A customer service semantic retrieval method and system for owner demand identification

Abstract

The application provides a customer service semantic retrieval method and system for owner demand identification, relates to the technical field of customer service semantic retrieval, identifies non-standardized expressions in owner input demand information, is associated and bound with standard service items and service context data, constructs exclusive memory anchor points, when the owner inputs target demand information containing non-standardized expressions again, the exclusive memory anchor points are called, service context data is obtained, dynamic evolution characteristics of the owner's service preferences are analyzed, a personalized preference weight set is generated, each service scheme in a preset standard service scheme library is prioritized based on the personalized preference weight set, a personalized service recommendation result is generated, and the association rules between non-standardized expressions and standard service items and their service context data are corrected, the personalized preference weight set is iteratively updated, the service efficiency, accuracy and owner satisfaction are significantly improved, and a more intelligent solution is provided for the property service field.

Description

Technical Field

[0001] This application relates to the field of customer service semantic retrieval technology, and more specifically, to a customer service semantic retrieval method and system for identifying customer needs. Background Technology

[0002] In the property management sector, accurately identifying the unique and personalized needs of homeowners and providing appropriate service solutions has always been a significant challenge. Existing customer service semantic retrieval systems often struggle to provide accurate service recommendations when processing various service requests submitted by homeowners via text or voice. This is due to the unique ways homeowners express themselves, the interference of environmental noise during voice interactions, and the system's own limitations in understanding changes in homeowners' deep preferences. Consequently, these systems fail to dynamically adapt to the evolving needs of homeowners.

[0003] Specifically, when describing their needs, homeowners often use a large number of domain-specific, highly colloquial, and even dialectal words or phrases. For example, regarding a leaky roof, a homeowner might not directly say "the roof waterproofing has failed," but rather use phrases like "the ceiling is dripping," "the walls are wet," or, with a local accent, "the roof is leaking." These non-standardized expressions may be assigned relatively vague or inaccurate values ​​in general language processing methods due to insufficient training data or an emphasis on general contexts. This means that the system's semantic parsing precision is limited when understanding the deeper meanings and contextual relationships of these domain-specific terms.

[0004] Because the domain-specific and colloquial expressions input by homeowners cannot be accurately converted into numerical values ​​by general language processing methods, discrepancies arise in the distance calculations between these ambiguous numerical values ​​and the standard, regulated service descriptions in property service knowledge (which have also undergone numerical processing). This discrepancy makes it difficult for the system to effectively distinguish between needs with similar meanings but significantly different actual service content during the initial matching phase. For example, a homeowner might describe "a dripping faucet," and the system might calculate the numerical distance between this and "a burst water pipe" very closely, because both involve "water" and "damage." However, in actual property services, a "dripping faucet" might only require replacing a gasket, while a "burst water pipe" requires emergency water outages and large-scale repairs; the two represent completely different levels of urgency, resource allocation, and handling procedures. This semantic confusion makes the system prone to misjudging when identifying the homeowner's true needs, failing to capture the subtle differences behind the needs.

[0005] Furthermore, with prolonged interaction between homeowners and the customer service system, they may gradually develop a unique set of non-standardized communication habits. These habits might stem from trust in specific property service personnel or tacit understanding formed through repeated communication. For example, after repeatedly reporting the same equipment problem, a homeowner might, upon encountering the issue again, simply use the phrase "the same as always" to refer to a specific equipment malfunction that was previously repaired. This expression of "the same as always" contains an implicit reference to historical events and an expectation that the system should have a memory of them. When the system relies solely on the aforementioned matching mechanism based on fuzzy numerical distance, it struggles to capture these deep-seated, time-accumulated personalized expressions and the true underlying needs and intentions. The system cannot understand which specific equipment or fault point "the same as always" refers to, nor can it correlate it with the homeowner's preferences for how the problem was previously handled.

[0006] Because the system fails to effectively identify the homeowner's deep-seated personalized needs and communication habits, it tends to return service options that "appear" to be highly relevant in terms of knowledge, but are actually generalized or the most common. This "safe" selection often results in simplistic and unspecific recommendations that fail to truly meet the homeowner's unique and implicit expectations. For example, for a homeowner who explicitly requests a quick solution, the system might only recommend routine repair services without prioritizing emergency repairs or expedited processing, because it failed to identify a strong preference for "speed" from the homeowner's tone or past interactions. This simplistic approach not only fails to solve the homeowner's problem but may also exacerbate their dissatisfaction. Summary of the Invention

[0007] This application provides a customer service semantic retrieval method and system for identifying homeowner needs, aiming to address the shortcomings of existing customer service semantic retrieval systems in handling non-standardized expressions of homeowners, understanding deep-seated preference changes, and providing accurate service recommendations.

[0008] On the one hand, this application provides a customer service semantic retrieval method for identifying owner needs, including: Identify non-standard expressions in the demand information input by the owner, associate and bind the non-standard expressions with the corresponding standard service items and matching service context data, and construct and store the exclusive memory anchor point for the owner. When the system detects that the homeowner has entered target demand information containing non-standardized expressions again, it retrieves the homeowner's unique memory anchor points and obtains the associated service context data. Based on the service context data and target demand information, the dynamic evolution characteristics of the owner's service preferences are analyzed, and a personalized preference weight set that adapts to the owner's current demand characteristics is generated. Based on the personalized preference weight set, the service plans in the preset standard service plan library are prioritized and sorted to generate personalized service recommendation results that match the owner's current needs and preferences. Collect interactive feedback information from homeowners regarding the personalized service recommendation results. Based on this feedback information, correct the association rules between non-standardized expressions in the exclusive memory anchor and standard service items and their service context data. Iteratively update the personalized preference weight set to achieve closed-loop adaptive optimization of semantic retrieval and service recommendation strategies.

[0009] On the other hand, this application provides a customer service semantic retrieval system for identifying owner needs, the system comprising: The association and memory recording module is used to identify non-standard expressions in the demand information input by the owner, associate and bind the non-standard expressions with the corresponding standard service items and matching service context data, and construct and store the exclusive memory anchor point for the owner. The service context data acquisition module is used to retrieve the owner's unique memory anchor points and obtain the associated service context data when the system detects that the owner has re-entered target demand information containing non-standardized expressions. The preference weight set generation module is used to analyze the dynamic evolution characteristics of the owner's service preferences based on the service context data and target demand information, and generate a personalized preference weight set that adapts to the owner's current demand characteristics. The service recommendation result generation module is used to prioritize each service plan in the preset standard service plan library based on the personalized preference weight set, and generate personalized service recommendation results that match the owner's current needs and preferences. The feedback and correction module is used to collect interactive feedback information from homeowners regarding the personalized service recommendation results, correct the association rules between non-standardized expressions in the exclusive memory anchor and standard service items and their service context data based on the interactive feedback information, iteratively update the personalized preference weight set, and realize closed-loop adaptive optimization of semantic retrieval and service recommendation strategies.

