A deep learning-based outsourcing service quality evaluation method

Abstract

The application relates to the technical field of machine learning, and discloses a deep learning-based outsourcing service quality evaluation method, which comprises the following steps: capturing the operational micro-habit dynamic of a service subject through light-weighted representation data, establishing an adaptively updated reference normal model, monitoring the deviation in real time and generating an early disturbance signal, then correlatively analyzing multiple signals to identify a predefined disturbance mode, and finally dynamically evaluating the service quality based on mode matching. Through fine-grained perception and patternized correlation of micro-habits, early risk early warning that cannot be captured by traditional macro-indicators is realized, the determination threshold is dynamically calibrated in combination with a situation beacon, the early warning accuracy and timeliness are significantly improved, and the multi-dimensional monitoring capability from individual abnormality to collective disorder is expanded by means of group behavior entropy change analysis.

Description

Technical Field

[0001] This invention relates to a method for evaluating the quality of outsourced services based on deep learning, belonging to the field of machine learning technology. Background Technology

[0002] Currently, mainstream technical solutions in this field typically build evaluation models based on predefined service quality indicator systems. By collecting key performance data within the service cycle, they use deep learning algorithms for post-event quality scoring and anomaly detection. Typical implementation paths include: constructing a multi-dimensional service quality indicator system, collecting historical service data to train a supervised learning model, generating quality scores based on real-time data input, and triggering threshold alarms. Such methods have basic applicability in standardized service scenarios, but they face fundamental technical bottlenecks when dealing with complex outsourced service ecosystems.

[0003] As outsourcing service scenarios evolve towards diversified and distributed collaboration models, traditional static indicator systems and post-hoc analysis mechanisms are gradually revealing systemic defects. Taking software outsourcing development as an example, while existing technologies can monitor explicit indicators such as code submission frequency and defect repair cycle, they struggle to capture process signals such as subtle changes in developers' coding habits and atypical fluctuations in team communication rhythm. When service providers experience hidden quality risks due to skill bottlenecks or collaboration mismatches, existing methods often require waiting for the problem to become explicit (such as project delays or customer complaints) before triggering an alert, missing the best intervention opportunity. More importantly, traditional models rely on massive amounts of historical data to establish evaluation benchmarks, making it difficult to adapt to dynamic changes in the service environment. They are prone to misjudging reasonable behavioral fluctuations caused by context switching as abnormalities. Although the industry has attempted to introduce incremental learning to optimize model adaptability, it is difficult to achieve real-time dynamic calibration due to complex feature engineering and computational resource consumption.

[0004] Existing technologies suffer from three fundamental technical contradictions: 1. The static indicator system with fixed evaluation dimensions conflicts with the inherent dynamic evolution of service quality, leading to systematic missed detection of early risk signals; 2. Centralized data processing architecture is incompatible with the real-time requirements of distributed service scenarios, causing significant delays in risk warnings; 3. The isolation of individual behavior monitoring and the emergent nature of group collaboration create a cognitive gap, making it impossible to identify systemic quality decline caused by micro-behavioral disorder. Although the industry has sought improvements by increasing data collection density and model complexity, it has fallen into a new predicament of increased data privacy risks and soaring computational costs. Therefore, how to build a lightweight, adaptive, and process-aware real-time evaluation system has become a key challenge to break through the existing technological paradigm and upgrade the service quality control paradigm. Summary of the Invention

[0005] This invention provides a deep learning-based method for evaluating the quality of outsourced services. Its main purpose is to solve the problems of insufficient early risk identification, poor adaptability to dynamic environments, and lack of early warning of disordered group behavior caused by the reliance on static indicator systems and lag analysis mechanisms in traditional quality evaluation methods.

[0006] To achieve the above objectives, this invention provides a deep learning-based method for evaluating the quality of outsourced services, the method comprising the following steps:

[0007] Step 1: Obtain lightweight representation data of at least one operational micro-habit of one or more service entities during the outsourcing service process. The lightweight representation data reflects the dynamic changes of operational micro-habits over time.

[0008] Step 2: For each operational micro-habit, based on its corresponding lightweight representation data, a reference normality model is established and adaptively maintained using machine learning methods. The reference normality model is used to represent the behavioral pattern of the operational micro-habit under the baseline service state.

[0009] Step 3: Monitor the lightweight characterization data in real time and determine the degree of deviation between it and the reference normal model. When the degree of deviation meets the statistical significance judgment condition set in advance by the method, generate an early atypical disturbance signal.

[0010] Step 4: Perform correlation analysis on one or more early atypical disturbance signals received to identify disturbance patterns that correspond to the service risk scenario and are predefined by the method.

