Enterprise digital diagnosis platform and use method thereof

Abstract

The invention belongs to the technical field of digital diagnosis, and discloses an enterprise digital diagnosis platform and a use method thereof, and the platform comprises a diagnosis management user unit which is used for carrying out the comprehensive management of a management user based on the enterprise diagnosis information; the enterprise user diagnosis unit is used for analyzing the diagnosis task of the enterprise user and providing diagnosis service and personalized support for the enterprise user according to the diagnosis task; the business authority management unit is used for defining the function access range of the enterprise user and the management user, and performing unified management on the function authority of the enterprise user and the management user based on the function access range; and the user role management unit is used for constructing a diagnosis management role and a diagnosis enterprise role. The method focuses on maintenance of global data, distribution of diagnosis tasks and comprehensive analysis, so that efficient organization and coordination of platform resources are facilitated, and stable operation of the platform and smooth development of enterprise diagnosis work are ensured.

Description

Technical Field

[0001] The present invention relates to the field of digital diagnosis technology, and in particular to an enterprise digital diagnosis platform and a method for using the same. Background Art

[0002] Enterprise digital consulting and diagnosis is a consulting service centered on digital technology. It provides customized digital strategies, solutions and implementation paths by comprehensively analyzing the current situation, problems and needs of enterprises in the process of digital transformation to help enterprises improve their competitiveness and achieve sustainable development.

[0003] Enterprise digital consulting covers the aspects of status quo assessment, problem diagnosis, strategic planning, solution design and implementation evaluation. Through scientific methods and tools, enterprises can achieve transformation goals faster and more accurately in digital transformation, enhance core competitiveness and achieve sustainable development. Since there are a large number of enterprises participating in digital consulting and diagnosis in the current region, most of the participation methods are for enterprises to fill out paper questionnaires, which leads to problems such as low diagnostic efficiency, great statistical difficulty, difficulty in ensuring diagnostic quality, and inability to analyze diagnostic results.

[0004] Therefore, how to provide an enterprise digital diagnostic platform and its usage method is a problem that needs to be solved urgently. Summary of the invention

[0005] The embodiment of the present invention provides an enterprise digital diagnosis platform and a method for using the same to solve the problems in the prior art of low diagnosis efficiency, high statistical difficulty, difficulty in ensuring diagnosis quality, and inability to analyze diagnosis results.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, an enterprise digital diagnosis platform is provided.

[0008] In one embodiment, the enterprise digital diagnosis platform includes:

[0009] The diagnostic management user unit is used to screen enterprise diagnostic information in multiple dimensions and conduct comprehensive management of management users based on the enterprise diagnostic information;

[0010] The diagnostic enterprise user unit is used to analyze the diagnostic tasks of enterprise users and provide diagnostic services and personalized support to enterprise users based on the diagnostic tasks;

[0011] The business authority management unit is used to define the functional access scope of enterprise users and management users, and to uniformly manage the functional permissions of enterprise users and management users based on the functional access scope;

[0012] The user role management unit is used to build diagnostic management roles and diagnostic enterprise roles to achieve permission division and user grouping under a multi-role system.

[0013] In one embodiment, the diagnosis management user unit includes a diagnosis information retrieval module, a diagnosis comprehensive analysis module, a diagnosis problem tailoring module, a diagnosis result revision module and a diagnosis result cancellation module, wherein:

[0014] The diagnostic information retrieval module is used to obtain enterprise diagnostic information and establish a diagnostic question library, classify the enterprise diagnostic information in the diagnostic database, and establish an index in the diagnostic question library using semantic indexing technology;

[0015] The diagnostic comprehensive analysis module is used to perform statistical analysis on the enterprise diagnostic information to obtain the enterprise diagnostic results, and generate an enterprise diagnostic report based on the enterprise diagnostic results;

[0016] A diagnostic question tailoring module is used to tailor diagnostic questions based on predefined enterprise personalized diagnostic requirements to allow management users to select, remove and modify diagnostic questions;

[0017] A diagnosis result revision module is used to review the enterprise diagnosis result, and based on the review result, revise the enterprise diagnosis result to generate a new enterprise diagnosis report, and update the enterprise diagnosis information associated with the new enterprise diagnosis report;

[0018] The diagnosis result invalidation module is used to detect anomalies in enterprise diagnosis results, invalidate enterprise diagnosis results with anomalies, and mark the corresponding enterprise diagnosis information as invalid records.

[0019] In one embodiment, the diagnostic information retrieval module, when acquiring the enterprise diagnostic information and establishing the diagnostic question library, classifies the enterprise diagnostic information in the diagnostic database, and establishes an index in the diagnostic question library using the semantic indexing technology, includes:

[0020] Obtain enterprise diagnostic information and convert it into text data, extract problem items from the text data, and construct a diagnostic problem library based on the problem items in a hierarchical form;

[0021] The text data stored in the diagnostic question library is embedded and vectorized using a semantic embedding model, and the processing results are mapped to a high-dimensional vector space to form a semantic vector representation;

[0022] Approximate nearest neighbor and inverted indexing techniques are used to index text data after embedding vectorization to improve the efficiency of enterprise diagnostic information retrieval.

[0023] In one embodiment, the enterprise diagnosis information includes diagnosis enterprise ID, diagnosis status, diagnosis score, diagnosis rating, diagnosis start time, diagnosis end time and diagnosis report address.