[0010] This application relates to a customer service semantic retrieval method and system for identifying homeowner needs. By identifying non-standardized expressions in homeowner input and associating them with standard service items and service context data, it constructs and stores exclusive memory anchors, effectively solving the problem of inaccurate semantic parsing caused by the special, colloquial, and dialectal nature of homeowner expressions in existing technologies. When a homeowner inputs target demand information containing non-standardized expressions again, the exclusive memory anchors can be retrieved to obtain the associated service context data, thus overcoming the limitations of general language processing methods in understanding the deep meaning and contextual association of words in specific domains. Furthermore, based on service context data and target demand information, this method analyzes the dynamic evolution characteristics of homeowner service preferences, generates a personalized preference weight set adapted to the homeowner's current demand characteristics, and prioritizes each service solution in a preset standard service solution library, generating personalized service recommendation results adapted to the homeowner's current needs and preferences. This mechanism effectively solves the problem of difficulty in capturing the deep-seated, time-accumulated personalized expressions of homeowners and the true intentions behind them, avoiding service misjudgments caused by semantic confusion, and making the recommendation results no longer a single generalized option, but a highly personalized solution that accurately meets the multidimensional needs of homeowners. Furthermore, this method collects interactive feedback from homeowners regarding personalized service recommendations. Based on this feedback, it corrects the association rules between non-standardized expressions in the exclusive memory anchor and standard service items and their service context data, and iteratively updates the personalized preference weight set, achieving closed-loop adaptive optimization of semantic retrieval and service recommendation strategies. This innovative design enables continuous learning and evolution, overcoming the shortcomings of existing technologies where service recommendation results cannot dynamically adapt to the evolving needs of homeowners. It significantly improves service efficiency, accuracy, and homeowner satisfaction, providing a more intelligent and humanized solution for the property management service sector. Attached Figure Description

[0011] To illustrate this application more clearly, the accompanying drawings used in the embodiments will be briefly described below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0012] Figure 1 The diagram above illustrates a flowchart of a customer service semantic retrieval method for identifying owner needs. Figure 2 The diagram above illustrates the structure of a customer service semantic retrieval system oriented towards identifying customer needs.

[0013] Figure labels: 100, Customer service semantic retrieval system for identifying owner needs; 10, Association and memory recording module; 20, Service context data acquisition module; 30, Preference weight set generation module; 40, Service recommendation result generation module; 50, Feedback and correction module. Detailed Implementation

[0014] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0015] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0016] In the property management service sector, accurately identifying the unique and personalized needs of homeowners and providing appropriate service solutions has always been a significant challenge. Existing customer service semantic retrieval systems often struggle to accurately recommend services and dynamically adapt to evolving needs when processing various service requests submitted by homeowners via text or voice. This is due to the specific nature of homeowners' expressions, environmental noise interference during voice interactions, and the system's own limitations in understanding changes in homeowners' deep preferences. Specifically, homeowners often use a large number of domain-specific, highly colloquial, and even dialect-influenced words or phrases when describing their needs. These non-standardized expressions may be assigned relatively vague or inaccurate values ​​in general language processing methods due to insufficient training data or an emphasis on general contexts. This means that the system's semantic parsing precision is limited when understanding the deeper meanings and contextual relationships of these domain-specific terms. Because these domain-specific and colloquial expressions input by homeowners are not accurately converted into numerical values ​​by general language processing methods, discrepancies arise in the distance calculation between these vague values ​​and the standard, regulated service descriptions in property management service knowledge (which are also numerically processed). This bias makes it difficult for the system to effectively distinguish between needs with similar meanings but significantly different actual service content during the initial matching stage. Furthermore, as homeowners interact with the customer service system over a long period, they may gradually develop a unique set of non-standardized expression habits. When the system relies solely on the aforementioned matching mechanism based on fuzzy numerical distance, it struggles to capture these deep-seated, time-accumulated personalized expressions and the true intentions behind them. Because the system fails to effectively identify homeowners' deep-seated personalized needs and expression habits, it tends to return service options that "appear" to have a high degree of matching in knowledge, but are actually generalized or the most common. This "safe" choice often results in recommendations that are singular, lack specificity, and fail to truly meet the homeowner's unique, implicit expectations.

[0017] S10, Identify non-standardized expressions in the demand information input by the owner, associate and bind the non-standardized expressions with the corresponding standard service items and matching service context data, and construct and store the exclusive memory anchor point corresponding to the owner; Non-standard expressions refer to words, phrases, or sentences used by homeowners when describing their service needs that are inconsistent with the standardized expressions in the property service vocabulary database, and that have colloquial, regional, or personalized characteristics. For example, a homeowner might describe "a leaking faucet" as "a dripping faucet" or "a broken water pipe."

[0018] Standard service items refer to various services in the property service field that have been defined and classified in a standardized manner, such as "pipeline repair", "electrical appliance repair" and "public area cleaning".

[0019] Service context data refers to environmental, historical, and status information related to the owner's service needs. Examples include the specific attributes of the faulty equipment, the type of service, the exact location of the fault, the initial handling plan provided by the system or manually, and the current real-time status of the service. This data helps to more comprehensively understand the owner's needs.

[0020] A personalized memory anchor is a data structure independently built for each homeowner. It stores the unique, non-standardized expressions of that homeowner, the corresponding standard service items, and the binding relationships between related service context data. This anchor can be continuously updated as the homeowner's service needs progress and interaction feedback occurs, thereby achieving the memorization and learning of the homeowner's personalized expressions and preferences.

[0021] S20: When it is detected that the owner inputs target demand information containing non-standardized expressions again, the owner's exclusive memory anchor point is retrieved to obtain the relevant service context data. S30, Based on the service context data and target demand information, analyze the dynamic evolution characteristics of the owner's service preferences and generate a personalized preference weight set that adapts to the owner's current demand characteristics. The personalized preference weight set is a set of weight parameters generated by analyzing the dynamic evolution characteristics of the homeowner's service preferences based on service context data and target demand information. These weight parameters are used to prioritize service options in a pre-set standard service option library to ensure that the recommendation results can adapt to the homeowner's current specific needs and preferences.

[0022] S40, based on the personalized preference weight set, prioritize each service plan in the preset standard service plan library to generate a personalized service recommendation result that matches the owner's current needs and preferences; The pre-set standard service solution library refers to a pre-stored database containing various standard service items and their corresponding solutions.

[0023] S50: Collect interactive feedback information from homeowners regarding the personalized service recommendation results; based on the interactive feedback information, correct the association rules between non-standardized expressions in the exclusive memory anchor and standard service items and their service context data; iteratively update the personalized preference weight set; and realize closed-loop adaptive optimization of semantic retrieval and service recommendation strategies.

[0024] Interactive feedback refers to the responses that homeowners make to personalized service recommendations through various means (such as acceptance, rejection, or objection). This feedback is crucial for adaptive optimization.

[0025] This application constructs exclusive memory anchors to dynamically capture the association between owners' non-standardized expressions and service contexts, and combines this with the dynamic evolution of owners' service preferences to generate a personalized preference weight set. This enables accurate ranking and adaptive optimization of service recommendation results, effectively solving the problems of inaccurate semantic understanding and simplistic service recommendations in existing technologies.

[0026] The customer service semantic retrieval method proposed in this application, which is oriented towards identifying the needs of homeowners, is based on a series of steps to achieve accurate identification of homeowner needs, personalized service recommendations, and closed-loop adaptive optimization of strategies.

