Government affair service business evaluation method and device, computer equipment and storage medium

Abstract

The invention belongs to the field of data processing, and relates to a government affair service business evaluation method and device, computer equipment and a storage medium. The government affair service business evaluation comprises the following steps: preprocessing multi-channel user evaluation information to obtain standard user evaluation information; associating the service operations according to the standard user evaluation information, and constructing a service behavior relation graph; mapping data corresponding to each service in the service behavior relation graph to a unified data structure to obtain a service data set; performing association fusion on the service data set based on a preset data fusion technology to obtain a fused service data set; associating the service operation with the user evaluation information according to the fused service data set to construct a multi-terminal service evaluation model; and inputting the real-time evaluation information into the multi-terminal service evaluation model to obtain a service evaluation score. According to the invention, the service evaluation score can be effectively generated according to the real-time evaluation of the user on the service business, so that the service quality can be effectively and comprehensively considered.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a government service business evaluation method, device, computer equipment and storage medium. Background Art

[0002] With the continuous development of Internet technology, government services are gradually moving online, and government service evaluation is an important means to measure service quality and improve service processes. However, there is a significant problem in the current government service business evaluation system: users often make subjective evaluations based only on service results, while ignoring the process difficulty and execution time during the service operation. This evaluation method results in service business personnel receiving unsatisfactory evaluations even if they have made great efforts in the process and even achieved the best service standards because the results did not fully meet user expectations.

[0003] Specifically, the service process may involve multiple complex links, each of which requires business personnel to invest a lot of time and energy. However, when evaluating, users often only focus on the final service results and lack sufficient knowledge and understanding of the difficulties and challenges in the process. This information asymmetry makes it difficult for service personnel to obtain accurate and comprehensive service quality feedback from user evaluations, which in turn affects the optimization and improvement of subsequent business.

[0004] In addition, due to inaccurate service evaluation, departments and business personnel may not be able to clearly define their service direction and improvement focus in their work, which makes it difficult to carry out the service business smoothly. Therefore, how to establish a more scientific and objective service evaluation system to comprehensively reflect various factors in the service process has become an urgent problem to be solved in the current service industry. Summary of the invention

[0005] The purpose of the embodiments of the present application is to propose a government service business evaluation method, device, computer equipment and storage medium to solve the problem of being unable to generate corresponding service evaluation scores based on users' real-time evaluation of service business to effectively and comprehensively evaluate service quality.

[0006] In order to solve the above technical problems, the present application embodiment provides a government service business evaluation method, which adopts the following technical solution:

[0007] Acquire multi-channel user evaluation information, and pre-process the multi-channel user evaluation information to obtain standard user evaluation information;

[0008] Associating service operations according to the standard user evaluation information to construct a service behavior relationship map;

[0009] Mapping the data corresponding to each service in the service behavior relationship graph to a unified data structure according to a preset data mapping rule to obtain a service data set;

[0010] Based on a preset data fusion technology, the service data set is associated and fused to obtain a fused service data set;

[0011] Associating service operations and user evaluation information of different channels according to the fusion service data set to construct a multi-terminal service evaluation model;

[0012] Real-time evaluation information is obtained, and the real-time evaluation information is input into the multi-terminal service evaluation model to obtain a service evaluation score.

[0013] Furthermore, the step of obtaining multi-channel user evaluation information and preprocessing the multi-channel user evaluation information to obtain standard user evaluation information specifically includes:

[0014] Obtain user original evaluation information from the preset web page and software end;

[0015] The original evaluation information is identified and encoded according to a preset unified data standard specification to obtain the multi-channel user evaluation information;

[0016] Clean and filter the multi-channel user evaluation information according to preset data field definitions, data types, and value range rules to obtain valid user evaluation information;

[0017] The effective user evaluation information is subjected to natural language processing to obtain the standard user evaluation information.

[0018] Furthermore, the step of associating service operations according to the standard user evaluation information to construct a service behavior relationship map specifically includes:

[0019] Extracting corresponding service operation data from a database according to the standard user evaluation information;

[0020] Normalizing the service operation data to obtain standardized service operation data;

[0021] Generating a globally unique number for each service operation in the canonical service operation data;

[0022] According to the globally unique number, similar service operations of different channels are associated to construct a directed graph of service operations;

[0023] The service behavior relationship graph is constructed based on the service operation directed graph.

[0024] Furthermore, the step of constructing the service behavior relationship graph based on the service operation directed graph specifically includes:

[0025] Performing community division on the service operation directed graph based on a community discovery algorithm to obtain a service operation community set;

[0026] Performing association rule mining on each service operation community in the service operation community set according to an association rule mining algorithm to obtain a service association pattern;

[0027] The service behavior relationship graph is constructed according to the service operation community set and the service association pattern.

[0028] Furthermore, the step of mapping the data corresponding to each service in the service behavior relationship graph to a unified data structure according to a preset data mapping rule to obtain a service data set specifically includes:

[0029] Obtaining service original data corresponding to each service operation in the service behavior relationship graph;

[0030] Extracting key information from the original service data to obtain key service information;

[0031] Organizing the key service information into a unified data structure and constructing a standard service data record;

[0032] A relational data table is created, and the standard service data record is inserted into the relational data table to obtain the service data set.

