Data processing method and device, server, medium and program product

Abstract

The invention provides a data processing method and device, a server, a medium and a program product. According to the method, in response to an evaluation request for the property service quality of a to-be-evaluated subject initiated by a target terminal, various index data for evaluating the property service quality of the to-be-evaluated subject is obtained based on multiple channels, and the index data are input into a pre-trained artificial intelligence model; and processing each item of index data based on an artificial intelligence model to obtain a property service quality score for each item of index data of the to-be-evaluated subject, the artificial intelligence model being a model obtained based on historical sample data set training and used for evaluating the property service quality score. And generating a property service evaluation report based on the property service quality score, and sending the property service evaluation report to the target terminal. According to the technical scheme, a property service quality evaluation mode for evaluating the subject matter more efficiently and accurately is provided for a bank, so that the bank business processing efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of big data, and in particular to a data processing method, device, server, medium and program product. Background Art

[0002] When banks issue mortgage loans, if the subject matter involves real estate and property management financial products and services, they usually need to evaluate the property. The service quality of the property directly affects its market value and attractiveness, which in turn affects the risk assessment of the loan and the determination of the loan amount.

[0003] In the relevant technology, the bank's evaluation method for the service quality of properties is usually based on offline or online surveys, collecting users' evaluation results on the properties and evaluating the service quality of the target properties. On the one hand, this method requires a lot of manpower and material resources, and on the other hand, it relies too much on the subjective ideas of users, resulting in an evaluation result that is not objective enough.

[0004] Therefore, there is an urgent need to propose a technical solution that can efficiently and accurately evaluate the quality of property services, thereby improving the efficiency of risk assessment and loan amount determination of bank loans. Summary of the invention

[0005] The present application provides a data processing method, device, server, medium and program product, which are used to solve the problem of low efficiency and low accuracy in evaluating the property service quality of the subject matter when a bank issues a mortgage loan.

[0006] In a first aspect, the present application provides a data processing method, comprising:

[0007] In response to a request for evaluating the property service quality of the subject to be evaluated initiated by the target terminal, obtaining various indicator data for evaluating the property service quality of the subject to be evaluated based on multiple channels;

[0008] Input each indicator data into a pre-trained artificial intelligence model, process each indicator data based on the artificial intelligence model, and obtain a property service quality score for each indicator data of the subject to be evaluated; wherein the artificial intelligence model is a model trained based on a historical sample data set and used to evaluate the property service quality score;

[0009] A property service evaluation report is generated based on the property service quality score, and the property service evaluation report is sent to the target terminal, so that the target terminal determines whether the property service quality evaluation of the subject to be evaluated has passed based on the property evaluation report.

[0010] In one embodiment, the multiple channels include at least one of the following: IoT devices, mobile applications, and social media, and the indicator data includes at least one of the following: hardware facility data, maintenance response time data, user feedback data, security management data, and service record data;

[0011] The various indicator data for evaluating the property service quality of the subject to be evaluated obtained through multiple channels include at least one of the following:

[0012] Based on the Internet of Things device, obtaining at least one of the hardware facility data and the maintenance response time data of the subject to be evaluated;

[0013] Based on the mobile application, obtaining at least one of the security management data and service record data of the subject to be evaluated;

[0014] Based on social media, user feedback data of the subject to be evaluated is obtained.

[0015] In one embodiment, the obtaining at least one of the hardware facility data and the maintenance response time data of the subject to be evaluated based on the Internet of Things device includes:

[0016] Based on the location information of the target to be evaluated, determine the Internet of Things devices within the area of ​​the target to be evaluated, and obtain at least one of the hardware facility data and maintenance response time data of the target to be evaluated from the Internet of Things devices; and / or,

[0017] The obtaining, based on the mobile application, at least one of the security management data and the service record data of the subject to be evaluated comprises:

[0018] Based on the property security management section of the mobile application, obtaining security management data of the subject to be evaluated, and / or based on the service record section of the mobile application, obtaining service record data of the subject to be evaluated; and / or,

[0019] The obtaining of user feedback data of the subject to be evaluated based on social media includes:

[0020] The comment data about the subject to be evaluated is queried from social media, and word segmentation and keyword extraction are performed on the comment data to obtain user feedback data of the subject to be evaluated.

[0021] In one embodiment, the training method of the artificial intelligence model includes:

[0022] Acquire a historical sample data set, wherein the historical sample data set includes sample indicator data of property service quality of multiple sample targets and corresponding scoring results;

[0023] The sample indicator data are used as the input of the initial artificial intelligence model, and the scoring results corresponding to the sample indicator data sets are used as the output of the initial artificial intelligence model. The initial artificial intelligence model is iteratively trained until a preset number of iterations is reached or the loss value meets a preset loss condition, so as to obtain the artificial intelligence model.

[0024] In one embodiment, the initial artificial intelligence model adopts at least one of the following: a recurrent neural network model, a long short-term memory network model, and a large language model.

[0025] In one embodiment, the step of inputting various indicator data into a pre-trained artificial intelligence model includes:

[0026] Preprocessing each indicator data to obtain preprocessed indicator data; wherein the preprocessing includes at least one of the following: data cleaning, data integration and feature extraction;

[0027] Input the preprocessed indicator data into the pre-trained artificial intelligence model.

[0028] In one implementation, generating a property service evaluation report based on the property service quality score includes:

[0029] Using data visualization technology, data analysis is performed on the indicator data whose property service quality scores are lower than the score thresholds corresponding to the relevant indicators to obtain the points for improvement of the indicator data;

[0030] A property service evaluation report is generated based on the property service score and the areas for improvement of the indicator data.

[0031] In one embodiment, the method further comprises:

[0032] Determine optimization strategies for various indicator data based on the property service evaluation report;

[0033] The property service evaluation report and the corresponding optimization strategy are sent to the property management platform.

