Method and system for generating equipment management strategy for railway passenger stations

Abstract

The present invention provides a method and device for generating equipment management strategies for railway passenger stations, including obtaining equipment data of target equipment and target user information, obtaining data classification of target equipment based on target user information, determining a processing method for equipment data, generating triplet target equipment data and triplet target user data according to the processing method for equipment data and target user information; performing knowledge fusion processing on triplet target equipment data and triplet target user data to generate a complete knowledge description of the target equipment; constructing a user portrait according to the complete knowledge description, and constructing a target equipment portrait that matches the user portrait; integrating the target equipment portrait with the equipment management system, generating an equipment management strategy corresponding to the target user information, and sending the equipment management strategy to the user. The present invention can provide users with targeted demand data according to business needs and passenger operation classification, and improve equipment management operation efficiency and passenger service quality.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and in particular to a method and system for generating equipment management strategies for railway passenger stations. Background Art

[0002] With the substantial increase in railway transport capacity, railway passenger transport has undergone positive and profound changes in service concepts, service methods, management levels, management methods, and the development of related information systems. Railway station equipment is also trending towards digitalization, networking, and intelligentization. Due to the diverse, large, and highly intelligent nature of railway station equipment, the vast amount of operational data and related information generated during equipment operation has resulted in the inability of existing equipment management systems to provide users with targeted demand data based on business needs and passenger operation classifications. This has hindered equipment management efforts and, even more so, has been detrimental to improving passenger operation efficiency and passenger service levels.

[0003] Therefore, how to provide users with targeted demand data based on business needs and passenger operation classification, thereby improving equipment management efficiency and passenger service quality, is a technical problem that needs to be urgently solved in this project. Summary of the Invention

[0004] The present invention provides a method and device for generating equipment management strategies for railway passenger stations, which are used to solve the defect in the prior art that it is impossible to provide users with targeted demand data based on business needs and passenger operation classification, thereby improving the efficiency of equipment management operations and the quality of passenger service.

[0005] The present invention provides a method for generating an equipment management strategy for a railway passenger station, comprising:

[0006] Acquire device data of a target device and target user information, acquire data classification of the target device based on the target user information, determine a processing method for the device data, and generate triplet target device data and triplet target user data according to the processing method for the device data and the target user information;

[0007] Performing knowledge fusion processing on the triplet target device data and the triplet target user data to generate a complete knowledge description of the target device;

[0008] Constructing a user profile based on the complete knowledge description, and constructing a target device profile that matches the user profile;

[0009] The target device portrait is integrated with the device management system to generate a device management policy corresponding to the target user information, and the device management policy is sent to the user.

[0010] According to a method for generating an equipment management strategy for a railway passenger station provided by the present invention, after constructing a user profile based on the complete knowledge description and constructing a target equipment profile that matches the user profile, the method further includes:

[0011] Generate a target device knowledge graph based on the target device portrait, and determine missing labels and initial labels of the target device knowledge graph with reference to an existing knowledge base;

[0012] Generate a complete passenger station device portrait based on the missing tags and the initial tags;

[0013] The integrating the target device profile with the device management system includes:

[0014] The complete passenger station equipment profile generated based on missing tags and initial tags is integrated with the equipment management system.

[0015] According to a method for generating a device management strategy for a railway passenger station provided by the present invention, data classification of the target device is obtained based on the target user information, a processing method of the device data is determined, and triplet target device data and triplet target user data are generated according to the processing method of the device data and the target user information, including:

[0016] According to the structured degree of the target user data and the target device data, the device data is divided into unstructured data, semi-structured data and original structured data;

[0017] Extracting knowledge from the target user information and unstructured data based on international standards, and converting the knowledge extraction results into the triplet target device data;

[0018] The semi-structured data is confirmed and knowledge is extracted, the semi-structured data is converted into target structured data according to the extraction result, and the original structured data and the target structured data are converted into triple target device data.

[0019] According to a method for generating equipment management strategies for a railway passenger station provided by the present invention, the steps of confirming the semi-structured data and extracting knowledge, and converting the semi-structured data into target structured data according to the extraction results, include:

[0020] Extract and confirm semi-structured data from the target device's device data and target user information;

[0021] Performing entity extraction, relationship extraction, and attribute extraction on the semi-structured data;

[0022] According to the extraction results, the semi-structured data is cleaned, annotated, and clustered into web page sets, a packaging space is generated and evaluated, and an evaluated wrapper is obtained;

[0023] Manual extraction is performed on the semi-structured data, a final wrapper is obtained based on the manual extraction result and the evaluated wrapper, and target structured data is generated based on the final wrapper.

[0024] According to a method for generating an equipment management strategy for a railway passenger station provided by the present invention, the step of constructing a user profile based on the complete knowledge description includes:

[0025] Dynamically tracking the target user's behavior trajectory based on the complete knowledge description, wherein the complete knowledge description includes: the application scenario of the target device, the type of information provided by the target device to the user, and the channel through which the target user accesses the target device;

[0026] Evaluate the value of the target user from a static perspective based on the target user's attribute data, wherein the attribute data includes demographic attributes, business attributes, usage intention, work status, user status, and user relationships;

[0027] Extracting common feature values ​​of the target users based on the attribute data of the target users, listing the correlations between the feature values ​​and the target devices and prioritizing them;

[0028] Key elements are extracted and a visualization model is constructed based on the priority ranking results of the target user's value and the target device's relevance, and a target user portrait is output based on the visualization model.

