Object screening method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an object screening method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of screening out selected evaluation demand information from preset evaluation demand information according to service categories and evaluation time period information of candidate objects; wherein the selected evaluation demand information comprises selected evaluation dimension information, a selected dimension weight value, selected evaluation index information and a selected index weight value; collecting object monitoring data of the candidate object according to the selected evaluation index information; performing satisfaction evaluation on the candidate objects according to the object monitoring data to obtain satisfaction evaluation data; splicing the selected evaluation dimension information, the selected dimension weight value, the selected index weight value and the satisfaction evaluation data into target evaluation data; and screening out a target object from the candidate objects according to the target evaluation data. The object screening method and device can be applied to business systems needing a large amount of data such as financial science and technology and health medical treatment, and the accuracy and objectivity of object screening can be improved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and is applied to the fields of fintech and healthcare, particularly to an object screening method and apparatus, electronic device and storage medium. Background Technology

[0002] An object management platform is a digital system for enterprises to manage and maintain relationships between multiple objects (such as product suppliers and service providers). Therefore, the quality of these objects impacts the construction of the enterprise's supply chain. Taking a fintech scenario as an example, a bank platform connects to multiple fund project suppliers, who provide investment projects. To improve operational stability, the bank platform will screen these fund project suppliers according to set service standards to select those suitable for building the fund project supply chain. Similarly, in a healthcare scenario, to increase the usage rate of a home healthcare service platform, the platform will screen its participating home healthcare service providers to form a home healthcare service supply chain.

[0003] In related technologies, object selection requires multi-faceted evaluation and screening based on evaluation data. However, object evaluation lacks quantitative evaluation tools and mainly relies on manual evaluation by administrators of the object management platform. This evaluation is too subjective and lacks comprehensiveness and scientific rigor, resulting in low accuracy in object selection. Therefore, improving the accuracy and objectivity of object selection has become an urgent technical problem to be solved. Summary of the Invention

[0004] The main objective of this application is to provide an object screening method, apparatus, electronic device, and storage medium, which aims to improve the accuracy and objectivity of object screening.

[0005] To achieve the above objectives, a first aspect of this application proposes an object filtering method, the method comprising:

[0006] Obtain information on the service category and evaluation period of the candidate;

[0007] The preset evaluation requirement information is filtered according to the service category and the evaluation period information to obtain the selected evaluation requirement information; wherein, the selected evaluation requirement information includes: selected evaluation dimension information, selected dimension weight value, selected evaluation indicator information and selected indicator weight value;

[0008] Based on the selected evaluation index information, object monitoring data of the candidate objects is collected; wherein, the object monitoring data is the operational data and service feedback data of the candidate objects when performing service tasks;

[0009] Based on the object monitoring data, a satisfaction assessment is performed on the candidate objects to obtain satisfaction assessment data;

[0010] The selected evaluation dimension information, the selected dimension weight value, the selected indicator weight value, and the satisfaction evaluation data are concatenated to obtain the target evaluation data of the candidate object;

[0011] The candidate objects are filtered based on the target evaluation data to obtain the target objects.

[0012] In some embodiments, the step of filtering preset evaluation requirement information based on the service category and the evaluation period information to obtain selected evaluation requirement information includes:

[0013] The service tag information for obtaining the preset evaluation requirements information;

[0014] The preset assessment requirements information is filtered based on the service category and the service tag information to obtain preliminary assessment requirements information;

[0015] The preliminary assessment requirements information is filtered based on the assessment period information to obtain the selected assessment requirements information.

[0016] In some embodiments, the preliminary assessment requirement information includes: preliminary assessment dimension parameters and preliminary assessment indicator parameters. The preliminary assessment dimension parameters include: preliminary assessment dimension information and preliminary dimension weight values. The preliminary assessment dimension information includes: product dimension information, order dimension information, service process dimension information, service result dimension information, and object risk dimension information.

[0017] The step of filtering the preliminary assessment requirement information based on the assessment period information to obtain selected assessment requirement information includes:

[0018] The preliminary evaluation dimension parameters are filtered based on the evaluation period information to obtain the selected evaluation dimension parameters; wherein, the evaluation period information is at least one period of the early, middle and late stages of the candidate object's execution of the service task, and the selected evaluation dimension parameters include: the selected evaluation dimension information and the selected dimension weight value;

[0019] The preliminary evaluation indicator parameters are filtered based on the evaluation period information and the selected evaluation dimension parameters to obtain the selected evaluation indicator parameters; wherein, the selected evaluation indicator parameters include: the selected evaluation indicator information and the selected indicator weight value.

[0020] In some embodiments, the step of evaluating the satisfaction of the candidate objects based on the object monitoring data to obtain satisfaction evaluation data includes:

[0021] Obtain the data category of the object monitoring data;

[0022] Based on the data categories, a target satisfaction assessment model is selected from the preset satisfaction assessment models;

[0023] The target satisfaction assessment model is used to assess the satisfaction of the monitored object data, and the satisfaction assessment data is obtained.

[0024] In some embodiments, the step of concatenating the selected evaluation dimension information, the selected dimension weight value, the selected indicator weight value, and the satisfaction evaluation data to obtain the target evaluation data of the candidate object includes:

[0025] The selected indicator weight values ​​and the satisfaction evaluation data are weighted to obtain indicator evaluation data;

[0026] The target evaluation data is obtained by weighting and summing the indicator evaluation data according to the selected evaluation dimension information and the selected dimension weight value.

[0027] In some embodiments, the step of filtering the candidate objects based on the target evaluation data to obtain the target object includes:

[0028] The preset candidate thresholds are filtered according to the service category to obtain the selected thresholds;

[0029] The candidate objects are filtered based on the target evaluation data and the selected threshold to obtain the target object.

[0030] In some embodiments, after filtering the candidate objects based on the target evaluation data to obtain the target object, the method further includes:

[0031] The target evaluation data of the target object is updated according to a preset time period to obtain updated evaluation data;

[0032] By using a preset service defect identification model and the updated evaluation data, service defects are identified in the object monitoring data of the target object to obtain service defect information.

[0033] Based on the service deficiency information, preset optimization measures are filtered to obtain target optimization measures;

[0034] The target optimization measures are performed on the target object.