[0034] According to a second aspect of the present application, there is provided a data processing device, comprising:

[0035] A data acquisition module, which is configured to respond to an evaluation request for the property service quality of the subject to be evaluated initiated by the target terminal, and obtain various indicator data for evaluating the property service quality of the subject to be evaluated based on multiple channels;

[0036] A data input module, which is configured to input various indicator data into a pre-trained artificial intelligence model;

[0037] A model processing module, which is configured to process various indicator data based on the artificial intelligence model to obtain a property service quality score for each indicator data of the subject to be evaluated; wherein the artificial intelligence model is a model trained based on a historical sample data set and used to evaluate the property service quality score;

[0038] An evaluation generation module is configured to generate a property service evaluation report based on the property service quality score, and send the property service evaluation report to the target terminal so that the target terminal determines whether the property service quality evaluation of the subject to be evaluated has passed based on the property evaluation report.

[0039] According to a third aspect of the present application, a server is provided, the server comprising: a processor, and a memory communicatively connected to the processor;

[0040] The memory stores computer-executable instructions;

[0041] The processor executes the computer-executable instructions stored in the memory to implement the data processing method provided in any one of the first aspects above.

[0042] According to a third aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the data processing method provided in any one of the first aspects above.

[0043] According to a fourth aspect of the present application, a computer program product is provided, comprising a computer program, which, when executed by a processor, performs the data processing method provided in any one of the first aspects above.

[0044] The data processing method, device, server, medium and program product provided by the present application, through the application embodiment, responds to the evaluation request for the property service quality of the subject to be evaluated initiated by the target terminal, obtains various indicator data for evaluating the property service quality of the subject to be evaluated based on multiple channels, inputs the various indicator data into the pre-trained artificial intelligence model, processes the various indicator data based on the artificial intelligence model, obtains the property service quality score for the various indicator data of the subject to be evaluated, the artificial intelligence model is a model trained based on the historical sample data set and used to evaluate the property service quality score, generates a property service evaluation report based on the property service quality score, and sends the property service evaluation report to the target terminal, so that the target terminal determines whether the property service quality evaluation of the subject to be evaluated is passed based on the property evaluation report. In this process, various indicator data of property service quality are collected through multiple channels, and the collected data are analyzed and processed using artificial intelligence technology to realize intelligent evaluation of property service quality, which can provide banks with a more efficient and accurate evaluation method for evaluating the property service quality of the subject matter, thereby improving the efficiency of bank business processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] Figure 1 A possible scenario diagram provided for an embodiment of the present application;

[0047] Figure 2 A flowchart of a data processing method provided in an embodiment of the present application;

[0048] Figure 3 for Figure 2 Schematic diagram of the process of step S201 in ;

[0049] Figure 4 A flowchart of another data processing method provided in an embodiment of the present application;

[0050] Figure 5 A flowchart of a data processing method provided for an exemplary embodiment of the present application;

[0051] Figure 6 A schematic diagram of the structure of a data processing device provided in an embodiment of the present application;

[0052] Figure 7 A schematic diagram of the structure of a server provided in an embodiment of the present application.

[0053] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0054] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0055] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of financial data or user data and other information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals. It should be noted that in the embodiments of this application, some existing solutions in the industry such as certain software, components, models, etc. may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of the implementation of the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0056] With the acceleration of urbanization and the improvement of residents' living standards, property management services, as an important part of community operations, have a direct impact on residents' quality of life and satisfaction. Therefore, when banks issue mortgage loans, if the subject involves financial products and services such as real estate and property management, they also need to evaluate the property. Traditional property service quality evaluation usually relies on manual surveys and feedback, which has problems such as low efficiency, incomplete data collection, and strong subjectivity. Therefore, it is necessary to provide an improvement plan for property service quality evaluation to enable banks to more efficiently and accurately evaluate the property service quality of the subject matter, while better meeting residents' needs and improving the quality and efficiency of property services.

[0057] In view of this, an embodiment of the present application proposes a property service quality assessment and improvement scheme based on artificial intelligence technology. The embodiment of the present application responds to an assessment request for the property service quality of the subject to be assessed initiated by the target terminal, obtains various indicator data for assessing the property service quality of the subject to be assessed based on multiple channels, inputs the various indicator data into a pre-trained artificial intelligence model, processes the various indicator data based on the artificial intelligence model, and obtains a property service quality score for each indicator data of the subject to be assessed. The artificial intelligence model is a model trained based on a historical sample data set and used to assess the property service quality score. A property service evaluation report is generated based on the property service quality score, and the property service evaluation report is sent to the target terminal, so that the target terminal determines whether the property service quality assessment of the subject to be assessed has passed based on the property evaluation report. In this process, various indicator data of property service quality are collected through multiple channels, and artificial intelligence technology is used to analyze and process the collected property service information, so as to realize intelligent evaluation of property service quality and user satisfaction, and provide banks with a more efficient and accurate property service quality evaluation method for evaluating the subject matter; in addition, the embodiment of the present application can also provide improvement suggestions for the property management platform based on the evaluation results. This solution has high practicality and market potential for financial institutions or property management platforms that evaluate property service quality.