[0033] Furthermore, the step of associating and fusing the service data set based on the preset data fusion technology to obtain a fused service data set specifically includes:

[0034] Extracting standard service data records from the service data set, and preprocessing the standard service data records to obtain service key features;

[0035] Calculating similarities between the standard service data records according to the service key features, and clustering the standard service data records according to the calculated similarities to obtain service data clustering results;

[0036] Identifying a data association pattern according to the service data clustering result, and constructing a service association rule between a service behavior and a service result based on the data association pattern;

[0037] The service data sets are fused according to the service association rules to obtain the fused service data set.

[0038] Furthermore, the step of associating service operations and user evaluation information of different channels according to the fusion service data set to construct a multi-terminal service evaluation model specifically includes:

[0039] Construct a multidimensional service data cube based on the fusion service data set, taking service platform, service business type, and service time as dimensions, and taking user evaluation score and completion rate as measurement values;

[0040] Performing a metric value difference analysis on the multidimensional service data cube to obtain key influencing factors;

[0041] The multi-terminal service evaluation model is constructed according to the fusion service data set and the key influencing factors.

[0042] In order to solve the above technical problems, the embodiment of the present application also provides a government service business evaluation device, which adopts the following technical solution:

[0043] An information processing module, used to obtain multi-channel user evaluation information, pre-process the multi-channel user evaluation information, and obtain standard user evaluation information;

[0044] An information association module, used to associate service operations according to the standard user evaluation information and construct a service behavior relationship map;

[0045] A data mapping module, used to map the data corresponding to each service in the service behavior relationship map to a unified data structure according to a preset data mapping rule to obtain a service data set;

[0046] An association and fusion module, used to associate and fuse the service data set based on a preset data fusion technology to obtain a fused service data set;

[0047] A model building module, used to associate service operations and user evaluation information of different channels according to the fusion service data set to build a multi-terminal service evaluation model;

[0048] The service evaluation module is used to obtain real-time evaluation information, input the real-time evaluation information into the multi-terminal service evaluation model, and obtain a service evaluation score.

[0049] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0050] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the government service business evaluation method as described in any one of the above items are implemented.

[0051] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0052] A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement the steps of the government service business evaluation method as described in any one of the above items.

[0053] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0054] This embodiment obtains multi-channel user evaluation information, pre-processes the multi-channel user evaluation information, and obtains standard user evaluation information; associates service operations according to the standard user evaluation information to construct a service behavior relationship map; maps the data corresponding to each service in the service behavior relationship map to a unified data structure according to a preset data mapping rule to obtain a service data set; associates and fuses the service data set based on a preset data fusion technology to obtain a fused service data set; associates service operations and user evaluation information of different channels according to the fused service data set to construct a multi-terminal service evaluation model; obtains real-time evaluation information, and inputs the real-time evaluation information into the multi-terminal service evaluation model to obtain a service evaluation score. In this way, the corresponding service evaluation score is effectively generated according to the user's real-time evaluation of the service business, so as to effectively and comprehensively consider the service quality and provide an effective and reliable basis for the optimization of subsequent service business. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A flowchart of an embodiment of a method for evaluating government service business according to the present application;

[0057] Figure 2 yes Figure 1 A flowchart of a specific implementation of step S10;

[0058] Figure 3 yes Figure 1 A flowchart of a specific implementation of step S20;

[0059] Figure 4 yes Figure 3 A flowchart of a specific implementation of step S205;

[0060] Figure 5 yes Figure 1 A flowchart of a specific implementation of step S30;

[0061] Figure 6 yes Figure 1 A flowchart of a specific implementation of step S40;

[0062] Figure 7 yes Figure 1 A flowchart of a specific implementation of step S50;

[0063] Figure 8 It is a structural diagram of an embodiment of a government service business evaluation device according to the present application;

[0064] Fig. 9 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0066] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0067] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0068] Continue to refer Figure 1 , shows a flow chart of an embodiment of a method for speaker production testing according to the present application. The government service business evaluation method comprises the following steps:

[0069] Step S10, obtaining multi-channel user evaluation information, and pre-processing the multi-channel user evaluation information to obtain standard user evaluation information;

[0070] In this embodiment, the multi-channel user evaluation information is user evaluation data collected from multiple terminals such as a preset web page terminal and a software terminal, and the user evaluation data is a service evaluation conducted by a user on a service business of a unified service business platform, wherein the service evaluation may include evaluation text content, evaluation score, evaluation level, etc. The preprocessing of the multi-channel user evaluation information includes data cleaning, filtering, and natural language processing, etc. By performing the above preprocessing on the multi-channel user evaluation information, standard user evaluation information that conforms to the subsequent processing format is obtained.

[0071] Step S20, associating the service operations according to the standard user evaluation information to construct a service behavior relationship map;

[0072] In this embodiment, the service operation is an operation record of the service business corresponding to the standard user evaluation information, and the operation service includes information such as operation time, operation type, operation result, and associated user ID. The standard user evaluation information includes information such as evaluation content, evaluation time, emotional tendency (positive, negative, neutral), and associated service operation ID. The service behavior relationship map is a map data used to represent the corresponding relationship between service operations, where the nodes of the service behavior relationship map represent service operations, and the edges represent the association relationship between service operations.