[0027] Firstly, several methods can be used to identify non-standardized expressions in the user's input requests. One approach is to preprocess the text using natural language processing (NLP) techniques, such as word segmentation and part-of-speech tagging, after receiving the user's input. Then, the processed text is compared with a pre-defined property service standard vocabulary, and a similarity score is calculated. If the similarity score is below a pre-defined matching threshold, or cannot be directly mapped to a standard service item, the statement is considered a non-standardized expression. For example, if a user inputs "the toilet is clogged," it will be compared with "drain unclogging" in the standard vocabulary. If the user inputs "the outhouse is blocked," it may be found to have a low matching degree with the standard vocabulary, thus being identified as a non-standardized expression. Another approach is to combine comparison with a dialect vocabulary database specific to the property management context, in addition to the standard vocabulary database. For example, in some regions, "faucet" may be called "water spout," and with the assistance of a dialect vocabulary database, these non-standardized expressions can be identified more accurately.

[0028] Next, the identified non-standard expressions are associated with corresponding standard service items and matching service context data, and a unique memory anchor is constructed and stored for each homeowner. In one implementation, when a non-standard expression is first identified, customer service personnel are prompted, or further semantic analysis is conducted, to manually or automatically associate it with one or more standard service items. Simultaneously, current service context data, such as the type of faulty equipment, the location of the fault, and the type of service, is recorded and bound together with the non-standard expression and standard service items to form the homeowner's unique memory anchor. For example, when a homeowner first uses "roof leak" to describe their need for "roof waterproofing repair," "roof leak" is bound to "roof waterproofing repair" and the current service context (such as "top floor," "heavy rain"), and stored in the homeowner's unique memory anchor. In another implementation, the unique memory anchor can be bound to the homeowner's unique identifier, ensuring that each homeowner's memory anchor is independent. These memory anchors can be stored in a database and continuously updated with service context data as service needs progress and homeowner feedback is received.

[0029] When the system detects that a homeowner has entered a target demand that contains a non-standardized expression, it will retrieve the homeowner's unique memory anchor points to obtain the associated service context data. For example, when a homeowner enters "roof leaking" again, the system will quickly retrieve the previously associated standard service item "roof waterproofing repair" and the previous service context data from the homeowner's unique memory anchor points based on the homeowner's identity.

[0030] Subsequently, based on the service context data and target demand information, the dynamic evolution characteristics of the homeowner's service preferences are analyzed to generate a personalized preference weight set adapted to the homeowner's current demand characteristics. One implementation method is to compare the service requests and service completion status corresponding to each service request initiated by the homeowner using the same non-standardized expression. For example, the homeowner's fixed and dynamically changing demands in dimensions such as service response speed, service consumable type, and service personnel selection are analyzed. If the homeowner repeatedly emphasizes "processing as soon as possible," their strong preference for service response speed is identified. These demands are quantified to generate multi-dimensional preference indices. For example, the preference for "processing as soon as possible" can be quantified as a higher response speed preference index. These multi-dimensional preference indices are used to construct a demand evolution vector representing the dynamic changes in the homeowner's personalized needs, and this vector is used to generate a personalized preference weight set for service solution screening and ranking.

[0031] Next, based on the personalized preference weight set, the service solutions in the preset standard service solution library are prioritized and sorted to generate personalized service recommendations that match the homeowner's current needs and preferences. Specifically, all available service solutions in the preset standard service solution library that match the standard service items corresponding to the non-standardized expressions in the target demand information are retrieved. For example, for "roof waterproofing repair," there may be "ordinary repair solution," "emergency repair solution," and "environmentally friendly material repair solution," etc. Then, the weight scores of all available service solutions are quantified according to the dimensions of service response speed, service consumable type, and service personnel selection corresponding to the personalized preference weight set, and a weighted score is calculated and sorted for all options. For example, if the homeowner's response speed preference weight is high, the "emergency repair solution" will score higher and will be prioritized. Thus, multiple types of personalized service recommendations that match the homeowner's real-time needs and dynamic preferences are generated.

[0032] Finally, the system collects user feedback on the personalized service recommendations. Based on this feedback, it corrects the association rules between non-standardized expressions and standard service items and their contextual data within the dedicated memory anchor, iteratively updating the personalized preference weight set to achieve closed-loop adaptive optimization of semantic retrieval and service recommendation strategies. It continuously collects user feedback, such as which solutions the user adopted, rejected, or raised objections to personalized needs. Based on this feedback, it matches preset feedback correction rules, adaptively adjusting the preference index weights of the three dimensions—service response speed, service consumable type, and service personnel selection—in the demand evolution vector. For example, if a user repeatedly rejects the recommended "normal repair solution" and chooses the "emergency repair solution," its preference index weight for response speed will be increased. Simultaneously, it corrects the binding association rules between non-standardized expressions, standard service items, and service contextual data within the dedicated memory anchor. For example, if a user reports a "leaky roof" but it's actually caused by a "blocked drainpipe," the association between "leaky roof" and "drainpipe unblocking" will be corrected. Based on the adjusted preference index weights, a new set of personalized preference weights will be generated iteratively, thereby completing the closed-loop adaptive update and optimization of semantic retrieval and service recommendation strategies.

[0033] The customer service semantic retrieval method proposed in this application, which focuses on identifying customer needs, achieves a deep understanding of non-standardized expressions and dynamic capture of personalized preferences by constructing and utilizing exclusive memory anchors. When a customer inputs their needs, the method identifies the non-standardized expressions and associates them with standard service items and service context data, forming exclusive memory anchors for that customer. When the customer initiates a service request again, these memory anchors are retrieved to obtain the associated service context data. Combined with the target need information, the method analyzes the dynamic evolution characteristics of the customer's service preferences and generates a personalized preference weight set adapted to their current needs. Based on this weight set, service solutions in a pre-set standard service solution library can be prioritized, thereby generating highly personalized service recommendation results. More importantly, this application introduces a closed-loop adaptive optimization mechanism. By collecting customer feedback on the recommendation results, the association rules in the exclusive memory anchors are continuously corrected, and the personalized preference weight set is iteratively updated.

[0034] Compared with existing technologies, the advantages of this application are as follows: Existing technologies often rely on general language processing models when dealing with non-standardized expressions from homeowners, making it difficult to accurately capture domain-specific, colloquial, or even dialectal expressions. This results in insufficient semantic parsing precision and generalized, unspecific service recommendations. For example, when a homeowner uses "as usual" to describe a device malfunction, existing technologies may not understand its deeper meaning or connect it to the homeowner's historical handling preferences for that problem. This application introduces "dedicated memory anchors," establishing a personalized semantic memory for each homeowner. This allows for the binding of the homeowner's unique non-standardized expressions with specific standard service items and service context data, continuously updated as the interaction deepens. This enables the system to understand the specific device, malfunction point, and the homeowner's historical handling preferences behind the homeowner's use of "as usual," much like an experienced dedicated customer service representative, thus providing more accurate and personalized services. Furthermore, this application analyzes the dynamic evolution characteristics of homeowner service preferences, generates a personalized preference weight set, and adaptively optimizes it by combining interactive feedback information. This allows the service recommendation strategy to continuously learn and adapt to the homeowner's ever-changing needs, effectively solving the problems of simplistic service recommendations and the inability to dynamically adapt to the evolving needs of homeowners in existing technologies. This closed-loop adaptive optimization mechanism enables continuous improvement in the accuracy of semantic retrieval and the satisfaction of service recommendations, significantly enhancing the intelligence level of property services and user experience.