[0058] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0059] Take the bank's mortgage loan business as an example. Figure 1 FIG. 1 is an application scenario diagram applicable to the method of the embodiment of the present application. Figure 1As shown, the application scenario includes: a server 10, and a target terminal 20 electrically connected to the server, wherein the target terminal 20 can be a device for business personnel of a bank (in some application scenarios, it can also be other enterprises, organizations or individuals that have the need to evaluate the property service quality of the target, which is not particularly limited in this embodiment) to perform business processing. The business personnel selects the target to be evaluated on the target terminal device 20 and initiates an evaluation request for the property service quality of the target to be evaluated to the server 10. After receiving the evaluation request, the server 20 obtains the indicator data corresponding to the target to be evaluated, and uses the artificial intelligence model to evaluate and analyze the indicator data to output the property service quality score, and generates a corresponding evaluation report based on the property service quality score and feeds it back to the target terminal 20. The target terminal further determines whether the property service quality of the target is passed based on the evaluation report, thereby determining whether to enter the downstream business process. For example, in the mortgage loan issuance business, if the property service quality of the target to be evaluated is passed and other evaluation items are passed, the subsequent mortgage loan business approval process can be entered. Optionally, it may also include a property management platform 30 electrically connected to the server 10. After generating the evaluation report, the server 10 may store the evaluation report in a cloud platform or other storage space. When the property management platform 30 initiates an evaluation report request, the evaluation report and the corresponding optimization strategy may be sent to the property management platform 30 to facilitate the property management platform 30 to improve the property service quality.

[0060] Among them, the server 10 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms; the terminal device 20 can include but is not limited to computers, smart phones, tablets, e-book readers, Moving Picture experts group audio layer III (MP3) players, Moving Picture experts group audio layer IV (MP4) players, portable computers, car computers, wearable devices, desktop computers, set-top boxes, smart TVs, etc.

[0061] based on Figure 1 In the application scenario shown, an embodiment of the present application provides a data processing method, and the execution entity is a server 10. Figure 2A flow chart of a data processing method provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the method may include the following steps S201-S203:

[0062] Step S201: In response to a request for evaluating the property service quality of an object to be evaluated initiated by a target terminal, various indicator data for evaluating the property service quality of the object to be evaluated are obtained based on multiple channels.

[0063] In this embodiment, the target terminal can be a terminal device (e.g., a user's smartphone or computer) used by a bank staff to handle business. By initiating a request to the server on the target terminal, the server requests to evaluate the quality of property services of a specific subject. The request may include information about the subject to be evaluated, such as the name, location, or other identification features of the property. After receiving the evaluation request, the server collects indicator data related to the quality of the property service through multiple channels. The multiple channels may include at least one of the following: an Internet of Things device, a mobile application, and social media, wherein the Internet of Things device may be a sensor device (such as a camera, a temperature and humidity sensor, etc.), and the mobile application may be an application software capable of loading an electronic device, which may be a property management application that provides functions such as property service requests, bill payments, and announcement notifications, or a community interactive application that provides functions such as community announcements, event organization, and neighborhood interactions, etc., wherein the mobile application may be integrated with the Internet of Things device to improve data acquisition efficiency. In some embodiments, in addition to the above channels, other channels such as a property management platform, a third-party service platform, and the like may also be included.

[0064] In this embodiment, the subject to be evaluated can be a property project, such as a specific property project or building complex, such as a residential community, a commercial building, an office building, etc.; or a property management company, that is, a company that provides property management services, to evaluate its overall service quality; or a specific service area, a specific area or functional area in property management, such as a parking lot, a public area, a fitness facility, etc.

[0065] It can be understood that in response to the conditions or states on which the executed operations depend, when the dependent conditions or states are met, one or more operations executed can be in real time or have a set delay; unless otherwise specified, there is no restriction on the order of execution of the multiple operations executed.

[0066] It can be understood that property services refer to the activities of owners selecting and hiring property service companies, and the owners and property service companies repairing, maintaining and managing houses and supporting facilities and related venues in accordance with the property service contract, and maintaining environmental sanitation and related order within the property management area.

[0067] Step S202: input each indicator data into a pre-trained artificial intelligence model, process each indicator data based on the artificial intelligence model, and obtain a property service quality score for each indicator data of the subject to be evaluated; wherein the artificial intelligence model is a model trained based on a historical sample data set and used to evaluate the property service quality score.

[0068] Instead of using online or offline surveys in related technologies, users' evaluation results on properties are collected to evaluate the property service quality of the target. After acquiring property services through multiple channels, this embodiment inputs them into a pre-trained artificial intelligence model, and the artificial intelligence model analyzes and processes the indicator data to obtain property quality scores for each indicator data. Among them, the artificial intelligence model can be a model based on machine learning or deep learning, such as a recurrent neural network model, a long short-term memory network model and a large language model, or a decision tree, a random forest, a support vector machine, etc.

[0069] In an optional embodiment, the training process of the artificial intelligence model can be carried out in the following manner: obtaining a historical sample data set, wherein the historical sample data set includes sample indicator data of property service quality of multiple sample targets and corresponding scoring results; using the sample indicator data as the input of the initial artificial intelligence model, and using the scoring results corresponding to the sample indicator data sets as the output of the initial artificial intelligence model, iteratively training the initial artificial intelligence model until a preset number of iterations is reached or the loss value satisfies a preset loss condition, thereby obtaining the artificial intelligence model.

[0070] In this embodiment, the model is trained by acquiring a sufficient amount of historical sample data sets to improve the accuracy of the model. These data sets may include various indicators of property service quality of multiple sample targets, as well as known scoring results of these sample targets. The scoring results may be evaluated by experts (such as standard evaluations set by bank mortgage business, which may be determined based on actual conditions). After acquiring the historical sample data sets, in order to ensure that all data formats are consistent, the sample data sets may be formatted first to facilitate subsequent processing and input into the model, and to remove missing values, outliers, and noise data to improve data quality, and to select and extract the most useful features for model prediction (such as feature scaling, encoding categorical variables, generating new features, etc.). Next, the sample data sets may be segmented, and the data sets may be divided into training sets, validation sets, and test sets, so that model validation and final evaluation can be performed during the training process. After selecting the initial artificial intelligence model (such as a recurrent neural network model), set the initial model parameters and prepare for training: use the sample indicator data as the input of the model, and the corresponding scoring results as the output of the model. Select a suitable loss function (such as mean square error, cross entropy, etc.) to measure the gap between the model prediction results and the actual results, and use iterative optimization algorithms (such as gradient descent, Adam, etc.) to continuously adjust the model parameters to minimize the loss function. The training process continues until the preset number of iterations is reached or the loss value meets the preset loss conditions.