[0073] Step S30, mapping the data corresponding to each service in the service behavior relationship graph to a unified data structure according to a preset data mapping rule to obtain a service data set;

[0074] In this embodiment, the preset data mapping rules include data extraction, key information extraction, unified data structure organization and data insertion. The data corresponding to each service in the service behavior relationship map refers to the service original data corresponding to the service operation, including service operation data, user information data, service time data, evaluation content data, etc. It can be extracted from the standard user evaluation information and database according to the type of service operation. The service data set is a data set that effectively organizes the key information of the above-mentioned service operation data, user information data, service time data, and evaluation content data. By organizing the service data set, it is convenient for subsequent processing.

[0075] Step S40, associating and fusing the service data set based on a preset data fusion technology to obtain a fused service data set;

[0076] In this embodiment, the preset data fusion technology includes feature extraction, similarity calculation, association rule construction and data fusion. The fused service data set is data set information obtained by performing a series of processing on the service data set according to the above preset data fusion technology and finally performing data fusion.

[0077] Step S50, associating service operations and user evaluation information of different channels according to the fusion service data set to construct a multi-terminal service evaluation model;

[0078] In this embodiment, the service operations and user evaluation information of different channels refer to different collection channels corresponding to the service operations and user evaluation information, and the collection channels include the above-mentioned preset web pages, mobile terminals, websites, APP applications, public accounts, etc. By performing difference analysis on the service operations and user evaluation information of different channels, the key influencing factors of the evaluation between different channels can be identified, and then a multi-terminal service evaluation model can be effectively constructed based on the key influencing factors, so that the model has the ability to adapt to the evaluation data obtained from multiple terminals and conduct real service evaluation.

[0079] Step S60, obtaining real-time evaluation information, inputting the real-time evaluation information into the multi-terminal service evaluation model, and obtaining a service evaluation score.

[0080] In this embodiment, the real-time evaluation information is user evaluation data collected from multiple terminals such as the preset web page and software terminals of the service business system. The collected user evaluation data is input into the multi-terminal service evaluation model to obtain a service evaluation score that effectively reflects the true evaluation of the service business. The service evaluation score takes into account the relationship between service operations and user evaluations to conduct a true and effective service effect assessment.

[0081] This embodiment obtains multi-channel user evaluation information, pre-processes the multi-channel user evaluation information, and obtains standard user evaluation information; associates service operations according to the standard user evaluation information to construct a service behavior relationship map; maps the data corresponding to each service in the service behavior relationship map to a unified data structure according to a preset data mapping rule to obtain a service data set; associates and fuses the service data set based on a preset data fusion technology to obtain a fused service data set; associates service operations and user evaluation information of different channels according to the fused service data set to construct a multi-terminal service evaluation model; obtains real-time evaluation information, and inputs the real-time evaluation information into the multi-terminal service evaluation model to obtain a service evaluation score. In this way, the corresponding service evaluation score is effectively generated according to the user's real-time evaluation of the service business, so as to effectively and comprehensively consider the service quality and provide an effective and reliable basis for the optimization of subsequent service business.

[0082] In this embodiment, the above-mentioned government service business evaluation method can be applied to the government service system. The user evaluation data collected from multiple terminals by the government service system is analyzed and evaluated to obtain a service evaluation score that effectively reflects the actual service effect, thereby effectively providing reliable data basis and support for subsequent adjustment and optimization of service business.

[0083] Continue to refer Figure 2 In some optional implementations of this embodiment, step S10 includes the following steps:

[0084] Step S101, obtaining user original evaluation information from a preset web page and software end;

[0085] In this embodiment, the preset web page and software sides belong to different service sides of the same service business system. The web page side can use web crawler technology (such as Python's BeautifulSoup, Scrapy or Selenium and other tools) to crawl users' original evaluation information from related websites, and the software side can obtain users' original evaluation information through API interface or database query.

[0086] Step S102, marking and encoding the original evaluation information according to a preset unified data standard specification to obtain the multi-channel user evaluation information;

[0087] In this embodiment, the preset unified data standard specification refers to a data dictionary containing channel identifiers (such as web pages, Android terminals, iOS terminals, etc.) and platform identifiers (such as website platforms, WeChat public accounts, service business apps, etc.). According to the data dictionary, the corresponding channel and platform identifiers are added to each data item of the original evaluation information.

[0088] Step S103, cleaning and filtering the multi-channel user evaluation information according to preset data field definitions, data types, and value range rules to obtain valid user evaluation information;

[0089] In this embodiment, the preset data field definition is used to clarify which fields the user evaluation information needs to include, such as user ID, evaluation content, evaluation time, score, etc. Data type and value range: define the data type (such as string, integer, date, etc.) and value range (such as the score should be between 1-5) for each field. Use a unique identifier (such as user ID + evaluation time) to remove duplicate records through data field definition, data type, and value range rules, and remove data that does not meet the standards (such as the score is not between 1-5, the evaluation content is empty, etc.) according to the data type and value range rules.

[0090] Step S104: performing natural language processing on the valid user evaluation information to obtain the standard user evaluation information.

