Business health degree self-service detection method and system and electronic equipment

Abstract

The invention relates to the technical field of communication network operation and maintenance, in particular to a service health degree self-service detection method and system and electronic equipment, and fundamentally solves the core pain point of lack of autonomy of customer detection in the prior art through full automation of a detection process. According to the invention, the client can trigger and quickly obtain the result by one key at any time without waiting for the intervention of operation and maintenance personnel, so that the operation and maintenance response efficiency and the client experience are greatly improved, and the real-time and autonomous monitoring of the service health state is realized.

Description

Technical Field

[0001] This application relates to the field of communication network operation and maintenance technology, specifically to a self-service detection method, system, and electronic equipment for business health. Background Technology

[0002] In the internet and communications services sector, ensuring the healthy and stable operation of privacy-preserving communication services (such as voice calls and SMS) is a core task for service providers. Currently, monitoring the health of services in this sector generally adopts a back-end periodic inspection model led by the operations and maintenance team. Specifically, when customers need to understand the real-time status of their services, they must submit an application to the operations and maintenance personnel through channels such as work orders, emails, or telephones. Subsequently, the operations and maintenance personnel manually collect key indicators such as voice call success rate and SMS delivery rate from various network devices and systems. These indicators are then manually compiled and analyzed in conjunction with operational data such as call detail records and call volume before the results are finally fed back to the customer.

[0003] However, this passive detection mechanism, which heavily relies on manual intervention, has a fundamental bottleneck: customers lack the ability to initiate detection independently. This bottleneck directly leads to two significant negative effects: First, the entire process, from the customer submitting an application to receiving the final result, is lengthy, averaging over 4 hours, completely failing to meet customers' operational needs for real-time and rapid status confirmation in the event of sudden business anomalies; second, it transforms a process that should be automated into a high-cost, low-efficiency manual service, not only increasing the workload of the operations team but also severely limiting customers' initiative and flexibility in managing their own businesses.

[0004] Therefore, this fundamental flaw in existing technologies makes it difficult to improve customer experience and operational efficiency. The industry urgently needs a solution that can be triggered autonomously by customers with a single click, and can automatically complete detection and report generation in a short time, to overcome the inherent delays of passive detection modes and truly achieve real-time and self-service business health monitoring. Summary of the Invention

[0005] The purpose of this application is to provide a self-service business health assessment method, system, and electronic device to solve the problem that existing privacy communication business health assessments rely on manual intervention, resulting in customers being unable to obtain assessment results independently.

[0006] To achieve the above objectives, the technical solution adopted in this application is as follows: According to one aspect of the embodiments of this application, a self-service business health assessment method is provided, comprising: responding to a self-service assessment instruction triggered by a customer on a client interface, generating a assessment task corresponding to the current business; invoking a assessment engine to execute the assessment task, thereby collecting multiple business health indicators from a preset dual-dimensional indicator system, wherein the business health indicators include customer operation indicators, voice service health indicators, and SMS service health indicators; determining the current business health score based on all business health indicators through a preset differentiated weighted health assessment model; generating a structured assessment report containing the health score, and returning the report to the client interface for display.

[0007] Based on the aforementioned technical means, by automating the entire testing process, the core pain points of existing technologies—such as lack of customer autonomy in testing and long processing times—are fundamentally solved. Customers can trigger testing with a single click at any time and quickly obtain results without waiting for maintenance personnel to intervene, greatly improving maintenance response efficiency and customer experience, and enabling real-time, autonomous monitoring of business health status.

[0008] Furthermore, multiple business health indicators from a pre-defined dual-dimensional indicator system are collected, including: customer operation indicators collected at a first pre-defined frequency, with the average of multiple continuously collected data points, where customer operation indicators include real-time call detail records, call volume, average call duration, and peak concurrent call count; voice service health indicators collected at a second pre-defined frequency, with the average of multiple continuously collected data points, where voice service health indicators include call success rate, call drop rate, voice latency, and voice clarity score; and SMS service health indicators collected at a third pre-defined frequency, with the average of multiple continuously collected data points, where SMS service health indicators include SMS sending success rate, SMS delivery latency, and SMS loss rate.

[0009] Based on the aforementioned technical means, by setting differentiated collection frequencies for different types of indicators and taking the average of continuous data points, this step effectively ensures the representativeness and stability of the collected data, avoids interference from instantaneous fluctuations on the evaluation results, and lays a reliable data foundation for subsequent accurate evaluation.

[0010] Furthermore, based on all business health indicators, a pre-defined differentiated weighted health assessment model is used to determine the current business health score. This includes: calculating the voice service health score and the SMS service health score separately using a pre-defined single-service health score formula; wherein, the voice service health score is obtained by weighted summation of the standardized values ​​of its corresponding multiple voice service health indicators according to a first weight set; the SMS service health score is obtained by weighted summation of the standardized values ​​of its corresponding multiple SMS service health indicators according to a second weight set; and the voice service health score, the SMS service health score, and the mean of the standardized customer operation indicators are weighted and fused to obtain the current business health score.

[0011] Based on the aforementioned technical means, a hierarchical evaluation model that "calculates by business segment and then integrates them as a whole" overcomes the shortcomings of traditional unified indicators in evaluating business performance, which suffers from weak targeting. It applies differentiated weighting to voice and SMS services and comprehensively considers business health and operational status, resulting in a final health score that more accurately and comprehensively reflects the overall condition of the business.

[0012] Furthermore, before calculating the health scores of the voice service and the SMS service respectively using the preset single-service health scoring formula, the process includes: comparing the actual value of each service health indicator with the preset maximum and minimum thresholds; based on the comparison results, calculating the standardized value of each service health indicator using the interval mapping method: when the actual value is greater than or equal to the maximum threshold, the standardized value is 100; when the actual value is less than or equal to the minimum threshold, the standardized value is 0; when the actual value is between the two, the standardized value is calculated using linear interpolation.

[0013] Based on the above technical means, the interval mapping method is used to unify different indicators with different dimensions and meanings into dimensionless standardized scores, which solves the problem that multiple indicators cannot be directly compared and weighted, and enables the evaluation model to scientifically and reasonably integrate and calculate all indicators.

[0014] Furthermore, when generating a structured inspection report, the above method also includes: generating a dynamic watermark string based on the operation traceability information of this inspection task, wherein the operation traceability information includes at least the operation number, operation interface name and operation time; and embedding the dynamic watermark string into the structured inspection report using a semi-transparent overlay method.