[0035] In some embodiments, the service context data includes faulty device attributes, service type, fault location, initial handling plan, and real-time service status. The exclusive memory anchor is bound to the owner's unique identity identifier and can be continuously updated with the progress of service needs and owner interaction feedback.

[0036] Service context data provides a detailed description of the environment and state in which a homeowner's service request occurs. This includes multiple dimensions such as faulty equipment attributes, service type, fault location, initial handling plan, and real-time service status. Faulty equipment attributes specify the type, model, and age of the faulty equipment; service type clarifies the specific category of service required by the homeowner, such as repair requests, inquiries, or complaints; fault location pinpoints the exact location of the problem; the initial handling plan records the first measures taken to address the issue; and real-time service status reflects the current progress of the service processing. These detailed data dimensions collectively constitute a comprehensive portrayal of the homeowner's service context.

[0037] Furthermore, the unique memory anchor is designed to be linked to the owner's unique identifier. This means that each owner's unique memory anchor is personalized and can be accurately traced back to a specific owner. This unique memory anchor is not static but is configured to continuously update service context data as service needs progress and as owner feedback is received. For example, when a service request changes from "pending" to "processing" or "completed," the real-time service status is updated; when owners provide feedback on the service outcome, the relevant context data is also corrected or supplemented.

[0038] This application's solution, by refining service context data and binding and continuously updating exclusive memory anchors to the owner's unique identifier, can more accurately capture and record the owner's specific needs and preferences in different service scenarios. Specifically, detailed context data such as faulty equipment attributes, service type, fault location, initial handling plan, and real-time service status provide a rich and multi-dimensional information foundation for subsequent analysis of the dynamic evolution of owner service preferences. For example, by analyzing the service history under specific equipment attributes, specific requirements of owners for such equipment repair services can be discovered; by tracking changes in real-time service status, the owner's sensitivity to service response speed can be understood. Simultaneously, the binding of exclusive memory anchors to the owner's unique identifier ensures that all context data and preference analysis can be accurately attributed to a specific owner, avoiding data confusion. Its continuous update mechanism ensures that the context data stored in the exclusive memory anchors remains up-to-date, reflecting the latest progress and changes in owner service needs in real time, thus providing dynamic and accurate input for subsequent generation of personalized preference weight sets and personalized service recommendation results.

[0039] Through the aforementioned technical solutions, this application significantly improves the refinement of identifying homeowner needs and the accuracy of personalized service recommendations. Specifically, detailed service context data enables a deeper understanding of the homeowner's service background and specific demands from multiple dimensions, rather than merely remaining superficial. The binding of exclusive memory anchors to the homeowner's unique identifier ensures that all data and analysis results are highly individualized, thus avoiding the "one-size-fits-all" recommendation problem. Furthermore, the continuous updating mechanism of exclusive memory anchors based on service progress and homeowner feedback allows for dynamic learning and adaptation to the homeowner's ever-changing preferences, ensuring that the generated personalized preference weight set and recommendation results always highly align with the homeowner's current actual needs and preferences. This effectively solves the problem of inaccurate and non-personalized service recommendations caused by insufficient or untimely updates in traditional methods.

[0040] In some embodiments, the scenarios for updating the dedicated memory anchor point include: a change in the processing status of a service request, a homeowner completing a service evaluation and providing feedback, and a homeowner initiating a service request again using the same non-standardized expression.

[0041] Specifically, a change in the processing status of a service request refers to a change in the current status of the service order or task related to the homeowner's service request, such as changing from "pending" to "processing," "completed," "cancelled," or "paused." When the service status changes, this event is recognized, triggering an update to the relevant service context data in the dedicated memory anchor to ensure it reflects the latest service progress. Homeowner feedback on service evaluations refers to homeowners rating or describing their service experience, service quality, and service personnel through the provided evaluation mechanism after service completion. This feedback is crucial for learning homeowner preferences and revising association rules. When a homeowner submits an evaluation, this feedback is used to update the association rules between non-standardized expressions and standard service items and their service context data in the dedicated memory anchor. A homeowner re-initiating a service request using the same non-standardized expression means that the homeowner uses the same non-standardized expression to describe their service needs at different times. This repetitive behavior indicates a potentially stable association between the non-standardized expression and a specific service context or homeowner preference, triggering an update to the dedicated memory anchor to strengthen or adjust this association, making it more accurately match the homeowner's intent.

[0042] This application's solution addresses the lack of specificity in the update mechanism of dedicated memory anchors in the basic solution by clearly defining the update scenarios for dedicated memory anchors. When the processing status of a service request changes, the latest information on the service progress, such as service completion, cancellation, or progress, can be captured in a timely manner, thereby updating the service context data related to that service in the dedicated memory anchor to ensure that the memory anchor always reflects the latest service status. When the customer provides service evaluation feedback, this direct feedback information can be used to revise the association rules between non-standardized expressions in the dedicated memory anchor and standard service items and their service context data, resulting in a more accurate understanding of customer preferences. Furthermore, when the customer initiates a service request again using the same non-standardized expression, this repetitive behavior can be identified, and the association strength or content between the non-standardized expression and the specific service context in the dedicated memory anchor can be adjusted accordingly, thereby strengthening the learning ability of the customer's specific expression habits and potential needs.

[0043] Through the aforementioned technical solution, this application ensures that dedicated memory anchors are updated promptly and effectively at critical moments, avoiding data lag or inaccuracy caused by untimely updates or unclear update strategies. This enables more accurate capture of the dynamic evolution of homeowner service preferences, improving the accuracy of semantic retrieval and the adaptability of personalized service recommendations. Simultaneously, clearly defined update scenarios help optimize system resource utilization, avoid unnecessary frequent updates, and enhance the efficiency and intelligence of the entire customer service semantic retrieval method.

[0044] In some embodiments, the exclusive memory anchor is stored in encrypted form and can only be accessed and retrieved through the owner's unique identity to prevent leakage of owner demand data and service preference data.

[0045] Specifically, encrypted storage refers to encrypting all data stored in the dedicated memory anchor, including non-standardized expressions, standard service items, service context data, and owner's unique identifiers, before writing them to the storage medium. The purpose is to ensure that even if the data storage medium is illegally accessed, unauthorized third parties cannot directly read or understand the sensitive information. The owner's unique identifier can be understood as a sequence of numbers or characters used to uniquely identify a specific owner, such as the owner's mobile phone number, user ID, or a unique code assigned by the system. Access and retrieval can only be performed through the owner's unique identifier. This means that any attempt to access or retrieve data in the dedicated memory anchor must first verify whether the requester's owner's unique identifier matches the identifier bound to that anchor. Only after successful verification is data operation permitted. The purpose is to ensure that data in the dedicated memory anchor can only be legally accessed by its corresponding owner or a system module authorized by the owner, thereby strictly controlling data usage permissions.