[0091] In this embodiment, natural language processing includes word segmentation, part-of-speech tagging, named entity recognition, keyword extraction and sentiment tendency. Word segmentation can be performed by using a Chinese word segmentation tool (such as jieba) to divide the evaluation content into words or phrases. Part-of-speech tagging refers to marking the part of speech (such as noun, verb, adjective, etc.) for each word or phrase, which helps with subsequent sentiment analysis. Named entity recognition refers to identifying and extracting entities (such as names, place names, product names, etc.) in the evaluation content, which helps to understand the specific objects of the evaluation. Keyword extraction can extract keywords from the evaluation content by using algorithms such as TF-IDF and TextRank. Sentiment tendency analysis can use sentiment dictionaries or machine learning models (such as SVM, BERT, etc.) to judge the sentiment tendency (such as positive, negative, neutral) of the evaluation content.

[0092] This embodiment obtains original user evaluation information from a preset web page and software end; identifies and encodes the original evaluation information according to a preset unified data standard specification to obtain the multi-channel user evaluation information; performs data cleaning and filtering on the multi-channel user evaluation information according to preset data field definitions, data types, and value range rules to obtain valid user evaluation information; performs natural language processing on the valid user evaluation information to obtain standard user evaluation information that meets subsequent processing requirements, so as to facilitate the subsequent construction of a service behavior relationship map based on the user evaluation information.

[0093] Continue to refer Figure 3 In some optional implementations of this embodiment, step S20 includes the following steps:

[0094] Step S201, extracting corresponding service operation data from a database according to the standard user evaluation information;

[0095] In this embodiment, the evaluation information identifier corresponding to the standard user evaluation information is used as a query condition to perform a matching query in the system database to query and extract the service operation data corresponding to the evaluation information.

[0096] Step S202, normalizing the service operation data to obtain standardized service operation data;

[0097] In this embodiment, the normalization process includes data cleaning, data conversion, and data standardization, wherein data cleaning includes removing duplicate data, processing missing values, correcting erroneous data, etc. Data conversion refers to converting data of different formats into a unified format, such as converting a date from a string to a date type. Data standardization refers to the need to perform normalization processing on certain numerical data, such as normalization or standardization to a certain range. By performing normalization processing on the service operation data including the above processing steps, standard and effective normalized service operation data can be obtained.

[0098] Step S203, generating a globally unique number for each service operation in the standard service operation data;

[0099] In this embodiment, a globally unique number (GUID or UUID) generated for each service operation in the normalized service operation data is used to uniquely identify each service operation and serves as a basis for subsequent association and construction of a directed graph.

[0100] Step S204, associating similar service operations of different channels according to the globally unique number to construct a service operation directed graph;

[0101] In this embodiment, the generated globally unique number is used to associate similar service operations of different channels. Similar service operations refer to service operations with the same or similar functions, such as "service consultation", "personal information query", etc. The association process of similar service operations of different channels may include identifying similar service operations: identifying which service operations belong to the same category according to the type or description of the service operation. Establishing an association relationship: Using a globally unique number, similar service operations of different channels are connected to form an association relationship. A service operation directed graph is a graphical representation method, in which the nodes of the service operation directed graph represent service operations, and each node corresponds to a service operation with a globally unique number. The directed edges of the service operation directed graph represent the association relationship between service operations, and the directed edges point from one service operation to another service operation, indicating the sequence or dependency relationship in the service process. When constructing a service operation directed graph, a suitable graphical representation tool or library, such as Graphviz, D3.js, etc., can be selected according to actual needs. In this embodiment, Graphviz can be used.

[0102] Step S205: construct the service behavior relationship graph based on the service operation directed graph.

[0103] In this embodiment, the service behavior relationship graph is used to describe the associated behaviors or association degrees between service operations. The service operation directed graph is processed by community division, association rule mining, etc. to obtain the association information between service operations, and the service behavior relationship graph is constructed based on the association information.

[0104] This embodiment extracts corresponding service operation data from the database according to the standard user evaluation information; normalizes the service operation data to obtain standard service operation data; generates a global unique number for each service operation in the standard service operation data; associates similar service operations of different channels according to the global unique number to construct a service operation directed graph; and constructs the service behavior relationship graph based on the service operation directed graph. This effectively realizes the construction of a service behavior relationship graph representing the relationship between service operations according to the service operation data corresponding to the standard user evaluation information, so as to facilitate the subsequent acquisition of service data sets.

[0105] Continue to refer Figure 4 In some optional implementations of this embodiment, step S205 includes the following steps:

[0106] Step S2051, dividing the service operation directed graph into communities based on a community discovery algorithm to obtain a service operation community set;

[0107] In this embodiment, the community discovery algorithm can adopt the Louvain algorithm. The step of dividing the service operation directed graph into communities by the Louvain algorithm includes setting parameters: determining the parameters required by the Louvain algorithm, such as the resolution parameter (used to control the coarse and fine granularity of the number of communities) and the maximum number of iterations (to prevent the algorithm from looping infinitely). Calculating modularity: Modularity is an indicator of the quality of community division. It reflects the tightness of the connection between nodes within the community relative to the tightness of the connection between nodes between communities. In the initial stage of the Louvain algorithm, each node is regarded as an independent community. Node movement: The algorithm traverses each node in the graph and tries to move it to the community to which its neighboring nodes belong to maximize the gain of modularity. If moving a node can increase the modularity, the move is performed. Aggregate nodes: Aggregate nodes belonging to the same community into a new node. The weight (or "size") of the new node can be set to the sum of the weights of all nodes in the original community. Construct new edges: In the new graph, the edge weights between the aggregated nodes are calculated according to the strength of the connection between the corresponding communities in the original graph. This step can be achieved by calculating the sum of the weights of the edges between all possible node pairs between the two communities. Iteration phase: Repeat the above two phases, that is, perform modularity optimization and node aggregation on the new graph until a certain stopping condition is reached (such as modularity no longer increases significantly, the maximum number of iterations is reached, etc.). Community partitioning: The algorithm ultimately outputs a community partitioning result, in which each service operation is assigned to a community, and all communities are aggregated to generate a service operation community set.