[0035] To achieve the above objectives, a second aspect of this application provides an object filtering apparatus, the apparatus comprising:

[0036] The acquisition module is used to obtain the service category and evaluation period information of the candidate objects;

[0037] The information filtering module is used to filter preset evaluation requirement information according to the service category and the evaluation period information to obtain selected evaluation requirement information; wherein, the selected evaluation requirement information includes: selected evaluation dimension information, selected dimension weight value, selected evaluation indicator information and selected indicator weight value;

[0038] The data acquisition module is used to collect object monitoring data of the candidate object according to the selected evaluation index information; wherein, the object monitoring data is the operation data and service feedback data of the candidate object when performing service tasks;

[0039] The evaluation module is used to evaluate the satisfaction of the candidate object based on the object monitoring data, and obtain satisfaction evaluation data.

[0040] The splicing module is used to splice the selected evaluation dimension information, the selected dimension weight value, the selected indicator weight value and the satisfaction evaluation data to obtain the target evaluation data of the candidate object;

[0041] The object filtering module is used to filter the candidate objects based on the target evaluation data to obtain the target object.

[0042] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0043] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0044] The object screening method, apparatus, electronic device, and storage medium proposed in this application, in order to achieve targeted satisfaction assessment during object screening, selects matching preset assessment requirement information as selected assessment requirement information based on the service category and assessment period information of the candidate objects. This selected assessment requirement information includes selected assessment dimension information, selected assessment indicator information, selected dimension weight values, and selected indicator weight values. Then, according to the selected assessment indicator information, the operational data and service feedback data of the candidate objects when performing service tasks are collected as object monitoring data. The satisfaction of the candidate objects is then assessed based on the object monitoring data to obtain satisfaction assessment data, achieving automated satisfaction assessment, saving manpower, and ensuring objectivity. Next, the satisfaction assessment data is concatenated into target assessment data according to the selected assessment dimension information, selected dimension weight values, and selected indicator weight values ​​to assess the candidate objects from multiple dimensions, resulting in more accurate target assessment data. Finally, target objects are screened from the candidate objects according to the target assessment data, achieving objective and accurate object screening. The screening process is fully automated, saving manpower and improving the efficiency of object screening. Attached Figure Description

[0045] Figure 1 This is a flowchart of the object filtering method provided in the embodiments of this application;

[0046] Figure 2 yes Figure 1 The flowchart of step S102 in the document;

[0047] Figure 3 yes Figure 2 The flowchart of step S203 in the process;

[0048] Figure 4 yes Figure 1 The flowchart of step S104 in the process;

[0049] Figure 5 yes Figure 1 The flowchart of step S105 in the process;

[0050] Figure 6 This is a schematic diagram of the scoring criteria corresponding to the service result dimension and the object risk dimension in the object screening method provided in this application embodiment;

[0051] Figure 7 yes Figure 1 The flowchart of step S106 in the process;

[0052] Figure 8 This is a flowchart of an object filtering method provided in another embodiment of this application;

[0053] Figure 9 This is a schematic diagram illustrating the application of the object filtering method provided in this embodiment to a home healthcare service platform;

[0054] Figure 10 This is a schematic diagram of the object screening device provided in the embodiments of this application;

[0055] Figure 11 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0059] First, let's analyze some of the terms used in this application:

[0060] Artificial intelligence (AI) is a new branch of computer science that studies, develops, and applies theories, methods, technologies, and systems to simulate, extend, and expand human intelligence. It aims to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thought. Furthermore, AI utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results.

[0061] Supplier management is a comprehensive management function that includes understanding, selecting, developing, using, and controlling suppliers. It aims to ensure stable supply, control costs, guarantee quality, and promote supplier development.

[0062] Supply chain: It is a functional network structure model that connects suppliers, manufacturers, distributors, retailers and end users around a core enterprise by controlling the flow of information, logistics and capital. It starts from the procurement of raw materials, produces intermediate and final products, and finally delivers the products to consumers through the sales network. It can be divided into two categories: internal supply chain and external supply chain.

[0063] Object profiling refers to a virtual representation of a real object, a target object model built upon a series of attribute data. With the development of the internet, object profiling has acquired new connotations—typically, it is a labeled object model abstracted from information such as the object's demographic characteristics, web browsing content, online social activities, and consumption behavior.

[0064] Dynamic evaluation, an economic term, refers to a method that considers the time value of money and comprehensively assesses a project's economic benefits through dynamic indicators. Its core characteristics include: reflecting the time value of money, examining economic data throughout the project's entire lifecycle, and applicability to decision-making stages such as feasibility studies. Commonly used dynamic evaluation indicators include net present value (NPV), internal rate of return (IRR), and dynamic payback period.

[0065] Multidimensional assessment is a comprehensive method for analyzing problems. It evaluates the target object in a comprehensive and systematic way from multiple perspectives or indicators, avoiding the limitations of a single dimension.

[0066] Throughout the lifecycle of an object management platform, the lack of detailed control points in daily operations and dynamic adjustments leads to a lack of scientific and comprehensive criteria for object selection. Furthermore, the platform lacks in-depth examination of object service qualifications, production capacity, and compliance information, resulting in insufficient transparency and high risk. The absence of quantitative evaluation tools also contributes to overly subjective object selection. For example, in fintech, applications from fund product suppliers to join financial product platforms are primarily reviewed manually, and post-joining service satisfaction is mainly determined by customer feedback. Therefore, the lack of objective and comprehensive evaluation and selection in setting up fund product supply chains on financial product platforms leads to instability in supply chain setup. Similarly, in healthcare, multiple home service providers join medical home service platforms, and the setup of home service supply chains is generally done manually, relying heavily on manual analysis of service feedback and qualifications of each provider. This not only affects the stability and efficiency of home service supply chain setup but also impacts its competitiveness. Therefore, how to evaluate objects from multiple dimensions and perform real-time dynamic monitoring and intelligent analysis to improve the efficiency, accuracy, and objectivity of object selection has become a pressing technical problem.

