Maintenance service package generation method and device based on home file, equipment and medium

Abstract

The invention provides a home file-based maintenance service package generation method, apparatus and device, and a medium, and relates to the technical field of computer data processing and artificial intelligence. The method comprises the following steps: after a maintenance condition is triggered, matching home file data of a target user with a maintenance rule in a maintenance knowledge base, and generating an expired maintenance item list of the target user; wherein the home file data comprises basic attributes and use state information of home assets, and maintenance rules corresponding to various home assets are stored in the maintenance knowledge base; selecting a main service item and a value-added service item from the expired maintenance item list based on a service item screening rule; wherein the service item screening rule comprises a value priority rule and a security priority rule; and integrating the main service item and the value-added service item to obtain a maintenance service package of the target user. The method can actively and intelligently generate and push the personalized periodic maintenance service combination with high cost performance for the user.

Description

Technical Field

[0001] This invention relates to the fields of computer data processing and artificial intelligence technology, and in particular to a method, apparatus, equipment and medium for generating maintenance service packages based on home archives. Background Technology

[0002] The living environment of modern urban families is becoming increasingly complex, integrating a large number of household appliances, sophisticated water, electricity, and gas systems, and a variety of furniture, building materials, and soft furnishings. The normal operation, safety, and lifespan of these home assets depend on a series of professional maintenance procedures with varying frequencies and methods.

[0003] However, the existing home service market suffers from the following significant pain points: 1. Passive service model: Traditional housekeeping or repair services are mostly reactive, meaning users only seek service after a problem is discovered. This model cannot prevent problems from occurring, and users often only report repairs when the problem has escalated into a serious malfunction, leading to high repair costs and potentially causing safety accidents or health damage. 2. Information gap: Ordinary users generally lack systematic home maintenance knowledge, are unaware of the maintenance cycles and correct methods for various equipment and building materials, and are unable to identify hidden but significant safety risks such as faulty residual current devices (RCDs), aging gas hoses, and tempered glass spontaneously shattering. 3. Commercialization dilemma of key "small tasks": Many preventative checks or minor maintenance tasks crucial to home safety and health (e.g., monthly RCD testing, quarterly range hood filter cleaning, gas stove flameout protection function checks, etc.) have low value per service, making it difficult to cover the cost of professional on-site visits. This results in service providers lacking the commercial incentive to provide such services, and users also feel that paying for individual small tasks is extremely uneconomical, leading to a lack of systematic maintenance. 4. Lack of personalized and forward-looking services: Existing service platforms typically offer standardized service menus, failing to provide truly personalized and forward-looking maintenance plans based on each family's unique asset composition (such as appliance brands and models, renovation year, materials, etc.) and usage habits. Furthermore, their underlying maintenance knowledge bases rely heavily on manual input and maintenance, resulting in outdated updates and an inability to dynamically adapt to the ever-emerging new products and standards in the market. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for generating maintenance service packages based on home archives, in order to solve the problem that current home asset maintenance services are unable to meet user needs.

[0005] In a first aspect, embodiments of the present invention provide a method for generating a maintenance service package based on home records, including: Once the maintenance conditions are triggered, the target user's home profile data is matched with the maintenance rules in the maintenance knowledge base to generate a list of due maintenance items for the target user. The home profile data includes the basic attributes and usage status information of home assets, and the maintenance knowledge base stores maintenance rules corresponding to various home assets. Based on the service item selection rules, the main service items and value-added service items are selected from the list of maintenance items due for completion; the service item selection rules include the value priority rule and the safety priority rule. By integrating core services and value-added services, a maintenance service package can be obtained for the target users.

[0006] In one possible implementation, each maintenance rule includes the applicable home asset type, maintenance cycle, risk level, and maintenance service items; the target user's home profile data is matched with maintenance rules in the maintenance knowledge base to generate a list of due maintenance items for the target user, including: For each home asset of the target user, the basic attributes of the home asset are matched with the applicable home asset types of various maintenance rules in the maintenance knowledge base. If the match is successful, it is determined whether the maintenance period of the maintenance rule and the usage status information of the home asset are used to determine whether it has expired. Combine all expiring maintenance service items to obtain the target user's list of expiring maintenance items.