[0029] According to a method for generating a device management strategy for a railway passenger station provided by the present invention, the step of constructing a target device profile that matches the user profile includes:

[0030] Screening all users involved in the target device to obtain the target user and the target user's portrait data;

[0031] Extracting characteristic information of the target user from the portrait data;

[0032] Constructing a target device profile that matches the characteristic information;

[0033] Obtain feedback information from the target user on the target device portrait, and modify the target device portrait based on the feedback information.

[0034] The present invention also provides a device for generating equipment management strategies for a railway passenger station, comprising:

[0035] a triplet target device data generation module, configured to obtain device data of a target device and target user information, obtain a data classification of the target device based on the target user information, determine a processing method for the device data, and generate triplet target device data and triplet target user data based on the processing method for the device data and the target user information;

[0036] a complete knowledge description generating module, configured to perform knowledge fusion processing on the triplet target device data and the triplet target user data to generate a complete knowledge description of the target device;

[0037] A target device portrait generation module, configured to construct a user portrait based on the complete knowledge description and to construct a target device portrait that matches the user portrait;

[0038] The device management policy generation module is used to integrate the target device portrait with the device management system, generate a device management policy corresponding to the target user information, and send the device management policy to the user.

[0039] The present invention also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for generating equipment management strategies for railway passenger stations as described above are implemented.

[0040] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for generating equipment management strategies for railway passenger stations.

[0041] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for generating equipment management strategies for railway passenger stations.

[0042] The present invention provides a method and apparatus for generating equipment management strategies for railway passenger stations. This method generates triplet data from target device data and target user information, fuses the triplet target device data and user data, and then constructs user and device profiles based on a complete knowledge description. After integrating the constructed device profile with the device management system, the generated equipment management strategy is sent to the user. By integrating the target device profile with the device management system, the target user is provided with a personalized, intelligent target device management strategy and decision-making assistance. This provides users with targeted demand data based on business needs and passenger transport operation classifications, improving equipment management efficiency and passenger transport service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is one of the flow charts of the method for generating equipment management strategies for railway passenger stations provided by the present invention;

[0045] Figure 2 This is the second flow chart of the method for generating equipment management strategy for a railway passenger station provided by the present invention;

[0046] Figure 3 This is the third flow chart of the method for generating equipment management strategy for a railway passenger station provided by the present invention;

[0047] Figure 4 This is the fourth flow chart of the method for generating equipment management strategy for a railway passenger station provided by the present invention;

[0048] Figure 5 This is the fifth flow chart of the method for generating equipment management strategy for a railway passenger station provided by the present invention;

[0049] Figure 6 This is the sixth flow chart of the method for generating equipment management strategy for a railway passenger station provided by the present invention;

[0050] Figure 7 This is the seventh flow chart of the method for generating equipment management strategy for a railway passenger station provided by the present invention;

[0051] Figure 8 This is the eighth flow chart of the method for generating equipment management strategy for a railway passenger station provided by the present invention;

[0052] Figure 9 This is the ninth flow chart of the method for generating equipment management strategy for a railway passenger station provided by the present invention;

[0053] Figure 10 It is a structural diagram of the equipment management strategy generating device for a railway passenger station provided by the present invention;

[0054] Figure 11 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0056] The following combination Figures 1-9 The invention describes a method for generating equipment management strategies for a railway passenger station.

[0057] Step 110: Obtain device data of a target device and target user information, obtain a data classification of the target device based on the target user information, determine a processing method for the device data, and generate triplet target device data and triplet target user data based on the processing method for the device data and the target user information;

[0058] Specifically, in this embodiment, device data and target user information are acquired through real-time collection and interfaces. Device data includes tables, graphics, audio, video, text, and web pages. Target users include railway bureaus, stations, passenger sections, departments, teams, suppliers, maintenance providers, individuals, and other diverse users. User information includes history, login information, usage information, status information, and relationship information.

[0059] Target user information specifically includes demographic attributes such as name, ethnicity, cultural background, and political affiliation; business attributes such as device administrator, maintenance personnel, purchasing personnel, supplier, or maintenance provider; usage intentions such as device operation and maintenance, device management, and backend monitoring; work styles such as 24-hour operation and scheduled shifts; user statuses such as online, offline, undergoing maintenance, and dispatched; and user relationships such as between administrator and user. Data classification is performed based on the aforementioned target user data and target device data.

[0060] In this embodiment, the target device's device data type is determined based on the target user information. Specifically, it can be categorized as unstructured data, semi-structured data, and structured data. The data processing method is then determined based on the device data's classification structure. Based on the different processing methods, the device data of different classifications is converted into triplet target device data. Accordingly, the target user information is converted into triplet target user data.

[0061] Step 120: performing knowledge fusion processing on the triplet target device data and the triplet target user data to generate a complete knowledge description of the target device;

[0062] Specifically, in this embodiment, the triple target device data and the triple target user data are logically attributed and redundant / error filtered through knowledge fusion to generate a complete knowledge description of the target device, so as to eliminate contradictions and ambiguities in entity expressions, increase knowledge links, and enhance the application value of knowledge.