[0067] Based on this, embodiments of this application provide an object screening method, apparatus, electronic device, and storage medium. By determining selected evaluation requirement information for each candidate object's corresponding service category and evaluation period, and including selected evaluation dimension information, selected dimension weight values, selected evaluation indicator information, and selected indicator weight values, candidate objects are evaluated from multiple dimensions and their corresponding evaluation indicators, achieving multi-dimensional evaluation. When evaluating candidate objects, object monitoring data is collected, and satisfaction evaluation data is obtained by assessing the candidate objects' satisfaction based on this data. Target evaluation data for candidate objects is then determined based on the satisfaction evaluation data, selected evaluation dimension information, selected dimension weight values, and selected indicator weight values. Finally, target objects are selected from the candidate objects based on the target evaluation data. Therefore, objective and accurate object screening is achieved, saving manpower and improving the efficiency of object screening.

[0068] The object filtering method, apparatus, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, the object filtering method in this application is described.

[0069] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0070] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0071] The object filtering method provided in this application relates to the field of artificial intelligence technology and is applied in the fields of fintech and healthcare. The object filtering method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the object filtering method, but is not limited to the above forms.

[0072] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0073] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0074] Figure 1 This is an optional flowchart of the object filtering method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0075] Step S101: Obtain the service category and evaluation period information of the candidate objects;

[0076] Step S102: Filter the preset assessment requirement information according to the service category and assessment period information to obtain the selected assessment requirement information; wherein, the selected assessment requirement information includes: selected assessment dimension information, selected dimension weight value, selected assessment indicator information and selected indicator weight value;

[0077] Step S103: Collect object monitoring data of candidate objects based on the selected evaluation index information; wherein, the object monitoring data is the operation data and service feedback data of the candidate objects when performing service tasks.

[0078] Step S104: Evaluate the satisfaction of candidate objects based on object monitoring data to obtain satisfaction evaluation data;

[0079] Step S105: The selected evaluation dimension information, selected dimension weight values, selected indicator weight values ​​and satisfaction evaluation data are concatenated to obtain the target evaluation data of the candidate object.

[0080] Step S106: Based on the target evaluation data, the candidate objects are screened to obtain the target objects.

[0081] Steps S101 to S106 of this embodiment involve obtaining selected evaluation requirement information corresponding to the service category and evaluation period information of the candidate object. This selected evaluation requirement information includes: selected dimension evaluation information, selected dimension weight values, selected evaluation indicator information, and selected indicator weight values. Based on the selected evaluation indicator information, operational data and service feedback data of the candidate object during the execution of service tasks are collected as object monitoring data. Then, satisfaction evaluation data is obtained by performing a satisfaction evaluation on the candidate object based on the object monitoring data, achieving automated satisfaction evaluation of the candidate object. The evaluation is completed according to the evaluation indicators corresponding to the service category and evaluation period information, eliminating the need for manual evaluation and saving manpower. Finally, the satisfaction evaluation data is concatenated into target evaluation data according to the selected evaluation dimension information, selected dimension weight values, and selected indicator weight values, achieving multi-dimensional evaluation of the candidate object. Target objects are selected from the candidate objects based on the target evaluation data, improving the efficiency, accuracy, and objectivity of object selection, thereby constructing a more stable object service link.

[0082] In step S101 of some embodiments, the candidate can be a product supplier or a service supplier. If the application field is fintech, the candidate can be a financial product supplier. The candidate selection method is applied to a financial product platform, where financial product suppliers register and the platform manages multiple suppliers. It should be noted that financial product suppliers capable of building a financial product supply chain are selected from among these suppliers. Taking healthcare as an example, the candidate selection method is applied to a medical home service platform, where home service suppliers are the candidates. The platform manages these suppliers, selecting a home service supply chain and providing users with the corresponding medical home services, thereby increasing the platform's usage rate.

[0083] Specifically, the object screening method is applied to the object management platform, and the service category represents the type of service tasks that candidate objects perform on the platform. Different service categories correspond to different evaluation indicators and dimensions, so the evaluation indicators and dimensions for each candidate object are determined based on the service category. The evaluation period information refers to the time period used to evaluate the satisfaction of candidate objects, and the evaluation indicators and dimensions for candidate objects differ in different evaluation periods. It should be noted that the evaluation period information can be at least one of the early, middle, and late stages of a candidate object's service task execution, to achieve a full lifecycle satisfaction evaluation of the candidate object.

[0084] In step S102 of some embodiments, the preset evaluation requirement information consists of pre-set comprehensive evaluation indicators for candidate objects. This preset evaluation requirement information is divided into five main dimensions and ten sub-dimensions. The five main dimensions are: product dimension, order dimension, service process dimension, service result dimension, and object risk dimension. The ten sub-dimensions are: product management and price management sub-dimensions within the product dimension; order acceptance management and fulfillment management sub-dimensions within the order dimension; service personnel management and service process management sub-dimensions within the service process dimension; service result management and service complaint management sub-dimensions within the service result dimension; and collection and payment on behalf and security deposit sub-dimensions within the object risk dimension. Therefore, by constructing an object profile for candidate objects through these five main dimensions and ten sub-dimensions, an accurate satisfaction assessment of the objects can be achieved.

[0085] Please see Figure 2 In some embodiments, step S102 may include, but is not limited to, steps S201 to S203:

[0086] Step S201: Obtain service tag information for preset assessment requirements;

[0087] Step S202: Filter the preset assessment requirements information according to the service category and service tag information to obtain preliminary assessment requirements information;

[0088] Step S203: Based on the assessment period information, the preliminary assessment requirement information is filtered and processed to obtain the selected assessment requirement information.

[0089] In step S201 of some embodiments, the service tag information represents the service category that matches the preset evaluation requirement information. The preset evaluation requirement information that matches the service category can be found through the service tag information.

[0090] In step S202 of some embodiments, the service category and service tag information are compared, the service tag information that is the same as the service category is selected as the selected tag information, and the preset demand evaluation information corresponding to the selected tag information is used as the preliminary evaluation demand information.

[0091] For example, if the application scenario is a medical home service platform, and the service category is any of the following: home delivery, in-store delivery, online delivery, or product category, with different evaluation tables configured for different service categories, the corresponding evaluation table will be selected as the target table based on the service category. The target table contains preliminary evaluation requirements information. It should be noted that if the service category is in-store delivery, the evaluation of the product dimension mainly focuses on in-store service evaluation; if the service category is product category, the evaluation of the product dimension involves product management and product pricing. Therefore, different preliminary evaluation requirements information will be determined for different service categories.