[0011] Step 5: Based on the identified disturbance patterns, assess the current quality status of the outsourced services, or output early warning information, and dynamically adjust the focus parameters used for subsequent quality assessments.

[0012] Preferably, operational micro-habits include: the changing trend of the average interval time for responding to customer interactions in manual services, or the fluctuation of the time taken to process tasks, or the change of commonly used operation sequences, or the increase or decrease of communication interaction frequency; operational micro-habits also include: the fluctuation of the success rate of application programming interface calls in automated services, or the change in the frequency of occurrence of specific types of error logs, or the peak occurrence pattern of system resource utilization.

[0013] Preferably, the lightweight characterization data is formed by extracting statistical features from the raw data of operational micro-habits within a pre-set sliding time window. The statistical features include at least one of the mean, variance, rate of change, and information entropy value of the operational micro-habits.

[0014] Preferably, the establishment and adaptive maintenance of the reference normal model are performed using an unsupervised learning algorithm. The unsupervised learning algorithm can learn from historical data of operational micro-habits and periodically update behavioral patterns to adapt to changes in the baseline state of the service environment.

[0015] Preferably, the step of performing correlation analysis on one or more received early atypical disturbance signals to identify the disturbance patterns predefined by the method includes: Step 1, aggregating multiple early atypical disturbance signals that occur at a predetermined frequency within a pre-set time window and reach a predetermined threshold, or multiple early atypical disturbance signals that logically belong to the same service process chain; Step 2, using a pattern recognition algorithm selected from at least one of the following: a decision tree-based classification algorithm, a Bayesian network inference algorithm, and a heuristic rule set initialized based on historical data and capable of being optimized later through machine learning, to identify the disturbance patterns associated with the service risk scenario from the aggregated combination of early atypical disturbance signals.

[0016] Preferably, the method further includes, before or during the step of performing real-time monitoring of lightweight representation data and determining the degree of deviation between it and a reference normality model: capturing at least one context beacon predefined by the method of the current macroscopic operational context of the representation outsourcing service; based on the captured context beacon and according to the context-micro-habitual normality expectation mapping rule pre-set by the method, determining a calibration parameter for calibrating the deviation detection threshold corresponding to the reference normality model, wherein the calibration parameter is specifically a context adjustment factor f. context ; and application context adjustment factor f context Adjust the deviation detection threshold to obtain the context-calibrated deviation detection threshold T. adj The calculation method is as follows:

[0017] T adj =T ref ×(1+f context ),

[0018] Among them, T ref To establish a baseline deviation detection threshold based on the normal model in the absence of contextual beacon influence, a context-calibrated deviation detection threshold T is used to determine whether the degree of deviation meets the pre-defined statistical significance criteria of this method. adj This serves as a standard for judgment, ensuring that the determination of the degree of deviation is adapted to the macro-operational context.

[0019] Preferably, the method further includes: for a predefined service subject group, selecting at least one similar micro-habit shared by the members of the group; within a continuous time window, acquiring lightweight representation data of each member in the group on the similar micro-habit, and constructing a group micro-habit spectrum entropy sequence based on the lightweight representation data to represent the diversity of the group's behavioral performance on the similar micro-habit, the group micro-habit spectrum entropy being used to quantify the dispersion of the similar micro-habit's behavioral performance within the group; and monitoring the dynamic changes of the group micro-habit spectrum entropy sequence, and when its deviation from its historical baseline or its changing trend meets the atypical entropy change judgment conditions pre-set by the method, generating an early warning signal indicating that the service subject group has a risk of collaborative disorder or behavioral solidification, and using the early warning signal as a supplementary basis for assessing the current quality status of the outsourced service.

[0020] Preferably, the steps of dynamically adjusting the parameters of concern for subsequent quality assessment include: based on the currently identified disturbance pattern, increasing the assessment weight of the service quality dimension directly related to the disturbance pattern in the subsequent assessment model, or increasing the granularity of the collection and analysis frequency of monitoring data related to the service quality dimension.

[0021] Preferably, the step of obtaining lightweight representation data of operational micro-habits prioritizes the use of existing and easily accessible digital records from the outsourcing process as data sources. These digital records include timestamp information from email systems, code commit logs from version control systems, status update data from task management tools, or application programming interface call logs.

[0022] Preferably, the method predefines the mapping relationship between the disturbance pattern and the service risk scenario, as well as the rules for dynamically adjusting the focus parameters used for subsequent quality assessment. The initial configuration is set based on the domain expert knowledge base, and during the operation of the method, the mapping relationship and rule parameters are automatically iteratively optimized through a reinforcement learning mechanism based on the accumulated quality event cases and the evaluation results of the accuracy of the early warning and the effectiveness of the risk judgment.