[0063] It should be noted that knowledge fusion requires two processes: entity linking and knowledge merging. For unstructured and semi-structured data, entity disambiguation (eliminating ambiguity caused by entities with the same name) and coreference resolution (ensuring correspondence between the referent and the entity object) are performed by calculating similarity between given entity referents. Once the correct entity referent is confirmed, the entity referent is linked to the corresponding entity in the knowledge base. For structured data, knowledge merging is primarily used to merge structured data.

[0064] Step 130: constructing a user profile based on the complete knowledge description, and constructing a target device profile that matches the user profile;

[0065] Specifically, the device portrait may include various attribute relationships such as basic attributes, customer station attributes, usage attributes, technical attributes, and shared attributes.

[0066] Among them, technical attributes include but are not limited to: name, type, category, quantity, model, number and function; passenger station attributes include but are not limited to: installation location, supplier, maintenance provider, procurement channel, railway bureau (station / department) and value; usage attributes include but are not limited to: purpose, usage method, maintenance method, user object, operating status, usage frequency, failure rate and repair time; technical attributes include but are not limited to: composition principle, usage mechanism, manufacturing technology and operating characteristics; sharing attributes include but are not limited to: multiple attribute relationships such as the number of users supported, access method, virtual technology and data interaction mechanism.

[0067] Step 140: Integrate the target device portrait with the device management system, generate a device management policy corresponding to the target user information, and send the device management policy to the user.

[0068] Specifically, after generating the target device portrait, the target device portrait is integrated with the device management system, and personalized target device management strategy recommendations are given according to the target device category, usage, maintenance status, fault level, user type, and urgency of demand, so as to facilitate the intelligent and proactive operation and maintenance and collaborative management of the target device.

[0069] The method for generating equipment management policies for railway passenger stations provided in an embodiment of the present invention generates triplet data from the device data of a target device and target user information, fuses the triplet target device data and user data, and then constructs user and device profiles based on a complete knowledge description. After the constructed device profile is integrated with the device management system, the generated equipment management policy is sent to the user. By integrating the target device profile with the device management system, a personalized, intelligent target device management policy and decision-making assistance method are provided to the target user. This method can provide users with targeted demand data based on business needs and passenger operation classification, thereby improving equipment management efficiency and passenger service quality.

[0070] Based on the above embodiment, after constructing a user profile according to the complete knowledge description and constructing a target device profile that matches the user profile, the method further includes:

[0071] Generate a target device knowledge graph based on the target device portrait, and determine missing labels and initial labels of the target device knowledge graph with reference to an existing knowledge base;

[0072] Generate a complete passenger station device portrait based on the missing tags and the initial tags;

[0073] The integrating the target device profile with the device management system includes:

[0074] The complete passenger station equipment profile generated based on missing tags and initial tags is integrated with the equipment management system.

[0075] In this embodiment, a knowledge graph corresponding to the target device is generated through the generated target device portrait, and then further knowledge calculation is performed based on the knowledge graph with reference to the existing knowledge base, and the missing labels of the target device are determined based on the initial labels of the knowledge graph.

[0076] After generating missing tags, the generated target device profile is refined by combining it with the initial tags to create a more complete passenger station device profile. This profile is then combined with the railway passenger station device management system to recommend personalized target device management strategies based on the target user's needs.

[0077] Based on the above embodiment, obtaining the data classification of the target device based on the target user information, determining the processing method of the device data, and generating triplet target device data and triplet target user data according to the processing method of the device data and the target user information include:

[0078] According to the structured degree of the target user data and the target device data, the device data is divided into unstructured data, semi-structured data and original structured data;

[0079] Extracting knowledge from the target user information and unstructured data based on international standards, and converting the knowledge extraction results into the triplet target device data;

[0080] The semi-structured data is confirmed and knowledge is extracted, the semi-structured data is converted into target structured data according to the extraction result, and the original structured data and the target structured data are converted into triple target device data.

[0081] It should be noted that, in this embodiment, the device data is classified according to the user target information and the structured degree of the target device, and the classification results are: unstructured data, semi-structured data and original structured data.

[0082] Based on the data classification results, different processing is performed on unstructured data, semi-structured data, and original structured data, including:

[0083] Data processing method 1: Extract named entities, attributes of entities, and relationships between entities from unstructured target device data; unstructured data generally refers to information contained in text, images, sounds, videos, etc.; based on the content of the unstructured data and in accordance with international standards, extract five major types of entities, namely names, places, organization names, times, and numbers, from the target device information and target user information; convert the data after entity extraction into triple target device data.

[0084] Data processing method 2: Confirmation and knowledge extraction of semi-structured data, specifically including: extracting semi-structured data such as tables and lists from target device data and user information; for the confirmed semi-structured data, generate a wrapper based on the HTML web page input content through manual extraction, wrapper induction, and automatic extraction, and output the required structured data.