[0092] In step S203 of some embodiments, as disclosed above, the preset evaluation requirement information corresponding to different evaluation time periods is different, that is, the evaluation indicators for candidate objects are different in different time periods. Therefore, the adaptation time period information of the preliminary evaluation requirement information is obtained, and the selected evaluation requirement information is selected from the preliminary evaluation requirement information based on the adaptation time period information and the evaluation time period information.

[0093] Specifically, this embodiment sets the evaluation period information to at least one period in the early, middle, and late stages of the candidate's service task execution. In the early stage, the evaluation mainly assesses the candidate's satisfaction in product management, price management, order management, and service personnel management sub-dimensions. The order management sub-dimension primarily evaluates order timeliness and order result indicators. Therefore, the selected evaluation dimension information and evaluation indicator information differ for the candidate in different evaluation periods.

[0094] In steps S201 to S203 of this embodiment, preliminary evaluation requirements are selected from the preset evaluation requirements information according to the service category and service tag information, and then selected evaluation requirements are selected from the preliminary evaluation requirements information according to the evaluation time period information, so as to select the selected evaluation requirements information that matches the service category and evaluation time period information, and realize the targeted evaluation of the candidate objects.

[0095] In some embodiments, the preliminary assessment requirements information includes: preliminary assessment dimension parameters and preliminary assessment indicator parameters. The preliminary assessment dimension parameters include: preliminary assessment dimension information and preliminary dimension weight values; the preliminary assessment indicator parameters include: preliminary assessment indicator information and preliminary indicator weight values. The preliminary assessment dimension information is the dimension information used for candidate satisfaction assessment, and the preliminary dimension weight values ​​are the weight values ​​of each preliminary assessment dimension information, representing the importance of the preliminary assessment dimension information in the satisfaction assessment. The preliminary assessment indicator information is the indicator information used for candidate satisfaction assessment, and the preliminary indicator weight values ​​are the weight values ​​of the preliminary assessment indicator information, representing the importance of the preliminary assessment indicator information in the satisfaction assessment.

[0096] The initial assessment dimensions include: product dimension information, order dimension information, service process dimension information, service result dimension information, and target risk dimension information. By evaluating the satisfaction of candidates across these five dimensions, accurate assessment and effective screening of candidates can be achieved, thereby constructing a supply chain that can enhance the target management platform.

[0097] Please see Figure 3 In some embodiments, step S203 may include, but is not limited to, steps S301 to S302:

[0098] Step S301: The preliminary evaluation dimension parameters are filtered based on the evaluation period information to obtain the selected evaluation dimension parameters; wherein, the evaluation period information is at least one period of the early, middle and late stages of the candidate object's service task, and the selected evaluation dimension parameters include: selected evaluation dimension information and selected dimension weight value.

[0099] Step S302: Based on the evaluation period information and the selected evaluation dimension parameters, the preliminary evaluation indicator parameters are filtered to obtain the selected evaluation indicator parameters; wherein, the selected evaluation indicator parameters include: selected evaluation indicator information and selected indicator weight values.

[0100] In step S301 of some embodiments, the preliminary evaluation dimension parameters corresponding to different evaluation time periods are different. Therefore, it is necessary to first determine the appropriate time period information for each preliminary evaluation dimension parameter, and then select the chosen evaluation dimension parameter from the preliminary evaluation dimension parameters based on the evaluation time period information and the appropriate time period information. It should be noted that the evaluation time period information is at least one time period in the early, middle, and late stages of the candidate object's execution of the service task, so as to realize the satisfaction evaluation of the candidate object in the early, middle, and late stages of the execution of the service task, so as to carry out a systematic, refined, and full life cycle satisfaction evaluation, which can realize the satisfaction evaluation of the entire process from service preparation to execution and then to service feedback.

[0101] Furthermore, the selected evaluation dimension parameters include selected evaluation dimension information and selected dimension weight values. The selected evaluation dimension information is filtered from the preliminary evaluation dimension information based on the evaluation period information, and the selected dimension weight values ​​are filtered from the preliminary dimension weight values ​​based on the evaluation period information. For example, if the evaluation period information is in the middle of the service task execution, the focus is on satisfaction evaluation around the service process, specifically including service personnel management sub-dimensions, service process management sub-dimensions, and service result management sub-dimensions. If the evaluation period information is in the later stage of the service task execution, the focus is on service structure management sub-dimensions and service complaint management sub-dimensions. Therefore, different evaluation dimensions are selected for different evaluation period information.

[0102] In step S302 of some embodiments, selected evaluation indicator parameters are filtered from the preliminary evaluation indicator parameters based on the selected evaluation dimension information, and then selected evaluation indicator parameters are filtered from the selected evaluation indicator parameters based on the evaluation period information. The selected evaluation indicator parameters include: selected evaluation indicator information and selected indicator weight values. The selected evaluation indicator information is an indicator of the satisfaction of the candidate object during the evaluation period by the evaluation management platform. The selected indicator weight values ​​represent the weight values ​​of the selected evaluation indicator information, indicating its importance in the satisfaction evaluation.

[0103] As previously disclosed, the preliminary assessment dimensions include: product dimension information, order dimension information, service process dimension information, service result dimension information, and target risk dimension information. The product dimension information includes: product management sub-dimension information and price management sub-dimension information; the order dimension information includes: order acceptance management sub-dimension information and fulfillment management sub-dimension information; the service process dimension information includes: service personnel management sub-dimension information and service process management sub-dimension information; the service result dimension information includes: service result management sub-dimension information and service complaint management sub-dimension information; and the target risk dimension information includes: collection and payment on behalf sub-dimension information and security deposit sub-dimension information. Therefore, by evaluating the satisfaction of candidate targets through five major dimensions and ten sub-dimensions, a comprehensive and accurate assessment of candidate targets can be achieved, thereby constructing more accurate satisfaction assessment data.

[0104] Specifically, the evaluation indicators for the product management sub-dimension mainly include product information source information, product information completeness, exclusive product supply information, non-standard product information, and inventory information. The evaluation indicators for the price management sub-dimension include price adjustment timeliness information and price competitiveness information, with price competitiveness information primarily determined by comparing the prices of multiple candidate products using a price comparison model. Therefore, by evaluating the object management platform's satisfaction with candidate products using multiple evaluation indicators within the product dimension, more competitive target products can be selected. The object management platform can then provide users with higher-quality products and services based on these target products.