[0015] By adding a dynamic watermark containing operational traceability information to the report using the aforementioned technical methods, this step significantly enhances the authenticity, credibility, and traceability of the test report. It not only allows for rapid identification of the source of the operation in case of disputes but also effectively prevents the forgery and alteration of the report, meeting the stringent compliance and auditing requirements of privacy-focused communication services.

[0016] Furthermore, based on the operational traceability information of this detection task, a dynamic watermark string is generated, including: extracting the operation number, operation interface name, and operation time, connecting them using pipes to form the original string, and then encoding the original string using BASE64 to obtain the target encoded string; determining the coordinates of the watermark in the report through perturbation calculation based on the page width and height of the report to be embedded; generating a dynamic key based on the coordinates of the watermark in the report using a preset key generation formula; encoding the dynamic key using BASE64 and calculating the offset sequence using a preset offset formula; transforming the target encoded string using the offset sequence, and generating the final watermark string using a preset watermark generation formula.

[0017] Using the aforementioned technical methods, by introducing a dynamic key bound to coordinates and complex encoding transformations, it is ensured that each watermark is unique and difficult to predict and crack. This strong correlation prevents the watermark from being copied or removed in batches, thus achieving high-security traceability protection at the level of a single report.

[0018] Furthermore, based on the page width and height of the report to be embedded, the coordinates of the watermark in the report are determined through perturbation calculation, including: calculating the maximum perturbation range of the watermark in the horizontal and vertical directions respectively according to the page width and height of the report to be embedded, using a preset perturbation formula; generating random perturbation values ​​of the watermark in the horizontal and vertical directions respectively within the maximum perturbation range; and calculating the final coordinates of the watermark in the report after boundary constraints based on the random perturbation values ​​and the preset initial coordinates, using a preset boundary value constraint formula.

[0019] Based on the aforementioned technical methods, the embedding coordinates are determined through random perturbation, making the watermark's distribution position in the report random and dynamic. This method avoids the weakness of fixed-position watermarks being easily identified and removed, significantly increasing the difficulty for malicious parties to completely remove the watermark without damaging the report content, and further enhancing anti-tampering capabilities.

[0020] According to another aspect of the embodiments of this application, a self-service business health detection device is also provided, comprising: an instruction response module, used to respond to a self-service detection instruction triggered by a customer on a client interface and generate a detection task corresponding to the current business; an indicator collection module, used to call a detection engine to execute the detection task, so as to collect multiple business health indicators in a preset dual-dimensional indicator system, wherein the business health indicators include customer operation indicators, voice business health indicators, and SMS business health indicators; a scoring determination module, used to determine the health score of the current business based on all business health indicators through a preset differentiated weighted health assessment model; and a report generation module, used to generate a structured detection report containing the health score and return the report to the client interface for display.

[0021] According to another aspect of the embodiments of this application, an electronic device is also provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; wherein the memory is used to store a computer program; and the processor is used to execute the steps of the business health self-testing method in any of the above embodiments by running the computer program stored in the memory.

[0022] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein the storage medium stores a computer program, wherein the computer program is configured to execute the business health self-service detection method steps of any of the above embodiments when running.

[0023] The beneficial effects of this application are: This application fundamentally solves the core pain point of lack of customer autonomy in testing by automating the entire testing process. Customers can trigger the process with a single click at any time and quickly obtain results without waiting for maintenance personnel to intervene, greatly improving maintenance response efficiency and customer experience, and enabling real-time, autonomous monitoring of business health status. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the hardware environment for an optional self-service business health detection method provided in an embodiment of this application; Figure 2 This is a flowchart illustrating an optional self-service business health assessment method provided in an embodiment of this application; Figure 3 This is a structural block diagram of an optional self-service business health detection device provided in an embodiment of this application; Figure 4 This is a structural block diagram of an optional electronic device provided in an embodiment of this application. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] According to one aspect of the embodiments of this application, a self-service business health detection method is provided. Optionally, in this embodiment, the above-mentioned self-service business health detection method can be applied to a hardware environment consisting of a terminal and a server. The server is connected to the terminal via a network and can be used to provide services to the terminal or clients installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services to the server.

[0030] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network, metropolitan area network, local area network. The aforementioned wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity), Bluetooth. The terminal is not limited to PC, mobile phone, tablet computer, etc.

[0031] The self-service business health check method of this application embodiment can be executed by a server, a terminal, or both. Specifically, the self-service business health check method of this application embodiment can also be executed by a client installed on the terminal.

[0032] Taking the self-service business health check method in this embodiment, executed by the server, as an example, please refer to [link to relevant documentation]. Figure 1 , Figure 1This is a schematic diagram of the hardware environment for an optional self-service business health detection method provided in an embodiment of this application, such as... Figure 1 As shown, the hardware environment of this self-service health assessment method includes: a terminal 102 and a server 104 connected to the terminal 102 via a network. The server 104 is used to deploy a detection engine and a health assessment model, which executes the self-service health assessment method of this application embodiment to assess the health status of privacy communication services. The terminal 102 is used to display the detection progress and the generated health report, which can be obtained by processing the detection engine and health assessment model deployed on the server 104.

[0033] The self-service health detection method for services in this embodiment can be applied to scenarios such as communication service operation and maintenance, and cloud service quality management. For example, enterprise users can monitor the quality of their voice and SMS services themselves. This embodiment uses the health detection of privacy communication services as an example to illustrate the above-mentioned self-service health detection method for services.

[0034] In existing technologies, the health monitoring of privacy communication services heavily relies on manual intervention by operations and maintenance personnel. Customers cannot initiate monitoring and obtain results independently and in real time, resulting in a lengthy process from application to feedback. At the same time, traditional monitoring methods are often limited to the network layer in terms of indicator coverage, failing to integrate core customer operational data, and the output results lack standardized reporting carriers and effective traceability mechanisms, which cannot meet customers' operational and maintenance needs for real-time, comprehensive, and reliable assessment of business status.

[0035] To address the aforementioned issues, this embodiment provides a self-service method for detecting the health of services running on the aforementioned server. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a flowchart illustrating an optional self-service business health assessment method provided in an embodiment of this application, such as... Figure 2 As shown, the self-service business health detection method of this application embodiment specifically includes the following steps: Step S201: In response to the self-service testing instruction triggered by the customer on the client interface, generate the testing task corresponding to the current business. Step S202: Call the detection engine to execute the detection task to collect multiple business health indicators in the preset dual-dimensional indicator system. The business health indicators include customer operation indicators, voice service health indicators and SMS service health indicators. Step S203: Based on all business health indicators, determine the current business health score using a preset differentiated weighted health assessment model; Step S204: Generate a structured detection report containing a health score and return the report to the client interface for display.