[0085] Based on the above embodiment, the steps of confirming the semi-structured data and extracting knowledge, and converting the semi-structured data into target structured data according to the extraction results, include:

[0086] Extract and confirm semi-structured data from the target device's device data and target user information;

[0087] Performing entity extraction, relationship extraction, and attribute extraction on the semi-structured data;

[0088] According to the extraction results, the semi-structured data is cleaned, annotated, and clustered into web page sets, a packaging space is generated and evaluated, and an evaluated wrapper is obtained;

[0089] Manual extraction is performed on the semi-structured data, a final wrapper is obtained based on the manual extraction result and the evaluated wrapper, and target structured data is generated based on the final wrapper.

[0090] Based on the second data processing method in the above embodiment, in this embodiment, web page cleaning and web page annotation are performed on semi-structured data, the input HTML web page content is cleaned, and the web page information to be processed is annotated; WrapperSpace packaging space is generated and the Wrapper is evaluated, and the wrapper is output.

[0091] Using supervised learning methods, the algorithm extracts target data from web pages with the same label or template based on data extraction rules learned from a set of labeled training examples. This is done by clustering the web pages. Wrapper spaces are generated for the clustered web pages, and wrappers are evaluated, outputting wrappers. The wrappers generate target structured data based on the manually extracted and evaluated web pages.

[0092] Based on the above embodiment, constructing the user profile according to the complete knowledge description includes:

[0093] Dynamically tracking the target user's behavior trajectory based on the complete knowledge description, wherein the complete knowledge description includes: the application scenario of the target device, the type of information provided by the target device to the user, and the channel through which the target user accesses the target device;

[0094] The primary application scenarios for the target equipment include, but are not limited to, equipment inspection, operational status monitoring, fault prediction and early warning, operation and maintenance plan development, coordination with passenger operations, decision support, health status assessment, supplier selection, and equipment maintenance results analysis. Target users include, but are not limited to, equipment management personnel, equipment maintenance personnel, equipment suppliers, equipment procurement personnel, equipment inspection personnel, and equipment control platform operators at railway bureaus, stations, and passenger sections. Based on the characteristics of the application scenarios and corresponding users, the target users, equipment management methods, data access channels, and their specific needs in different application scenarios are matched, resulting in clear and dynamic tracking results of user behavior trajectories.

[0095] Evaluate the value of the target user from a static perspective based on the target user's attribute data, wherein the attribute data includes demographic attributes, business attributes, usage intention, work status, user status, and user relationships;

[0096] The specific attributes of the target user data include: demographic attributes such as name, ethnicity, cultural background, and political background; business attributes such as equipment administrator, maintenance personnel, purchasing personnel, supplier, and maintenance provider; usage intentions such as equipment operation and maintenance, equipment management and control, and background viewing; work modes such as 24-hour work and scheduling according to preset plans; user status such as online, offline, in maintenance, and on-duty; and user relationships such as between managers and users.

[0097] The above user attributes can be used to analyze the user's static information from multiple dimensions, and the value of the target user can be evaluated from the perspectives of railway passenger station equipment selection, procurement, management, use, operation and maintenance.

[0098] Extracting common feature values ​​of the target users based on the attribute data of the target users, listing the correlations between the feature values ​​and the target devices and prioritizing them;

[0099] Specifically, by conducting the above-mentioned multi-dimensional and multi-angle analysis on the target users, the common characteristic values ​​of a certain type of target users are extracted, and the correlation of the characteristic values ​​is correlated with the category, purpose, location, importance, etc. of the passenger station equipment, and then the priority is sorted according to the level of importance to passenger services.

[0100] Key elements are extracted and a visualization model is constructed based on the priority ranking results of the target user's value and the target device's relevance, and a target user portrait is output based on the visualization model.

[0101] Among them, visualization models include but are not limited to: matrix diagrams, scatter plots, curve diagrams, probability diagrams, grayscale diagrams, bar charts, area charts and other charts, as well as 2D plane models and 3D twin models.

[0102] This embodiment constructs user profiles from both dynamic and static perspectives. Different user tags are assigned based on dynamic and static user information, from which common feature values ​​are extracted and prioritized for relevance to the target device. A visualization model is constructed from the results of this ranking, and the target user profile is output.

[0103] Based on the above embodiment, the step of constructing a target device profile that matches the user profile includes:

[0104] Screening all users involved in the target device to obtain the target user and the target user's portrait data;

[0105] Extracting characteristic information of the target user from the portrait data;

[0106] Constructing a target device profile that matches the characteristic information;

[0107] Obtain feedback information from the target user on the target device portrait, and modify the target device portrait based on the feedback information.

[0108] This embodiment screens all users for which the target device is designed to identify the target user, generates profile data for the target user, and extracts feature information from the profile data to create a device profile that matches the feature information. The target device profile is then modified based on the target user's feedback on the device profile, resulting in a more accurate target device profile.

[0109] Reference Figure 2-Figure 9 , the following is a detailed description of the equipment management strategy generation method for a railway passenger station provided by the present invention.