[0024] In one embodiment, the comprehensive diagnosis analysis module performs statistical analysis on the enterprise diagnosis information to obtain the enterprise diagnosis result, and generates the enterprise diagnosis report based on the enterprise diagnosis result, including:

[0025] Preprocess the enterprise diagnostic information, and use the clustering feature tree algorithm to divide the enterprises with similar characteristics corresponding to the preprocessed enterprise diagnostic information into the same group;

[0026] Formulate diagnostic indicators for the divided enterprise groups, and evaluate the performance of enterprises in different diagnostic indicators according to predefined evaluation standards to obtain enterprise diagnostic results;

[0027] Based on the enterprise diagnosis results, the first-level indicator radar chart and the second-level indicator radar chart are formulated, and the industry of the diagnosed enterprise is displayed in the form of a box-whisker chart. The industry distribution range of the diagnosed enterprise is obtained by analyzing the first-level indicator radar chart, the second-level indicator radar chart and the box-whisker chart;

[0028] The enterprise diagnosis results are updated based on the industry distribution range to form an enterprise diagnosis report, and the enterprise diagnosis report is provided in the form of a document.

[0029] In one embodiment, the method of using a clustering feature tree algorithm to classify enterprises with similar features corresponding to the pre-processed enterprise diagnostic information into the same group includes:

[0030] Randomly select enterprise samples from the preprocessed enterprise diagnostic information as the cluster center of the root node, and use the cluster center as the starting point of the cluster feature tree;

[0031] Add new enterprise sample points to the cluster feature tree, calculate the distance between the enterprise sample points and all cluster centers and compare them with the preset threshold;

[0032] If the distance is less than the preset threshold, the new enterprise sample point is added to the cluster with the smallest distance, and the clustering features corresponding to the leaf node are updated in sequence from the corresponding leaf node to the parent node; if the distance is greater than the preset threshold, the new sample point is used as a new cluster;

[0033] Determine the number of clusters in the leaf node. If the number of clusters is greater than the preset threshold, split the leaf node until no new enterprise sample points are generated. Otherwise, repeatedly calculate the distance between the enterprise sample point and the cluster center.

[0034] When all enterprise sample points have been processed and the number of clusters in the leaf nodes meets the preset threshold, enterprises with similar characteristics are divided into the same group.

[0035] In one embodiment, the diagnosis enterprise user unit includes an enterprise information reporting module, a diagnosis question selection module, a customer service information display module and a diagnosis result viewing module, wherein:

[0036] The enterprise information filling module is used for enterprise users to fill in basic enterprise information, verify the basic enterprise information to obtain verification results, and standardize and store the enterprise information according to the verification results;

[0037] The diagnostic question selection and answering module is used to help enterprise users complete the selection of diagnostic questions, extract keywords from the answers to the diagnostic questions, and record and associate the answers to the diagnostic questions with the enterprise diagnostic tasks;

[0038] The customer service information display module is used to extract the target text between the enterprise user behavior and the customer service end, analyze the enterprise user sentiment from the target text, and dynamically adjust the customer service response strategy based on the enterprise user sentiment;

[0039] The diagnosis result viewing module is used to provide visual query for enterprise users using the charts generated by enterprise diagnosis results.

[0040] In one embodiment, the customer service information display module extracts the target text between the enterprise user behavior and the customer service end, analyzes the enterprise user emotion from the target text, and dynamically adjusts the customer service response strategy based on the enterprise user emotion, including:

[0041] The target text between the enterprise user behavior and the customer service end is used as the text to be subjected to sentiment analysis, and the target text is vectorized to obtain the first sentiment vector matrix and the second sentiment vector of the target text;

[0042] A dual-channel attention mechanism is used to assign weights to the emotional features in the first emotional vector matrix and the second emotional vector matrix respectively, and the emotional features after weight assignment are subjected to weighted processing and feature fusion processing in turn;

[0043] The activation function is used to classify the first sentiment vector matrix and the second sentiment vector matrix after feature fusion to obtain the sentiment classification result of the target text, and the customer service response strategy is dynamically adjusted based on the sentiment classification result.

[0044] In one embodiment, the expression of the emotional feature after weight allocation is:

[0045]

[0046] In the formula, represents the attention weight of the first sentiment vector matrix; represents the attention weight of the second sentiment vector matrix; w h represents the first sentiment vector matrix; w c represents the second sentiment vector matrix; e h represents the first bias term; e c represents the second bias term; y i Represents the first weight vector of the target text processed by the activation function; y j The second weight vector representing the target text processed by the activation function; h i Represents global features; c j Represents local features.

[0047] According to a second aspect of an embodiment of the present invention, a method for using an enterprise digital diagnosis platform is provided.

[0048] In one embodiment, the method for using the enterprise digital diagnosis platform includes:

[0049] Screen enterprise diagnostic information from multiple dimensions and conduct comprehensive management of management users based on enterprise diagnostic information;

[0050] Analyze the diagnostic tasks of enterprise users and provide diagnostic services and personalized support to enterprise users based on the diagnostic tasks;

[0051] Define the functional access scope of enterprise users and management users, and centrally manage the functional permissions of enterprise users and management users based on the functional access scope;

[0052] Build diagnostic management roles and diagnostic enterprise roles to achieve authority division and user grouping under the multi-role system.