[0046] This application's solution effectively addresses the risk of sensitive data leakage in dedicated memory anchors by introducing encrypted storage and a strict access control mechanism. Specifically, when a dedicated memory anchor is built or updated, the owner's needs and service preferences data it contains are encrypted before storage, transforming the original data into unreadable ciphertext. Therefore, even if the storage medium is physically stolen or the system is maliciously compromised, attackers without the decryption key cannot directly obtain meaningful owner information. Simultaneously, by binding the access permissions of the dedicated memory anchor to the owner's unique identifier and mandating that all data retrieval operations be authenticated through this unique identifier, a robust defense is constructed at the logical level. This ensures that only the legitimate owner associated with the dedicated memory anchor or their authorized agent can access and manipulate their personalized data, fundamentally preventing unauthorized data access and misuse.

[0047] Through the aforementioned technical solution, this application significantly improves the security and privacy protection level of the customer service semantic retrieval method for identifying homeowner needs. Homeowner demand data and service preference data in the dedicated memory anchor are effectively encrypted, greatly reducing the risk of data leakage. Simultaneously, a strict identity verification mechanism ensures the legality and exclusivity of data access, effectively preventing unauthorized third parties from obtaining or using homeowners' sensitive information. This not only enhances homeowners' trust in the customer service system and encourages their more active participation in service feedback and interaction, but also complies with current legal and regulatory requirements for data privacy protection, providing a solid security foundation for the continuous optimization of personalized service recommendations.

[0048] In some embodiments, the step of identifying non-standardized expressions in the owner's input demand information includes: Based on a pre-set property service standard vocabulary database and a property scenario colloquial dialect vocabulary database, the similarity of the demand information entered by the owners is compared to obtain a matching score; Statements with a matching score below the preset matching threshold that cannot accurately correspond to standard service items are judged as non-standard expressions.

[0049] The pre-set property service standard vocabulary database can be understood as a collection of standardized service item names, descriptions, and related professional terms in the property management field, such as "burst water pipe repair," "elevator malfunction reporting," and "public area cleaning." The property scenario colloquial vocabulary database aims to collect informal, colloquial, or locally-specific expressions that residents might use in daily communication. For example, "burst water pipe" is described as "water pipe leaking" or "water pipe is broken," and "elevator malfunction" is described as "elevator stuck" or "elevator not moving." The establishment of these two databases aims to comprehensively cover various expressions that residents might use, providing foundational data for subsequent similarity comparisons.

[0050] Specifically, comparing the similarity of the needs input by homeowners involves calculating the semantic or textual similarity between the original text input and various entries in a pre-defined property service standard vocabulary database and a property-related colloquial vocabulary database. This comparison process can employ various natural language processing techniques, such as word vector models, TF-IDF algorithms, and deep learning models like BERT, to quantify the similarity between the homeowner's input and standard or common expressions, thus obtaining a matching score. A higher matching score indicates that the homeowner's input is closer to a specific standard service item or common expression in the vocabulary database.

[0051] Furthermore, the preset matching threshold is a configurable value used to define whether the owner's input information is sufficiently close to the standard service items. When the matching score of the owner's input request information is lower than the preset matching threshold after similarity comparison, it indicates that there is a significant difference between the input information and existing standard service items or known colloquial expressions, and it cannot be accurately identified and categorized. In this case, the statement will be judged as a non-standardized expression. The inability to accurately correspond to standard service items means that the expression cannot be directly mapped to any standard service item in the property service standard vocabulary, and may require further semantic understanding or manual intervention.

[0052] This application's solution, by constructing a comprehensive vocabulary database and combining it with a similarity comparison and threshold determination mechanism, can effectively and accurately identify non-standard expressions that do not conform to standard formats or spoken habits from the massive and diverse needs input by homeowners. It is precisely this meticulous identification process that enables the capture of homeowners' personalized, non-standard language habits, laying the foundation for subsequent construction of exclusive memory anchors and personalized service recommendations. By classifying statements with matching scores below a preset matching threshold as non-standard expressions, it can filter out ambiguous, semantically unclear, or expressions beyond the current knowledge base, ensuring the accuracy and effectiveness of subsequent processing.

[0053] Through the above technical solution, this application overcomes the difficulties and inaccuracies of traditional semantic retrieval methods in handling non-standardized, colloquial expressions of homeowner needs. By introducing a standard property service vocabulary database and a colloquial dialect vocabulary database for property scenarios, and combining similarity comparison and threshold judgment mechanisms, it can more comprehensively and accurately identify non-standardized expressions in homeowner input information, thereby avoiding misunderstandings or service delays caused by language differences. This accurate non-standardized expression recognition capability provides reliable input for subsequently building personalized memory anchors, significantly improving the intelligence level of customer service semantic retrieval and user experience.

[0054] In some embodiments, the step of analyzing the dynamic evolution characteristics of homeowner service preferences based on the service context data and target demand information, and generating a personalized preference weight set adapted to the current demand characteristics of homeowners, includes: Based on the service context data and target demand information, the service requests and service completion status corresponding to the previous service requests initiated by the owner using the same non-standard expression are compared to explore the owner's fixed and dynamic demands in three dimensions: service response speed, service consumable type, and service personnel selection. The fixed and dynamic demands in each dimension are quantified to generate a multi-dimensional preference index. A demand evolution vector representing the dynamic changes in the personalized needs of homeowners is constructed by using a multi-dimensional preference index. The demand evolution vector is then used to generate a set of personalized preference weights for service plan selection and ranking.

[0055] Specifically, comparing the service requests and service completion statuses of homeowners using the same non-standardized expression across different service requests involves tracking multiple service requests initiated by homeowners for a specific non-standardized expression (e.g., "leaky faucet") using historical service data recorded in dedicated memory anchors. The service request can be understood as the specific expectations expressed by the homeowner in each service request, such as requirements for service response time, quality of repair materials, and qualifications of service personnel. The service completion status refers to the final outcome of the service request, such as whether it was completed on time, whether the homeowner was satisfied, and whether there was a need for rework. By comparing and analyzing this historical data, we can delve into the homeowner's preferences in three core dimensions: service response speed, service consumable type, and service personnel selection. The service response speed dimension focuses on the homeowner's requirement for timely service; the service consumable type dimension focuses on the homeowner's preferences for the quality, brand, or cost of repair or replacement materials; and the service personnel selection dimension focuses on the homeowner's preferences for the professional skills, service attitude, or specific personnel of service personnel.

[0056] Fixed demands refer to the consistent and stable preferences of service providers across multiple service requests, such as always demanding the fastest response time or specifying a particular service provider. Dynamically changing demands refer to preferences that adjust with time, context, or service experience; for example, preferences for consumable types may differ in different seasons, or service provider selection criteria may change after a single unsatisfactory service experience. Quantifying both fixed and dynamically changing demands across these dimensions involves transforming these qualitative preferences into calculable numerical indicators. For instance, "very fast" can be quantified as a high score, and "average" as a medium score, thus generating a multi-dimensional preference index. These indices comprehensively reflect the strength and direction of the service provider's preferences across various dimensions.