[0110] Reference Figure 2 The specific process of the method for generating equipment management strategy for a railway passenger station provided by the present invention includes the following steps:

[0111] Step 210: Obtain the structured degree of railway passenger station equipment and target user information through the data engine;

[0112] Step 220: Determine the data classification of the target device based on the target user information;

[0113] Step 230: Determine a processing method for the target device data based on the data classification and target user information, and generate corresponding triplet target device data and user data;

[0114] Step 240: performing knowledge fusion processing based on the triplet target device data and user data to generate a complete knowledge description of the target device;

[0115] Step 250: Build a user profile and a matching target device profile based on the complete knowledge description of the target device;

[0116] Step 260: Preliminarily construct a target device knowledge graph based on the target device profile and user profile;

[0117] Step 270: Perform further knowledge calculation based on the target device knowledge graph to determine the missing tags of the target device;

[0118] Step 280: Generate a relatively complete target device profile based on the determined missing tags of the target device and the initial tags of the target device;

[0119] Step 290: Based on the railway passenger station equipment management system, a relatively complete target equipment portrait is combined with it, and a personalized target equipment management strategy is recommended according to the target user's needs.

[0120] Reference Figure 3 , generating corresponding triplet target device data and user data according to the target device data and user information, specifically comprising the following steps:

[0121] Step 310: Obtaining extraction objects corresponding to the unstructured target device data, including entities, entity attributes, and relationships between entities;

[0122] Step 320: Determine a data processing method 1 corresponding to the unstructured target device data according to the extraction object;

[0123] Step 330: extracting named entities, attributes of entities, and relationships between entities from the unstructured target device data according to the first data processing method;

[0124] Step 340: Generate triplet target device data corresponding to the unstructured target device data based on the named entities, the attributes of the entities, and the relationships between the entities;

[0125] Step 350: extracting the semi-structured target device data according to the second data processing method, and restoring it to the structured target device data;

[0126] Step 360: Convert all acquired structured target device data into triplet target device data through data processing method three.

[0127] It should be noted that, first, the unstructured passenger station equipment data is extracted, including entities, attributes, and inter-entity relationships. Then, corresponding triples of passenger station equipment data are generated using methods such as entity extraction, entity recognition and linking, and relationship extraction. Secondly, triples of semi-structured data are generated using a wrapper. Finally, the structured data is converted into a knowledge graph through methods such as graph mapping or D2R conversion.

[0128] It should be noted that entity extraction in knowledge extraction for unstructured data is the extraction of atomic information elements in the original data, which usually includes labels such as key information. The specific labels can be adjusted according to user needs and management tasks.

[0129] In addition, for entity recognition in knowledge extraction for unstructured data, the entity is identified in the sentences or texts of the provided data, and the link is to link the entity with the corresponding entity in the knowledge base (including the third-party knowledge base and the railway passenger station existing equipment management knowledge base), including: identifying the entity reference through named entity recognition or dictionary matching technology, and then disambiguating the candidate entity to obtain a unique entity candidate and then connect it with the entity in the knowledge base.

[0130] In addition, for relationship extraction in knowledge extraction for unstructured data, the semantic relationship between two or more entities is extracted using trigger-based methods such as Pattern, Pipeline, and Bootstrapping.

[0131] Reference Figure 4 , Figure 4 This is the knowledge extraction flow chart of semi-structured data provided by the present invention, such as Figure 4 As shown, semi-structured data such as XML and HTML web page information are cleaned and annotated to generate Wrapper Space and complete Wrapper evaluation, and then output to the wrapper.

[0132] Cluster similar web page group 1, similar web page group 2...similar web page group M into web page sets (similarly cluster web page 3, web page 4...web page N into web page sets), generate several wrappers Wrapper Space, and input the extracted information into the generated wrapper, and input it into the final wrapper together with the information that has been evaluated by Wrapper Space and manually extracted knowledge, output the required information, and thus complete the extraction of the required knowledge.

[0133] Reference Figure 5 The entire user portrait construction method includes but is not limited to the following steps:

[0134] Step 510: Accurately identify and collect target user data based on the client station device's own attributes and operating characteristics;

[0135] Step 520: Dynamically track the target user's behavior trajectory based on the main application scenarios of the guest station device, the type of information provided by the guest station device to the target user, the channel through which the target user accesses the device, etc.;

[0136] Step 530: Evaluate the target user's value from a static perspective by combining the target user's demographic attributes, business attributes, usage intention, work style, user status, user relationships, etc.

[0137] Step 540: extracting common feature values ​​based on the features reflected by the target user's tag and prioritizing their relevance to the guest station equipment;

[0138] Step 550: Extract key elements based on the priority ranking results of the relevance between the target user and the guest station equipment, and construct a visualization model to provide a target user portrait.

[0139] Reference Figure 6 , building a device profile that matches the user profile, including but not limited to the following steps:

[0140] Step 610: Screen all users involved in the guest station device to obtain user portrait data of the target user;

[0141] Step 620: Extract characteristic information of the target user related to device management from the user portrait data;

[0142] Step 630: Constructing a guest station device profile that matches the target user profile based on the feature information;

[0143] Step 640: Obtaining feedback data of the target user on the device portrait;

[0144] Step 650: Modify the device portrait based on the above feedback data.

[0145] Reference Figure 7 , Figure 7 It is a schematic diagram of the device portrait attributes of the present invention.

[0146] The passenger station equipment portrait includes but is not limited to:

[0147] Basic attributes 710, such as name, type, category, quantity, model, number, function, etc.;

[0148] Passenger station attributes 720, such as: installation location, supplier, maintenance provider, procurement channel, railway bureau / station / department, value, etc.