[0105] In some embodiments, the evaluation indicators corresponding to the order management sub-dimension information include: order acceptance method information and order acceptance capability information, with the order acceptance capability information determined based on the order acceptance rate and order success rate within a preset time range. The evaluation indicators corresponding to the fulfillment management sub-dimension information include: fulfillment progress information and fulfillment capability information, with the fulfillment progress information determined based on the fulfillment status information and service on-time rate, and the fulfillment capability information determined based on the service full-duration rate and environmental assessment capability. Therefore, by setting multiple evaluation indicators in the order management and fulfillment management sub-dimensions to evaluate candidate objects, a more refined evaluation can be made in terms of order acceptance and fulfillment. The selected target objects can then efficiently process orders and fulfill fulfillment, thereby improving the user's service experience on the object management platform. For example, if the candidate object is a home service provider, evaluating the home service provider in terms of multiple evaluation indicators related to order acceptance and fulfillment will result in output satisfaction evaluation data covering evaluation data in both order acceptance and fulfillment. The selected target home service providers will have better order acceptance efficiency and fulfillment capability, which can improve the user experience of the medical home service platform.

[0106] In some embodiments, the evaluation metrics corresponding to the service personnel management sub-dimensional information are service personnel mode, service personnel professional competence information, service personnel screening information, and service personnel information. It should be noted that the service personnel mode is used to determine whether the service personnel in the candidate pool are part-time or full-time; the service personnel professional competence information is determined based on the service personnel's professional skills and training; the service personnel screening information represents the dispatch rate of the candidate pool in the whitelist; and the service personnel information represents the probability that the candidate pool will provide service personnel information when performing service tasks. The evaluation metric corresponding to the service process management sub-dimensional information is whether the service process formulated by the candidate pool conforms to the process standards set by the object management platform. Therefore, by evaluating the satisfaction of the candidate pool from multiple evaluation metrics corresponding to service personnel management and service process management, the selected target pool possesses better service personnel and service processes, thereby improving user service satisfaction on the object management platform.

[0107] In some embodiments, the evaluation metrics corresponding to the service outcome management sub-dimensional information are acceptance satisfaction and customer satisfaction, while the evaluation metrics corresponding to the service complaint management sub-dimensional information are complaint rate and complaint withdrawal rate. Therefore, by evaluating candidates in multiple aspects such as acceptance satisfaction, customer satisfaction, complaint rate, and complaint withdrawal rate, target candidates with higher service quality and customer satisfaction are selected.

[0108] In some embodiments, the evaluation indicators corresponding to the collection and payment sub-dimension information are the order collection and payment ratio, payment period, and commission rate. The evaluation indicator corresponding to the margin deposit sub-dimension information is margin deposit information, which indicates whether the candidate accepts margin deposit. Therefore, by evaluating the candidate through collection and payment and margin deposit information, the risk of the candidate can be determined, low-risk target objects can be selected, and the management risk of the object management platform can be reduced.

[0109] In step S103 of some embodiments, after determining the selected evaluation requirement information corresponding to the service category and evaluation period information of each candidate object, and since the selected evaluation requirement information involves multiple dimensions and multiple indicators, and determining the dimension weight value corresponding to the dimension and the indicator weight value corresponding to the indicator, the candidate object can be accurately and comprehensively evaluated. Before evaluating the candidate object, it is necessary to collect the running data and service feedback data of the candidate object when performing service tasks according to the selected evaluation indicator information to obtain object monitoring data. As disclosed above, if the selected evaluation indicator information is product information completeness, then the completeness of the service product when the candidate object performs service tasks is collected as object monitoring data. Therefore, the object monitoring data to be collected is different for different selected evaluation indicator information.

[0110] Please see Figure 4 In some embodiments, step S104 may include, but is not limited to, steps S401 to S403:

[0111] Step S401: Obtain the data category of the object monitoring data;

[0112] Step S402: Select the target satisfaction assessment model from the preset satisfaction assessment models according to the data category;

[0113] Step S403: Use the target satisfaction assessment model to assess the satisfaction of the object monitoring data and obtain satisfaction assessment data.

[0114] In step S401 of some embodiments, as disclosed above, different object monitoring data correspond to different selected evaluation index information, and the corresponding evaluation standards are also different. Therefore, by obtaining the data category of the object monitoring data, the evaluation standard of the object monitoring data is determined according to the data category.

[0115] In step S402 of some embodiments, the preset satisfaction assessment model is a pre-trained model capable of performing satisfaction assessments. It should be noted that each preset satisfaction assessment model is configured with adaptation category information. The target satisfaction assessment model is obtained by filtering the preset satisfaction assessment models using the data category and adaptation category information. For example, if the data category is the price category, the target satisfaction assessment model is a comparison model. The price reasonableness of candidate objects is calculated using the price comparison model, and the object management platform's satisfaction with the candidate objects is determined based on the price reasonableness. If the data category is the order acceptance category, i.e., the object monitoring data is the order acceptance result, the target satisfaction assessment model is a feedback analysis model. The user satisfaction in the order acceptance result is judged using the feedback analysis model, so as to evaluate the object management platform's satisfaction with the candidate objects based on user satisfaction.

[0116] In step S403 of some embodiments, satisfaction evaluation data is obtained by evaluating the object monitoring data through the target satisfaction evaluation model, which simplifies the satisfaction evaluation operation of candidate objects.

[0117] For example, if the evaluation period is the early stage of a candidate's service task, the target monitoring data could include: product competitiveness information such as price adjustment timeliness and payment price; order acceptance method information; order acceptance capability information such as preset time range order acceptance rate and order success rate; order dispatch timeliness information; and service personnel professional competence information such as service personnel professional competence and training, service personnel screening information, and service personnel information. The satisfaction evaluation model would then assign scores to the target monitoring data, setting different scores for different target monitoring data. For example, regarding price adjustment timeliness and payment price: if price adjustment timeliness indicates timely price adjustments, the score would be 120; otherwise, the score would be 60. If 100% of orders have a lower payment price than other candidates, the score would be 120; if 90% of orders have a lower payment price than other candidates, the score would be 100; and if less than 90% of orders have a lower payment price than other candidates, the score would be 80. Therefore, different target satisfaction evaluation models are used for different target monitoring data to achieve a comprehensive and simple satisfaction assessment.