[0023] Compared to the problems in the background technology, the beneficial effects of the present invention are:

[0024] 1. This invention captures lightweight micro-habit representation data generated by service providers during operation and combines it with unsupervised learning algorithms to construct an adaptively updated reference normality model. This enables the system to perceive atypical deviations in subtle behavioral patterns in real time. This mechanism avoids the static lag of traditional indicator systems and shifts quality assessment from macro-result analysis to dynamic process monitoring. By timely capturing early disturbance signals, it triggers warnings before risks become apparent. In particular, when deviation signals of multiple related micro-habits form specific disturbance patterns within a time window, the system can identify complex risk scenarios that are difficult to detect using traditional methods, achieving a leap from isolated anomaly detection to systemic risk correlation analysis.

[0025] 2. By introducing contextual beacons that represent macro-operational situations, the system can dynamically adjust the threshold for judging deviations from micro-habits at different service stages. When the service enters a critical task cycle, it automatically increases the sensitivity to micro-habits such as response efficiency; during routine maintenance, it relaxes the tolerance for innovative and exploratory behaviors. This context-based dynamic calibration mechanism enables the reference normal model to reflect the historical behavioral patterns of individuals and actively adapt to the current task objectives, effectively avoiding misjudgments and omissions caused by environmental changes, and significantly improving the signal-to-noise ratio and decision-making value of early warning signals.

[0026] 3. Based on the collaborative characteristics of the service group, by calculating the entropy value sequence of the performance distribution of similar micro-habits within the group, the system can capture collective behavioral anomalies that are difficult to detect through individual monitoring. When the diversity of group behavior surges abnormally, it can warn of potential collaborative disorder risks; while when the entropy value continues to decline, it can identify early signs of innovation stagnation. This mechanism, which combines individual micro-habit monitoring with group spectrum entropy analysis, forms a multi-level quality assessment system from micro to macro, which is particularly suitable for identifying chronic quality decline problems caused by the evolution of team dynamics.

[0027] 4. The system continuously optimizes the risk pattern library and evaluation weight configuration through reinforcement learning. After each warning is triggered, the mapping relationship between disturbance patterns and service risks is automatically corrected based on the reverse verification of actual quality events. At the same time, the focus dimensions and analysis granularity of subsequent monitoring are dynamically adjusted. This closed-loop optimization mechanism enables the evaluation model to adapt to the specificity of different service scenarios. While maintaining the lightweight nature of the core algorithm, it gradually builds an intelligent evaluation system with domain adaptability, effectively avoiding the bottleneck of the generalization ability of traditional static models in new scenarios. Furthermore, by prioritizing the use of existing digital footprints to build micro-habit representations, the system significantly reduces the deployment cost and privacy risks of data collection. Meanwhile, the reference normal modeling and deviation detection are distributed and executed on the service terminals, and only lightweight disturbance signals are uploaded, which ensures real-time performance and avoids computational overload of the central node. This edge-based architecture design enables the present invention to achieve fine-grained monitoring of large-scale service networks with extremely low resource overhead, providing small and medium-sized outsourcing service providers with quality management capabilities that are difficult to achieve with traditional solutions. Attached Figure Description

[0028] Figure 1 This is a comparison chart of the variation trends of the perturbation signal and the spectral entropy in this invention;

[0029] Figure 2 This is a functional structure framework diagram of the deep learning-based outsourcing service quality assessment system of the present invention;

[0030] Figure 3 This is a flowchart of the service quality assessment process based on dynamic threshold and spectral entropy feedback in this invention.

[0031] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0032] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0033] This application provides a method for evaluating the quality of outsourced services based on deep learning, the method comprising the following steps:

[0034] Step 1: Obtain lightweight representation data of at least one operational micro-habit of one or more service entities during the outsourcing service process. The lightweight representation data reflects the dynamic changes of operational micro-habits over time.

[0035] Step 2: For each operational micro-habit, based on its corresponding lightweight representation data, a reference normality model is established and adaptively maintained using machine learning methods. The reference normality model is used to represent the behavioral pattern of the operational micro-habit under the baseline service state.

[0036] Step 3: Monitor the lightweight characterization data in real time and determine the degree of deviation between it and the reference normal model. When the degree of deviation meets the statistical significance judgment condition set in advance by the method, generate an early atypical disturbance signal.