[0036] Through steps S201 to S204 above, the core pain points of existing technologies—namely, the lack of customer autonomy in testing and the long processing time—are fundamentally solved by automating the entire testing process. Customers can trigger the process with a single click at any time and quickly obtain results without waiting for maintenance personnel to intervene, greatly improving maintenance response efficiency and customer experience, and enabling real-time, autonomous monitoring of business health status.

[0037] The following is combined with Figure 2 The self-service business health detection method in the embodiments of this application will be explained.

[0038] In the technical solution of step S201, in response to the self-service testing instruction triggered by the customer on the client interface, a testing task corresponding to the current business is generated.

[0039] In this embodiment, the client interface refers to the web or application interface deployed on the operation platform for direct access and operation by customers. Within the "Operation and Maintenance Management" function module, this interface features a specific, highlighted "One-Click Detection" button, which serves as the direct interactive medium for customer-triggered actions in this step.

[0040] The self-service testing command refers to a digital request signal generated by the client interface after the customer actively clicks the "One-Click Testing" button mentioned above. The core purpose of this command is to request the system to perform a real-time health check for its current business, which is essentially a shift from "manual application" to "automatic triggering".

[0041] A detection task refers to a complete, schedulable unit of work created in the background after the system receives a "self-service detection instruction." This task is a structured data object that contains at least the following key information: task ID (unique identifier), task type (self-service health check), associated business ID, task status (e.g., "pending execution"), the currently logged-in customer's operator ID, and creation timestamp. This information is used to uniquely identify and track the lifecycle of this detection within the entire system.

[0042] This step is the starting point for achieving "self-service" testing, aiming to transform a simple customer interaction into an executable automated process within the system. Its specific implementation is as follows: The customer first clicks the highlighted "One-Click Detection" button in the "Operations and Maintenance Management" module on the operations platform's client interface. This action generates a self-service detection command (a digital signal requesting the system to perform a real-time health check). This interaction is captured by the front-end interface, marking the official start of a self-service detection process.

[0043] To ensure real-time feedback on testing progress and prevent customers from triggering tests repeatedly, the system immediately establishes a persistent long-term connection with the client via the WebSocket protocol upon receiving a self-service testing instruction. This connection serves as the sole channel for the server to proactively push progress status (such as "indicator collection in progress," "evaluation in progress," and "report generation in progress") to the client in real time throughout the entire testing process. This solves the latency problem of traditional polling methods and effectively prevents customers from triggering tests repeatedly due to waiting.

[0044] Following this, upon receiving the self-service testing instruction, the backend service creates a structured testing task for this request. This task is a data object containing key information such as a unique task ID, associated business ID, operator ID, and precise timestamp, and is persistently stored in a relational database (such as MySQL), with its status marked as "pending execution." This transforms a momentary user request into a persistent task entity that is verifiable and traceable within the system, providing a data foundation for end-to-end traceability and accountability.

[0045] Finally, the system schedules this task to the detection engine layer, seamlessly triggering subsequent metric collection and analysis processes. Specifically: after the task is generated and persisted, the system automatically sends a task scheduling signal to the detection engine layer, adding the newly created task ID to the execution queue. At this point, a detection task initiated by the customer and automatically accepted by the system is officially generated.

[0046] In step S202, the detection engine is invoked to execute a detection task, collecting multiple business health indicators from a preset dual-dimensional indicator system. This step is the core data acquisition stage of the self-service detection process. Through an automated detection engine, it collects dual-dimensional indicator systems covering both customer operational status and network service quality in parallel, thereby solving the problems of "incomplete indicator coverage" and "weak business targeting" in existing technologies.

[0047] In this embodiment, the detection engine refers to the background service module in the system responsible for executing the core detection logic. It receives the detection task from step S201 and schedules its internal indicator acquisition units to concurrently collect data from different data sources according to preset rules.

[0048] The dual-dimensional indicator system refers to a set of indicators specifically designed for this testing solution, consisting of two complementary dimensions. The first dimension is customer operation indicators, reflecting the activity and load of the business; the second dimension is communication service health indicators, which are further divided into voice and SMS services, reflecting the service quality of core services. This design breaks through the limitations of traditional methods that only focus on network-level indicators.

[0049] Business health metrics are a collective term for all specific monitoring items in this system, and are the basic data units constituting the two dimensions mentioned above. Business health metrics include customer operation metrics, voice service health metrics, and SMS service health metrics. This embodiment constructs a two-dimensional system of "operation metrics + business health metrics" to cover core customer-focused data. Please refer to Table 1 below for the specific business health metrics included in each metric type.

[0050] Table 1. Specific Composition of the Two-Dimensional Indicator System

[0051] As shown in Table 1, the number and content of business health indicators included in each indicator type are different, and the collection frequency of indicators for each indicator type is also different.

[0052] As an optional implementation, multiple business health indicators from a preset dual-dimensional indicator system are collected, including: collecting customer operation indicators at a first preset frequency and averaging the data from multiple consecutively collected data points, wherein the customer operation indicators include real-time call detail records, call volume, average call duration, and peak concurrent call count; collecting voice service health indicators at a second preset frequency and averaging the data from multiple consecutively collected data points, wherein the voice service health indicators include call success rate, call drop rate, voice latency, and voice clarity score; and collecting SMS service health indicators at a third preset frequency and averaging the data from multiple consecutively collected data points, wherein the SMS service health indicators include SMS sending success rate, SMS delivery latency, and SMS loss rate.

[0053] This step is the core of data acquisition. It employs a carefully designed, differentiated data collection strategy to ensure the comprehensiveness, stability, and business relevance of the data used in subsequent evaluations. The specific execution is as follows: 1. Collection of customer operation metrics In practical applications, the system starts with a detection task triggered by the front-end interface and automatically collects customer operational metrics at a preset frequency (once every 10 seconds). This process involves the detection engine calling the corresponding data interface to sample four key operational data points within a continuous 10-second window: real-time call detail records (reflecting current business volume), call volume (measuring total call duration), average call duration (characterizing user call behavior), and peak call concurrency (reflecting the system's maximum load). The system continuously collects five valid data points and then immediately calculates their arithmetic mean. This aims to effectively filter out noise interference caused by instantaneous system fluctuations or sudden abnormal traffic, thereby obtaining stable operational status data that represents the true business load level within that short period, providing a solid data foundation for subsequent evaluation.