[0053] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0054] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0055] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0056] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0057] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0058] 1. The present invention divides permissions by constructing diagnostic management roles and diagnostic enterprise roles, and grants diagnostic enterprise management, diagnostic information retrieval and diagnostic comprehensive analysis to the diagnostic management role, so as to support the management work of the diagnostic platform manager, and grants new diagnosis, invalid diagnosis and other tasks to the diagnostic enterprise role, so as to support the enterprise to fill in diagnostic information and answer diagnostic questions on its own, so that the platform can focus on global data maintenance, diagnostic task allocation and comprehensive analysis, which in turn helps to efficiently organize and coordinate platform resources, ensure the stable operation of the platform and the smooth progress of enterprise diagnostic work.

[0059] 2. The present invention allows enterprises to manage diagnostic tasks by themselves, including creating or canceling diagnostic operations, thereby reducing dependence on platform managers and improving the autonomy and flexibility of enterprise users. It helps enterprises to complete the reporting and adjustment of diagnostic information in a timely manner according to their own needs, and the clear division of permissions ensures that different roles can only access and operate data within the authorized scope, reducing the risk of misoperation or data leakage.

[0060] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0062] Figure 1 is a principle block diagram of an enterprise digital diagnosis platform according to an exemplary embodiment;

[0063] Figure 2 is a flow chart showing a method for using an enterprise digital diagnosis platform according to an exemplary embodiment;

[0064] Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment;

[0065] Figure 4 It is a process layer design diagram of an enterprise digital diagnosis platform according to an exemplary embodiment;

[0066] Figure 5 It is a technical architecture diagram of an enterprise digital diagnosis platform according to an exemplary embodiment. DETAILED DESCRIPTION

[0067] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0068] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like used herein to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this article and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description herein, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal connection of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0069] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0070] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0071] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0072] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure 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 the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0073] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0074] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0075] Figure 1 An embodiment of an enterprise digital diagnosis platform of the present invention is shown.

[0076] In this optional embodiment, the enterprise digital diagnosis platform includes:

[0077] The diagnosis management user unit 101 is used to screen enterprise diagnosis information in multiple dimensions and perform comprehensive management of management users based on the enterprise diagnosis information;

[0078] The diagnostic enterprise user unit 103 is used to analyze the diagnostic tasks of the enterprise users and provide diagnostic services and personalized support for the enterprise users according to the diagnostic tasks;

[0079] The business authority management unit 105 is used to define the function access scope of the enterprise user and the management user, and to uniformly manage the function authority of the enterprise user and the management user based on the function access scope;

[0080] The user role management unit 107 is used to construct diagnosis management roles and diagnosis enterprise roles to achieve authority division and user grouping under a multi-role system.

[0081] In this optional embodiment, the diagnosis management user unit includes a diagnosis information retrieval module, a diagnosis comprehensive analysis module, a diagnosis problem tailoring module, a diagnosis result revision module and a diagnosis result cancellation module, wherein:

[0082] The diagnostic information retrieval module is used to obtain enterprise diagnostic information and establish a diagnostic question library, classify enterprise diagnostic information in the diagnostic database, and establish an index in the diagnostic question library using semantic indexing technology;

[0083] The diagnostic comprehensive analysis module is used to perform statistical analysis on the enterprise diagnostic information to obtain the enterprise diagnostic results, and generate an enterprise diagnostic report based on the enterprise diagnostic results;

[0084] A diagnostic question tailoring module is used to tailor diagnostic questions based on predefined enterprise personalized diagnostic requirements to allow management users to select, remove and modify diagnostic questions;

[0085] A diagnosis result revision module is used to review the enterprise diagnosis result, and revise the enterprise diagnosis result based on the review result to generate a new enterprise diagnosis report, and update the enterprise diagnosis information associated with the new enterprise diagnosis report;

[0086] The diagnosis result invalidation module is used to detect anomalies in enterprise diagnosis results, invalidate enterprise diagnosis results with anomalies, and mark the corresponding enterprise diagnosis information as invalid records.

[0087] In this optional embodiment, the diagnostic information retrieval module, when acquiring the enterprise diagnostic information and establishing the diagnostic question library, classifies the enterprise diagnostic information in the diagnostic database, and establishes an index in the diagnostic question library using the semantic indexing technology, includes:

[0088] Obtain enterprise diagnostic information and convert it into text data, extract problem items from the text data, and construct a diagnostic problem library based on the problem items in a hierarchical form;

[0089] The text data stored in the diagnostic question library is embedded and vectorized using a semantic embedding model, and the processing results are mapped to a high-dimensional vector space to form a semantic vector representation;

[0090] Approximate nearest neighbor and inverted indexing techniques are used to index text data after embedding vectorization to improve the efficiency of enterprise diagnostic information retrieval.

[0091] In this optional embodiment, the method of using the approximate nearest neighbor and inverted index technology to index the text data after embedding vectorization processing to improve the efficiency of enterprise diagnosis information retrieval includes:

[0092] Associate text with its keywords or feature vectors to build an inverted index structure to quickly locate text data containing specific keywords; based on the embedded vector, use the approximate nearest neighbor search algorithm (such as HNSW, Faiss or Annoy) to build an efficient index structure and optimize the similarity matching speed of high-dimensional vectors; when searching, input the query vector and use the approximate nearest neighbor algorithm to quickly find the most similar embedded vector; sort the text matched by the inverted index and return the enterprise diagnosis information most relevant to the query; for newly added diagnostic data, update the inverted index and ANN index in real time to maintain retrieval efficiency and accuracy.

[0093] In this optional embodiment, the enterprise diagnosis information includes the diagnosis enterprise ID, diagnosis status, diagnosis score, diagnosis rating, diagnosis start time, diagnosis end time and diagnosis report address.