[0007] In one possible implementation, based on service item selection rules, key service items and value-added service items are selected from the list of expiring maintenance items, including: The value score of each maintenance service item in the list of expiring maintenance items is calculated based on the value assessment model, and maintenance service items with a value score higher than the first preset threshold are selected as the main service items. Based on the safety priority rule, maintenance service items with a risk level higher than the second preset threshold are selected as value-added service items from the list of maintenance items due for expiration.

[0008] In one possible implementation, a value score is calculated for each maintenance service item on the expiring maintenance item list based on a value assessment model, including: For the first maintenance service item, the risk level, maintenance cycle type, and historical failure impact are determined based on the value assessment model; where the first maintenance service item is any maintenance service item. The value score of the first maintenance service item is obtained by weighting and summing the individual scores.

[0009] In one possible implementation, before matching the target user's home profile data with maintenance rules in the maintenance knowledge base, the following is also included: Based on user input, automatic data collection, or uploads from IoT devices, home profile information for target users is created and stored in a home profile database.

[0010] In one possible implementation, before matching the target user's home profile data with maintenance rules in the maintenance knowledge base, the following is also included: Data related to home maintenance is crawled from preset information sources, including electronic manuals, official maintenance guides, national safety standard documents, or technical discussions in professional communities. The crawled data is analyzed using natural language processing technology to form structured maintenance rules, which are then stored in a maintenance knowledge base.

[0011] In one possible implementation, maintenance conditions include timed triggering, event triggering, or user-manual triggering; wherein, timed triggering is based on a preset time period, event triggering is based on fault alarms of home assets or environmental changes, and user-manual triggering is based on maintenance requests initiated by the user.

[0012] Secondly, embodiments of the present invention provide a maintenance service package generation device based on home records, comprising: The matching module is used to match the target user's home profile data with the maintenance rules in the maintenance knowledge base after the maintenance conditions are triggered, and generate a list of the target user's due maintenance items. The home profile data includes the basic attributes, usage status information and location information of the home assets, and the maintenance knowledge base stores maintenance rules corresponding to various home assets. The selection module is used to select main service items and value-added service items from the list of expiring maintenance items based on service item screening rules; the service item screening rules include value priority rules and safety priority rules. The integration module is used to combine the main service items and value-added service items to obtain the maintenance service package for the target user.

[0013] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0015] The present invention provides a method, apparatus, equipment, and medium for generating maintenance service packages based on home records. By utilizing the home record data of target users, it transforms traditional passive response services into a new model of proactive prediction, proactive reminders, and proactive planning, systematically preventing home safety hazards and equipment malfunctions. Through service item selection rules, it intelligently bundles "major" services with high perceived value to users with "minor" services that are crucial to safety but have low perceived value, improving the efficiency and effectiveness of maintenance service order fulfillment. Users no longer need to remember complicated maintenance tasks, greatly enhancing user experience and time efficiency. It can proactively and intelligently generate and push personalized, cost-effective periodic maintenance service combinations to users. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of the method for generating a maintenance service package based on home records provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the structure of the maintenance service package generation device based on home records provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] See Figure 1 The document illustrates a flowchart of the method for generating a maintenance service package based on home records, as provided in an embodiment of the present invention. Details are as follows: Step 101: After the maintenance conditions are triggered, the target user's home archive data is matched with the maintenance rules in the maintenance knowledge base to generate a list of due maintenance items for the target user; wherein, the home archive data includes the basic attributes and usage status information of home assets, and the maintenance knowledge base stores maintenance rules corresponding to various home assets.

[0019] In this embodiment, home archive data refers to a structured collection of information recording all home assets under the target user's name, including basic attributes and usage status information. Among them, basic attributes are the inherent characteristics of home assets (such as type, brand, model, material, and installation location); usage status information are characteristics that change dynamically over time (such as installation year, cumulative usage time, last maintenance time, and historical fault records).

[0020] Maintenance rules are maintenance guidelines for specific home assets, including applicable home asset types (such as wall-mounted air conditioners and solid wood flooring), maintenance cycles (such as once every 6 months or once a year), risk levels (quantifying the degree of risk of not performing maintenance items, such as high / medium / low), and service item classifications (distinguishing between basic maintenance, deep maintenance, and other types).