[0057] Furthermore, constructing a demand evolution vector representing the dynamic changes in the homeowner's personalized needs through multi-dimensional preference indices means combining the aforementioned quantified multi-dimensional preference indices into a single vector. This vector can dynamically capture and represent the evolving trends of the homeowner's personalized needs. For example, one component of the vector might represent a preference index for service response speed, while another component might represent a preference index for consumable types. Generating a set of personalized preference weights for service plan selection and ranking by mapping the demand evolution vector means dynamically generating a set of weights based on the real-time state of the demand evolution vector using preset mapping rules or machine learning models. This set of weights will be used to prioritize and rank the service plans in the preset standard service plan library, ensuring that the recommended service plans best match the homeowner's current and dynamically changing personalized needs.

[0058] This application's solution, by introducing a detailed comparison of the service requests and completion status of each service request from homeowners, can deeply uncover their fixed and dynamic preferences across three key dimensions: service response speed, service consumable types, and service personnel selection. This multi-dimensional and dynamic analysis mechanism moves beyond relying solely on single or static preference information, enabling a comprehensive and real-time perception of changes in homeowner needs. By quantifying these qualitative preferences into multi-dimensional preference indices and further constructing a demand evolution vector, this solution achieves a precise representation of the dynamic changes in homeowners' personalized needs. Consequently, the personalized preference weight set generated by mapping the demand evolution vector more accurately reflects the homeowner's current true needs and preferences, thus providing a more precise basis for subsequent service plan selection and ranking, effectively solving the problem of insufficiently refined and dynamic preference weight generation that may exist in the basic solution.

[0059] Through the aforementioned technical solution, this application enables in-depth analysis and precise capture of the dynamic evolution characteristics of homeowner service preferences, significantly improving the generation accuracy and adaptability of personalized preference weight sets. Specifically, by meticulously comparing past service requests, it can distinguish between homeowners' fixed and dynamically changing needs, quantifying them into multi-dimensional preference indices, thereby constructing a demand evolution vector that reflects the real-time evolution of homeowner needs. This mechanism ensures that the generated personalized preference weight set not only adapts to the current needs of homeowners but also dynamically adjusts to accommodate changes in their preferences, avoiding service recommendation biases caused by lagging or incomplete preference information. Therefore, this solution can provide homeowners with service recommendations that better match their personalized and dynamic needs, greatly improving their service satisfaction and experience.

[0060] In some embodiments, the step of prioritizing each service solution in the preset standard service solution library based on the personalized preference weight set and generating a personalized service recommendation result that matches the current needs and preferences of the homeowner includes: Retrieve all available service solutions from the preset standard service solution library that match the standard service items corresponding to the non-standardized expressions in the target requirement information; Based on the weighted scores of personalized preferences, corresponding to various dimensions such as service response speed, service consumables type, and service personnel selection, all available service options are weighted, scored, and ranked. Service options that meet the multidimensional personalized needs of homeowners are prioritized, generating multi-type personalized service recommendation results that adapt to the real-time needs and dynamic preferences of homeowners.

[0061] Specifically, "retrieving all available service solutions from the preset standard service solution library that match the standard service items corresponding to the non-standardized expressions in the target demand information" means that after receiving the owner's target demand information, the system first maps the identified non-standardized expressions in the information to preset standard service items. Then, it retrieves and extracts all service solutions from the preset standard service solution library that are related to or applicable to that standard service item. These solutions constitute a preliminary candidate set, laying the foundation for subsequent personalized recommendations.

[0062] Furthermore, "weighting and ranking all available service options based on the quantified weight scores of various dimensions corresponding to service response speed, service consumable type, and service personnel selection within the personalized preference weight set" means that all available service options retrieved above will be evaluated based on the previously generated personalized preference weight set. This weight set includes the quantified preference weight scores of the homeowner on multiple dimensions such as service response speed, service consumable type, and service personnel selection. Each available service option typically has preset attributes or performance in these dimensions. The attributes of each available option will be weighted together with the homeowner's preference weights in the corresponding dimensions to obtain a comprehensive score. For example, if the homeowner has a higher preference weight for service response speed, then the service option with a faster response speed will receive a higher weighted score.

[0063] Therefore, "prioritizing service solutions that meet the multidimensional and personalized needs of homeowners, and generating multiple types of personalized service recommendations that adapt to their real-time needs and dynamic preferences" means that after weighting and scoring all available service solutions, they will be ranked from highest to lowest based on their comprehensive scores. The top-ranked service solutions are considered to better meet the homeowner's current multidimensional and personalized needs; for example, they may consider the homeowner's requirements for speed, as well as their preferences for consumable quality or specific service personnel. Ultimately, these high-priority service solutions will be output to the homeowner as personalized service recommendations. These results can be a single optimal solution or multiple recommended options that meet different emphases and preferences, adapting to the homeowner's constantly changing needs and preferences.

[0064] This application's solution first accurately retrieves all available service options related to the homeowner's needs, ensuring comprehensive recommendations. Building on this, it weights the homeowner's personalized preferences across key dimensions such as service response speed, service consumables type, and service personnel selection with the actual attributes of each available service option, enabling the recommendation process to deeply consider the homeowner's multifaceted and nuanced needs. This weighted scoring and ranking mechanism effectively avoids recommendation biases that may arise from traditional single-dimensional or simple ranking methods, ensuring that the recommended results not only match the homeowner's basic needs but also closely align with their dynamically changing personalized preferences. It is precisely this multi-dimensional, weighted evaluation that allows the final service plan to truly meet the homeowner's multi-dimensional and personalized needs, thereby significantly improving the accuracy and satisfaction of service recommendations.

[0065] Through the aforementioned technical solution, this application overcomes the shortcomings of traditional service recommendations in terms of personalized matching. Specifically, by introducing multi-dimensional quantitative weight scores to weightedly score and rank service options, the recommendation results can more precisely reflect the specific preferences of homeowners regarding service response speed, service consumable types, and service personnel selection. This generates a variety of personalized service recommendation results that highly adapt to the homeowner's real-time needs and dynamic preferences. This mechanism not only improves the accuracy of service recommendations but also enhances the flexibility and diversity of the recommendation results, better meeting the complex and ever-changing personalized service needs of homeowners and significantly improving their service experience and satisfaction.

[0066] In some embodiments, the steps of collecting interactive feedback information from homeowners regarding the personalized service recommendation results, correcting the association rules between non-standardized expressions in the exclusive memory anchor and standard service items and their service context data based on the interactive feedback information, iteratively updating the personalized preference weight set, and realizing closed-loop adaptive optimization of semantic retrieval and service recommendation strategies include: Continuously collect feedback from homeowners regarding personalized service recommendations; Based on the interactive feedback information, the preset feedback correction rules are matched and the preference index weights of the three dimensions of service response speed, service consumable type, and service personnel selection in the demand evolution vector are adaptively adjusted. At the same time, the binding and association rules between non-standardized expressions, standard service items, and service context data within the exclusive memory anchor are corrected. Based on the adjusted preference index weights, a personalized preference weight set is generated iteratively, completing the closed-loop adaptive update and optimization of semantic retrieval and service recommendation strategies.

[0067] The phrase "continuously collecting feedback from homeowners regarding personalized service recommendations" refers to collecting various responses from homeowners to these recommendations in real-time or periodically after they have been provided. This feedback can include whether homeowners have adopted the recommended solutions, reasons for rejecting them, evaluations of the service process or results, and any objections to personalized needs. Data collection can be conducted through various methods, such as clicking, selecting, text input, and voice input on the user interface.