[0094] In this optional embodiment, the comprehensive diagnosis analysis module performs statistical analysis on the enterprise diagnosis information to obtain the enterprise diagnosis result, and generates the enterprise diagnosis report based on the enterprise diagnosis result, including:

[0095] Preprocess the enterprise diagnostic information, and use the clustering feature tree algorithm to divide the enterprises with similar characteristics corresponding to the preprocessed enterprise diagnostic information into the same group;

[0096] Formulate diagnostic indicators for the divided enterprise groups, and evaluate the performance of enterprises in different diagnostic indicators according to predefined evaluation standards to obtain enterprise diagnostic results;

[0097] Based on the enterprise diagnosis results, the first-level indicator radar chart and the second-level indicator radar chart are formulated, and the industry of the diagnosed enterprise is displayed in the form of a box-whisker chart. The industry distribution range of the diagnosed enterprise is obtained by analyzing the first-level indicator radar chart, the second-level indicator radar chart and the box-whisker chart;

[0098] The enterprise diagnosis results are updated based on the industry distribution range to form an enterprise diagnosis report, and the enterprise diagnosis report is provided in the form of a document.

[0099] In this optional embodiment, the use of a clustering feature tree algorithm to classify the enterprises with similar features corresponding to the pre-processed enterprise diagnostic information into the same group includes:

[0100] Randomly select enterprise samples from the preprocessed enterprise diagnostic information as the cluster center of the root node, and use the cluster center as the starting point of the cluster feature tree;

[0101] Add new enterprise sample points to the cluster feature tree, calculate the distance between the enterprise sample points and all cluster centers and compare them with the preset threshold;

[0102] If the distance is less than the preset threshold, the new enterprise sample point is added to the cluster with the smallest distance, and the clustering features corresponding to the leaf node are updated in sequence from the corresponding leaf node to the parent node; if the distance is greater than the preset threshold, the new sample point is used as a new cluster;

[0103] Determine the number of clusters in the leaf node. If the number of clusters is greater than the preset threshold, split the leaf node until no new enterprise sample points are generated. Otherwise, repeatedly calculate the distance between the enterprise sample point and the cluster center.

[0104] When all enterprise sample points have been processed and the number of clusters in the leaf nodes meets the preset threshold, enterprises with similar characteristics are divided into the same group.

[0105] In this optional embodiment, the diagnosis enterprise user unit includes an enterprise information filling module, a diagnosis question selection module, a customer service information display module and a diagnosis result viewing module, wherein:

[0106] The enterprise information filling module is used for enterprise users to fill in basic enterprise information, verify the basic enterprise information to obtain verification results, and standardize and store the enterprise information according to the verification results;

[0107] The diagnostic question selection and answering module is used to help enterprise users complete the selection of diagnostic questions, extract keywords from the answers to the diagnostic questions, and record and associate the answers to the diagnostic questions with the enterprise diagnostic tasks;

[0108] The customer service information display module is used to extract the target text between the enterprise user behavior and the customer service end, analyze the enterprise user sentiment from the target text, and dynamically adjust the customer service response strategy based on the enterprise user sentiment;

[0109] The diagnosis result viewing module is used to provide visual query for enterprise users using the charts generated by enterprise diagnosis results.

[0110] In this optional embodiment, the customer service information display module extracts the target text between the enterprise user behavior and the customer service end, analyzes the enterprise user emotion from the target text, and dynamically adjusts the customer service response strategy based on the enterprise user emotion, including:

[0111] The target text between the enterprise user behavior and the customer service end is used as the text to be subjected to sentiment analysis, and the target text is vectorized to obtain the first sentiment vector matrix and the second sentiment vector of the target text;

[0112] A dual-channel attention mechanism is used to assign weights to the emotional features in the first emotional vector matrix and the second emotional vector matrix respectively, and the emotional features after weight assignment are subjected to weighted processing and feature fusion processing in turn;

[0113] The activation function is used to classify the first sentiment vector matrix and the second sentiment vector matrix after feature fusion to obtain the sentiment classification result of the target text, and the customer service response strategy is dynamically adjusted based on the sentiment classification result.

[0114] In this optional embodiment, the use of an activation function to classify the first emotion vector matrix and the second emotion vector matrix after feature fusion to obtain an emotion classification result of the target text, and dynamically adjusting the customer service response strategy based on the emotion classification result includes:

[0115] The first sentiment vector matrix and the second sentiment vector matrix are fused in a specific way (such as splicing, weighted average or attention mechanism) to generate a comprehensive sentiment feature matrix to uniformly represent the multi-level sentiment information of the text; an activation function (such as Tanh activation function) is applied to perform nonlinear transformation on the fused sentiment feature matrix to enhance the expressive ability of the model and capture complex sentiment features and interactive relationships in high-dimensional space; the activated sentiment feature matrix is ​​input into a classification model (such as Softmax classifier or Logistic regression) to classify the text sentiment and output the probability distribution of sentiment categories (such as positive, neutral, negative) or sentiment intensity; according to the sentiment classification results, the customer service response strategy is dynamically adjusted: recommend value-added services to users with positive emotions, guide further communication to users with neutral emotions, and give priority to providing soothing replies or transferring to manual customer service to users with negative emotions.

[0116] In this optional embodiment, the expression of the emotional feature after weight allocation is:

[0117]

[0118]

[0119] In the formula, represents the attention weight of the first sentiment vector matrix; represents the attention weight of the second sentiment vector matrix; w h represents the first sentiment vector matrix; w c represents the second sentiment vector matrix; e h represents the first bias term; e c represents the second bias term; y i Represents the first weight vector of the target text processed by the activation function; y j The second weight vector representing the target text processed by the activation function; h i Represents global features; c j Represents local features.