[0071] In addition, during the training process, the validation set can be used to evaluate the model performance to prevent overfitting. After the training is completed, the test set can be used to evaluate the generalization ability of the model to ensure that the model performs well on unseen data. A variety of performance indicators (such as accuracy, precision, recall, F1 score, etc.) can also be used to comprehensively evaluate the model.

[0072] After the model training is completed, the trained model can be deployed to the production environment for actual property service quality evaluation. In this embodiment, the model can be deployed in the server for use by the server. The server can monitor the performance of the model in actual applications and regularly update the model to adapt to new data and environmental changes.

[0073] In this way, an effective artificial intelligence model can be trained to evaluate the quality of property services, helping banks to efficiently and accurately evaluate the property service levels of the properties to be evaluated, thereby improving the bank's business processing efficiency.

[0074] Step S203: Generate a property service evaluation report based on the property service quality score, and send the property service evaluation report to the target terminal, so that the target terminal determines whether the property service quality evaluation of the subject to be evaluated has passed based on the property evaluation report.

[0075] Optionally, in the process of generating a property service evaluation report, the property service evaluation report template can be combined. For example, when the target terminal initiates a request for an evaluation of the quality of property services, the evaluation request may carry a template for a property service evaluation report. The template may be determined or adjusted by a person skilled in the art according to the adaptability of the actual application. For example, the template may include formats such as a title, cover, directory, and text content. The text content may include fixed items and fillable items. Fixed items are filled-in format content (such as the type of each indicator data). The server fills the analysis results (scores) corresponding to the corresponding indicator data into the fillable items based on the property service quality score to generate a property service evaluation report and send it to the target terminal for bank query and processing. It should be noted that in addition to the above examples, other templates or forms may be used to generate property service evaluation reports. For example, the template may also be a template for icon display, etc. This is not particularly limited in this embodiment.

[0076] In some embodiments, the above steps generate a property service evaluation report based on the property service quality score, and can also be carried out in the following manner: using data visualization technology, data analysis is performed on the indicator data whose property service quality score is lower than the score threshold corresponding to the relevant indicator, and the points for improvement of the indicator data are obtained; based on the property service score and the points for improvement of the indicator data, a property service evaluation report is generated.

[0077] In this embodiment, the indicators with scores lower than the set threshold are screened out. These indicators represent the weak links of property services and need to be focused on and improved. By arranging the data of low-scoring indicators into a format that is easy to analyze and preparing for visualization, data visualization tools can be selected to display the data (such as the way of chart creation), and the causes of low scores can be identified by in-depth analysis of low-scoring indicators. It can be a detailed analysis of specific events, user feedback or operation processes, and based on the analysis results, the specific points to be improved for each low-scoring indicator are determined. For example, a machine learning algorithm can be used to analyze the collected data, identify the strengths and weaknesses of service quality, and use a pre-built property service evaluation model to comprehensively score various indicators of property services (such as excellent, good, general, poor), achieve a quantitative assessment of service quality, and generate a service evaluation report. It should be noted that the above-mentioned score threshold can be determined by a technician in this field based on industry standards or historical data adaptability. In this way, a more detailed property evaluation report can be generated, which is convenient for users to view and further process and analyze.

[0078] In some embodiments, the target terminal determines whether the property service quality evaluation of the subject to be evaluated has passed according to the property evaluation report. The following method can be used: the bank pre-sets the evaluation criteria and stores them in the target terminal. The evaluation criteria are used to evaluate whether the property service quality meets the requirements. It can be determined based on industry specifications, internal policies or historical data, and weight distribution can be set for different indicator data. Different indicators may have different importance. The system needs to conduct a comprehensive evaluation of each indicator according to the preset weight. Specifically, the comprehensive score and the score of each indicator in the report can be compared with the preset scoring criteria. The final calculation result is calculated according to the scoring criteria and weights using logical judgment tools such as rule engines or decision trees. According to the calculation results, the system automatically determines whether the property service quality evaluation has passed. If the comprehensive score and the key indicator score both meet or exceed the standard, it can be regarded as passed. For situations close to the standard boundary, manual review or further investigation may be required. Further, an evaluation result notification can be generated and sent to relevant stakeholders, such as property management companies, bank internal audit teams, etc.

[0079] In this way, the target terminal can efficiently and accurately determine whether the property service quality evaluation has passed based on the property evaluation report. This automated and systematic evaluation process helps improve decision-making efficiency, reduce human errors, and ensure transparency and consistency in the evaluation process.

[0080] In this embodiment, the indicator data may include at least one of the following: hardware facility data, maintenance response time data, user feedback data, security management data, and service record data.

[0081] Among them, hardware facility data can be usage status data or usage time data of facilities such as elevators, water supply systems, power supply systems, HVAC equipment, and security equipment; maintenance response time data can be the maintenance response time of property maintenance personnel for hardware facilities (such as the time from user initiation to property maintenance personnel's on-site maintenance); user feedback data can be service quality data for the target to be evaluated fed back by effective users of the target (such as residents of a residential community), which may include satisfaction surveys, complaint records, suggestions and opinions, etc.; safety management data can be data recorded by property management personnel (such as security) during the safety management process of the target to be evaluated; service record data can include cleaning service data, greening data, and so on.