[0149] Usage attributes 730, such as: purpose, usage method, maintenance method, user object, operating status, usage frequency, failure rate, repair time, etc.;

[0150] Technical attributes 740, such as: construction principle, usage mechanism, manufacturing technology, operating characteristics, etc.;

[0151] Shared attributes 750, such as the number of supported users, access method, virtualization technology, data interaction mechanism, etc.

[0152] Reference Figure 8 The method for generating equipment management strategies for railway passenger stations provided by the present invention utilizes the following technologies, including but not limited to:

[0153] Step 810: Based on multi-angle, multi-dimensional, and multi-modal big data hashing technology and graph database storage technology, complete the collection and storage of multi-angle, multi-dimensional, and multi-modal big data from the client station equipment. Specifically, a machine learning mechanism based on metric learning is used to reduce the dimensionality of the input data, obtaining a real vector representation in a low-dimensional space. This is then transformed using the hash function h and mapped into a binary hash code, significantly reducing data storage and communication overhead.

[0154] Step 820: Entity and relationship extraction, entity alignment, and data fusion based on the neural language model. Complete entity extraction and relationship extraction from multi-source information.

[0155] Step 830: Based on the relational reasoning and entity dynamic update technology of the dynamic memory network model, the dynamic evolution and expansion of the domain knowledge graph are realized.

[0156] Specifically, it performs dynamic evolution and deductive updating of the target device's knowledge graph, in which the dynamic memory network (including: input data, word vector generation, extraction of input information, retrieval of useful input, and inference generation of final information representation) can be used to process the output question sequence, form semantic and situational memory based on the input data and questions, generate corresponding memory fragments and parse to generate the final answer.

[0157] Step 840: Based on deep learning knowledge reasoning and decision-making support technology, construct a guest station equipment portrait based on the knowledge graph.

[0158] Step 850: Assist decision-making technology based on big data analysis and apply it to the equipment management system based on knowledge graph.

[0159] Reference Figure 9 , Figure 9 This is a schematic diagram of the knowledge graph construction process provided by the present invention. The construction of the knowledge graph includes knowledge extraction, knowledge storage, knowledge fusion, knowledge modeling, knowledge calculation and knowledge application.

[0160] Knowledge storage includes third-party knowledge bases and existing knowledge bases, where the existing knowledge bases are generated based on domain knowledge and related knowledge.

[0161] Knowledge extraction involves extracting knowledge and data from raw data, including structured, semi-structured, and unstructured data. Knowledge extraction from semi-structured and structured data includes entity extraction, relationship extraction, and attribute extraction. After extracting structured data, data is integrated with third-party and existing knowledge bases to form a preliminary knowledge representation.

[0162] The preliminary knowledge representations are then fused. This involves knowledge alignment, which involves resolving ambiguities and unifying the preliminary knowledge representations, including entity disambiguation and coreference resolution. Data is supplemented based on the data model framework constructed during knowledge modeling to form a standard knowledge representation. Knowledge computation is then performed on the standard knowledge representation, which involves reasoning about the standard knowledge representation. Specifically, this involves performing knowledge reasoning and discovery based on attribute characteristics, strength merging, and relationship links within the standard knowledge representation.

[0163] Based on existing data models and data, new knowledge or conclusions that meet semantic and logical requirements are obtained according to inference rules. The resulting data is evaluated and the qualified data is placed in the knowledge graph. The results of knowledge discovery are evaluated for quality to obtain a knowledge graph, which is then used for knowledge application.

[0164] Knowledge applications, based on the resulting knowledge graph and specific application scenarios, provide targeted scenario applications and diverse data presentations tailored to the specific needs of target users. This includes: knowledge representation within the knowledge graph, model construction based on the stored domain knowledge and related knowledge, generation of a data model based on the model construction, and knowledge storage of the knowledge application and data model.

[0165] The following describes the device for generating equipment management strategies for a railway passenger station provided by the present invention. The device for generating equipment management strategies for a railway passenger station described below and the method for generating equipment management strategies for a railway passenger station described above can refer to each other.

[0166] Reference Figure 10 The present invention provides a device for generating equipment management strategies for a railway passenger station, comprising:

[0167] A triplet target device data generation module 101 is configured to obtain device data of a target device and target user information, obtain a data classification of the target device based on the target user information, determine a processing method for the device data, and generate triplet target device data and triplet target user data based on the processing method for the device data and the target user information;

[0168] A complete knowledge description generating module 102 is configured to perform knowledge fusion processing on the triplet target device data and the triplet target user data to generate a complete knowledge description of the target device;

[0169] A target device portrait generation module 103 is configured to construct a user portrait based on the complete knowledge description and to construct a target device portrait that matches the user portrait;

[0170] The device management policy generation module 104 is used to integrate the target device portrait with the device management system, generate a device management policy corresponding to the target user information, and send the device management policy to the user.

[0171] The device for generating equipment management policies for railway passenger stations provided by the present invention generates triplet data from the device data of target devices and target user information, fuses the triplet target device data and user data, and then constructs user and device profiles based on a complete knowledge description. After integrating the constructed device profile with the device management system, the generated equipment management policy is sent to the user. By integrating the target device profile with the device management system, a personalized, intelligent target device management policy and auxiliary decision-making method are provided to the target user. This provides users with targeted demand data based on business needs and passenger operation classification, thereby improving equipment management efficiency and passenger service quality.