[0120] Figure 2 An embodiment of a method for using an enterprise digital diagnosis platform of the present invention is shown.

[0121] In this optional embodiment, the method for using the enterprise digital diagnosis platform includes:

[0122] Step S201, screening enterprise diagnostic information in multiple dimensions, and performing comprehensive management of management users based on the enterprise diagnostic information;

[0123] Step S203, analyzing the diagnostic tasks of the enterprise users, and providing diagnostic services and personalized support for the enterprise users according to the diagnostic tasks;

[0124] Step S205, defining the function access scope of the enterprise user and the management user, and uniformly managing the function permissions of the enterprise user and the management user based on the function access scope;

[0125] Step S207, constructing diagnosis management roles and diagnosis enterprise roles to achieve authority division and user grouping under the multi-role system.

[0126] In order to facilitate understanding of the above technical solution of the present invention, the enterprise digital diagnosis platform of the present invention in the actual process is described in detail below:

[0127] like Figure 5 As shown in the figure, the digital diagnosis platform adopts B / S architecture and front-end and back-end separation development and deployment mode. The platform is developed based on the self-developed support platform and data middle platform, making full use of business components and improving the platform security, stability and development efficiency.

[0128] Design ideas:

[0129] By constructing diagnostic platform management roles and diagnostic enterprise roles to divide permissions, the diagnostic platform management role is granted diagnostic enterprise management, diagnostic information retrieval and diagnostic comprehensive analysis to support the management work of the diagnostic platform manager; the diagnostic enterprise role is granted tasks such as creating new diagnoses and canceling diagnoses to support enterprises in filling in diagnostic information and answering diagnostic questions on their own.

[0130] The digital diagnosis platform mainly includes "diagnosis information retrieval", "diagnosis problem cutting", "diagnosis result revision", "diagnosis result cancellation", "online diagnosis result display and analysis" and "diagnosis problem cutting". It realizes the whole process of online diagnosis for enterprises, including self-diagnosis, diagnosis cutting and diagnosis cancellation, and realizes diagnosis management, diagnosis revision and diagnosis result analysis of the diagnosis platform.

[0131] Platform functional requirements:

[0132] The platform is divided into diagnostic management users and diagnostic enterprise users. As shown in the digital diagnostic platform business architecture diagram, the invalidation of diagnostic results, tailoring of diagnostic problems, and display of customer service information are common functions. The platform matches different functions according to the different user roles.

[0133] Online diagnosis result display and analysis:

[0134] Based on the actual diagnosis situation of the enterprise, the platform can view and analyze the diagnosis results of the enterprise.

[0135] Diagnosis result page display: Conduct statistical analysis on the enterprise's diagnosis results and present the analysis results in a graphical manner.

[0136] Data collection scope: For relevant statistics such as average values ​​involved in enterprise diagnosis and analysis, the latest diagnosis results of each enterprise are taken as the diagnosis results of the enterprise and included in the statistical analysis.

[0137] Diagnostic analysis: Conduct statistical analysis on diagnostic enterprises. You can select the diagnostic scope of the statistical analysis enterprise. It supports screening the diagnostic enterprises that need to be analyzed by combining the two dimensions of industry and region. All analysis is performed by default.

[0138] Regional distribution interface:

[0139] As shown in Table 1, the regional distribution interface is mainly used to intuitively display the enterprise's diagnostic information data in the form of a radar chart. The radar chart is divided into a primary indicator radar chart and a secondary indicator radar chart. Each radar chart counts different indicators according to the category of the topic.

[0140] Table 1: Regional distribution interfaces

[0141]

[0142] Industry distribution interface:

[0143] As shown in Table 2, the industry distribution interface is mainly used to clearly display the industry of diagnostic companies in the form of a box-and-whisker plot, so that users can intuitively understand the distribution of different industries of diagnostic companies.

[0144] Table 2: Industry distribution interface

[0145]

[0146]

[0147] Diagnostic report export: The enterprise diagnostic report is presented in the form of a file, and statistical analysis is performed on the diagnostic information submitted by the enterprise at the time of diagnosis. Both word and pdf formats are supported.

[0148] Export answer results: Export the company's diagnostic answer results in Excel to show which options the company has selected. If the company answers the questions multiple times, the company's multiple diagnostic answer information can be displayed, and the final diagnosis can be displayed separately.

[0149] Question answering progress statistics: The progress of answering the latest questions diagnosed by the enterprise can be counted.

[0150] Question bank import:

[0151] According to the needs of diagnostic business, a diagnostic question bank is added and maintained through the diagnostic question bank template file. This function is used to import a new question bank and realize the function of adding diagnostic question banks by yourself. Question bank import supports the following:

[0152] Formulate the question bank catalogue;

[0153] Question bank question formulation;

[0154] Question bank question score setting;

[0155] Question bank question option formulation;

[0156] Formulate score values ​​for question options in the question bank;

[0157] Question bank question option type formulation.

[0158] The diagnostic enterprise completes the diagnosis of the enterprise by filling in the enterprise information and selecting and answering the diagnostic questions through the new diagnosis. The diagnostic platform can revise the completed diagnosis, modify and correct the diagnostic information filled in by the enterprise according to actual needs, complete the diagnosis of the enterprise, and form a diagnostic analysis report.