[0068] "Matching pre-defined feedback correction rules based on interactive feedback information" refers to the internally pre-defined series of rules used to interpret and process different types of homeowner interactive feedback. For example, if a homeowner adopts a recommended solution, the weights of that solution and its associated preference dimensions may be strengthened; if a homeowner rejects a solution and provides reasons for rejection, the weights of relevant skewness dimensions will be adjusted based on these reasons to avoid recommending solutions that do not conform to the homeowner's preferences in the future. These rules aim to transform unstructured feedback information into structured data that can be used for model adjustment.

[0069] "Adaptive adjustment of preference index weights for the three dimensions of service response speed, service consumable type, and service personnel selection in the demand evolution vector" refers to the dynamic adjustment of preference index weights for these three key dimensions—service response speed, service consumable type, and service personnel selection—in the demand evolution vector after receiving feedback from the homeowner, based on preset correction rules. For example, if the homeowner is dissatisfied with the service response speed, the weight of this dimension in future recommendations will be reduced, or the preference weight for rapid response solutions will be increased. This adjustment is adaptive, meaning that it can flexibly adjust its understanding of homeowner preferences based on the latest feedback.

[0070] "Synchronously correcting the binding and association rules among non-standardized expressions, standard service items, and service context data within the dedicated memory anchor" means that, in addition to adjusting preference weights, the association relationships between non-standardized expressions, standard service items, and service context data stored in the dedicated memory anchor will be corrected based on customer feedback. For example, if a customer's request for a service through a non-standardized expression is ultimately proven to be not a perfect match for a standard service item, or generates negative feedback in a specific context, the association strength or type between that non-standardized expression and the standard service item or service context data will be adjusted to improve the accuracy of future semantic retrieval.

[0071] "Iterative generation of personalized preference weight sets based on adjusted preference index weights" refers to the process of recalculating and generating a new set of personalized preference weights using updated information after the preference index weights and association rules have been revised. This process is iterative, meaning it continuously learns and optimizes, enabling the personalized preference weight set to more accurately reflect the current real needs and preferences of homeowners.

[0072] "Completing the closed-loop adaptive update and optimization of semantic retrieval and service recommendation strategies" means that through the above series of feedback collection, rule matching, weight adjustment and association correction, a complete learning and optimization cycle can be formed.

[0073] This application's solution provides valuable learning data by continuously collecting interactive feedback from homeowners regarding personalized service recommendations. This feedback is used to match pre-defined feedback correction rules, enabling accurate identification of changes in homeowner preferences for key dimensions such as service response speed, service consumable types, and service personnel selection. By adaptively adjusting the preference index weights of these dimensions in the demand evolution vector, the evolutionary characteristics of homeowner service preferences can be dynamically captured. Simultaneously, the binding and association rules between non-standardized expressions within the dedicated memory anchor, standard service items, and service context data are corrected, ensuring the accuracy of semantic retrieval and the effectiveness of association. Therefore, a personalized preference weight set is iteratively generated based on the adjusted preference index weights, allowing for continuous optimization of the understanding and prediction of homeowner needs, thereby achieving a closed-loop adaptive update and optimization of semantic retrieval and service recommendation strategies.

[0074] The aforementioned technical solution learns from the actual interactions between homeowners and the recommended results, rather than relying solely on preset rules or historical data. This continuous feedback mechanism dynamically adapts to the ever-changing needs and preferences of homeowners, significantly improving the accuracy and personalization of service recommendations. Specifically, through adaptive adjustments to the preference index weights and unique memory anchor association rules, it can more accurately understand the true intent behind non-standardized expressions and recommend service solutions that better meet the real-time needs of homeowners. This effectively improves homeowner service satisfaction and enables intelligent and adaptive optimization of customer service semantic retrieval and service recommendation strategies.

[0075] In some embodiments, the interactive feedback information includes feedback on solution adoption, feedback on solution rejection, and feedback on objections to personalized needs.

[0076] Among them, the feedback on the adoption of the solution refers to the feedback from the owner after receiving the recommended personalized service solution, which clearly indicates that the owner accepts and chooses to implement the solution. This shows that the recommended solution is highly matched with the owner's current needs and preferences.

[0077] A rejection feedback refers to feedback from homeowners who do not accept the recommended personalized service plan or choose other options. This feedback may suggest that the recommended plan does not fully meet the homeowner's needs or that there are other better alternatives.

[0078] Personalized needs objection feedback refers to the feedback from homeowners after receiving the recommended plan, in which they raise objections, supplements, or suggestions for modification regarding certain aspects of the plan. For example, homeowners may have specific preferences regarding service response speed, types of consumables, or selection of service personnel, and hope that these preferences will be taken into consideration in subsequent recommendations.

[0079] This application's solution refines the types of interactive feedback information, enabling a more accurate understanding of the homeowner's true attitude and specific needs regarding recommended service solutions. Upon receiving feedback on solution adoption, the effectiveness of the current recommendation strategy can be confirmed, and relevant association rules and preference weights can be strengthened. Upon receiving feedback on solution rejection, the shortcomings of the current recommendation strategy are identified, and the association rules and personalized preference weight sets in the exclusive memory anchor are adjusted based on the reason for rejection to avoid recommending solutions that do not meet the homeowner's preferences again. Upon receiving feedback on objections to personalized needs, deeper personalized needs of the homeowner can be captured, such as specific requirements for service response speed, consumable types, or service personnel selection. This allows for refined adjustments to the corresponding preference index weights in the demand evolution vector, ensuring that the subsequently generated personalized preference weight set more accurately reflects the homeowner's dynamically changing preferences.

[0080] The aforementioned technical solutions enable the acquisition of richer and more targeted homeowner feedback data. This allows for more precise correction of the association rules between non-standardized expressions in the personalized memory anchor and standard service items and their service context data, and more effective iterative updates to the personalized preference weight set. This significantly enhances the adaptive optimization capabilities of semantic retrieval and service recommendation strategies, ensuring continuous learning and adaptation to the ever-changing needs and preferences of homeowners, ultimately providing more accurate and satisfactory personalized services.

[0081] This application also proposes a customer service semantic retrieval system oriented towards identifying owner needs, such as... Figure 2 As shown, a customer service semantic retrieval system 100 for identifying homeowner needs includes: The association and memory recording module 10 is used to identify non-standard expressions in the demand information input by the owner, associate and bind the non-standard expressions with the corresponding standard service items and matching service context data, and construct and store the exclusive memory anchor point corresponding to the owner. The service context data acquisition module 20 is used to retrieve the owner's exclusive memory anchor point and obtain the associated service context data when the owner re-enters target demand information containing non-standardized expressions. The preference weight set generation module 30 is used to analyze the dynamic evolution characteristics of the owner's service preferences based on the service context data and target demand information, and generate a personalized preference weight set that adapts to the current demand characteristics of the owner. The service recommendation result generation module 40 is used to prioritize each service plan in the preset standard service plan library based on the personalized preference weight set, and generate personalized service recommendation results that match the owner's current needs and preferences. The feedback and correction module 50 is used to collect interactive feedback information from homeowners regarding the personalized service recommendation results, correct the association rules between non-standardized expressions in the exclusive memory anchor and standard service items and their service context data based on the interactive feedback information, iteratively update the personalized preference weight set, and realize closed-loop adaptive optimization of semantic retrieval and service recommendation strategies.