[0021] Once the maintenance conditions are triggered, the system extracts all home asset information of the target user from the home archive database, matches each item with the maintenance rules in the maintenance knowledge base, and generates a personalized maintenance item list for the target user based on the specific situation of the target user's home assets.

[0022] Step 102: Based on the service item screening rules, select the main service items and value-added service items from the list of maintenance items due for completion; the service item screening rules include the value priority rule and the safety priority rule.

[0023] In this embodiment, different maintenance service items can be preset as "major items", "medium items" and "minor items". Then the service combination algorithm is configured to select services classified as "major items" or "medium items" from the due maintenance list as "main service items" and select services classified as "minor items" as "value-added service items".

[0024] Step 103: Integrate the main service items and value-added service items to obtain the maintenance service package for the target user.

[0025] In this embodiment, the main service items and value-added service items are integrated into structured data and pushed to target users, making it easier for users to subscribe to maintenance service packages and avoiding redundancy or omissions in general packages. "Value-added service items" are marked as free in personalized service packages. Simultaneously with pushing personalized service packages, self-service maintenance tutorials are provided for the remaining maintenance items in the list that were not selected for service packages.

[0026] The maintenance knowledge base can also store consumable information for various home assets. While generating a list of due maintenance items for the target user, it can also query and match suitable consumable information in the maintenance knowledge base based on the equipment model in the home file, and push the purchase link of the consumables and personalized service package to the user.

[0027] This invention transforms traditional passive response services into a proactive prediction, reminder, and planning model by utilizing the home profile data of target users. This systematically prevents home safety hazards and equipment malfunctions. Through service item selection rules, it intelligently bundles high-value "major" services with safety-critical but low-value "minor" services, improving the efficiency and effectiveness of maintenance service order fulfillment. Users no longer need to remember cumbersome maintenance tasks, greatly enhancing user experience and time efficiency. It can proactively and intelligently generate and push personalized, cost-effective periodic maintenance service combinations to users.

[0028] In one possible implementation, each maintenance rule includes the applicable home asset type, maintenance cycle, risk level, and maintenance service items; the target user's home profile data is matched with maintenance rules in the maintenance knowledge base to generate a list of due maintenance items for the target user, including: For each home asset of the target user, the basic attributes of the home asset are matched with the applicable home asset types of various maintenance rules in the maintenance knowledge base. If the match is successful, it is determined whether the maintenance period of the maintenance rule and the usage status information of the home asset are used to determine whether it has expired. Combine all expiring maintenance service items to obtain the target user's list of expiring maintenance items.

[0029] In this embodiment, for each home asset of the target user (such as "a washing machine of brand B purchased in 2021"), the system extracts its basic attributes (type: washing machine; brand: B; model: Y2) and compares them with the "applicable home asset type" of all maintenance rules in the maintenance knowledge base. For example, if the rule corresponding to "brand B washing machine (model Y2)" in the knowledge base is "maintenance cycle of 6 months, service items are 'drum cleaning + drain pipe unclogging'", then a successful match is determined.

[0030] For successfully matched rules, further analysis of the usage status information of the home assets is conducted to determine whether the lease has expired. If the rule maintenance cycle is "6 months", and the washing machine's "last maintenance time was January 2024" and the current time is August 2024 (7 months interval > 6 months), then the project is deemed to have expired; If an asset is newly installed (such as a water heater installed in September 2024), and the rule maintenance cycle is "12 months", then it has not yet expired and will not be included in the list.

[0031] Finally, all expiring projects are compiled, and duplicates are removed (if the same asset may match multiple rules, they need to be merged into a comprehensive project), resulting in a final list of expiring maintenance projects for the target user.

[0032] In one possible implementation, based on service item selection rules, key service items and value-added service items are selected from the list of expiring maintenance items, including: The value score of each maintenance service item in the list of expiring maintenance items is calculated based on the value assessment model, and maintenance service items with a value score higher than the first preset threshold are selected as the main service items. Based on the safety priority rule, maintenance service items with a risk level higher than the second preset threshold are selected as value-added service items from the list of maintenance items due for expiration.