[0108] Step S2052, performing association rule mining on each service operation community in the service operation community set according to an association rule mining algorithm to obtain a service association pattern;

[0109] In this embodiment, the association rule mining algorithm can adopt the Apriori algorithm, and the Apriori algorithm is used to perform association rule mining on each service operation community in the service operation community set, and the steps include finding frequent item sets: setting a minimum support threshold, counting the number of occurrences (i.e., support) of each item set (service operation) in the service operation community set, and finding all item sets with support greater than or equal to the minimum support threshold as frequent item sets. Generate association rules: set a minimum credibility (confidence) threshold, generate all possible association rules based on frequent item sets, calculate the credibility (confidence) of each rule, that is, the ratio of the number of times the rule antecedent appears in the database to the number of times the rule appears as a whole, and find all association rules with credibility greater than or equal to the minimum credibility threshold. And organize the finally obtained association rules to obtain the service association pattern.

[0110] Step S2053: construct the service behavior relationship graph according to the service operation community set and the service association pattern.

[0111] In this embodiment, the service operations in the service operation community set are used as nodes of the service behavior relationship graph, and the edges of the service behavior relationship graph are constructed in a service association mode to represent the association relationship between service operations, thereby effectively constructing a service behavior relationship graph.

[0112] This embodiment divides the service operation directed graph into communities based on a community discovery algorithm to obtain a service operation community set; performs association rule mining on each service operation community in the service operation community set based on an association rule mining algorithm to obtain a service association pattern; and constructs the service behavior relationship graph based on the service operation community set and the service association pattern. This effectively achieves the description of the specific association relationship between service operations, so as to facilitate the subsequent acquisition of a service data set based on the service behavior relationship graph.

[0113] Continue to refer Figure 5 In some optional implementations of this embodiment, step S30 includes the following steps:

[0114] Step S301, obtaining service original data corresponding to each service operation in the service behavior relationship graph;

[0115] In this embodiment, the service original data includes service operation data (service type, operation action, operation parameters), user information data (user ID, user attributes), service time data (service start time, service end time, service duration), evaluation content data (evaluation text, evaluation level, evaluation time), etc.

[0116] Step S302, extracting key information from the original service data to obtain key service information;

[0117] In this embodiment, the key service information extracted from the service raw data includes service operations (such as the type of service, the actions performed, etc.), evaluation content (user feedback on the service), user information (such as the user's ID, name, etc.) and service time (timestamp when the service occurs).

[0118] Step S303, organizing the key service information into a unified data structure to construct a standard service data record;

[0119] In this embodiment, the format, type, length, etc. of the service data record are defined, and then the data structure of the service key information is organized according to the defined format, type, length, etc. of the service data record to ensure that each record contains the same information and is presented in the same way, thereby effectively realizing the construction of standard service data records.

[0120] Step S304: create a relational data table, insert the standard service data record into the relational data table, and obtain the service data set.

[0121] In this embodiment, a relational database is a table-based data storage method that allows the definition of relationships between data. According to the extracted standard service data records, a corresponding relational data table needs to be created in the relational database, wherein each row of the data table represents a service record, and the columns correspond to each service key information. The insert operation of inserting the standard service data record into the relational data table can be performed by writing an SQL statement or using a database management tool to obtain a service data set.

[0122] This embodiment obtains the service original data corresponding to each service operation in the service behavior relationship graph; extracts key information from the service original data to obtain service key information; organizes the service key information into a unified data structure to construct a standard service data record; creates a relational data table, and inserts the standard service data record into the relational data table, thereby obtaining a service data set represented by a unified data structure to facilitate subsequent data association and fusion.

[0123] Continue to refer Figure 6In some optional implementations of this embodiment, step S40 includes the following steps:

[0124] Step S401, extracting standard service data records from the service data set, and preprocessing the standard service data records to obtain service key features;

[0125] In this embodiment, the preprocessing of the standard service data record includes steps such as data conversion and feature extraction. The standard service data record is converted into suitable data, and the converted standard service data record is subjected to feature extraction to obtain the key service features. Among them, data conversion includes data formatting: ensuring that the format of the data is consistent, such as date format, numerical format, etc. Data type conversion: converting certain data types to another type, such as converting a string to a numerical type (if possible), or converting a date time to a specific timestamp format. Data scaling: scaling numerical data to eliminate dimensional differences between different features. Common methods include standardization (converting data to a distribution with a mean of 0 and a standard deviation of 1) and normalization (scaling data to a specific range, such as between 0 and 1). Data discretization: converting continuous data into discrete data, such as segmenting age into different intervals. Missing value processing: filling or deleting missing values. Common methods include using the mean, median, mode filling, or using interpolation methods. Feature extraction includes statistical features: calculating statistics of data, such as mean, median, mode, variance, standard deviation, etc. Text features: For text data, you can use methods such as bag-of-words model and TF-IDF (term frequency-inverse document frequency) to extract text features. Time series features: For time series data, you can extract features such as trends, seasonality, and periodicity. Domain-specific features: Extract features with business significance in specific business domains, such as user behavior characteristics and service performance indicators.