[0054] 2. Collection of health indicators for voice services Given the high sensitivity of voice services to real-time performance and call quality, the system employs a higher second preset frequency (i.e., once every 5 seconds) to collect its health indicators. The detection engine obtains four core quality data points in parallel from network probes and the service platform: call success rate (ability to establish a call), call drop rate (stability of call persistence), voice latency (call real-time performance), and voice clarity score (call quality). Within each collection cycle, the system rapidly and continuously collects 10 data points and randomly averages them. This higher frequency and more sampling point design enables more precise capture of instantaneous fluctuations and trends in voice service quality, providing high-fidelity data for accurately assessing its Service Level Agreement (SLA) compliance.

[0055] 3. Collection of SMS service health indicators For SMS services, health metrics focus more on delivery reliability and efficiency. Therefore, the system collects SMS service health indicators at a third preset frequency (once every 10 seconds). During this period, the detection engine monitors and records three indicators: SMS sending success rate (measuring the reliability of the delivery process), SMS delivery latency (reflecting delivery efficiency), and SMS loss rate (characterizing link integrity). The system collects five consecutive data points and then calculates the average. This collection strategy ensures data stability while also aligning with the near real-time communication characteristics of SMS services, ensuring an accurate assessment of its overall service quality performance.

[0056] After initial cleaning, the collected raw indicator data is temporarily stored in a cache or database and associated with the task ID for this task.

[0057] Based on the aforementioned technical means, by setting differentiated collection frequencies for different types of indicators and taking the average of continuous data points, this step effectively ensures the representativeness and stability of the collected data, avoids interference from instantaneous fluctuations on the evaluation results, and lays a reliable data foundation for subsequent accurate evaluation.

[0058] In step S203, the current business health score is determined based on all business health indicators using a pre-defined differentiated weighted health assessment model. This step is the core algorithm of the entire method, aiming to transform the collected multi-dimensional and multi-dimensional raw indicators into an intuitive and unified comprehensive health score through a scientific and objective model. This step directly addresses the shortcomings of existing technologies, such as "weak business targeting" and "incomplete indicator coverage."

[0059] In this embodiment, the differentiated weighted health assessment model is a pre-defined calculation engine containing specific mathematical formulas and rules. Its "differentiated weighting" is the core feature, which means assigning different importance (i.e., weight) to different types of services (such as voice and SMS) and their internal different indicators, rather than using a uniform standard, so that the assessment results can more accurately reflect the actual operational characteristics and quality requirements of different services.

[0060] The health score is a quantitative and comprehensive numerical indicator defined in this solution, typically ranging from 0 to 100. It is not a measurement of a single network parameter, but rather a normalized result output after standardizing, weighting, and integrating all collected "dual-dimensional indicator systems" (including customer operation indicators and communication service health indicators) through a "differentiated weighted health assessment model."

[0061] The rating system is designed to intuitively and comprehensively reflect the overall operational status and service quality of a privacy communication service at a specific point in time.

[0062] It consolidates dozens of underlying technical indicators and operational data with different dimensions and meanings into a simple and easy-to-understand score, greatly reducing the understanding threshold and decision-making cost for customers. Because all indicators have undergone standardization and are ultimately aggregated into a unified score, customers can objectively compare the health status of their different services (such as voice and SMS) or track the health trends of the same service over different time periods. This score is the core conclusion for generating the final inspection report. Based on this score, the system can automatically trigger alerts or generate specific optimization suggestions, thereby driving operational actions.

[0063] As an optional implementation, the current business health score is determined based on all business health indicators using a preset differentiated weighted health assessment model. This includes: calculating the voice service health score and the SMS service health score using a preset single-service health score formula; wherein the voice service health score is obtained by weighted summation of the standardized values ​​of its corresponding multiple voice service health indicators according to a first weight set; the SMS service health score is obtained by weighted summation of the standardized values ​​of its corresponding multiple SMS service health indicators according to a second weight set; and the voice service health score, the SMS service health score, and the average of the standardized customer operation indicators are weighted and fused to obtain the current business health score.

[0064] In this embodiment, standardized values ​​refer to the process of mathematically transforming all original indicators (such as call success rate percentage, SMS latency in milliseconds, and number of call details) with different dimensions and value ranges to uniformly map them to dimensionless values ​​between 0 and 100. This process solves the fundamental problem of the inability to directly compare and weightedly calculate different indicators, and is a prerequisite for the model to run.

[0065] A weight set refers to a pre-defined set of importance coefficients for various business metrics. For example, the first weight set is dedicated to voice services, with the following weight allocation: {Call success rate: 0.35, Call drop rate: 0.25, Voice latency: 0.2, Voice clarity score: 0.2}, where "Call success rate" is assigned the highest weight. The second weight set is dedicated to SMS services, with the following weight allocation: {SMS sending success rate: 0.4, SMS delivery latency: 0.3, SMS loss rate: 0.3}, where "SMS sending success rate" is assigned the highest weight. The sum of the weights is 1, reflecting the differentiated allocation of resources across different services. This weight allocation is based on historical data statistics, highlighting the most critical quality indicators for each type of service.

[0066] In practical applications, the voice service score calculation process is as follows: Weights are obtained from the first weight set, and the voice service health score is calculated using the single-service health score formula. The SMS service score calculation process is as follows: Weights are obtained from the second weight set, and the SMS service health score is calculated using the single-service health score formula. The formula for scoring the health of this single business is as follows:

[0067] In the formula, Assign a health score to a single service (x=V represents voice service, x=S represents SMS service). The weight of the i-th indicator in the x-th business category ( =1), The standardized score (0-100 points) for the i-th indicator in the x-th business category.

[0068] The process of calculating the overall health score of the current business is as follows: Calculate the standardized mean Ō of customer operation indicators; and then... , Substitute Ō into the overall health score formula to calculate and output the final health score. The overall health score formula is as follows:

[0069] In the formula, H is the overall health score (0-100 points), 0.4 and 0.6 are the weights of the health indicators and operation indicators of communication services, respectively, and Ō is the standardized mean of the operation indicators (the average of four indicators, including real-time call detail records, after standardization).

[0070] Based on the aforementioned technical means, a hierarchical evaluation model that "calculates by business segment and then integrates them as a whole" overcomes the shortcomings of traditional unified indicators in evaluating business performance, which suffers from weak targeting. It applies differentiated weighting to voice and SMS services and comprehensively considers business health and operational status, resulting in a final health score that more accurately and comprehensively reflects the overall condition of the business.