[0082] Next, the various indicator data for evaluating the property service quality of the subject to be evaluated in the above step S201 based on multiple channels are further introduced. Figure 3 As shown, the above step S201 may include at least one of the following steps S201a-S201c.

[0083] In one example, step S201 may be included: based on an IoT device, obtaining at least one of the hardware facility data and maintenance response time data of the subject to be evaluated.

[0084] IoT devices may include temperature sensors, humidity sensors, current sensors, vibration sensors, smart meters, smart valves, etc., which are used to monitor and collect status data of hardware facilities. These IoT devices can communicate with the server (or through the property management platform) to transmit the hardware device data of each sensor to the server.

[0085] Similarly, maintenance response time data can also be monitored through sensors, such as installing sensors on facilities or equipment that can detect equipment status or faults. Specifically, sensors in elevators, air conditioners, or water supply systems can monitor their operating status in real time. Once a fault is detected, the sensor can automatically generate a maintenance request and record the time of the request. Alternatively, maintenance response time data can also use an automated work order system, such as IoT devices can be integrated with property management systems to automatically generate and assign maintenance work orders. When a sensor detects a problem, the system will immediately create a work order and assign it to the appropriate maintenance personnel. The system can track the entire process from work order generation to maintenance completion, recording the timestamp of each step. In addition, Radio Frequency Identification (RFID) and Global Positioning System (GPS) technologies can also be combined. Maintenance personnel can be equipped with devices with RFID tags or GPS tracking so that the property management system can track their location and work status in real time. When maintenance personnel receive the work order and start working, the system can record their response time and the time they arrived at the site.

[0086] In some embodiments, maintenance personnel can use a dedicated mobile application to receive work orders, update maintenance status, and record completion time. The application can automatically record the time from receiving the work order to completing the maintenance, and upload this data to the management system.

[0087] Furthermore, considering that the server may need to conduct property service quality assessment for multiple targets to be assessed in actual applications, in order to efficiently obtain the indicator data of the corresponding targets, this embodiment further refines the above-mentioned step of obtaining at least one of the hardware facility data and maintenance response time data of the target to be assessed based on the IoT device. Specifically, based on the positioning information of the target to be assessed, the IoT devices within the area of ​​the target to be assessed can be determined, and at least one of the hardware facility data and maintenance response time data of the target to be assessed can be obtained from the IoT devices. In this way, through the method of positioning acquisition, it is possible to effectively avoid confusing the data of the IoT devices, thereby improving data accuracy.

[0088] In another example, step S201b may be included: based on the mobile application, obtaining at least one of the security management data and service record data of the subject to be evaluated.

[0089] Among them, the mobile application can be a dedicated mobile application developed by the property to record property-related data. The application can include sections for security management and service records, support data input, upload, viewing and analysis, and relevant property management personnel, security personnel, and service personnel can register accounts on the mobile application, and different permissions can be assigned according to user roles to ensure data security and privacy. For example, security personnel can enter security event records, while service personnel can enter service execution records. The server initiates a request to the mobile application and obtains security management data, service record data, etc. through the application or background system.

[0090] As mentioned above, the mobile application can be designed with different sections, such as the security management and service record sections. In the above steps, obtaining at least one of the security management data and service record data of the subject to be evaluated based on the mobile application may include the following steps: obtaining the security management data of the subject to be evaluated based on the property security management section of the mobile application, and / or obtaining the service record data of the subject to be evaluated based on the service record section of the mobile application. In this way, the server can obtain the corresponding indicator data through different sections, thereby improving the efficiency of data acquisition.

[0091] In yet another example, step S201c may also be included: obtaining user feedback data of the subject to be evaluated based on social media.

[0092] In this embodiment, social media can be a social media platform where users are active, such as relevant property forums in WeChat and Weibo, etc., or forums created by the authorities, etc. By setting keywords and tags related to the property subject, such as property name, service type, FAQ, etc., relevant discussions and feedback can be accurately captured. Exemplarily, in the above steps, user feedback data of the subject to be evaluated is obtained based on social media, including: querying comment data about the subject to be evaluated from social media, performing word segmentation and keyword extraction processing on the comment data, so as to obtain user feedback data of the subject to be evaluated.

[0093] The corresponding comment data can be queried from social media using the relevant tags of the property (such as the name of the property), and the comment data can be segmented using a word segmentation tool to decompose the text into independent words or phrases, and the most representative and frequently occurring keywords in the comments can be identified by keyword recognition means, reflecting the main issues and topics of user concern, thereby obtaining user feedback data of the subject to be evaluated. In some embodiments, the sentiment analysis model can also be applied to analyze the user's sentiment tendency analysis (positive, neutral, negative) of the comments, thereby assigning a sentiment score to each comment to help identify the user's satisfaction and emotional attitude. Through these steps, the server can effectively utilize social media comment data to efficiently obtain user feedback data of the subject to be evaluated. Among them, the above-mentioned word segmentation tool, keyword recognition means and sentiment analysis model can be implemented using natural language processing technology.

[0094] It should be noted that the above process of obtaining various types of indicator data may be to select and obtain any one or any combination of indicator data, which is not particularly limited in this embodiment. In some embodiments, in addition to the indicator data or channels listed above, other indicator data or channels may also be included, which is not particularly limited in this embodiment.

[0095] Next, the above step S202 of inputting various indicator data into the pre-trained artificial intelligence model is further introduced: preprocessing various indicator data to obtain preprocessed indicator data; wherein, the preprocessing includes at least one of the following: data cleaning, data integration and feature extraction; and inputting the preprocessed indicator data into the pre-trained artificial intelligence model.

