + 0.4*0.2 = 0.68.

[0109] For example, the device type in real-time asset information is "brand AS5735 switch," while the device type in reference asset information is "brand A S5735-48G-PWR." The initial similarity of the device types can be calculated as follows: Extract the keywords "brand A" and "S5735." Using the NLP model, the semantic similarity score is 0.92. A 0.05 point deduction is applied for any differences in the model suffix (-48G-PWR). The initial similarity of the device types is 0.87.

[0110] For example, topological location can be device neighbor relationships. The device's topological location in real-time asset information is: connected to "Switch 06, Building A, 3F." The device's topological location in reference asset information is: currently connected to "Switch 12, Building B, 1F." The initial similarity of topological locations can be calculated as follows: Original location neighbor devices: 10 (Finance Department devices). New location neighbor devices: 2 (Conference Room devices). Initial topological location similarity = Number of shared neighbors / Total number of neighbors = 0 / 12 = 0.

[0111] After obtaining the initial similarity of each feature, the target similarity is calculated using the following formula:

[0112] Target similarity = first weight value * initial IP address similarity + second weight value * initial MAC address similarity + third weight value * initial device type similarity + fourth weight value * initial topological location similarity.

[0113] For example, if the first weight is 0.4, the second weight is 0.3, the third weight is 0.2, and the fourth weight is 0.1, then the target similarity = 0.4*0.97 + 0.3*0.68 + 0.2*0.87 + 0.1*0 = 0.802.

[0114] It should be noted that the embodiments of the present disclosure do not impose any specific restrictions on the type of target features and the weight value corresponding to each target feature, and they can be adjusted according to actual circumstances.

[0115] It is understandable that verifying asset information from multiple dimensions can improve the accuracy of asset information verification.

[0116] Figure 7 The second flowchart of the method for comparing real-time asset information with reference asset information according to an embodiment of the present disclosure is schematically shown.

[0117] As mentioned above, if Figure 7 As shown, the asset verification method of this embodiment may further include operations S710 to S730.

[0118] In operation S710 , a historical accuracy rate of target similarity of a target feature in each dimension is obtained, where the historical accuracy rate represents an accuracy rate of historical asset information verification of the target feature.

[0119] In operation S720 , the weight value is updated according to the historical accuracy rate.

[0120] In operation S730 , the target similarity is determined according to the updated weight value.

[0121] In one example, historical accuracy refers to the frequency with which the results of a verification dimension (such as IP, MAC, device type, etc.) in past asset verifications matched actual conditions. This refers to the accuracy of the target feature's initial similarity calculated over a historical period. For example, if the algorithm calculates an initial similarity of 0.7 for an IP address, and a manual calculation yields an initial similarity of 0.8, this initial similarity calculation is considered inaccurate.

[0122] The updated weight value can be calculated using the following formula:

[0123] Updated weight value = weight value × (1 + historical accuracy)

[0124] If the historical accuracy of the MAC dimension is 90%, the weight is adjusted to 0.3×(1+0.9)=0.57.

[0125] It should be noted that the sum of the updated weight values ​​of each target feature is 1.

[0126] It is understandable that dynamically adjusting the weight value according to the accuracy of the target feature similarity judgment in each dimension can improve the accuracy of the target similarity calculation, thereby making the asset information verification more reliable.

[0127] As described above, the asset verification method of this embodiment may further include the following operations: when the target similarity is less than a preset threshold, determining that an abnormality exists in the asset configuration and generating an alarm message.

[0128] In one example, if the target similarity is ≥ 0.9 (preset threshold), the real-time asset information fully matches the reference asset information, indicating no asset changes. If the target similarity is < 0.9 (preset threshold), an anomaly between the real-time and reference asset information exists, indicating an asset change. A pop-up window (alarm message) immediately appears on the network management platform, alerting staff to the asset change. The information to be pushed is submitted to the administrator for confirmation, and the network management platform's data revision module is dispatched to complete the update.

[0129] Based on the above asset information verification method, the present disclosure also provides an asset information verification device. Figure 8 The device is described in detail.

[0130] Figure 8 The structural block diagram of the asset information verification device according to an embodiment of the present disclosure is schematically shown.

[0131] like Figure 8 As shown, the asset information verification device 800 of this embodiment includes a processing module 810, an acquisition module 820 and a comparison module 830.

[0132] Processing module 810 is configured to, in response to receiving an asset verification request, input the request into the intelligent model to obtain an asset verification instruction, where the accuracy of the asset verification information in the instruction is higher than the accuracy of the asset verification information in the request. In one embodiment, processing module 810 may be configured to perform operation S210 described above, which will not be further described here.