[0071] As an optional embodiment, before calculating the voice service health score and SMS service health score respectively using a preset single-service health score formula, the method includes: comparing the actual value of each service health indicator with preset maximum and minimum thresholds; and calculating the standardized value of each service health indicator using an interval mapping method based on the comparison results: when the actual value is greater than or equal to the maximum threshold, the standardized value is 100; when the actual value is less than or equal to the minimum threshold, the standardized value is 0; and when the actual value is between the two, the standardized value is calculated using linear interpolation.

[0072] In this embodiment, the interval mapping algorithm is invoked to convert the actual values ​​of all indicators collected in step S202. one by one with the preset maximum threshold and minimum threshold By comparing the results, the standardized value of each indicator is calculated using linear interpolation. This standardizes all indicators to a scale of 0-100 points. The interval mapping algorithm can be expressed by the following interval mapping formula:

[0073] In the formula, For the standardized score of the i-th indicator in the x-th business category, The actual value of the indicator. The maximum threshold for the indicator. This is the minimum threshold for the indicator.

[0074] Based on the above technical means, the interval mapping method is used to unify different indicators with different dimensions and meanings into dimensionless standardized scores, which solves the problem that multiple indicators cannot be directly compared and weighted, and enables the evaluation model to scientifically and reasonably integrate and calculate all indicators.

[0075] The operation of the health assessment model in step S203 follows a logic of "layered calculation and final fusion." The specific process for determining the current business health score is as follows: 1. Indicator Standardization Processing: First, the interval mapping algorithm is called to standardize the actual values ​​of all indicators collected in step S202. one by one with the preset maximum threshold and minimum threshold By comparing the results, the standardized value of each indicator is calculated using linear interpolation. Standardize all indicators to a scale of 0-100 points. 2. Single business health score calculation: Subsequently, the model enters the stratified evaluation stage.

[0076] For voice services, use the formula Calculate the health score of the voice service. Among them, From the first weighted set {Call success rate: 0.35, Call drop rate: 0.25, Voice latency: 0.2, Voice clarity score: 0.2}.

[0077] For SMS services, use the formula. Calculate the health score of the SMS service. From the second weight set {SMS sending success rate: 0.4, SMS delivery delay: 0.3, SMS loss rate: 0.3}.

[0078] This step embodies "differentiated weighting," ensuring that key indicators play a dominant role in the final score.

[0079] 3. Overall Health Score Integration: Finally, the model integrates business and operational dimensions. It uses a formula... The final overall health score is calculated. Among them, the business health indicator (the average of voice and SMS scores) and the operational indicator (the standardized average of customer operation indicators) are assigned fixed weights of 0.4 and 0.6, respectively.

[0080] This design integrates a dual-dimensional indicator system, reflecting both network service quality and customer operational status, resulting in a comprehensive and balanced final evaluation. Through the above process, the model successfully transforms messy raw data into a single score that accurately and intuitively reflects the overall health of the business, achieving objectivity, quantification, and operability of the evaluation results.

[0081] In step S204, a structured testing report containing a health score is generated and returned to the client interface for display. This step is the final output of the self-service testing process, and its core task is to organize the analysis results into a professional, reliable, and easy-to-understand report and deliver it completely to the client. This step directly solves the problems of "insufficient report traceability" and "unintuitive result display" in existing technologies.

[0082] In this embodiment, a structured testing report refers to a standardized output document with a fixed format and chapter divisions, typically generated in PDF format. Unlike temporary data displays, it systematically organizes health scores, detailed data, analytical conclusions, and recommendations to ensure the completeness and professionalism of the content.

[0083] The specific steps to return the report to the client interface for display are as follows: 1. Status Update and Notification: The detection engine layer pushes the final status "Detection Complete" to the client via the WebSocket long connection established in S201. The client interface then displays a completion indicator on the detection progress bar and may display a pop-up window or sound notification to inform the user that the report is ready.

[0084] 2. Report Preview and Download: The "Report Preview and Download" module on the interface is activated. Clients can click the "Preview" button, usually in the built-in PDF reader or a new browser tab, to view the complete structured report content online. Clients can click the "Download" button to save the PDF report file with dynamic watermark to their local device.

[0085] 3. Interface Display Elements: During preview or download, clients can intuitively see all key content in the report, including: a prominent health score (H) and its level, detailed data and visualizations for each indicator, and a list of automatically generated optimization suggestions. A dynamic, semi-transparent watermark in the background ensures the report's professionalism, authority, and traceability. For example, the report content is automatically assembled according to six preset modules: Customer Information Summary: Customer Name, Business ID, Testing Time Range.

[0086] Operational metrics details: Real-time call detail records (CDRs), call volume, and other metrics are displayed in tables and trend charts, along with historical comparisons.

[0087] Voice service health analysis: Displays standardized scores, health score S_v, and weight distribution of indicators such as call success rate and call drop rate.

[0088] SMS service health analysis: Displays standardized scores, health score S_s, and weight distribution of indicators such as SMS sending success rate and delivery delay.

[0089] Overall health assessment: Highlights the final health score (H) and provides a rating (e.g., Excellent, Good, Average, Poor).

[0090] Optimization suggestions: The system automatically matches and generates text suggestions based on abnormal metric conditions. For example, if "SMS delivery delay" exceeds the standard, the system will generate "SMS delivery delay exceeds 5 seconds, it is recommended to check the SMS center routing configuration".

[0091] Through the above process, this step ensures that the test results can reach customers efficiently in a highly structured, reliable, and user-friendly manner, completing a closed loop from test triggering to result delivery.

[0092] As an optional embodiment, when generating a structured inspection report, the above method further includes: generating a dynamic watermark string based on the operation traceability information of this inspection task, wherein the operation traceability information includes at least the operation number, the operation interface name, and the operation time; and embedding the dynamic watermark string into the structured inspection report using a semi-transparent overlay method.

[0093] In this embodiment, the operation traceability information refers to the data tuple that uniquely identifies the source of this detection operation, including at least the operation employee number (the customer identity identifier that performs the detection), the operation interface name (the name of the specific front-end page that triggers the detection), and the operation time (accurate to the second, in the format YYYY-MM-DD HH:MM:SS).

[0094] Dynamic watermarks are semi-transparent identifiers embedded in the background of reports that contain information about the traceability of operations. They are not static images, but are dynamically generated using a specific algorithm based on the operator's ID, interface name, and operation time. They are unique and tamper-proof, and are used to achieve end-to-end traceability of operational behavior.

[0095] The client-side interface refers to the front-end interface of the operations platform where the client initially triggers the detection. In this step, it serves as the final display medium for the report, providing interactive functions such as report preview, download, and final progress status feedback.