[0096] In this embodiment, data cleaning may include identifying and processing missing values ​​in the data, such as deleting missing data points, filling with mean / median or using interpolation to process missing values; or identifying and processing outliers, which may need to be processed according to statistical methods (such as Z-score) or business rules; it may also include removing duplicate records in the data set to avoid bias in the analysis results, etc. Data integration may include merging data sets from different sources to ensure that all relevant indicator data are in a unified data framework. Feature extraction may be to extract or generate new features from the original data, select the most useful features for the model according to correlation analysis or business needs, reduce the dimension and computational complexity, and standardize the data (such as Z-score standardization) or normalize (such as Min-Max normalization) to ensure that different features are on the same scale. After the data is preprocessed as described above, the pre-trained artificial intelligence model is loaded, and the pre-processed indicator data is input into the model to ensure that the data format and dimension are consistent with the input requirements of the model. Accordingly, the processing of the historical sample data set during the model training process can refer to the data preprocessing method in this embodiment for data processing.

[0097] Through data preprocessing, the data is input into the model for processing, making the data input into the model more accurate, which can effectively improve the accuracy and reliability of model analysis and processing.

[0098] Figure 4 This is a flow chart of another data processing method provided by an embodiment of the present application. Based on the above embodiment, this embodiment combines the property service evaluation report to determine the optimization strategy corresponding to each indicator data, and transmits the optimization strategy to the property management platform to provide data and decision support for improving the quality of property services. Specifically, in addition to the above steps S201-S203, the method provided by this embodiment may also include steps S401 and S402:

[0099] Step S401: Determine an optimization strategy for each indicator data according to the property service evaluation report.

[0100] Exemplarily, the server combines the property service evaluation report to identify the scores of various indicators, indicators below the threshold, and potential areas for improvement. Through data analysis in the report, determine which indicators have problems or perform poorly, such as poor user feedback, frequent security incidents, and long maintenance response time. Through in-depth cause analysis of each low-scoring indicator, identify the specific factors that cause the problem, such as insufficient resources, improper processes, or technical problems, and design specific optimization strategies based on the root cause analysis, such as optimizing property management processes to improve efficiency and response speed; resource allocation, adjust resource allocation, and ensure that key areas have sufficient personnel and equipment support; technology upgrades, introduce or upgrade technical tools, such as more advanced monitoring systems or automated management software; training and development, provide training for employees to improve their skills and service awareness, and so on.

[0101] Specifically, deep learning technology can be used to analyze the root causes of identified problems, find out the bottlenecks and deficiencies in the service process, and provide support for decision-making by simulating the possible effects of different improvement plans. According to the results of problem diagnosis, expert systems and rule engines are used to generate targeted improvement suggestions, including process optimization, personnel training, facility upgrades, etc. For example, if the cleaning and sanitation score is found to be low, it may be recommended to increase the frequency of cleaning or replace cleaning equipment. At the same time, implementation effect predictions are provided to help property managers make more reasonable decisions. In some embodiments, an optimization strategy mapping table can be stored in the server, and the optimization strategy mapping table includes the corresponding relationship between the indicator (when the score is lower than the preset threshold) and the optimization strategy. Using the optimization strategy mapping table, the corresponding optimization strategy can be quickly determined. In some embodiments, the optimization strategies can also be prioritized according to the degree of impact and feasibility, so that users can view the items that need to be optimized first.

[0102] Step S402: Send the property service evaluation report and the corresponding optimization strategy to the property management platform.

[0103] Through the above method, optimization measures are proposed for the problems analyzed in the property service evaluation report and simultaneously provided to the property management platform, which will help the property management platform to improve the overall level of property services and thus bring better experience to users.

[0104] In an exemplary embodiment, if Figure 5 As shown, the following process is included:

[0105] S1. Data mobile phones collect property service-related data through various channels such as IoT devices (such as sensors, surveillance cameras), mobile applications, and social media, which can include user feedback, service records, facility usage, security management, maintenance response time, etc. In this process, natural language processing technology can be used to perform sentiment analysis and keyword extraction on text data, providing a basis for subsequent intelligent analysis.

[0106] S2. Use data preprocessing technology to clean and integrate the collected data, and extract key indicators through features, such as service response speed, problem solving rate, user satisfaction, etc.

[0107] S3. Use artificial intelligence models (such as machine learning algorithm models) to analyze the collected data and identify the strengths and weaknesses of service quality. By building a property service evaluation model, it is possible to comprehensively score various indicators of property services (such as excellent, good, average, and poor), achieve quantitative evaluation of service quality, and generate a service evaluation report.

[0108] In this process, the artificial intelligence model can analyze and evaluate the collected data based on the service quality satisfaction dimension. The satisfaction evaluation dimensions can be as follows: Facility maintenance: the maintenance of public areas and facilities, such as whether green areas, elevators, fitness facilities, etc. are properly managed; Service response speed: evaluate the response time of the property service team to residents' needs and problems, and the efficiency of solving problems; Service attitude: the service attitude of the property staff, including friendliness, professionalism and patience; Service professionalism: the professional skills and knowledge level of the property team in maintenance, cleaning, safety and other aspects; Environmental sanitation: the cleanliness of the community, including public areas and garbage disposal; Safety assurance : Community security measures, such as access control systems, monitoring equipment, patrols, etc.; Fee transparency: the rationality of property fees, the openness and transparency of charging standards, and the details of fee usage; Customer communication: whether the communication channels between the property company and the owners are unobstructed, and the mechanism for handling problem feedback and suggestions; Value-added services: additional services provided by the property company, such as housekeeping, maintenance, community activities, etc.; Emergency response capabilities: the response capabilities and efficiency of the property team in emergencies or emergencies; Continuous improvement: whether the property company has a mechanism for continuous service improvement, and whether it can adjust and optimize according to the feedback of the owners; Comprehensive impression: the overall evaluation and satisfaction of the owners with the overall service of the property company. By rating these dimensions as very dissatisfied, dissatisfied, average, very satisfied, very satisfied, or 1-5 points, the quality of property services can be evaluated, and targeted optimization measures and suggestions can be proposed.