[0126] Step S402, calculating the similarity between the standard service data records according to the service key features, and clustering the standard service data records according to the calculated similarity to obtain a service data clustering result;

[0127] In this embodiment, the similarity between the key service features and the standard service data records can be obtained by calculating the cosine similarity. First, these features and data records need to be converted into vector form. Each feature or data point can be mapped to a specific numerical value to form a corresponding multidimensional vector, and then the cosine similarity formula is used to calculate the similarity between the two vectors. For example, a service key feature vector A and a standard service data record vector B, both of which are composed of multiple dimensions (such as response time, quality, price, etc.). We can use the following formula to calculate the cosine similarity between them:

[0128] Cosine similarity = (A ·B) / (|A|×|B|);

[0129] Among them, A·B represents the dot product of vectors A and B, and |A| and |B| represent the modulus (i.e., length) of vectors A and B respectively.

[0130] After the similarity is calculated, the standard service data records are clustered by the K-means clustering method. The clustering steps include: determining the value of K and selecting K initial cluster centers (also called centroids). These centroids will be used as the starting point of the iteration to assign data points to the nearest cluster. Calculate the similarity (or distance) between each standard service data record (now regarded as a data point) and all K cluster centers, and assign each data point to the cluster where the cluster center closest to it is located. When all data points are assigned to clusters, recalculate the cluster center of each cluster, which can be achieved by calculating the average value (centroid) of all data points in the cluster. Repeat the steps of allocating data points and selecting cluster centers until the cluster centers no longer change significantly (i.e., the algorithm converges), or the preset number of iterations is reached. The result obtained at this time is the service data clustering result, in which the data records in each cluster have a high similarity, while the data records between different clusters have a low similarity.

[0131] Step S403, identifying a data association pattern according to the service data clustering result, and constructing a service association rule between a service behavior and a service result based on the data association pattern;

[0132] In this embodiment, the data association pattern can be expressed as associations between certain features, associations between clusters, or associations in time series, etc. Service association rules between service behaviors and service results are constructed based on the data association pattern, and these rules describe how service behaviors affect service results, or the association relationship between different service behaviors.

[0133] Step S404: fusing the service data set according to the service association rule to obtain the fused service data set.

[0134] In this embodiment, the service data set can be fused by merging similar service records, adjusting the weights or attributes of service records according to service association rules, etc. The purpose of fusion is to obtain a more refined, accurate service data set containing valuable association information, which is used for subsequent analysis, prediction or decision support.

[0135] This embodiment obtains indicator correlation information by performing correlation analysis on the industry advantage indicators, the regional advantage indicators, the enterprise's own risk information, and the enterprise-related risk information; obtains a preset weight allocation table, maps the industry advantage indicators, the regional advantage indicators, the enterprise's own risk information, and the enterprise-related risk information to the weight allocation table, and obtains indicator weight information; and fuses the industry advantage indicators, the regional advantage indicators, the enterprise's own risk information, and the enterprise-related risk information according to the indicator correlation information, thereby effectively obtaining a set of enterprise characteristic indicators that integrates multiple indicators and risk information, so as to facilitate subsequent text generation processing.

[0136] Continue to refer Figure 7 In some optional implementations of this embodiment, step S50 includes the following steps:

[0137] Step S501, constructing a multidimensional service data cube based on the fused service data set with service platform, service business type, and service time as dimensions and user evaluation score and completion rate as measurement values;

[0138] In this embodiment, the steps of constructing a multidimensional service data cube include determining the dimensions: service platform (such as PC, mobile, mini-program, etc.), service business type (such as query, submission for review, consultation, etc.), service time (can be a specific time point, time period or date). Determine the measurement value: user evaluation score (usually a numerical value, indicating the user's satisfaction with the service) and completion rate (indicating the percentage of the service successfully completed). Construct a data cube: Construct a multidimensional data cube based on the above dimensions and measurement values, which will contain measurement value data under different dimension combinations.

[0139] Step S502, performing a metric value difference analysis on the multi-dimensional service data cube to obtain key influencing factors;

[0140] In this embodiment, the step of metric value difference analysis includes: comparing the metric values ​​(user evaluation scores and completion rates) under different dimension combinations to find the difference items between them. The difference items are analyzed to identify which dimensions have a significant impact on the metric values. For example, the user evaluation scores on a service platform may be generally low, or the completion rate may be particularly high during a certain time period. If we find that the user evaluation scores on the mobile terminal are generally higher than those on the PC terminal, then the service experience on the mobile terminal may be better. If we find that the completion rate in a certain time period (such as at night) is particularly low, then we may need to investigate whether there is a problem with the service process during that time period.

[0141] Step S503: constructing the multi-terminal service evaluation model according to the fusion service data set and the key influencing factors.