[0082] The system proposed in this application achieves accurate identification of homeowner needs, personalized service recommendations, and closed-loop adaptive optimization of strategies through a modular design. Specifically, the association and memory recording module is responsible for establishing a unique semantic memory for each homeowner, effectively capturing and storing their unique non-standardized expressions and their association with service contexts; the service context data acquisition module ensures that this historical context information can be quickly retrieved and utilized when a homeowner makes a subsequent request; the preference weight set generation module generates a highly personalized weight set based on dynamically evolving service preferences; the service recommendation result generation module uses this weight set to accurately rank service options, providing recommendations tailored to the homeowner's current needs and preferences; finally, the feedback and correction module continuously collects homeowner interaction feedback, adaptively correcting and iteratively updating the memory anchor points and preference weight sets, thereby achieving closed-loop adaptive optimization of the entire semantic retrieval and service recommendation strategy. This system architecture effectively solves the problems of inaccurate semantic understanding and simplistic service recommendations in existing technologies, significantly improving the intelligence level of property services and user experience.

[0083] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A customer service semantic retrieval method for identifying homeowner needs, characterized in that, include: Identify non-standard expressions in the demand information input by the owner, associate and bind the non-standard expressions with the corresponding standard service items and matching service context data, and construct and store the exclusive memory anchor point for the owner. When the system detects that the homeowner has entered target demand information containing non-standardized expressions again, it retrieves the homeowner's unique memory anchor points and obtains the associated service context data. Based on the service context data and target demand information, the dynamic evolution characteristics of the owner's service preferences are analyzed, and a personalized preference weight set that adapts to the owner's current demand characteristics is generated. Based on the personalized preference weight set, the service plans in the preset standard service plan library are prioritized and sorted to generate personalized service recommendation results that match the owner's current needs and preferences. Collect interactive feedback information from homeowners regarding the personalized service recommendation results. Based on this feedback information, correct the association rules between non-standardized expressions in the exclusive memory anchor and standard service items and their service context data. Iteratively update the personalized preference weight set to achieve closed-loop adaptive optimization of semantic retrieval and service recommendation strategies.

2. The customer service semantic retrieval method for identifying owner needs according to claim 1, characterized in that, The service context data includes faulty equipment attributes, service type, fault location, initial handling plan, and real-time service status. The exclusive memory anchor is bound to the owner's unique identity identifier and can be continuously updated with the progress of service needs and owner interaction feedback.

3. The customer service semantic retrieval method for identifying owner needs according to claim 2, characterized in that, The scenarios for updating the exclusive memory anchor include: changes in the processing status of service requests, completion of service evaluation feedback by the owner, and the owner initiating a service request again using the same non-standardized expression.

4. The customer service semantic retrieval method for identifying owner needs according to claim 2, characterized in that, The dedicated memory anchors are stored in encrypted form and can only be accessed and retrieved through the owner's unique identification, preventing the leakage of owner's demand data and service preference data.

5. The customer service semantic retrieval method for identifying owner needs according to claim 1, characterized in that, The steps for identifying non-standardized expressions in the owner's input demand information include: Based on a pre-set property service standard vocabulary database and a property scenario colloquial dialect vocabulary database, the similarity of the demand information entered by the owners is compared to obtain a matching score; Statements with a matching score below the preset matching threshold that cannot accurately correspond to standard service items are judged as non-standard expressions.

6. The customer service semantic retrieval method for identifying owner needs according to claim 1, characterized in that, The step of analyzing the dynamic evolution characteristics of homeowner service preferences based on the service context data and target demand information, and generating a personalized preference weight set adapted to the current demand characteristics of homeowners, includes: Based on the service context data and target demand information, the service requests and service completion status corresponding to the previous service requests initiated by the owner using the same non-standard expression are compared to explore the owner's fixed and dynamic demands in three dimensions: service response speed, service consumable type, and service personnel selection. The fixed and dynamic demands in each dimension are quantified to generate a multi-dimensional preference index. A demand evolution vector representing the dynamic changes in the personalized needs of homeowners is constructed by using a multi-dimensional preference index. The demand evolution vector is then used to generate a set of personalized preference weights for service plan selection and ranking.

7. The customer service semantic retrieval method for identifying owner needs according to claim 1, characterized in that, The step of prioritizing each service plan in the preset standard service plan library based on the personalized preference weight set and generating a personalized service recommendation result that matches the current needs and preferences of the homeowner includes: Retrieve all available service solutions from the preset standard service solution library that match the standard service items corresponding to the non-standardized expressions in the target requirement information; Based on the weighted scores of personalized preferences, corresponding to various dimensions such as service response speed, service consumables type, and service personnel selection, all available service options are weighted, scored, and ranked. Service options that meet the multidimensional personalized needs of homeowners are prioritized, generating multi-type personalized service recommendation results that adapt to the real-time needs and dynamic preferences of homeowners.

8. The customer service semantic retrieval method for identifying owner needs according to claim 1, characterized in that, The steps of collecting user feedback on personalized service recommendations, revising the association rules between non-standardized expressions in the exclusive memory anchor and standard service items and their service context data based on the feedback, and iteratively updating the personalized preference weight set to achieve closed-loop adaptive optimization of semantic retrieval and service recommendation strategies include: Continuously collect feedback from homeowners regarding personalized service recommendations; Based on the interactive feedback information, the preset feedback correction rules are matched and the preference index weights of the three dimensions of service response speed, service consumable type, and service personnel selection in the demand evolution vector are adaptively adjusted. At the same time, the binding and association rules between non-standardized expressions, standard service items, and service context data within the exclusive memory anchor are corrected. Based on the adjusted preference index weights, a personalized preference weight set is generated iteratively, completing the closed-loop adaptive update and optimization of semantic retrieval and service recommendation strategies.

9. The customer service semantic retrieval method for identifying owner needs according to claim 8, characterized in that, The interactive feedback information includes feedback on solution adoption, feedback on solution rejection, and feedback on objections to personalized needs.

10. A customer service semantic retrieval system for identifying homeowner needs, characterized in that, The system includes: The association and memory recording module is used to identify non-standard expressions in the demand information input by the owner, associate and bind the non-standard expressions with the corresponding standard service items and matching service context data, and construct and store the exclusive memory anchor point for the owner. The service context data acquisition module is used to retrieve the owner's unique memory anchor points and obtain the associated service context data when the system detects that the owner has re-entered target demand information containing non-standardized expressions. The preference weight set generation module is used to analyze the dynamic evolution characteristics of the owner's service preferences based on the service context data and target demand information, and generate a personalized preference weight set that adapts to the owner's current demand characteristics. The service recommendation result generation module is used to prioritize each service plan in the preset standard service plan library based on the personalized preference weight set, and generate personalized service recommendation results that match the owner's current needs and preferences. The feedback and correction module is used to collect interactive feedback information from homeowners regarding the personalized service recommendation results, correct the association rules between non-standardized expressions in the exclusive memory anchor and standard service items and their service context data based on the interactive feedback information, iteratively update the personalized preference weight set, and realize closed-loop adaptive optimization of semantic retrieval and service recommendation strategies.

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