[0133] The acquisition module 820 is used to obtain the real-time asset information and reference asset information of the target area assets in response to receiving the asset verification instruction. In one embodiment, the acquisition module 820 can be used to perform the operation S220 described above, which will not be repeated here.

[0134] The comparison module 830 is used to compare the real-time asset information with the reference asset information to obtain a verification result. In one embodiment, the comparison module 830 can be used to perform the operation S230 described above, which will not be repeated here.

[0135] According to embodiments of the present disclosure, any multiple modules among the processing module 810, acquisition module 820, and comparison module 830 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the processing module 810, acquisition module 820, and comparison module 830 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of these. Alternatively, at least one of the processing module 810, acquisition module 820, and comparison module 830 may be at least partially implemented as a computer program module that, when executed, performs the corresponding functionality.

[0136] Figure 9 A block diagram of an electronic device suitable for implementing the asset information verification method according to an embodiment of the present disclosure is schematically shown.

[0137] like Figure 9 As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage unit 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0138] Various programs and data required for the operation of the electronic device 900 are stored in the RAM 903. The processor 901, ROM 902, and RAM 903 are connected to each other via a bus 904. The processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than the ROM 902 and RAM 903. The processor 901 may also perform various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in one or more memories.

[0139] According to an embodiment of the present disclosure, electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to bus 904. Electronic device 900 may also include one or more of the following components connected to I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 908 including a hard disk; and a communication section 909 including a network interface card such as a LAN card or modem. Communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to I / O interface 905 as needed. Removable media 911, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 910 as needed, so that computer programs read from the removable media can be installed into storage section 908 as needed.

[0140] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0141] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above, and / or one or more memories other than ROM 902 and RAM 903.

[0142] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code is used to enable the computer system to implement the asset information verification method provided by the embodiments of the present disclosure.

[0143] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the processor 901 executes the computer program. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0144] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0145] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from a removable medium 911. When the computer program is executed by the processor 901, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0146] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0147] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0148] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0149] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for verifying asset information, characterized in that: The method comprises: In response to receiving an asset verification request, inputting the request into the intelligent model to obtain an asset verification instruction, wherein the accuracy of the asset verification information in the instruction is higher than the accuracy of the asset verification information in the request; In response to receiving the asset verification instruction, obtaining real-time asset information and reference asset information of assets in the target area; The real-time asset information is compared with the reference asset information to obtain a verification result.

2. The method according to claim 1, characterized in that In response to receiving an asset verification request, the request is input into the intelligent model to obtain an asset verification instruction, including: extracting at least one original keyword from the request; In the case where the original keyword does not match the standard keyword, the original keyword is replaced with the standard keyword based on a mapping table to obtain the instruction, wherein the mapping table is a correspondence between standard keywords and common keywords, and the common keywords include the original keyword.

3. The method according to claim 1 or 2, characterized in that In response to receiving an asset verification request, the request is input into the intelligent model to obtain an asset verification instruction, including: extracting at least one ambiguous word from the request; According to the fuzzy word, an accurate word corresponding to the fuzzy word is determined to obtain the instruction.

4. The method according to claim 1, wherein In response to receiving an asset verification request, the request is input into the intelligent model to obtain an asset verification instruction, including: generating prompt information for at least one word according to the request; Acquiring supplementary information for the at least one word according to the prompt information; The instruction is generated according to the supplementary information and the request.

5. The method according to claim 1, wherein The real-time asset information is compared with the reference asset information to obtain verification results, including: Determining initial similarities of target features in the real-time asset information and the reference asset information with respect to multiple dimensions; Obtaining weight values ​​of target features of the multiple dimensions; According to the initial similarity and the weight value, a target similarity is determined to obtain a verification result.

6. The method according to claim 5, characterized in that The method further comprises: Obtaining a historical accuracy rate of the target similarity of the target feature of each dimension, wherein the historical accuracy rate represents the accuracy rate of historical asset information verification of the target feature; Update the weight value according to the historical accuracy; According to the updated weight value, the target similarity is determined.

7. The method according to claim 5, characterized in that The method further comprises: When the target similarity is less than a preset threshold, it is determined that there is an abnormality in the asset configuration and an alarm message is generated.

8. The method according to claim 5, characterized in that The target features of the multiple dimensions include: IP address, MAC address, device type and topological location of the device.

9. An asset information verification device, characterized in that: The device comprises: a processing module configured to, in response to receiving an asset verification request, input the request into an intelligent model to obtain an asset verification instruction, wherein the accuracy of the asset verification information in the instruction is higher than the accuracy of the asset verification information in the request; an acquisition module, configured to acquire real-time asset information and reference asset information of assets in a target area in response to receiving an asset verification instruction; and The comparison module is used to compare the real-time asset information with the reference asset information to obtain a verification result.

10. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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