[0037] Step 4: Perform correlation analysis on one or more early atypical disturbance signals received to identify disturbance patterns that correspond to the service risk scenario and are predefined by the method.

[0038] Step 5: Based on the identified disturbance patterns, assess the current quality status of the outsourced services, or output early warning information, and dynamically adjust the focus parameters used for subsequent quality assessments.

[0039] Preferably, operational micro-habits include: the changing trend of the average interval time for responding to customer interactions in manual services, or the fluctuation of the time taken to process tasks, or the change of commonly used operation sequences, or the increase or decrease of communication interaction frequency; operational micro-habits also include: the fluctuation of the success rate of application programming interface calls in automated services, or the change in the frequency of occurrence of specific types of error logs, or the peak occurrence pattern of system resource utilization.

[0040] Preferably, the lightweight characterization data is formed by extracting statistical features from the raw data of operational micro-habits within a pre-set sliding time window. The statistical features include at least one of the mean, variance, rate of change, and information entropy value of the operational micro-habits.

[0041] Preferably, the establishment and adaptive maintenance of the reference normal model are performed using an unsupervised learning algorithm. The unsupervised learning algorithm can learn from historical data of operational micro-habits and periodically update behavioral patterns to adapt to changes in the baseline state of the service environment.

[0042] Preferably, the step of performing correlation analysis on one or more received early atypical disturbance signals to identify the disturbance patterns predefined by the method includes: Step 1, aggregating multiple early atypical disturbance signals that occur at a predetermined frequency within a pre-set time window and reach a predetermined threshold, or multiple early atypical disturbance signals that logically belong to the same service process chain; Step 2, using a pattern recognition algorithm selected from at least one of the following: a decision tree-based classification algorithm, a Bayesian network inference algorithm, and a heuristic rule set initialized based on historical data and capable of being optimized later through machine learning, to identify the disturbance patterns associated with the service risk scenario from the aggregated combination of early atypical disturbance signals.

[0043] Preferably, the method further includes, before or during the step of performing real-time monitoring of lightweight representation data and determining the degree of deviation between it and a reference normality model: capturing at least one context beacon predefined by the method of the current macroscopic operational context of the representation outsourcing service; based on the captured context beacon and according to the context-micro-habitual normality expectation mapping rule pre-set by the method, determining a calibration parameter for calibrating the deviation detection threshold corresponding to the reference normality model, wherein the calibration parameter is specifically a context adjustment factor f. context ; and application context adjustment factor f context Adjust the deviation detection threshold to obtain the context-calibrated deviation detection threshold T. adj The calculation method is as follows:

[0044] T adj =T ref ×(1+f context ),

[0045] Among them, T ref To establish a baseline deviation detection threshold based on the normal model in the absence of contextual beacon influence, a context-calibrated deviation detection threshold T is used to determine whether the degree of deviation meets the pre-defined statistical significance criteria of this method. adj This serves as a standard for judgment, ensuring that the determination of the degree of deviation is adapted to the macro-operational context.

[0046] Preferably, the method further includes: for a predefined service subject group, selecting at least one similar micro-habit shared by the members of the group; within a continuous time window, acquiring lightweight representation data of each member in the group on the similar micro-habit, and constructing a group micro-habit spectrum entropy sequence based on the lightweight representation data to represent the diversity of the group's behavioral performance on the similar micro-habit, the group micro-habit spectrum entropy being used to quantify the dispersion of the similar micro-habit's behavioral performance within the group; and monitoring the dynamic changes of the group micro-habit spectrum entropy sequence, and when its deviation from its historical baseline or its changing trend meets the atypical entropy change judgment conditions pre-set by the method, generating an early warning signal indicating that the service subject group has a risk of collaborative disorder or behavioral solidification, and using the early warning signal as a supplementary basis for assessing the current quality status of the outsourced service.

[0047] Preferably, the steps of dynamically adjusting the parameters of concern for subsequent quality assessment include: based on the currently identified disturbance pattern, increasing the assessment weight of the service quality dimension directly related to the disturbance pattern in the subsequent assessment model, or increasing the granularity of the collection and analysis frequency of monitoring data related to the service quality dimension.

[0048] Preferably, the step of obtaining lightweight representation data of operational micro-habits prioritizes the use of existing and easily accessible digital records from the outsourcing process as data sources. These digital records include timestamp information from email systems, code commit logs from version control systems, status update data from task management tools, or application programming interface call logs.