[0033] In this embodiment, the first preset threshold can be based on historical data statistics, selecting the score corresponding to the item with a failure probability > 20% due to lack of timely maintenance as the threshold (e.g., by analyzing 1000 cases, it was found that the failure probability of items with a score ≥ 80 points when not maintained reaches 25%, so it is set to 80 points); the second preset threshold (value-added items) can refer to industry safety standards, setting the risk level of "potential property damage or minor safety hazards" (e.g., "medium risk") as the threshold (e.g., water pipe leakage detection is "medium risk" and needs to be included in value-added items). Through the dual screening of "value + safety", both core maintenance needs are guaranteed and potential safety risk items are supplemented, improving the comprehensiveness of the service package.

[0034] In one possible implementation, a value score is calculated for each maintenance service item on the expiring maintenance item list based on a value assessment model, including: For the first maintenance service item, the risk level, maintenance cycle type, and historical failure impact are determined based on the value assessment model; where the first maintenance service item is any maintenance service item. The value score of the first maintenance service item is obtained by weighting and summing the individual scores.

[0035] In this embodiment, the value assessment model is used to calculate the value score of a single maintenance item, with pre-set scoring criteria for various input parameters: Risk level rating: High risk (e.g., gas leak detection): 100 points (lack of maintenance may lead to personal safety accidents); Medium risk (e.g., water pipe inspection): 60 points (lack of maintenance may lead to property damage); Low risk (e.g., furniture dusting): 30 points (no maintenance only affects appearance).

[0036] Maintenance cycle type rating: Mandatory inspection period (e.g., annual elevator inspection, in compliance with national standards): 100 points; Recommended cycle (e.g., for air conditioner cleaning, brand recommendation): 60 minutes; Optional cycle (e.g., curtain cleaning, not mandatory): 30 minutes.

[0037] Historical Fault Impact Rating: Serious impact (lack of maintenance leading to asset obsolescence, such as circuit burnout): 100 points; General impact (parts needing replacement due to lack of maintenance, such as clogged filters): 60 points; No impact (no obvious consequences without maintenance): 30 points.

[0038] Based on the logic of "safety first, compliance second, and historical reference as supplementary," the following weights are set: Risk level: 40% (directly related to security); Maintenance cycle type: 30% (related to compliance); Impact of historical failures: 30% (based on past data to verify necessity).

[0039] By using quantitative models, we can achieve objective evaluation of project value, avoid the subjectivity of manual screening, and improve the accuracy of classifying core / value-added projects.

[0040] In one possible implementation, before matching the target user's home profile data with maintenance rules in the maintenance knowledge base, the following is also included: Based on user input, automatic data collection, or uploads from IoT devices, home profile information for target users is created and stored in a home profile database.

[0041] In this embodiment, the method for obtaining home archive data may include: User input: Through the APP / mini-program form, users can manually enter the basic attributes of their home assets (brand, model, purchase time, etc.) and upload photos (for appearance documentation). Automatic data collection: Connecting to e-commerce platforms (e.g., when a user purchases home appliances on the platform, simultaneously obtaining the brand, model, and installation time from the order); IoT device uploads: Smart home appliances (such as refrigerators with WiFi) automatically upload operating data (cumulative usage time, energy consumption, etc.) via API interface, and sensors (such as temperature and humidity sensors) upload environmental impact data.

[0042] In addition, it includes a mechanism for updating home archive data to provide accurate basic data for subsequent rule matching: Automatic updates: IoT device data is synchronized in real time (updated once per hour); Manual update: Users can modify asset information via the APP (such as updating the model after replacing parts); Regular verification: The system reminds users to check the status of their assets (such as whether they are still in use) every quarter.

[0043] In one possible implementation, before matching the target user's home profile data with maintenance rules in the maintenance knowledge base, the following is also included: Data related to home maintenance is crawled from preset information sources, including electronic manuals, official maintenance guides, national safety standard documents, or technical discussions in professional communities. The crawled data is analyzed using natural language processing technology to form structured maintenance rules, which are then stored in a maintenance knowledge base.