[0118] If the evaluation period is the middle of the candidate's service task execution, the monitoring data mainly focuses on the service quality and staff behavior during the service process. This ensures effective communication between service personnel and customers, confirms service content and time, and improves service readiness. It also collects data on staff tardiness / early departures, photos of their uniforms, and customer feedback. Therefore, by conducting a satisfaction assessment on the mid-term monitoring data, a comprehensive evaluation of the service process is achieved, enabling the selection of target candidates with high service quality and high user satisfaction based on the target evaluation data.

[0119] In the later stages of a candidate's service task, the monitoring data focuses on the continuous optimization of service quality and user satisfaction. This data mainly includes user feedback, service personnel performance, and service timeliness. The goal is to assess whether the candidate's behavior data after the service is completed can improve user satisfaction, thereby selecting target candidates with high user satisfaction.

[0120] In steps S401 to S403 of this embodiment, a target satisfaction assessment model matching the data category of the object monitoring data is selected, and the object monitoring data is evaluated for satisfaction using the target satisfaction assessment model. This achieves a targeted satisfaction assessment without the need for manual evaluation, and outputs more accurate and objective satisfaction assessment data.

[0121] Please see Figure 5 In some embodiments, step S105 may include, but is not limited to, steps S501 to S502:

[0122] Step S501: The selected indicator weight values ​​and satisfaction evaluation data are weighted and processed to obtain indicator evaluation data;

[0123] Step S502: Based on the selected evaluation dimension information and the selected dimension weight values, the indicator evaluation data is weighted and summed to obtain the target evaluation data.

[0124] The satisfaction assessment data is concatenated according to the selected assessment dimension information, the selected dimension weight value, and the selected indicator weight value. The concatenation method can be any one of weighted summation, weighted average calculation, and attention weighted operation. This embodiment does not restrict the concatenation method of the satisfaction assessment data.

[0125] In steps S501 to S502 of some embodiments, the selected indicator weight value and the corresponding satisfaction evaluation data are first weighted to obtain indicator evaluation data. Then, the indicator evaluation data corresponding to the selected evaluation dimension information are summed to obtain candidate evaluation data. Finally, the candidate evaluation data are weighted and summed according to the selected dimension weight value to obtain target evaluation data.

[0126] For example, let's take the service outcome dimension and the object risk dimension as examples, such as... Figure 6 As shown, the weight of the selected indicator corresponding to acceptance satisfaction is determined to be 50%, the weight of the selected indicator corresponding to customer satisfaction is determined to be 50%, while the weight of the selected dimension corresponding to the service result management sub-dimension is 5%, and the weight of the selected dimension corresponding to the service investment management sub-dimension is 5%. In the object risk dimension, the weight of the selected dimension corresponding to the collection and payment sub-dimension is 5%, and the weight of the selected dimension corresponding to the margin deposit sub-dimension is 5%. Therefore, as... Figure 6As shown, firstly, the satisfaction assessment data corresponding to each selected evaluation indicator is calculated, that is, the score value. Then, the score value and the weight value of the selected indicator are weighted and summed to obtain the indicator evaluation data. Then, the indicator evaluation data are weighted and summed according to the weight value of the selected dimension to obtain the target evaluation data, so as to obtain objective and accurate target evaluation data.

[0127] In steps S501 to S502 of this embodiment, the selected indicator weight value corresponding to each selected evaluation indicator information is set in advance, and the selected dimension weight value corresponding to each selected evaluation dimension information is set. Then, the satisfaction evaluation data is multi-dimensionally weighted and summed according to the selected dimension weight value and the selected indicator weight value to construct more accurate and objective target evaluation data and achieve accurate and comprehensive evaluation of candidate objects.

[0128] Please see Figure 7 In some embodiments, step S106 includes, but is not limited to, steps S701 to S702:

[0129] Step S701: Filter the preset candidate thresholds according to the service category to obtain the selected threshold;

[0130] Step S702: Based on the target evaluation data and the selected threshold, the candidate objects are filtered to obtain the target objects.

[0131] In step S701 of some embodiments, as disclosed above, if the candidate is a home service provider, and different home service providers have different screening criteria, a selected threshold is selected from the candidate thresholds according to the service category. It should be noted that the selected threshold is an evaluation threshold set for screening candidate objects.

[0132] In step S702 of some embodiments, the target evaluation data is compared with a selected threshold, and candidate objects whose target evaluation data is greater than the selected threshold are taken as target objects. A service supply chain is then built through the target objects to improve the utilization rate of the object management platform.

[0133] Taking a home healthcare service platform as an example, the candidate entities are home healthcare service providers. The target evaluation data is obtained through a full-cycle assessment of the providers across five dimensions: products, orders, service process, service results, and client risk. This data also represents the platform's satisfaction with the providers. Therefore, home healthcare service providers whose target evaluation data exceeds a selected threshold are designated as target service providers. This helps the platform select high-quality providers and establish long-term, stable partnerships. Based on these target providers, a home healthcare service supply chain is built, offering users choices on the platform, improving user experience, and enhancing the platform's market competitiveness.

[0134] In steps S701 to S702 of this embodiment, a corresponding selected threshold is selected for different service categories of candidate objects, and candidate objects whose target evaluation data is greater than the selected threshold are taken as target objects, so as to achieve targeted screening of target objects and improve the objectivity and accuracy of target object screening.

[0135] In some embodiments, candidate objects whose target evaluation data is lower than a selected threshold are selected objects, and target optimization suggestions are filtered from preset optimization suggestions based on the target evaluation data. The target evaluation data and target optimization suggestions are sent to the selected objects to prompt them to improve their service quality so that they can continue to run on the object management platform after optimization.

[0136] Please see Figure 8 In some embodiments, after step S106, the object filtering method may also include, but is not limited to, steps S801 to S804:

[0137] Step S801: Update the target evaluation data of the target object according to the preset time period to obtain updated evaluation data;

[0138] Step S802: Use a preset service defect identification model and updated evaluation data to identify service defects in the object monitoring data of the target object, and obtain service defect information.

[0139] Step S803: Based on the service deficiency information, the preset optimization measures are filtered to obtain the target optimization measures;

[0140] Step S804: Perform target optimization measures on the target object.