[0172] Optionally, the device for generating equipment management strategies for a railway passenger station provided by the present invention further includes the following modules:

[0173] A knowledge graph generation module is used to generate a target device knowledge graph based on the target device portrait, and determine missing labels and initial labels of the target device knowledge graph with reference to an existing knowledge base;

[0174] A complete passenger station device portrait generation module, configured to generate a complete passenger station device portrait based on the missing tags and the initial tags;

[0175] The device management strategy generation module is specifically used to integrate the complete passenger station device portrait generated based on missing tags and initial tags with the device management system.

[0176] Optionally, the triplet target device data generation module is specifically configured to: classify the device data into unstructured data, semi-structured data, and original structured data according to the structured degree of the target user data and the target device data;

[0177] Extracting knowledge from the target user information and unstructured data based on international standards, and converting the knowledge extraction results into the triplet target device data;

[0178] The semi-structured data is confirmed and knowledge is extracted, the semi-structured data is converted into target structured data according to the extraction result, and the original structured data and the target structured data are converted into triple target device data.

[0179] Optionally, the triplet target device data generation module is specifically configured to: extract and confirm semi-structured data from the device data of the target device and the target user information;

[0180] Performing entity extraction, relationship extraction, and attribute extraction on the semi-structured data;

[0181] According to the extraction results, the semi-structured data is cleaned, annotated, and clustered into web page sets, a packaging space is generated and evaluated, and an evaluated wrapper is obtained;

[0182] Manual extraction is performed on the semi-structured data, a final wrapper is obtained based on the manual extraction result and the evaluated wrapper, and target structured data is generated based on the final wrapper.

[0183] Optionally, the target device portrait generation module is specifically configured to: dynamically track the target user's behavior trajectory based on the complete knowledge description; wherein the complete knowledge description includes: the application scenario of the target device, the type of information provided by the target device to the user, and the channel through which the target user accesses the target device;

[0184] Evaluate the value of the target user from a static perspective based on the target user's attribute data, wherein the attribute data includes demographic attributes, business attributes, usage intention, work status, user status, and user relationships;

[0185] Extracting common feature values ​​of the target users based on the attribute data of the target users, listing the correlations between the feature values ​​and the target devices and prioritizing them;

[0186] Key elements are extracted and a visualization model is constructed based on the priority ranking results of the target user's value and the target device's relevance, and a target user portrait is output based on the visualization model.

[0187] Optionally, the target device portrait generation module is further configured to: screen all users involved in the target device to obtain the target user and the target user's portrait data;

[0188] Extracting characteristic information of the target user from the portrait data;

[0189] Constructing a target device profile that matches the characteristic information;

[0190] Obtain feedback information from the target user on the target device portrait, and modify the target device portrait based on the feedback information.

[0191] Figure 11 An example of a physical structure diagram of an electronic device is shown below. Figure 11As shown, the electronic device may include: a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other via the communication bus 1140. The processor 1110 may call the logic instructions in the memory 1130 to execute the device management strategy generation method for the railway passenger station, which includes:

[0192] Acquire device data of a target device and target user information, acquire data classification of the target device based on the target user information, determine a processing method for the device data, and generate triplet target device data and triplet target user data according to the processing method for the device data and the target user information;

[0193] Performing knowledge fusion processing on the triplet target device data and the triplet target user data to generate a complete knowledge description of the target device;

[0194] Constructing a user profile based on the complete knowledge description, and constructing a target device profile that matches the user profile;

[0195] The target device portrait is integrated with the device management system to generate a device management policy corresponding to the target user information, and the device management policy is sent to the user.

[0196] In addition, the logic instructions in the above-mentioned memory 1130 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0197] On the other hand, the present invention further provides a computer program product, comprising a computer program, which may be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for generating a railway passenger station equipment management strategy provided by the above methods, which includes:

[0198] Acquire device data of a target device and target user information, acquire data classification of the target device based on the target user information, determine a processing method for the device data, and generate triplet target device data and triplet target user data according to the processing method for the device data and the target user information;

[0199] Performing knowledge fusion processing on the triplet target device data and the triplet target user data to generate a complete knowledge description of the target device;

[0200] Constructing a user profile based on the complete knowledge description, and constructing a target device profile that matches the user profile;

[0201] The target device portrait is integrated with the device management system to generate a device management policy corresponding to the target user information, and the device management policy is sent to the user.

[0202] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the method for generating a device management strategy for a railway passenger station provided by the above methods, the method comprising:

[0203] Acquire device data of a target device and target user information, acquire data classification of the target device based on the target user information, determine a processing method for the device data, and generate triplet target device data and triplet target user data according to the processing method for the device data and the target user information;

[0204] Performing knowledge fusion processing on the triplet target device data and the triplet target user data to generate a complete knowledge description of the target device;

[0205] Constructing a user profile based on the complete knowledge description, and constructing a target device profile that matches the user profile;

[0206] The target device portrait is integrated with the device management system to generate a device management policy corresponding to the target user information, and the device management policy is sent to the user.