[0096] By adding a dynamic watermark containing operational traceability information to the report using the aforementioned technical methods, this step significantly enhances the authenticity, credibility, and traceability of the test report. It not only allows for rapid identification of the source of the operation in case of disputes but also effectively prevents the forgery and alteration of the report, meeting the stringent compliance and auditing requirements of privacy-focused communication services.

[0097] As an optional implementation, a dynamic watermark string is generated based on the operation traceability information of this detection task. This includes: extracting the operation number, operation interface name, and operation time, connecting them using pipes to form an original string, and encoding the original string using BASE64 to obtain the target encoded string; determining the coordinates of the watermark in the report by perturbation calculation based on the page width and height of the report to be embedded; generating a dynamic key using a preset key generation formula based on the coordinates of the watermark in the report; encoding the dynamic key using BASE64 and calculating an offset sequence using a preset offset formula; transforming the target encoded string using the offset sequence, and generating the final watermark string using a preset watermark generation formula.

[0098] This step is the core of achieving report traceability and anti-tampering functions. Its purpose is to generate a unique dynamic watermark string that is strongly associated with the specific report page and location by encrypting and encoding the operation traceability information through a series of encryption and encoding transformations.

[0099] In this embodiment, the target encoded string refers to the intermediate encoded string obtained by concatenating the operation tracing information and then encoding it using BASE64. It is the starting point for watermark generation, converting plaintext information into a format that facilitates subsequent calculations. This process can be represented by the following formula:

[0100] In the formula, B is the encrypted string after BASE64 encoding of the original watermark string, and P is the string concatenated by "|" for the operator ID, operation interface name, and operation time. Let i be the i-th character in the encoded string B.

[0101] A dynamic key is an encryption key that is not fixed but dynamically calculated using a key generation formula based on the initial coordinates (x, y) where the watermark will be embedded. This allows different watermarks to be generated at different locations even for the same traceability information, greatly enhancing resistance to attacks. The key generation formula is as follows:

[0102] In the formula, K is the dynamic key, and (x,y) are the preset initial coordinates of the watermark.

[0103] The offset sequence is a numerical sequence derived from the dynamic key, used to perform character-by-character shift transformations on the target encoded string, and is crucial for achieving encoding obfuscation. The offset formula is as follows:

[0104]

[0105] In the formula, Let be the ASCII code of the i-th character after BASE64 encoding of the key K. The i-th element in the offset sequence For floor function, O is the offset of dynamic key K after BASE64 conversion, and L is the length of encrypted string B after BASE64 encoding of the original watermark string.

[0106] The watermark string refers to the final generated string that has undergone complete encryption and encoding transformation; it is the final form that is actually embedded in the report. This watermark string is generated using a preset watermark generation formula, as follows:

[0107]

[0108] In the formula, Let E be the i-th character in the encoded string B, and let E be the final watermark string after encoding transformation. Let L be the i-th character in the watermark string E, and L be the length of the encrypted string B after BASE64 encoding of the original watermark string. Calculate... The formula indicates that for characters in B that are not padding characters '=", the addition is performed with the corresponding offset followed by a modulo operation (implemented implicitly by dividing by 64); for padding characters '=", the result remains unchanged.

[0109] In practical applications, the system simultaneously performs a watermark embedding process during the report rendering process to PDF format: 1. Generate watermark string: Call the watermark generation logic, concatenate the operation ID, interface name, and operation time, encode with BASE64 and encrypt with a dynamic key to generate the final watermark string E.

[0110] 2. Determine the embedding location: Pixel-level perturbation fusion technology is used to calculate multiple non-repeating, randomly distributed watermark coordinates for each page of the report.

[0111] 3. Render Overlay: Embed the generated watermark string E into the background layer of the PDF report at specified coordinates in a semi-transparent mode.

[0112] Using the aforementioned technical methods, by introducing a dynamic key bound to coordinates and complex encoding transformations, it is ensured that each watermark is unique and difficult to predict and crack. This strong correlation prevents the watermark from being copied or removed in batches, thus achieving high-security traceability protection at the level of a single report.

[0113] As an optional embodiment, the coordinates of the watermark in the report are determined by perturbation calculation based on the page width and height of the report to be embedded. This includes: calculating the maximum perturbation range of the watermark in the horizontal and vertical directions respectively using a preset perturbation formula based on the page width and height of the report to be embedded; generating random perturbation values ​​of the watermark in the horizontal and vertical directions respectively within the maximum perturbation range; and calculating the final coordinates of the watermark in the report after boundary constraints based on the random perturbation values ​​and preset initial coordinates using a preset boundary value constraint formula.

[0114] This step is one of the core components of dynamic watermarking technology. Its purpose is to determine a random and unique embedding position for each watermark instance on the report page. This randomization process makes the watermarks visually irregularly distributed, effectively preventing the mass identification and removal of watermarks through fixed positions, and greatly enhancing the watermark's resistance to damage and its concealment.

[0115] In this embodiment, page width and height refer to the actual size of the final generated PDF report page in pixels, and are the benchmark for calculating the area where the watermark can be placed.

[0116] The maximum perturbation range refers to the maximum distance the watermark coordinates are allowed to shift randomly in the horizontal and vertical directions. This range is calculated proportionally to the page size to ensure that the perturbation amplitude produces a significant positional change without pushing the watermark excessively to the page edges. This maximum perturbation range is calculated using a perturbation formula, as follows:

[0117]

[0118] In the formula, max_disturb_x is , for W represents the page width, and H represents the page height.

[0119] The random perturbation value refers to a random offset generated by a uniformly distributed random number generator for the watermark coordinates in both the horizontal and vertical directions within the maximum perturbation range. This value is crucial for achieving the uncertainty in the watermark position. This value is generated using the following formula:

[0120]

[0121] In the formula, max_disturb_x is the maximum random perturbation in the X direction, max_disturb_y is the maximum random perturbation in the Y direction, and Unif is a uniformly distributed random number.

[0122] The preset initial coordinates refer to a basic position coordinate set by the system for the watermark before the perturbation calculation begins. These coordinates are usually based on a predefined page layout, such as the center point of the page, or a starting point within a fixed area. The perturbation calculation will apply a random offset to this.

[0123] Boundary value constraint: This refers to a verification and correction operation performed on the coordinates after applying random perturbations. Its purpose is to ensure that the calculated final coordinates do not exceed the valid range of the page [0, W] and [0, H], preventing the watermark from being drawn outside the page and becoming invisible. The process of applying coordinate boundary constraints is shown in the boundary value constraint formula:

[0124]

[0125] In the formula, The X-axis pixel-level fusion coordinates are... The pixel-level fusion coordinates are in the Y direction, and disturb_x is... , for W represents the page width, and H represents the page height.