[0044] In this embodiment, the specific steps for parsing the crawled data using natural language processing technology to form structured maintenance rules may include: Entity recognition: Use the BERT model to identify "home asset type" (e.g., "drum washing machine"), "maintenance cycle" (e.g., "every 6 months"), and "risk description" (e.g., "potential electrical leakage") in the text; Rule extraction: Through dependency parsing, extract the association relationship of "applicable type → maintenance cycle → risk level → service content" (e.g., "drum washing machine → every 6 months → medium risk → clean drum + check circuit"). Structured processing: Convert the extracted results into a unified format.

[0045] By integrating multi-source data and using NLP analysis, a comprehensive and dynamic maintenance rule base is constructed to ensure the authority and timeliness of the rules.

[0046] In one possible implementation, maintenance conditions include timed triggering, event triggering, or user-manual triggering; wherein, timed triggering is based on a preset time period, event triggering is based on fault alarms of home assets or environmental changes, and user-manual triggering is based on maintenance requests initiated by the user.

[0047] In this embodiment, the specific triggering method for maintenance conditions may include: Timed trigger: The system has a preset cycle (which can be customized by the user, with the default being the 1st of each month) to automatically scan the target user's home archives and check whether all assets have reached their maintenance cycle; once triggered, it generates a "regular maintenance reminder" and pushes the due list to the system simultaneously.

[0048] Event triggered: Fault alarm: When an IoT device (such as a smart gas valve) detects an anomaly (such as excessive gas concentration), it sends an alarm signal to the system, triggering the maintenance process; Environmental changes: The temperature and humidity sensor detected that the ambient humidity was >80% (which may cause the wood flooring to become damp), triggering the "Wood Flooring Moisture Prevention and Maintenance" project.

[0049] User-triggered manually: Users can initiate a request through the APP and choose "whole house maintenance" or a specific asset (such as "maintain only the air conditioner"). The system will respond instantly and generate a service package.

[0050] In one specific embodiment, the present invention provides a personalized maintenance service package generation system based on user home records. This system is deployed on a server and includes: The home furnishing database stores structured home furnishing information for at least one user. This information includes at least the user's home assets data (such as the brand, model, purchase year, and material of appliances, building materials, and furniture). Each file is a structured collection of data that details various assets of the user's household. For example, for an air conditioner, it records its location (e.g., "master bedroom"), brand, model, installation year (e.g., "2021"), photos, and service history. This data can be collected and entered during the initial home visit via a client application (service personnel terminal) running on the service personnel's computing device, or it can be entered by the user themselves via a client application (user terminal) running on their computing device.

[0051] A maintenance knowledge base stores maintenance rules associated with home asset data. These rules include at least preset maintenance cycles, risk levels, and service item classifications. This is a core AI knowledge base storing a massive amount of home maintenance rules. These rules are linked to assets in the home asset database. For example: Association rule: For air conditioners of brand B with model number A, the official recommended replacement cycle for filter C is 6-12 months.

[0052] Risk level: The "flameout protection device" of the gas stove is marked as the highest safety risk level.

[0053] Service Classification: "Deep cleaning of air conditioners" is classified as a "major" service, while "testing the flameout protection device of gas stoves" is classified as a "minor" service.

[0054] Operating Procedures (SOPs): Stores the standard operating procedures for performing various services.

[0055] The knowledge automatic construction module is configured to: automatically acquire unstructured or semi-structured data related to home assets from multiple preset information sources on the Internet (such as equipment manufacturer websites, industry standard publishing agency websites, and professional technical forums) through web crawling technology; and parse the data through natural language processing technology to automatically extract and structure it into maintenance rules for dynamically creating and updating the maintenance knowledge base.

[0056] This module serves as the dynamic data source for the maintenance knowledge base. It is configured to perform the following tasks: 1. Data Collection: Using web crawlers, relevant publicly available information is retrieved from the Internet based on keywords such as equipment brands and models appearing in the home furnishing archive database. This includes electronic manuals, official maintenance guides, national safety standard documents, and technical discussions in professional communities.

[0057] 2. Information Extraction: Using a Natural Language Processing (NLP) model, the collected unstructured text is analyzed to extract key maintenance parameters, such as "cleaning frequency", "replacement cycle", "applicable cleaning agent", and "risk warning".