[0207] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0208] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for generating equipment management strategy for a railway passenger station, characterized in that: include: Acquire device data of a target device and target user information, acquire data classification of the target device based on the target user information, determine a processing method for the device data, and generate triplet target device data and triplet target user data according to the processing method for the device data and the target user information; Performing knowledge fusion processing on the triplet target device data and the triplet target user data to generate a complete knowledge description of the target device; Building a user profile according to the complete knowledge description, and building a target device profile that matches the user profile; Merging the target device portrait with the device management system, generating a device management policy corresponding to the target user information, and sending the device management policy to the user; Wherein, after constructing the user portrait according to the complete knowledge description and constructing the target device portrait matching the user portrait, the method further includes: Generate a target device knowledge graph based on the target device portrait, and determine missing labels and initial labels of the target device knowledge graph with reference to an existing knowledge base; Generate a complete guest station equipment portrait based on the missing tags and the initial tags; The integrating the target device profile with the device management system includes: Integrate the complete passenger station equipment portrait generated based on missing tags and initial tags with the equipment management system; Wherein, obtaining the data classification of the target device based on the target user information, determining the processing method of the device data, and generating triplet target device data and triplet target user data according to the processing method of the device data and the target user information include: According to the structured degree of the target user data and the target device data, the device data is divided into unstructured data, semi-structured data and original structured data; Extracting knowledge from the target user information and unstructured data based on international standards, and converting the knowledge extraction results into the triplet target device data; The semi-structured data is confirmed and knowledge is extracted, the semi-structured data is converted into target structured data according to the extraction result, and the original structured data and the target structured data are converted into triple target device data.

2. The method for generating equipment management strategy for a railway passenger station according to claim 1, characterized in that: The confirming and extracting knowledge from the semi-structured data, and converting the semi-structured data into target structured data according to the extraction result, includes: Extract and confirm semi-structured data from the target device's device data and target user information; Performing entity extraction, relationship extraction and attribute extraction on the semi-structured data; According to the extraction results, the semi-structured data is cleaned, annotated, and clustered into web page sets, a packaging space is generated and evaluated, and an evaluated wrapper is obtained; Manual extraction is performed on the semi-structured data, a final wrapper is obtained based on the manual extraction result and the evaluated wrapper, and target structured data is generated based on the final wrapper.

3. The method for generating equipment management strategy for a railway passenger station according to claim 1, characterized in that: The constructing the user portrait according to the complete knowledge description includes: Dynamically track the behavior trajectory of the target user according to the complete knowledge description, wherein the complete knowledge description includes: the application scenario of the target device, the type of information provided by the target device to the user, and the channel through which the target user accesses the target device; According to the attribute data of the target user, the value of the target user is evaluated from a static perspective; wherein the attribute data includes: demographic attributes, business attributes, usage intention, work form, user status and user relationship; Extracting common feature values ​​of the target users according to the attribute data of the target users, listing the correlations between the feature values ​​and the target devices and prioritizing them; According to the priority ranking result of the target user's value and the target device's relevance, key elements are extracted and a visualization model is constructed, and a target user portrait is output based on the visualization model.

4. The method for generating equipment management strategy for a railway passenger station according to claim 1, characterized in that: The constructing a target device profile that matches the user profile includes: Screening all users involved in the target device to obtain the target user and the portrait data of the target user; Extracting characteristic information of the target user from the portrait data; Constructing a target device profile that matches the characteristic information; Acquire feedback information of the target user on the target device portrait, and modify the target device portrait based on the feedback information.

5. A device for generating equipment management strategy for a railway passenger station, characterized in that: include: A triplet target device data generation module is used to obtain device data of a target device and target user information, obtain data classification of the target device based on the target user information, determine a processing method of the device data, and generate triplet target device data and triplet target user data according to the processing method of the device data and the target user information, including: according to the structured degree of the target user data and the target device data, classify the device data into unstructured data, semi-structured data and original structured data; Extracting knowledge from the target user information and unstructured data based on international standards, and converting the knowledge extraction results into the triplet target device data; Confirming and extracting knowledge from the semi-structured data, converting the semi-structured data into target structured data according to the extraction result, and converting the original structured data and the target structured data into triplet target device data; A complete knowledge description generating module, used for performing knowledge fusion processing on the triplet target device data and the triplet target user data to generate a complete knowledge description of the target device; A target device portrait generation module, used to construct a user portrait according to the complete knowledge description, and to construct a target device portrait matching the user portrait; After constructing a user profile according to the complete knowledge description and constructing a target device profile matching the user profile, the method further includes: Generate a target device knowledge graph based on the target device portrait, and determine missing labels and initial labels of the target device knowledge graph with reference to an existing knowledge base; Generate a complete guest station equipment portrait based on the missing tags and the initial tags; The device management policy generation module is used to integrate the target device portrait with the device management system, generate a device management policy corresponding to the target user information, and send the device management policy to the user.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for generating equipment management strategy for a railway passenger station as described in any one of claims 1 to 4 are implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for generating equipment management strategy for a railway passenger station as claimed in any one of claims 1 to 4 are implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for generating equipment management strategy for a railway passenger station as claimed in any one of claims 1 to 4 are implemented.

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