[0126] Based on the aforementioned technical methods, the embedding coordinates are determined through random perturbation, making the watermark's distribution position in the report random and dynamic. This method avoids the weakness of fixed-position watermarks being easily identified and removed, significantly increasing the difficulty for malicious parties to completely remove the watermark without damaging the report content, and further enhancing anti-tampering capabilities.

[0127] As an optional implementation, after the report is generated, it can be persisted, and a link to the report file can be generated. The generated watermarked PDF report is saved to an unstructured database (such as an object storage service), and a unique download link with access permissions and timeliness is generated. The system updates the record of this detection task in the relational database, marks the status as "complete," and associates it with the stored report file link.

[0128] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0129] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0130] According to another aspect of the embodiments of this application, a self-service business health detection apparatus for implementing the above-described self-service business health detection method is also provided. Please refer to... Figure 3 , Figure 3This is a structural block diagram of an optional self-service business health detection device provided in an embodiment of this application, such as... Figure 3 As shown, the self-service health assessment device 300 may include: The instruction response module 301 is used to respond to the self-service testing instruction triggered by the customer on the client interface and generate the testing task corresponding to the current business. The indicator collection module 302 is used to call the detection engine to execute detection tasks in order to collect multiple business health indicators in the preset dual-dimensional indicator system. Among them, the business health indicators include customer operation indicators, voice service health indicators and SMS service health indicators. The scoring determination module 303 is used to determine the current business health score based on all business health indicators through a preset differentiated weighted health assessment model. The report generation module 304 is used to generate a structured test report containing a health score and return the report to the client interface for display.

[0131] It should be noted that the instruction response module 301 in this embodiment can be used to execute the above step S201, the indicator acquisition module 302 in this embodiment can be used to execute the above step S202, the scoring determination module 303 in this embodiment can be used to execute the above step S203, and the report generation module 304 in this embodiment can be used to execute the above step S204.

[0132] Regarding the self-service business health detection device in this embodiment, the specific manner in which its instruction response module 301, indicator acquisition module 302, scoring determination module 303, and report generation module 304 execute the above method has been described in detail in the embodiments related to the self-service business health detection method, and will not be elaborated here.

[0133] In this embodiment, the indicator acquisition module 302 is specifically used to: acquire the customer operation indicators at a first preset frequency and take the average of multiple continuously acquired data points, wherein the customer operation indicators include real-time call detail records, call volume, average call duration, and peak call concurrency; acquire the voice service health indicators at a second preset frequency and take the average of multiple continuously acquired data points, wherein the voice service health indicators include call success rate, call drop rate, voice latency, and voice clarity score; and acquire the SMS service health indicators at a third preset frequency and take the average of multiple continuously acquired data points, wherein the SMS service health indicators include SMS sending success rate, SMS delivery latency, and SMS loss rate.

[0134] The scoring determination module 303 includes: a single-service scoring unit, used to calculate the voice service health score and the SMS service health score respectively using a preset single-service health score formula; wherein, the voice service health score is obtained by weighted summation of the standardized values ​​of its corresponding multiple voice service health indicators according to a first weight set; the SMS service health score is obtained by weighted summation of the standardized values ​​of its corresponding multiple SMS service health indicators according to a second weight set; and a fusion calculation unit, used to weightedly fuse the voice service health score, the SMS service health score, and the average value of the standardized customer operation indicators to obtain the health score of the current service.

[0135] The scoring determination module 303 further includes a standardization unit, used to compare the actual value of each service health indicator with a preset maximum threshold and a preset minimum threshold before calculating the voice service health score and SMS service health score respectively using a preset single service health score scoring formula; and to calculate the standardized value of each service health indicator using an interval mapping method based on the comparison results: when the actual value is greater than or equal to the maximum threshold, the standardized value is 100; when the actual value is less than or equal to the minimum threshold, the standardized value is 0; and when the actual value is between the two, the standardized value is calculated by linear interpolation.

[0136] The report generation module 304 further includes: a watermark generation unit, used to generate a dynamic watermark string based on the operation traceability information of this detection task, wherein the operation traceability information includes at least the operation employee number, operation interface name, and operation time; and a watermark embedding unit, used to embed the dynamic watermark string into the structured detection report using a semi-transparent overlay method. Specifically, the watermark generation unit is used to: extract the operation employee number, operation interface name, and operation time, connect them using the "|" symbol to form an original string, and encode the original string using BASE64 to obtain a target encoded string; determine the coordinates of the watermark in the report based on the page width and height of the report to be embedded using perturbation calculation; generate a dynamic key using a key generation formula based on the coordinates of the watermark in the report; encode the dynamic key using BASE64 and calculate an offset sequence using an offset formula; transform the target encoded string using the offset sequence, and generate the final watermark string using the watermark generation formula.

[0137] When determining the coordinates of the watermark in the report through perturbation calculation, the watermark generation unit specifically performs the following steps: based on the page width and height of the report to be embedded, calculates the maximum perturbation range of the watermark in the horizontal and vertical directions using a perturbation formula; within the maximum perturbation range, generates random perturbation values ​​for the watermark in the horizontal and vertical directions; and based on the random perturbation values ​​and a preset initial coordinates, calculates the final coordinates of the watermark in the report after boundary constraints using a boundary value constraint formula.

[0138] It is understood that the technical solution provided in this embodiment, through the fully automated detection process of each module in the self-service business health detection device, fundamentally solves the core pain points of existing technologies, such as lack of customer autonomy in detection and long process times. Customers can trigger the detection process with a single click at any time and quickly obtain results without waiting for maintenance personnel to intervene, greatly improving maintenance response efficiency and customer experience, and realizing real-time, autonomous monitoring of business health status.

[0139] In addition to the modules described above, the apparatus in this embodiment may also include modules that execute any method in any of the aforementioned self-service business health detection methods.

[0140] It should be noted that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can operate in ways such as... Figure 1 The method shown can be implemented in either software or hardware within a hardware environment, where the hardware environment includes a network environment.

[0141] According to another aspect of the embodiments of this application, an electronic device for implementing the above-described self-service business health detection method is also provided. The electronic device may be a server, a terminal, or a combination thereof.

[0142] According to another embodiment of this application, an electronic device is also provided; please refer to [link to relevant documentation]. Figure 4 , Figure 4 This is a structural block diagram of an optional electronic device provided in an embodiment of this application, such as... Figure 4 As shown, the electronic device may include: a processor 1501, a communication interface 1502, a memory 1503, and a communication bus 1504, wherein the processor 1501, the communication interface 1502, and the memory 1503 communicate with each other through the communication bus 1504.