[0058] 3. Structuring and Storage: The extracted parameters are transformed into structured maintenance rules and stored or updated in the maintenance knowledge base to form knowledge entries associated with specific home assets.

[0059] One or more processors.

[0060] A memory that stores instructions executable by one or more processors, which, when executed, cause the system to perform the following steps: 1. Receive file creation instructions, collect and create user home profile information through a client application running on the user's computing device, and store it in the home profile database. Process requests from user terminals or service personnel terminals, performing add, delete, modify, and query operations on the home profile database. User terminals and service personnel terminals refer to any client application that can run on the user's or service personnel's computing device (such as a smartphone, tablet, or personal computer). These applications can be implemented in ways including but not limited to WeChat mini-programs, native applications (APPs), or web applications (WebApps). Users use it to receive notifications, manage files, and place orders; service personnel use it to create files, view work orders, and record services.

[0061] 2. Generate a list of due maintenance items. The processor periodically or based on preset trigger conditions, regularly (e.g., once a month) or triggered by specific events (e.g., when a user adds a new device), analyzes home archive information and matches it with maintenance rules in the maintenance knowledge base to generate a list containing one or more due maintenance items for the user.

[0062] 3. Generate personalized service packages. The processor executes a service composition algorithm from the due maintenance list: (a) Based on a preset value assessment model, automatically select at least one high-value "core service item"; specifically, it can sort the items in the list according to parameters such as value, necessity, and risk level defined in the maintenance knowledge base, and then select 1-2 "core service items" (major items) with high perceived value by users and suitable for marketing and traffic generation from the top-ranked items. (b) Based on preset safety priority rules, automatically select at least one low-value "value-added service item" that is strongly related to safety or health; (c) Combine "main service items" with "value-added service items" to generate a personalized "buy one get one free" service package with bundled discounts. Calculate its total market value and the discounted price for this combination, and set a validity period. For example, select 1 to 2 "major" or "medium" services from the due maintenance list and intelligently combine them with 3 to 5 "minor" services as free gifts: Assuming the list includes: [A. Deep cleaning of cylindrical cabinet air conditioner (major), C. Range hood filter cleaning (minor), D. Gas stove flameout protection test (minor), E. Leakage protection device inspection (minor)]. The algorithm will select A as the "main service item" and forcibly combine it with C, D, and E as free "value-added service items," ultimately generating an "air conditioner combination cleaning package."

[0063] 4. Push service notifications: Personalized service packages and due maintenance lists are sent as structured messages to the client application via the network for viewing and scheduling. Users complete the scheduling on their client terminals after receiving the push notification. After service completion, service personnel submit service records through their service terminals. The server automatically updates the user's home profile based on these records. This updated profile serves as the basis for future analysis, forming a continuously optimized and evolving service loop.

[0064] In another preferred embodiment, the system further includes a consumables recommendation module, which is configured to: query and match suitable consumables information (such as filters and filter cartridges) in the maintenance knowledge base according to the device model in the home profile, and push the purchase link of the consumables and service notification to the user together.

[0065] As can be seen from the above, compared with the prior art, the present invention can assist in the marketing of home maintenance services and has the following significant beneficial effects: 1. Automated construction and iteration of core knowledge: By introducing an automated knowledge construction module and utilizing web crawling and NLP technologies, the maintenance knowledge base has been given dynamic and self-updating capabilities. This greatly improves the coverage, timeliness, and accuracy of knowledge, reduces reliance on manual maintenance, and builds a solid technological barrier.

[0066] 2. A fundamental shift in service model has been achieved: Through "one file per household" and AI analysis, the traditional passive response service has been transformed into a new model of proactive prediction, proactive reminders, and proactive planning, systematically preventing home safety hazards and equipment failures.

[0067] 3. Solved the commercialization problem of key "small tasks": The original "buy big, get small free" service package generation algorithm intelligently bundles "big" services with high perceived value to users with "small" services that are crucial to security but have low perceived value to users. This solves the industry pain point that "small tasks" are difficult to generate commercial orders independently through technical means, and ensures the systematic implementation of home security inspections.