[0142] In this embodiment, the step of constructing a multi-terminal service evaluation model includes selecting key influencing factors: based on the results of the metric difference analysis, we select dimensions that have a significant impact on the metric as key influencing factors. Construct a model: Use these key influencing factors and the fusion service data set to construct a multi-terminal service evaluation model, and use the validation data set to verify the accuracy of the model and optimize it as needed. The multi-terminal service evaluation model can be used to predict the service evaluation score under different dimensional combinations. The service evaluation score is used to reflect the actual score of the service business, so as to provide a reliable basis for subsequent service business optimization.

[0143] This embodiment constructs a multi-dimensional service data cube based on the fused service data set with service platform, service business type, and service time as dimensions, and user evaluation score and completion rate as measurement values; performs measurement value difference analysis on the multi-dimensional service data cube to obtain key influencing factors; and constructs the multi-terminal service evaluation model based on the fused service data set and the key influencing factors. Thus, a multi-terminal service evaluation model that can accurately evaluate the operation results of the service business is effectively constructed based on the key influencing factors of the fused data set.

[0144] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0145] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0146] Further references Figure 8 , as a response to the above Figure 1The present application provides an embodiment of a government service business evaluation device, and the device embodiment is similar to Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0147] like Figure 8 As shown, the government service business evaluation device 700 described in this embodiment includes: an information processing module 701, an information association module 702, a data mapping module 703, an association fusion module 704, a model building module 705, and a service evaluation module 706. Among them:

[0148] The information processing module 701 is used to obtain multi-channel user evaluation information, pre-process the multi-channel user evaluation information, and obtain standard user evaluation information;

[0149] An information association module 702, used to associate service operations according to the standard user evaluation information and construct a service behavior relationship map;

[0150] The data mapping module 703 is used to map the data corresponding to each service in the service behavior relationship map to a unified data structure according to a preset data mapping rule to obtain a service data set;

[0151] An association and fusion module 704 is used to associate and fuse the service data set based on a preset data fusion technology to obtain a fused service data set;

[0152] A model building module 705 is used to associate service operations and user evaluation information of different channels according to the fusion service data set to build a multi-terminal service evaluation model;

[0153] The service evaluation module 706 is used to obtain real-time evaluation information, input the real-time evaluation information into the multi-terminal service evaluation model, and obtain a service evaluation score.

[0154] This embodiment obtains multi-channel user evaluation information, pre-processes the multi-channel user evaluation information, and obtains standard user evaluation information; associates service operations according to the standard user evaluation information to construct a service behavior relationship map; maps the data corresponding to each service in the service behavior relationship map to a unified data structure according to a preset data mapping rule to obtain a service data set; associates and fuses the service data set based on a preset data fusion technology to obtain a fused service data set; associates service operations and user evaluation information of different channels according to the fused service data set to construct a multi-terminal service evaluation model; obtains real-time evaluation information, and inputs the real-time evaluation information into the multi-terminal service evaluation model to obtain a service evaluation score. In this way, the corresponding service evaluation score is effectively generated according to the user's real-time evaluation of the service business, so as to effectively and comprehensively consider the service quality and provide an effective and reliable basis for the optimization of subsequent service business.

[0155] To solve the above technical problems, the present application also provides a computer device. Fig. 9 , Fig. 9 This is a basic structural block diagram of the computer device in this embodiment.

[0156] The computer device 9 includes a memory 81, a processor 82, and a network interface 83 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 8 with components 81-83, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0157] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0158] The memory 81 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 81 can be an internal storage unit of the computer device 8, such as a hard disk or memory of the computer device 8. In other embodiments, the memory 81 can also be an external storage device of the computer device 8, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 8. Of course, the memory 81 can also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 81 is generally used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions of the government service business evaluation method, etc. In addition, the memory 81 can also be used to temporarily store various types of data that have been output or are to be output.

[0159] The processor 82 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 82 is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to run computer-readable instructions or process data stored in the memory 81, such as computer-readable instructions for running the government service business evaluation method.

[0160] The network interface 83 may include a wireless network interface or a wired network interface. The network interface 83 is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0161] By adopting the above-mentioned computer device, this embodiment can obtain multi-channel user evaluation information, pre-process the multi-channel user evaluation information to obtain standard user evaluation information; associate service operations according to the standard user evaluation information to construct a service behavior relationship map; map the data corresponding to each service in the service behavior relationship map to a unified data structure according to a preset data mapping rule to obtain a service data set; associate and fuse the service data set based on a preset data fusion technology to obtain a fused service data set; associate service operations and user evaluation information of different channels according to the fused service data set to construct a multi-terminal service evaluation model; obtain real-time evaluation information, input the real-time evaluation information into the multi-terminal service evaluation model to obtain a service evaluation score. In this way, the corresponding service evaluation score is effectively generated according to the user's real-time evaluation of the service business, so as to effectively and comprehensively consider the service quality and provide an effective and reliable basis for the optimization of subsequent service business.

[0162] The present application also provides another implementation, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the government service business evaluation method as described above.