[0159] The functional services of the digital diagnostic platform are mainly divided into two categories: management side and enterprise side. Different authority roles are divided according to the needs of the two types of services to support the authority control of platform functions and data.

[0160] The management role needs to support business operations including enterprise diagnosis management, enterprise diagnosis revision, enterprise diagnosis cancellation, diagnosis information viewing, diagnosis report generation, diagnosis report download, diagnosis information retrieval, diagnosis information management, diagnosis comprehensive analysis, and diagnosis question bank import. The management role can realize the full-process, paperless diagnosis management business through the information platform.

[0161] The enterprise-side roles need to support business operations including creating new diagnoses, filling in enterprise information, selecting and answering diagnostic questions, temporarily storing selected questions and answers, submitting selected questions and answers, generating diagnostic reports, downloading diagnostic reports, and invalidating enterprise diagnoses. The enterprise-side roles can complete the entire process and more conveniently fill in complete diagnostic information and view business through the information platform.

[0162] By designing and developing a digital diagnostic platform based on domestic operating platforms, domestic databases, and open source third-party component technologies, we can ensure the security and controllability of the platform from a technical perspective. At the same time, we use containerized deployment to achieve smooth expansion of the platform's performance at the hardware level. Secondly, we use a front-end and back-end separation development model to achieve standardized and efficient program development.

[0163] Based on the accumulation of existing technology research and development platforms, we can quickly, efficiently and with high quality implement the design, development, testing and deployment of digital diagnosis platform projects, meet the business needs of the platform, ensure the efficient operation of the platform after it goes online, and provide the basic guarantee for information services for digital consulting and diagnosis work.

[0164] like Figure 4 As shown, the overall process of the digital diagnosis platform involves the participation of two types of roles, the enterprise side and the management side. In addition to user authentication and login through a unified authentication platform, the main diagnostic business process includes filling in enterprise information, selecting diagnostic questions, submitting diagnostic results, viewing diagnostic information, revising diagnostic results, and generating diagnostic reports. The entire digital consulting and diagnosis business process is completed by the enterprise filling in complete information and selecting diagnostic options online, and the management revising the diagnostic results and generating diagnostic reports.

[0165] Data maintenance and collection:

[0166] The data maintenance and collection of the digital diagnosis platform mainly includes data initialization maintenance work before the platform goes online and data collection and maintenance work during the operation of the platform. The work content and methods of the above two stages are as follows:

[0167] 1. Data initialization and maintenance work before the platform goes online:

[0168] Technical data: including technical data at the platform level, which is initialized and maintained through data scripts;

[0169] Configuration data: including code and parameter data, which are initialized and maintained through data scripts;

[0170] Business data: including question bank data, which is initialized and maintained through data scripts;

[0171] Permission data: including function permissions, data permissions, and account permissions, which are initialized and maintained through data scripts.

[0172] The entity relationship design of the digital diagnosis platform can establish association relationships between different entities, including diagnostic enterprises, enterprise diagnostic information, enterprise diagnostic information details, diagnostic questions, diagnostic question options, diagnostic reports and other entities, and determine the data structure generated by the association relationships between entities.

[0173] 1) There is a one-to-many relationship between the diagnosis enterprise and the enterprise diagnosis information. An enterprise can complete multiple diagnosis questions and associate them through the enterprise ID;

[0174] 2) There is a one-to-many relationship between enterprise diagnosis information and enterprise diagnosis information details. One piece of enterprise diagnosis information can correspond to different questions. Each question can support single selection or multiple selection and is associated through the company diagnosis ID;

[0175] 3) There is a one-to-many relationship between diagnostic questions and diagnostic question options. Each diagnostic question can support single selection or multiple selection and can correspond to different question options. Each diagnostic question option is associated through the question id;

[0176] 4) There is a one-to-one relationship between the enterprise diagnostic information and the latest version of the diagnostic report. After completing the digital diagnosis, each piece of enterprise diagnostic information can generate a corresponding diagnostic report and record the version number. If the enterprise revise the diagnostic information, the latest version of the diagnostic report can be regenerated and associated through the company diagnostic ID.

[0177] 2. Data collection during platform operation:

[0178] User data: including business data of enterprise information reporting, enterprise diagnosis selection, and management-side diagnosis revision, collected through platform functions;

[0179] Business data: including question bank data, which is imported and collected through platform functions;

[0180] Log data: including platform operation log data and user operation log data, which are collected in real time through the platform log module.

[0181] Architecture and deployment form:

[0182] The digital diagnosis platform adopts B / S architecture, which divides the platform into the front-end (browser) and the back-end (server). The front-end is responsible for the user interface and interaction, and the back-end is responsible for business logic processing, data storage and management. The data transmission between the front-end and the back-end is encrypted through AES to ensure the security of data transmission.

[0183] The platform deployment is mainly based on the operation resource layer, platform component layer, and support platform layer for layer-by-layer deployment and configuration. The planning of each layer is as follows:

[0184] Operation resource layer: It is provided in the form of intranet cloud resource containers, and the security policies of resources and networks are configured according to unified requirements and specifications;

[0185] Platform component layer: Introduce qualified open source third-party components to support the needs of different technical scenarios of the platform;

[0186] Support platform layer: Integrate mature and stable self-developed support platform front-end and back-end components to reduce duplication of development, improve platform development efficiency, and shorten project delivery cycle.

[0187] Platform structure:

[0188] As a core component of the industrial Internet platform construction and expansion project, the digital diagnostic platform is based on the B / S architecture and uses technical means to separate the front-end and back-end to achieve a hierarchical layout of the platform, thereby improving the stability of the platform.