[0141] In step S801 of some embodiments, in this embodiment, in order to achieve periodic monitoring of candidate objects and improve the service stability of target objects on the object management platform, it is necessary to update the target evaluation data of the target objects according to a preset time period. That is, service supervision data of the target objects are collected periodically according to a preset time period, and the satisfaction evaluation of the target objects is performed according to the above-mentioned satisfaction evaluation method. The latest target evaluation data is used as the updated evaluation data. Therefore, updating the target evaluation data according to the preset time period realizes the dynamic evaluation of the target objects.

[0142] In step S802 of some embodiments, in order to continuously optimize the target object and enhance its competitiveness on the object management platform, a service defect identification model and updated evaluation data are used to identify service point defects in the target object's object monitoring data to obtain service defect information. It should be noted that the service defect identification model is a pre-trained model that can accurately identify the defects of the target object in each of the five dimensions of evaluation indicators, outputting the deficient evaluation indicator information. This deficient evaluation indicator information is then combined to form the service defect information. For example, if the target object has low customer satisfaction in after-sales service, low customer satisfaction is used as service defect information to facilitate satisfaction improvement operations based on low customer satisfaction.

[0143] In steps S803 to S804 of some embodiments, as disclosed above, preset optimization measures are selected as target optimization measures for different service defect information. For example, in the middle of the service task execution, it is necessary to evaluate the object monitoring data of the target object in the service process to determine the target evaluation data. It should be noted that the evaluation of the target object is mainly based on the effective communication between service personnel and customers, service image, service attitude, service professionalism, and service feedback results. The service image is mainly determined by the uniform and hairstyle of the staff through photo check-in, while the service professionalism is mainly determined by the dialogue data and lateness and early departure data monitored during the service process. If the service defect identification model identifies situations such as untidy uniforms and unfriendly service attitude, the target optimization measures are determined. The main measures are to strengthen the supervision of uniform requirements and service attitude, and control the target object to implement the target optimization measures in order to improve the service quality of the target object and enhance the competitiveness of the object management platform.

[0144] In steps S801 to S804 of this embodiment, the target evaluation data of the target object is updated periodically, and the service deficiencies of the candidate objects are identified based on the updated evaluation data. Optimization measures are then implemented for the target object in a targeted manner according to the service deficiencies, thereby improving user satisfaction with the target object on the object management platform, enhancing the stability of the target object and the object management platform, and thus improving the competitiveness of the object management platform.

[0145] Please refer to Figure 9This application example uses a medical home care service platform as an example, with home care service providers as the candidates. After a home care service provider joins the platform, clicking the "Provider Evaluation" button leads to the provider's evaluation settings interface. The platform presents a corresponding evaluation table based on the service category of the provider. This table records the selected evaluation indicators, their weights, dimensions, and weights for each evaluation period. It's important to note that the weights for selected indicators and dimensions can be customized, and additional evaluation dimensions and indicators can be added. The selected evaluation dimensions include: product dimension, order dimension, service process dimension, service result dimension, and provider risk dimension. Each dimension corresponds to at least two selected evaluation indicators. Operational data and / or user feedback data from the home care service provider during the pre-service, mid-service, and post-service phases are collected as monitoring data based on the selected evaluation indicators. The target monitoring data can include one of the following: product information completeness, non-standard product information, product price, order acceptance method information, order success rate, service personnel professional competence information, acceptance satisfaction, complaint rate, and deposit payment information. Then, based on the data category of the target monitoring data, a target satisfaction assessment model is selected from the preset candidate satisfaction assessment models. The home service provider's satisfaction is then assessed according to the target satisfaction assessment model and the target monitoring data to obtain satisfaction assessment data. Finally, the satisfaction assessment data and selected indicator weights are summed to obtain candidate assessment data. Then, the candidate assessment data is weighted and summed according to the selected assessment dimensions and selected dimension weights to obtain the target assessment data. Therefore, by evaluating home service providers from multiple aspects, and with the assessment process being fully automated and requiring no manual evaluation, accurate and objective target assessment data is obtained. Finally, based on the target assessment data, target service providers are selected and combined into a medical home service supply chain for the medical home service platform, providing users with options for medical home services. Therefore, by collecting real-time monitoring data from home service providers throughout the entire service process and conducting multi-dimensional evaluations of these providers, accurate and comprehensive target assessment data can be generated. Based on this target assessment data, the medical home service platform can select target home service providers to establish long-term and stable partnerships, thereby improving the platform's usage rate and competitiveness.

[0146] Please see Figure 10 This application also provides an object filtering device that can implement the above-described object filtering method. The device includes:

[0147] Module 1001 is used to obtain the service category and evaluation period information of the candidate objects;

[0148] The information filtering module 1002 is used to filter the preset evaluation requirement information according to the service category and evaluation period information to obtain the selected evaluation requirement information; wherein, the selected evaluation requirement information includes: selected evaluation dimension information, selected dimension weight value, selected evaluation indicator information and selected indicator weight value.

[0149] The data acquisition module 1003 is used to collect object monitoring data of candidate objects based on selected evaluation index information; wherein, the object monitoring data is the operation data and service feedback data of the candidate objects when they perform service tasks.

[0150] The evaluation module 1004 is used to evaluate the satisfaction of candidate objects based on object monitoring data and obtain satisfaction evaluation data.

[0151] The splicing module 1005 is used to splice the selected evaluation dimension information, the selected dimension weight value, the selected indicator weight value and the satisfaction evaluation data to obtain the target evaluation data of the candidate object.

[0152] The object filtering module 1006 is used to filter candidate objects based on target evaluation data to obtain target objects.

[0153] The specific implementation of this object screening device is basically the same as the specific embodiment of the object screening method described above, and will not be repeated here.

[0154] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described object filtering method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0155] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0156] The processor 1101 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0157] The memory 1102 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1102 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1102 and is called and executed by the processor 1101 using the object filtering method of the embodiments of this application.

[0158] Input / output interface 1103 is used to implement information input and output;

[0159] The communication interface 1104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0160] Bus 1105 transmits information between various components of the device (e.g., processor 1101, memory 1102, input / output interface 1103, and communication interface 1104);

[0161] The processor 1101, memory 1102, input / output interface 1103 and communication interface 1104 are connected to each other within the device via bus 1105.