[0109] S4. For the generated property service evaluation report, deep learning technology can be used to analyze the root causes of the identified problems and find out the bottlenecks and deficiencies in the service process. By simulating the possible effects of different improvement plans, support can be provided for decision-making.

[0110] S5. Based on the results of problem diagnosis, expert systems and rule engines can be used to generate targeted improvement suggestions, including process optimization, personnel training, facility upgrades, etc. For example, if the cleaning and sanitation score is low, it may be recommended to increase the cleaning frequency or replace the cleaning equipment. At the same time, it provides implementation effect predictions to help property managers make more reasonable decisions.

[0111] Figure 6 is a structural diagram of a data processing device provided by an embodiment of the present disclosure, such as Figure 6 As shown, the device 600 includes a data acquisition module 601, a data input module 602, a model processing module 603 and an evaluation generation module 604, wherein:

[0112] The data acquisition module 601 is configured to respond to an evaluation request for the property service quality of the subject to be evaluated initiated by the target terminal, and obtain various indicator data for evaluating the property service quality of the subject to be evaluated based on multiple channels;

[0113] A data input module 602, which is configured to input various indicator data into a pre-trained artificial intelligence model;

[0114] A model processing module 603 is configured to process various indicator data based on the artificial intelligence model to obtain a property service quality score for each indicator data of the subject to be evaluated; wherein the artificial intelligence model is a model trained based on a historical sample data set and used to evaluate the property service quality score;

[0115] The evaluation generation module 604 is configured to generate a property service evaluation report based on the property service quality score, and send the property service evaluation report to the target terminal so that the target terminal determines whether the property service quality evaluation of the subject to be evaluated has passed based on the property evaluation report.

[0116] In one embodiment, the multiple channels include at least one of the following: IoT devices, mobile applications, and social media, and the indicator data includes at least one of the following: hardware facility data, maintenance response time data, user feedback data, security management data, and service record data;

[0117] The data acquisition module 601 includes at least one of the following:

[0118] A first acquisition unit, configured to acquire at least one of the hardware facility data and the maintenance response time data of the subject to be evaluated based on an Internet of Things device;

[0119] A second acquisition unit, configured to acquire at least one of the security management data and the service record data of the subject to be evaluated based on a mobile application;

[0120] The third collection unit is configured to obtain user feedback data of the subject to be evaluated based on social media.

[0121] In one embodiment, the first acquisition unit is specifically configured to determine the IoT devices within the area of ​​the target to be evaluated based on the location information of the target to be evaluated, and obtain at least one of the hardware facility data and the maintenance response time data of the target to be evaluated from the IoT devices; and / or,

[0122] The second acquisition unit is specifically configured to obtain the security management data of the subject to be evaluated based on the property security management section of the mobile application, and / or obtain the service record data of the subject to be evaluated based on the service record section of the mobile application; and / or,

[0123] The third collection unit is specifically configured to query comment data about the subject to be evaluated from social media, perform word segmentation and keyword extraction processing on the comment data, so as to obtain user feedback data of the subject to be evaluated.

[0124] In one embodiment, a model training module is also included, including:

[0125] A sample acquisition unit, which is configured to acquire a historical sample data set, wherein the historical sample data set includes sample indicator data of property service quality of multiple sample targets and corresponding scoring results;

[0126] A training unit is configured to use the sample indicator data as the input of an initial artificial intelligence model, and use the scoring results corresponding to the sample indicator data sets as the output of the initial artificial intelligence model, and iteratively train the initial artificial intelligence model until a preset number of iterations is reached or the loss value meets a preset loss condition, thereby obtaining the artificial intelligence model.

[0127] In one embodiment, the initial artificial intelligence model adopts at least one of the following: a recurrent neural network model, a long short-term memory network model, and a large language model.

[0128] In one implementation, the data input module 602 includes:

[0129] A preprocessing unit, which is configured to preprocess various indicator data to obtain preprocessed indicator data; wherein the preprocessing includes at least one of the following: data cleaning, data integration and feature extraction;

[0130] An input unit is configured to input the preprocessed indicator data into the pre-trained artificial intelligence model.

[0131] In one implementation, the evaluation generation module 604 includes:

[0132] Using data visualization technology, data analysis is performed on the indicator data whose property service quality scores are lower than the score thresholds corresponding to the relevant indicators to obtain the points for improvement of the indicator data;

[0133] A property service evaluation report is generated based on the property service score and the areas for improvement of the indicator data.

[0134] In one embodiment, the device further comprises:

[0135] A strategy determination module, which is configured to determine an optimization strategy for each indicator data according to the property service evaluation report;

[0136] A report sending module is configured to send the property service evaluation report and the corresponding optimization strategy to the property management platform.

[0137] The above-mentioned device provided in the embodiment of the present application can be used to execute the technical solution of the data processing method in the above-mentioned embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0138] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, all or part of them can be integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the data acquisition module 603 can be a separately established processing element, or it can be integrated in a certain chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a certain processing element of the above device. The function of the above data acquisition module 603. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0139] The present application also provides a server. Figure 7 As shown, the server may include: a transceiver 701 , a processor 702 , and a memory 703 .

[0140] The processor 702 executes the computer execution instructions stored in the memory, so that the processor 702 executes the data processing scheme in the above embodiment. The processor 702 can be a general-purpose processor, including a central processing unit CPU, a network processor (NP), etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0141] The memory 703 is connected to the processor 702 via a system bus and completes communication between them. The memory 703 is used to store computer program instructions.