[0189] At the same time, the platform introduced a cache service to cache hot data and calculation results to reduce the pressure on the database; the platform also used an object storage layer to store unstructured data (mainly physical files storing diagnostic reports), which facilitates programs to upload, download, delete and other operations.

[0190] Operating environment:

[0191] When designing and implementing the digital diagnostic platform, an operating environment design that combines domestic technology with container technology is adopted to ensure that the platform can provide services to enterprises efficiently and stably.

[0192] The operating platform uses the Kylin Advanced Server Operating Platform V10, which meets the platform's requirements for security, stability and performance. By using a domestic operating platform, we can better master and control the underlying technology to ensure the security and controllability of the platform. The platform uses containerization technology to build a software operating environment to achieve rapid deployment, efficient management and flexible expansion of applications.

[0193] Program structure description:

[0194] The digital diagnosis platform adopts a development model with front-end and back-end separation at the technical level. This model allows the front-end to focus on display logic and interaction design to ensure the smoothness and response speed of the interface. The back-end focuses on business logic and data processing. The two communicate through interfaces to achieve data interaction. The specific program structure is as follows:

[0195] Configuration layer: such as database configuration, security configuration, application-level configuration, etc.

[0196] Data access layer: mapped with database tables and used to access the database;

[0197] Business logic layer: used to process business logic, usually interacting with the data access layer;

[0198] Control layer: processes http requests, collects data, and returns response results;

[0199] Presentation layer: processes and renders the response data and finally presents it on the page;

[0200] Others: such as unified exception handling, unified tool classes, log record printing, etc.

[0201] Platform detailed design:

[0202] The frontend and backend communicate with each other through RESTful API interfaces. These interfaces define the rules for the frontend to request data from the backend and the format for the backend to return data to the frontend.

[0203] The interface design follows the HTTP protocol and JSON data format to ensure the standardization and normalization of data interaction between the front-end and back-end.

[0204] The front end obtains the required data, such as user information, diagnostic results, etc., by calling the API interface provided by the back end. After receiving the request from the front end, the back end executes the corresponding business logic according to the content of the request, obtains or processes data from the database, and then returns the results to the front end in JSON format.

[0205] After receiving the data returned by the backend, the frontend parses it and displays it on the user interface. When the frontend and backend interact, the input parameters are encrypted with AES, the HTTPS protocol is used for data transmission, the API interface is authenticated and access controlled, and the user input is authenticated and filtered.

[0206] Diagnostic business management:

[0207] As shown in Table 3, the diagnosis enterprise list interface is mainly used to display the information reported by the enterprise, classification information, and the latest diagnosis status and diagnosis progress list of each enterprise. The list supports paging operations and supports fuzzy search of enterprise name keywords.

[0208] Table 3: Diagnostic enterprise list interface

[0209]

[0210]

[0211] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0212] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0213] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0214] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0215] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0216] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. An enterprise digital diagnosis platform, characterized in that: The platform includes: The diagnostic management user unit is used to screen enterprise diagnostic information in multiple dimensions and conduct comprehensive management of management users based on the enterprise diagnostic information; The diagnostic enterprise user unit is used to analyze the diagnostic tasks of enterprise users and provide diagnostic services and personalized support to enterprise users based on the diagnostic tasks; The business authority management unit is used to define the functional access scope of enterprise users and management users, and to uniformly manage the functional permissions of enterprise users and management users based on the functional access scope; The user role management unit is used to build diagnostic management roles and diagnostic enterprise roles to achieve permission division and user grouping under a multi-role system.

2. The enterprise digital diagnosis platform according to claim 1, characterized in that: The diagnosis management user unit includes a diagnosis information retrieval module, a diagnosis comprehensive analysis module, a diagnosis problem tailoring module, a diagnosis result revision module and a diagnosis result cancellation module, wherein: The diagnostic information retrieval module is used to obtain enterprise diagnostic information and establish a diagnostic question library, classify enterprise diagnostic information in the diagnostic database, and establish an index in the diagnostic question library using semantic indexing technology; The diagnostic comprehensive analysis module is used to perform statistical analysis on the enterprise diagnostic information to obtain the enterprise diagnostic results, and generate an enterprise diagnostic report based on the enterprise diagnostic results; A diagnostic question tailoring module is used to tailor diagnostic questions based on predefined enterprise personalized diagnostic requirements to allow management users to select, remove and modify diagnostic questions; A diagnosis result revision module is used to review the enterprise diagnosis result, and revise the enterprise diagnosis result based on the review result to generate a new enterprise diagnosis report, and update the enterprise diagnosis information associated with the new enterprise diagnosis report; The diagnosis result invalidation module is used to detect anomalies in enterprise diagnosis results, invalidate enterprise diagnosis results with anomalies, and mark the corresponding enterprise diagnosis information as invalid records.

3. The enterprise digital diagnosis platform according to claim 2, characterized in that: The diagnostic information retrieval module acquires the enterprise diagnostic information and establishes a diagnostic question library, classifies the enterprise diagnostic information in the diagnostic database, and uses semantic indexing technology to establish an index in the diagnostic question library, including: Obtain enterprise diagnostic information and convert it into text data, extract problem items from the text data, and construct a diagnostic problem library based on the problem items in a hierarchical form; The text data stored in the diagnostic question library is embedded and vectorized using a semantic embedding model, and the processing results are mapped to a high-dimensional vector space to form a semantic vector representation; Approximate nearest neighbor and inverted indexing techniques are used to index text data after embedding vectorization to improve the efficiency of enterprise diagnostic information retrieval.