[0068] 4. Significantly reduces users' decision-making costs and cognitive barriers: Users no longer need to memorize complicated maintenance tasks. The system automatically pushes highly personalized and cost-effective service plans at the appropriate time. Users can confirm with one click on their own client application (such as WeChat mini program, native APP or web application), which greatly improves user experience and time efficiency.

[0069] 5. Enhanced user stickiness and platform value: Through periodic proactive service and precise consumable recommendations, long-term trust relationships and continuous interaction have been established with users, significantly increasing repurchase rates. The continuous accumulation of service records makes user profiles increasingly accurate, forming a data-driven service optimization loop, enabling the platform's value to continue to grow.

[0070] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0071] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0072] Figure 2 A schematic diagram of the structure of the home maintenance service package generation device based on home records provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 2 As shown, the home maintenance service package generation device 2 based on home records includes: The matching module 21 is used to match the target user's home archive data with the maintenance rules in the maintenance knowledge base after the maintenance conditions are triggered, and generate a list of the target user's due maintenance items; wherein, the home archive data includes the basic attributes, usage status information and location information of home assets, and the maintenance knowledge base stores maintenance rules corresponding to various home assets; Module 22 is used to select main service items and value-added service items from the list of expiring maintenance items based on service item screening rules; the service item screening rules include value priority rules and safety priority rules. Integration module 23 is used to integrate the main service items and value-added service items to obtain the maintenance service package for the target user.

[0073] In one possible implementation, each maintenance rule includes the applicable home asset type, maintenance cycle, risk level, and maintenance service items; the matching module 21 is specifically used for: For each home asset of the target user, the basic attributes of the home asset are matched with the applicable home asset types of various maintenance rules in the maintenance knowledge base. If the match is successful, it is determined whether the maintenance period of the maintenance rule and the usage status information of the home asset are used to determine whether it has expired. Combine all expiring maintenance service items to obtain the target user's list of expiring maintenance items.

[0074] In one possible implementation, module 22 is specifically used for: The value score of each maintenance service item in the list of expiring maintenance items is calculated based on the value assessment model, and maintenance service items with a value score higher than the first preset threshold are selected as the main service items. Based on the safety priority rule, maintenance service items with a risk level higher than the second preset threshold are selected as value-added service items from the list of maintenance items due for expiration.

[0075] In one possible implementation, module 22 is specifically used for: For the first maintenance service item, the risk level, maintenance cycle type, and historical failure impact are determined based on the value assessment model; where the first maintenance service item is any maintenance service item. The value score of the first maintenance service item is obtained by weighting and summing the individual scores.

[0076] In one possible implementation, the matching module 21 is also used for: Before matching the target user's home profile data with the maintenance rules in the maintenance knowledge base, the target user's home profile information is established based on user input, automatic collection, or uploads from IoT devices, and then stored in the home profile database.

[0077] In one possible implementation, the matching module 21 is also used for: Before matching the target user's home profile data with the maintenance rules in the maintenance knowledge base, data related to home maintenance is crawled from preset information sources; these preset information sources include electronic manuals, official maintenance guides, national safety standard documents, or technical discussions in professional communities. The crawled data is analyzed using natural language processing technology to form structured maintenance rules, which are then stored in a maintenance knowledge base.

[0078] In one possible implementation, maintenance conditions include timed triggering, event triggering, or user-manual triggering; wherein, timed triggering is based on a preset time period, event triggering is based on fault alarms of home assets or environmental changes, and user-manual triggering is based on maintenance requests initiated by the user.

[0079] This invention transforms traditional passive response services into a proactive prediction, reminder, and planning model by utilizing the home profile data of target users. This systematically prevents home safety hazards and equipment malfunctions. Through service item selection rules, it intelligently bundles high-value "major" services with safety-critical but low-value "minor" services, improving the efficiency and effectiveness of maintenance service order fulfillment. Users no longer need to remember cumbersome maintenance tasks, greatly enhancing user experience and time efficiency. It can proactively and intelligently generate and push personalized, cost-effective periodic maintenance service combinations to users.

[0080] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 3 As shown, the electronic device 3 of this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the various method embodiments described above. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the various device embodiments described above.

[0081] For example, computer program 32 may be divided into one or more modules / units, which are stored in memory 31 and executed by processor 30 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in electronic device 3.