[0142] The transceiver 701 may be used to receive or send data.

[0143] The system bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The transceiver is used to realize the communication between the database access device and other computers (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM) and may also include non-volatile memory.

[0144] For related instructions, please refer to the above method embodiment, which will not be elaborated here.

[0145] The embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the data processing method provided in the above method embodiment. For relevant instructions, please refer to the above method embodiment, and no further details will be given here.

[0146] According to a fourth aspect of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the data processing method provided in the above method embodiment is implemented. For relevant descriptions, please refer to the above method embodiment, and no further details will be given here.

[0147] The embodiment of the present application also provides a chip for executing instructions, which is used to execute the technical solution of the data processing method in the above embodiment. For relevant instructions, please refer to the above method embodiment, and no further details will be given here.

[0148] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0149] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of this embodiment.

[0150] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The above-mentioned module-composed unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0151] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the methods of various embodiments of the present application.

[0152] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.

[0153] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0154] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0155] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0156] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic control unit or a main control device.

[0157] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A data processing method, characterized in that: include: In response to a request for evaluating the property service quality of the subject to be evaluated initiated by the target terminal, obtaining various indicator data for evaluating the property service quality of the subject to be evaluated based on multiple channels; Input each indicator data into a pre-trained artificial intelligence model, process each indicator data based on the artificial intelligence model, and obtain a property service quality score for each indicator data of the subject to be evaluated; wherein the artificial intelligence model is a model trained based on a historical sample data set and used to evaluate the property service quality score; A property service evaluation report is generated based on the property service quality score, and the property service evaluation report is sent to the target terminal, so that the target terminal determines whether the property service quality evaluation of the subject to be evaluated has passed based on the property evaluation report.

2. The method according to claim 1, characterized in that The multiple channels include at least one of the following: IoT devices, mobile applications, and social media, and the indicator data includes at least one of the following: hardware facility data, maintenance response time data, user feedback data, security management data, and service record data; The various indicator data for evaluating the property service quality of the subject to be evaluated obtained through multiple channels include at least one of the following: Based on the Internet of Things device, obtaining at least one of the hardware facility data and the maintenance response time data of the subject to be evaluated; Based on the mobile application, obtaining at least one of the security management data and service record data of the subject to be evaluated; Based on social media, user feedback data of the subject to be evaluated is obtained.

3. The method according to claim 2, characterized in that The obtaining, based on the IoT device, at least one of the hardware facility data and the maintenance response time data of the subject to be evaluated includes: Based on the location information of the target to be evaluated, determine the Internet of Things devices within the area of ​​the target to be evaluated, and obtain at least one of the hardware facility data and the maintenance response time data of the target to be evaluated from the Internet of Things devices; and / or, The obtaining, based on the mobile application, at least one of the security management data and the service record data of the subject to be evaluated comprises: Based on the property security management section of the mobile application, obtaining security management data of the subject to be evaluated, and / or based on the service record section of the mobile application, obtaining service record data of the subject to be evaluated; and / or, The obtaining of user feedback data of the subject to be evaluated based on social media includes: The comment data about the subject to be evaluated is queried from social media, and word segmentation and keyword extraction are performed on the comment data to obtain user feedback data of the subject to be evaluated.

4. The method according to claim 1, characterized in that: The training method of the artificial intelligence model comprises: Acquire a historical sample data set, wherein the historical sample data set includes sample indicator data of property service quality of multiple sample targets and corresponding scoring results; The sample indicator data are used as the input of the initial artificial intelligence model, and the scoring results corresponding to the sample indicator data sets are used as the output of the initial artificial intelligence model. The initial artificial intelligence model is iteratively trained until a preset number of iterations is reached or the loss value meets a preset loss condition, so as to obtain the artificial intelligence model.

5. The method according to claim 4, characterized in that The initial artificial intelligence model adopts at least one of the following: a recurrent neural network model, a long short-term memory network model and a large language model.

6. The method according to any one of claim 1, characterized in that: The inputting of various indicator data into the pre-trained artificial intelligence model includes: Preprocessing each indicator data to obtain preprocessed indicator data; wherein the preprocessing includes at least one of the following: data cleaning, data integration and feature extraction; Input the preprocessed indicator data into the pre-trained artificial intelligence model.

7. The method according to claim 1, characterized in that Generating a property service evaluation report based on the property service quality score includes: Using data visualization technology, data analysis is performed on the indicator data whose property service quality scores are lower than the score thresholds corresponding to the relevant indicators to obtain points for improvement for the indicator data; A property service evaluation report is generated based on the property service score and the areas for improvement of the indicator data.

8. The method according to any one of claims 1 to 7, characterized in that: Also includes: Determine optimization strategies for various indicator data based on the property service evaluation report; The property service evaluation report and the corresponding optimization strategy are sent to the property management platform.

9. A data processing device, characterized in that: include: A data acquisition module, which is configured to respond to an evaluation request for the property service quality of the subject to be evaluated initiated by the target terminal, and obtain various indicator data for evaluating the property service quality of the subject to be evaluated based on multiple channels; A data input module, which is configured to input various indicator data into a pre-trained artificial intelligence model; A model processing module, which is configured to process various indicator data based on the artificial intelligence model to obtain a property service quality score for each indicator data of the subject to be evaluated; wherein the artificial intelligence model is a model trained based on a historical sample data set and used to evaluate the property service quality score; An evaluation generation module is configured to generate a property service evaluation report based on the property service quality score, and send the property service evaluation report to the target terminal so that the target terminal determines whether the property service quality evaluation of the subject to be evaluated has passed based on the property evaluation report.

10. A server, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the data processing method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the data processing method according to any one of claims 1 to 8 when executed by a processor.

12. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the data processing method according to any one of claims 1 to 8.

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