4. The enterprise digital diagnosis platform according to claim 3, characterized in that: The enterprise diagnosis information includes the diagnosis enterprise ID, diagnosis status, diagnosis score, diagnosis rating, diagnosis start time, diagnosis end time and diagnosis report address.

5. The enterprise digital diagnosis platform according to claim 4, characterized in that: The comprehensive diagnosis analysis module performs statistical analysis on the enterprise diagnosis information to obtain the enterprise diagnosis results, and generates an enterprise diagnosis report based on the enterprise diagnosis results, including: Preprocess the enterprise diagnostic information, and use the clustering feature tree algorithm to divide the enterprises with similar characteristics corresponding to the preprocessed enterprise diagnostic information into the same group; Formulate diagnostic indicators for the divided enterprise groups, and evaluate the performance of enterprises in different diagnostic indicators according to predefined evaluation standards to obtain enterprise diagnostic results; Based on the enterprise diagnosis results, the first-level indicator radar chart and the second-level indicator radar chart are formulated, and the industry of the diagnosed enterprise is displayed in the form of a box-whisker chart. The industry distribution range of the diagnosed enterprise is obtained by analyzing the first-level indicator radar chart, the second-level indicator radar chart and the box-whisker chart; The enterprise diagnosis results are updated based on the industry distribution range to form an enterprise diagnosis report, and the enterprise diagnosis report is provided in the form of a document.

6. The enterprise digital diagnosis platform according to claim 5, characterized in that: The method of using a clustering feature tree algorithm to classify enterprises with similar characteristics corresponding to the pre-processed enterprise diagnostic information into the same group includes: Randomly select enterprise samples from the preprocessed enterprise diagnostic information as the cluster center of the root node, and use the cluster center as the starting point of the cluster feature tree; Add new enterprise sample points to the cluster feature tree, calculate the distance between the enterprise sample points and all cluster centers and compare them with the preset threshold; If the distance is less than the preset threshold, the new enterprise sample point is added to the cluster with the smallest distance, and the clustering features corresponding to the leaf node are updated in sequence from the corresponding leaf node to the parent node; if the distance is greater than the preset threshold, the new sample point is used as a new cluster; Determine the number of clusters in the leaf node. If the number of clusters is greater than the preset threshold, split the leaf node until no new enterprise sample points are generated. Otherwise, repeatedly calculate the distance between the enterprise sample point and the cluster center. When all enterprise sample points have been processed and the number of clusters in the leaf nodes meets the preset threshold, enterprises with similar characteristics are divided into the same group.

7. The enterprise digital diagnosis platform according to claim 6, characterized in that: The diagnosis enterprise user unit includes an enterprise information filling module, a diagnosis question selection module, a customer service information display module and a diagnosis result viewing module, wherein: The enterprise information filling module is used for enterprise users to fill in basic enterprise information, verify the basic enterprise information to obtain verification results, and standardize and store the enterprise information according to the verification results; The diagnostic question selection and answering module is used to help enterprise users complete the selection of diagnostic questions, extract keywords from the answers to the diagnostic questions, and record and associate the answers to the diagnostic questions with the enterprise diagnostic tasks; The customer service information display module is used to extract the target text between the enterprise user behavior and the customer service end, analyze the enterprise user sentiment from the target text, and dynamically adjust the customer service response strategy based on the enterprise user sentiment; The diagnosis result viewing module is used to provide visual query for enterprise users using the charts generated by enterprise diagnosis results.

8. The enterprise digital diagnosis platform according to claim 7, characterized in that: The customer service information display module extracts the target text between the enterprise user behavior and the customer service end, analyzes the enterprise user emotion from the target text, and dynamically adjusts the customer service response strategy based on the enterprise user emotion, including: The target text between the enterprise user behavior and the customer service end is used as the text to be subjected to sentiment analysis, and the target text is vectorized to obtain the first sentiment vector matrix and the second sentiment vector of the target text; A dual-channel attention mechanism is used to assign weights to the emotional features in the first emotional vector matrix and the second emotional vector matrix respectively, and the emotional features after weight assignment are subjected to weighted processing and feature fusion processing in turn; The activation function is used to classify the first sentiment vector matrix and the second sentiment vector matrix after feature fusion to obtain the sentiment classification result of the target text, and the customer service response strategy is dynamically adjusted based on the sentiment classification result.

9. The enterprise digital diagnosis platform according to claim 8, characterized in that: The expression of the emotional feature after the weight allocation is: In the formula, represents the attention weight of the first sentiment vector matrix; represents the attention weight of the second sentiment vector matrix; w h represents the first sentiment vector matrix; w c represents the second sentiment vector matrix; e h represents the first bias term; e c represents the second bias term; y i Represents the first weight vector of the target text processed by the activation function; y j The second weight vector representing the target text processed by the activation function; h i Represents global features; c j Represents local features.

10. A method for using an enterprise digital diagnosis platform, characterized in that: The method includes: Screen enterprise diagnostic information from multiple dimensions and conduct comprehensive management of management users based on enterprise diagnostic information; Analyze the diagnostic tasks of enterprise users and provide diagnostic services and personalized support to enterprise users based on the diagnostic tasks; Define the functional access scope of enterprise users and management users, and centrally manage the functional permissions of enterprise users and management users based on the functional access scope; Build diagnostic management roles and diagnostic enterprise roles to achieve authority division and user grouping under the multi-role system.

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