[0162] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described object filtering method.

[0163] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0164] The object screening method, apparatus, electronic device, and storage medium provided in this application embodiment achieve targeted screening of evaluation requirement information by selecting selected evaluation requirement information that matches the service category and evaluation period information of candidate objects from preset evaluation requirement information. The selected evaluation requirement information includes: selected evaluation dimension information, selected evaluation indicator information, selected dimension weight values, and selected indicator weight values. To accurately assess the satisfaction of candidate objects, operational data and service feedback data of candidate objects performing service tasks are collected as object monitoring data according to the selected evaluation indicator information. The satisfaction of candidate objects is then assessed based on the object monitoring data to obtain satisfaction evaluation data. The satisfaction evaluation data is then concatenated according to the selected evaluation dimension information, selected dimension weight values, and selected indicator weight values ​​to obtain target evaluation data, achieving multi-dimensional and automated evaluation of candidate objects and improving the efficiency, objectivity, and accuracy of satisfaction evaluation. Finally, target objects are screened from candidate objects based on the target evaluation data to achieve objective and accurate object screening, saving manpower and improving the efficiency of object screening.

[0165] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of this application, and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0166] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0167] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0168] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0169] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0170] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0172] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0174] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0175] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. An object filtering method, characterized in that, The method includes: Obtain information on the service category and evaluation period of the candidate; The preset evaluation requirement information is filtered according to the service category and the evaluation period information to obtain the selected evaluation requirement information; wherein, the selected evaluation requirement information includes: selected evaluation dimension information, selected dimension weight value, selected evaluation indicator information and selected indicator weight value; Based on the selected evaluation index information, object monitoring data of the candidate objects is collected; wherein, the object monitoring data is the operational data and service feedback data of the candidate objects when performing service tasks; Based on the object monitoring data, a satisfaction assessment is performed on the candidate objects to obtain satisfaction assessment data; The selected evaluation dimension information, the selected dimension weight value, the selected indicator weight value, and the satisfaction evaluation data are concatenated to obtain the target evaluation data of the candidate object; The candidate objects are filtered based on the target evaluation data to obtain the target objects.

2. The method according to claim 1, characterized in that, The step of filtering the preset evaluation requirement information based on the service category and the evaluation period information to obtain the selected evaluation requirement information includes: The service tag information for obtaining the preset evaluation requirements information; The preset assessment requirements information is filtered based on the service category and the service tag information to obtain preliminary assessment requirements information; The preliminary assessment requirements information is filtered based on the assessment period information to obtain the selected assessment requirements information.

3. The method according to claim 2, characterized in that, The preliminary assessment requirements information includes: preliminary assessment dimension parameters and preliminary assessment indicator parameters. The preliminary assessment dimension parameters include: preliminary assessment dimension information and preliminary dimension weight values. The preliminary assessment dimension information includes: product dimension information, order dimension information, service process dimension information, service result dimension information, and object risk dimension information. The step of filtering the preliminary assessment requirement information based on the assessment period information to obtain the selected assessment requirement information includes: The preliminary evaluation dimension parameters are filtered based on the evaluation period information to obtain selected evaluation dimension parameters; wherein, the evaluation period information is at least one period of the early, middle and late stages of the candidate object's execution of the service task, and the selected evaluation dimension parameters include: the selected evaluation dimension information and the selected dimension weight value; The preliminary evaluation indicator parameters are filtered based on the evaluation period information and the selected evaluation dimension parameters to obtain the selected evaluation indicator parameters; wherein, the selected evaluation indicator parameters include: the selected evaluation indicator information and the selected indicator weight value.

4. The method according to any one of claims 1 to 3, characterized in that, The step of evaluating the satisfaction of the candidate objects based on the object monitoring data to obtain satisfaction evaluation data includes: Obtain the data category of the object monitoring data; Based on the data categories, a target satisfaction assessment model is selected from the preset satisfaction assessment models; The target satisfaction assessment model is used to assess the satisfaction of the monitored object data, and the satisfaction assessment data is obtained.

5. The method according to any one of claims 1 to 3, characterized in that, The step of concatenating the selected evaluation dimension information, the selected dimension weight value, the selected indicator weight value, and the satisfaction evaluation data to obtain the target evaluation data of the candidate object includes: The selected indicator weight values ​​and the satisfaction evaluation data are weighted to obtain indicator evaluation data; The target evaluation data is obtained by weighting and summing the indicator evaluation data according to the selected evaluation dimension information and the selected dimension weight value.

6. The method according to any one of claims 1 to 3, characterized in that, The step of filtering the candidate objects based on the target evaluation data to obtain the target objects includes: The preset candidate thresholds are filtered according to the service category to obtain the selected thresholds; The candidate objects are filtered based on the target evaluation data and the selected threshold to obtain the target object.

7. The method according to any one of claims 1 to 3, characterized in that, After filtering the candidate objects based on the target evaluation data to obtain the target objects, the method further includes: The target evaluation data of the target object is updated according to a preset time period to obtain updated evaluation data; By using a preset service defect identification model and the updated evaluation data, service defects are identified in the object monitoring data of the target object to obtain service defect information. Based on the service deficiency information, preset optimization measures are filtered to obtain target optimization measures; The target optimization measures are performed on the target object.

8. An object screening device, characterized in that, The device includes: The acquisition module is used to obtain the service category and evaluation period information of the candidate objects; The information filtering module is used to filter preset evaluation requirement information according to the service category and the evaluation period information to obtain selected evaluation requirement information; wherein, the selected evaluation requirement information includes: selected evaluation dimension information, selected dimension weight value, selected evaluation indicator information and selected indicator weight value; The data acquisition module is used to collect object monitoring data of the candidate object based on the selected evaluation index information; wherein, the object monitoring data is the operation data and service feedback data of the candidate object when performing service tasks; The evaluation module is used to evaluate the satisfaction of the candidate object based on the object monitoring data, and obtain satisfaction evaluation data. The splicing module is used to splice the selected evaluation dimension information, the selected dimension weight value, the selected indicator weight value and the satisfaction evaluation data to obtain the target evaluation data of the candidate object; The object filtering module is used to filter the candidate objects based on the target evaluation data to obtain the target object.

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

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the object filtering method according to any one of claims 1 to 7.

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