[0163] By adopting the above-mentioned computer-readable storage medium, this embodiment can obtain multi-channel user evaluation information, pre-process the multi-channel user evaluation information to obtain standard user evaluation information; associate service operations according to the standard user evaluation information to construct a service behavior relationship map; map the data corresponding to each service in the service behavior relationship map to a unified data structure according to a preset data mapping rule to obtain a service data set; associate and fuse the service data set based on a preset data fusion technology to obtain a fused service data set; associate service operations and user evaluation information of different channels according to the fused service data set to construct a multi-terminal service evaluation model; obtain real-time evaluation information, input the real-time evaluation information into the multi-terminal service evaluation model to obtain a service evaluation score. In this way, the corresponding service evaluation score is effectively generated according to the user's real-time evaluation of the service business, so as to effectively and comprehensively consider the service quality and provide an effective and reliable basis for the optimization of subsequent service business.

[0164] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0165] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A government service business evaluation method, characterized in that: include: Acquire multi-channel user evaluation information, and pre-process the multi-channel user evaluation information to obtain standard user evaluation information; Associating service operations according to the standard user evaluation information to construct a service behavior relationship map; Mapping the data corresponding to each service in the service behavior relationship graph to a unified data structure according to a preset data mapping rule to obtain a service data set; Based on a preset data fusion technology, the service data set is associated and fused to obtain a fused service data set; Associating service operations and user evaluation information of different channels according to the fusion service data set to construct a multi-terminal service evaluation model; Real-time evaluation information is obtained, and the real-time evaluation information is input into the multi-terminal service evaluation model to obtain a service evaluation score.

2. The government service business evaluation method according to claim 1 is characterized in that: The step of obtaining multi-channel user evaluation information and preprocessing the multi-channel user evaluation information to obtain standard user evaluation information specifically includes: Obtain user original evaluation information from the preset web page and software end; The original evaluation information is identified and encoded according to a preset unified data standard specification to obtain the multi-channel user evaluation information; Clean and filter the multi-channel user evaluation information according to preset data field definitions, data types, and value range rules to obtain valid user evaluation information; The effective user evaluation information is subjected to natural language processing to obtain the standard user evaluation information.

3. The government service business evaluation method according to claim 1 is characterized in that: The step of associating service operations according to the standard user evaluation information to construct a service behavior relationship map specifically includes: Extracting corresponding service operation data from a database according to the standard user evaluation information; Normalizing the service operation data to obtain standardized service operation data; Generating a globally unique number for each service operation in the canonical service operation data; According to the globally unique number, similar service operations of different channels are associated to construct a directed graph of service operations; The service behavior relationship graph is constructed based on the service operation directed graph.

4. The government service business evaluation method according to claim 3 is characterized in that: The step of constructing the service behavior relationship graph based on the service operation directed graph specifically includes: Performing community division on the service operation directed graph based on a community discovery algorithm to obtain a service operation community set; Performing association rule mining on each service operation community in the service operation community set according to an association rule mining algorithm to obtain a service association pattern; The service behavior relationship graph is constructed according to the service operation community set and the service association pattern.

5. The government service business evaluation method according to claim 1 is characterized in that: The step of mapping the data corresponding to each service in the service behavior relationship graph to a unified data structure according to a preset data mapping rule to obtain a service data set specifically includes: Obtaining service original data corresponding to each service operation in the service behavior relationship graph; Extracting key information from the original service data to obtain key service information; Organizing the key service information into a unified data structure and constructing a standard service data record; A relational data table is created, and the standard service data record is inserted into the relational data table to obtain the service data set.

6. The government service business evaluation method according to claim 1 is characterized in that: The step of associating and fusing the service data sets based on the preset data fusion technology to obtain a fused service data set specifically includes: Extracting standard service data records from the service data set, and preprocessing the standard service data records to obtain service key features; Calculating similarities between the standard service data records according to the service key features, and clustering the standard service data records according to the calculated similarities to obtain service data clustering results; Identifying a data association pattern according to the service data clustering result, and constructing a service association rule between a service behavior and a service result based on the data association pattern; The service data sets are fused according to the service association rules to obtain the fused service data set.

7. The government service business evaluation method according to claim 1 is characterized in that: The step of associating service operations and user evaluation information of different channels according to the fusion service data set to construct a multi-terminal service evaluation model specifically includes: Construct a multidimensional service data cube based on the fusion service data set, taking service platform, service business type, and service time as dimensions, and taking user evaluation score and completion rate as measurement values; Performing a metric value difference analysis on the multidimensional service data cube to obtain key influencing factors; The multi-terminal service evaluation model is constructed according to the fusion service data set and the key influencing factors.

8. A government service business evaluation device, characterized in that: include: An information processing module, used to obtain multi-channel user evaluation information, pre-process the multi-channel user evaluation information, and obtain standard user evaluation information; An information association module, used to associate service operations according to the standard user evaluation information and construct a service behavior relationship map; A data mapping module, used to map the data corresponding to each service in the service behavior relationship map to a unified data structure according to a preset data mapping rule to obtain a service data set; An association and fusion module, used to associate and fuse the service data set based on a preset data fusion technology to obtain a fused service data set; A model building module, used to associate service operations and user evaluation information of different channels according to the fusion service data set to build a multi-terminal service evaluation model; The service evaluation module is used to obtain real-time evaluation information, input the real-time evaluation information into the multi-terminal service evaluation model, and obtain a service evaluation score.

9. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the steps of the government service business evaluation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the government service business evaluation method as described in any one of claims 1 to 7.

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