[0082] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 3 may also include input / output devices, network access devices, buses, etc.

[0083] The processor 30 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0084] The memory 31 can be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 3. Furthermore, the memory 31 can include both internal and external storage units of the electronic device 3. The memory 31 is used to store the computer program 32 and other programs and data required by the electronic device 3. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0085] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0086] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0087] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0088] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0089] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0090] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for generating a maintenance service package based on home records, characterized in that, include: After the maintenance conditions are triggered, the target user's home file data is matched with the maintenance rules in the maintenance knowledge base to generate a list of due maintenance items for the target user; wherein, the home file data includes the basic attributes and usage status information of home assets, and the maintenance knowledge base stores maintenance rules corresponding to various home assets; Based on the service item selection rules, main service items and value-added service items are selected from the list of expiring maintenance items; wherein, the service item selection rules include value priority rules and safety priority rules; The main service items and the value-added service items are integrated to obtain the maintenance service package for the target user.

2. The method for generating a maintenance service package based on home records according to claim 1, characterized in that, Each maintenance rule includes the applicable home asset type, maintenance cycle, risk level, and maintenance service items; The process of matching the target user's home archive data with maintenance rules in the maintenance knowledge base to generate a list of due maintenance items for the target user includes: For each home asset of the target user, the basic attributes of the home asset are matched with the applicable home asset types of each maintenance rule in the maintenance knowledge base. If the match is successful, it is determined whether the maintenance period of the maintenance rule and the usage status information of the home asset are used to determine whether the maintenance period has expired. Combine all the expiring maintenance service items to obtain the expiring maintenance item list for the target user.

3. The method for generating a maintenance service package based on home records according to claim 1, characterized in that, Based on the service item screening rules, the main service items and value-added service items are selected from the list of expiring maintenance items, including: The value score of each maintenance service item in the list of expiring maintenance items is calculated based on the value assessment model, and maintenance service items with a value score higher than the first preset threshold are selected as the main service items. Based on the safety priority rule, maintenance service items with a risk level higher than the second preset threshold are selected as value-added service items from the list of expiring maintenance items.

4. The method for generating a maintenance service package based on home records according to claim 3, characterized in that, The value score for each maintenance service item in the list of expiring maintenance items is calculated based on a value assessment model, including: For the first maintenance service item, the risk level, maintenance cycle type, and historical fault impact are determined based on the value assessment model; wherein, the first maintenance service item is any maintenance service item. The value score of the first maintenance service item is obtained by weighting and summing the individual scores.

5. The method for generating a maintenance service package based on home records according to claim 1, characterized in that, Before matching the target user's home profile data with the maintenance rules in the maintenance knowledge base, the process also includes: Based on user input, automatic data collection, or uploads from IoT devices, home profile information for the target user is established and stored in the home profile database.

6. The method for generating a maintenance service package based on home records according to claim 1, characterized in that, Before matching the target user's home profile data with maintenance rules in the maintenance knowledge base, the process also includes: Home maintenance-related data is crawled from preset information sources; wherein, the preset information sources include electronic manuals, official maintenance guides, national safety standard documents, or technical discussions in professional communities; The crawled data is analyzed using natural language processing technology to form structured maintenance rules, which are then stored in the maintenance knowledge base.

7. The method for generating a maintenance service package based on home records according to claim 1, characterized in that, The maintenance conditions include timed triggering, event triggering, or user-manual triggering; wherein, timed triggering is based on a preset time period, event triggering is based on fault alarms of home assets or environmental changes, and user-manual triggering is based on maintenance requests initiated by the user.

8. A device for generating maintenance service packages based on home records, characterized in that, include: The matching module is used to match the target user's home archive data with the maintenance rules in the maintenance knowledge base after the maintenance conditions are triggered, and generate a list of the target user's due maintenance items; wherein, the home archive data includes the basic attributes, usage status information and location information of home assets, and the maintenance knowledge base stores maintenance rules corresponding to various home assets; The selection module is used to select main service items and value-added service items from the list of expiring maintenance items based on service item screening rules; wherein, the service item screening rules include value priority rules and safety priority rules; The integration module is used to integrate the main service items and the value-added service items to obtain the maintenance service package for the target user.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

Images