[0143] Memory 1503 is used to store computer programs; When processor 1501 executes the program stored in memory 1503, it performs the following steps: Step S201: In response to the self-service testing instruction triggered by the customer on the client interface, generate the testing task corresponding to the current business. Step S202: Call the detection engine to execute the detection task to collect multiple business health indicators in the preset dual-dimensional indicator system. The business health indicators include customer operation indicators, voice service health indicators and SMS service health indicators. Step S203: Based on all business health indicators, determine the current business health score using a preset differentiated weighted health assessment model; Step S204: Generate a structured detection report containing a health score and return the report to the client interface for display.

[0144] It is understood that the technical solution provided in this embodiment, through the processor of the electronic device, fundamentally solves the core pain points of existing technologies, such as the lack of customer autonomy in testing and the long processing time, by automating the entire testing process. Customers can trigger and quickly obtain results with a single click at any time, without waiting for maintenance personnel to intervene, greatly improving maintenance response efficiency and customer experience, and achieving real-time, autonomous monitoring of business health status.

[0145] Optionally, in this embodiment, the communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used to represent it in the figure, but this does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned electronic device and other devices.

[0146] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0147] The processor mentioned above can be a general-purpose processor, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0148] This application also provides a computer-readable storage medium, which includes a stored program, wherein the program executes the method steps of the above method embodiments when it runs.

[0149] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0150] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0151] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0152] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0154] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the solution provided in this embodiment, depending on actual needs.

[0155] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0156] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A service health self-detection method, characterized in that, include: In response to the self-service testing command triggered by the customer on the client interface, generate the testing task corresponding to the current business. The detection engine is invoked to execute the detection task to collect multiple business health indicators from a preset two-dimensional indicator system. These business health indicators include customer operation indicators, voice service health indicators, and SMS service health indicators. Based on all the aforementioned business health indicators, the current business health score is determined using a preset differentiated weighted health assessment model. Generate a structured detection report containing the health score, and return the report to the client interface for display.

2. The self-service business health assessment method according to claim 1, characterized in that, Collect multiple business health indicators from a pre-defined two-dimensional indicator system, including: The customer operation indicators are collected at a first preset frequency, and the average value of multiple continuously collected data points is taken. The customer operation indicators include real-time call detail records, call volume, average call duration, and peak call concurrency. The voice service health indicators are collected at a second preset frequency, and the average value of multiple continuously collected data points is taken. The voice service health indicators include call success rate, call drop rate, voice latency and voice clarity score. The SMS service health indicators are collected at a third preset frequency, and the average value of multiple continuously collected data points is taken. The SMS service health indicators include SMS sending success rate, SMS delivery delay and SMS loss rate.

3. The self-service business health assessment method according to claim 2, characterized in that, Based on all the aforementioned business health indicators, a pre-defined differentiated weighted health assessment model is used to determine the current business health score, including: Using a preset single-service health scoring formula, the health scores for voice services and SMS services are calculated respectively. The voice service health score is obtained by weighted summation of the standardized values ​​of its corresponding multiple voice service health indicators according to a first weight set. The SMS service health score is obtained by weighted summation of the standardized values ​​of its corresponding multiple SMS service health indicators according to a second weight set. The health score of the current service is obtained by weighting and fusing the health score of the voice service, the health score of the SMS service, and the average of the standardized customer operation indicators.

4. The self-service business health assessment method according to claim 3, characterized in that, Before calculating the health scores for voice services and SMS services using a preset single-service health scoring formula, the following steps are included: For each business health indicator, its actual value is compared with the preset maximum and minimum thresholds; Based on the comparison results, the standardized value of each business health indicator is calculated using the interval mapping method: when the actual value is greater than or equal to the maximum threshold, the standardized value is 100; when the actual value is less than or equal to the minimum threshold, the standardized value is 0; when the actual value is between the two, the standardized value is calculated by linear interpolation.

5. The self-service business health assessment method according to claim 1, characterized in that, When generating a structured inspection report, the following are also included: Based on the operation traceability information of this testing task, a dynamic watermark string is generated, wherein the operation traceability information includes at least the operation employee number, the operation interface name, and the operation time. The dynamic watermark string is embedded into the structured detection report using a semi-transparent overlay method.

6. The self-service business health assessment method according to claim 5, characterized in that, Based on the operational traceability information of this detection task, a dynamic watermark string is generated, including: Extract the operator ID, operation interface name, and operation time, connect them using pipes to form a raw string, and then perform BASE64 encoding on the raw string to obtain the target encoded string. Based on the page width and height of the report to be embedded, the coordinates of the watermark in the report are determined through perturbation calculation; Based on the coordinates of the watermark in the report, a dynamic key is generated using a preset key generation formula; The dynamic key is BASE64 encoded, and the offset sequence is calculated using a preset offset formula. The target encoded string is transformed using the offset sequence, and the final watermark string is generated using a preset watermark generation formula.

7. The self-service business health assessment method according to claim 6, characterized in that, Based on the page width and height of the report to be embedded, the coordinates of the watermark in the report are determined through perturbation calculations, including: Based on the page width and height of the report to be embedded, the maximum perturbation range of the watermark in the horizontal and vertical directions is calculated using a preset perturbation formula. Within the maximum perturbation range, random perturbation values ​​are generated in the horizontal and vertical directions of the watermark, respectively. Based on the random disturbance value and the preset initial coordinates, the coordinates of the final watermark in the report after boundary constraint are calculated using a preset boundary value constraint formula.

8. A self-service business health assessment system, characterized in that, include: The instruction response module is used to respond to self-service testing instructions triggered by customers on the client interface and generate the testing task corresponding to the current business. The indicator collection module is used to call the detection engine to execute the detection task, so as to collect multiple business health indicators in the preset two-dimensional indicator system, including customer operation indicators, voice service health indicators and SMS service health indicators. The scoring determination module is used to determine the health score of the current business based on all the business health indicators and through a preset differentiated weighted health assessment model. The report generation module is used to generate a structured detection report containing the health score and return the report to the client interface for display.

9. An electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein, The processor, the communication interface, and the memory communicate with each other via the communication bus, characterized in that... The memory is used to store computer programs; The processor is configured to execute the self-service business health detection method according to any one of claims 1 to 7 by running the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the self-service business health detection method according to any one of claims 1 to 7 when it runs.

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