[0049] Preferably, the method predefines the mapping relationship between the disturbance pattern and the service risk scenario, as well as the rules for dynamically adjusting the focus parameters used for subsequent quality assessment. The initial configuration is set based on the domain expert knowledge base, and during the operation of the method, the mapping relationship and rule parameters are automatically iteratively optimized through a reinforcement learning mechanism based on the accumulated quality event cases and the evaluation results of the accuracy of the early warning and the effectiveness of the risk judgment.

[0050] Example 1: In a typical implementation scenario, a medium-sized technology service company manages multiple geographically dispersed outsourced development teams through remote collaboration. The services cover requirements analysis, coding implementation, unit testing, and delivery acceptance. Team collaboration primarily relies on tools such as task management tools, version control systems, instant messaging software, and interface debugging platforms. To effectively control the overall service quality, the company deploys the method of this invention. The specific process is as follows: First, during the service startup phase, the system automatically accesses the existing information management system in the outsourcing process based on a preset strategy. It prioritizes timestamp data from the email system, task status change logs from the task management tool, code commit records from the version control platform, and call logs from the interface service platform as raw data sources. The system extracts statistical features reflecting operational micro-habits from the raw data by setting a sliding time window (the time length can be adaptively adjusted according to the service rhythm; common settings include five minutes to one hour). These features include the mean and fluctuation of task processing intervals, short-term changes in interface call failure rates, and the time distribution deviation trend of code commit behavior. These statistical features constitute lightweight representation data, possessing high information density and not involving privacy, ensuring data processing security and controllable computational load.

[0051] Subsequently, the system constructs a reference normal model for the service subject based on an unsupervised deep learning algorithm. Specifically, during the initial service phase or a specified period without anomalies, the system utilizes a self-organizing map network to generate a multi-dimensional spatial distribution map of various micro-habitual features and updates the model periodically to adapt to dynamic changes in the service environment. When subsequent behavioral data of the service subject is input into the model, the system uses the spatial distance between the current feature and its normal model as a deviation criterion, combined with preset warning conditions, to determine whether a deviation signal has been triggered. To further improve the contextual adaptability of deviation determination, the system parses a set of contextual beacon information reflecting the current macro-service status in parallel, including... This includes task density levels (reflected by the number of tasks assigned per unit time), the current service stage (determined by project management progress tags), and service peak alert signals (predicted based on historical concurrency trends). Based on preset mapping rules between scenarios and micro-habit behaviors, the system automatically calculates calibration factors to dynamically adjust the applicable deviation threshold. For example, in the near delivery phase, the system appropriately increases its sensitivity to monitoring task response-related micro-habits to enhance its ability to identify potential service delay risks. When the system detects that a certain type of micro-habit deviates from its corresponding normal model and exceeds the deviation threshold after scenario calibration, it generates an early disturbance signal. To further enhance the accuracy and interpretability of risk identification, the system performs cluster analysis and causal relationship modeling on multiple disturbance signals within a preset time window, prioritizing the identification of two types of relationships: first, disturbance signals with highly overlapping occurrence times; and second, abnormal signal nodes logically belonging to the same service process chain. The system combines decision tree algorithms with Bayesian causal inference methods to comprehensively assess the likelihood of aggregated signals constituting a risk, and matches the analysis results with a predefined disturbance pattern library. When a risk pattern that meets the criteria is identified, the corresponding service quality warning information is output.

[0052] Taking a specific example, within 24 hours of entering the system testing preparation phase, a service team experienced a significant decrease in code submission frequency, frequent switching of multiple task statuses in the task update log, and a continuous decline in the success rate of test interface calls, as shown in the interface service call log. Although these signals did not reach the abnormal threshold in any single dimension, the system, through comprehensive analysis of these disturbance signals, identified a typical complex disturbance pattern where collaboration misalignment hindered functional integration. Based on this, a medium-level quality warning was generated, prompting managers to pay close attention to the team's status and intervene as necessary. After each disturbance pattern is identified, the system further dynamically adjusts the evaluation strategy parameters, prioritizing the monitoring weight of micro-habit features related to the disturbance pattern and correspondingly shortening its analysis refresh frequency. For example, in the above case, the system... The system prioritizes the operational micro-habits of the service team in API calls, shortening their data collection cycle from hourly to every ten minutes to improve response speed and monitoring granularity for potential issues. Simultaneously, based on the overall behavioral characteristics of the service group, the system implements a spectral entropy analysis mechanism. Over multiple consecutive time windows, the system collects lightweight representation data of group members on a specific homogeneous micro-habit dimension, calculating the spectral entropy change sequence of behavioral performance within the group. This reflects the dynamic changes in behavioral diversity. When the entropy value rises significantly, the system identifies it as a signal of abnormally high dispersion in group behavior, indicating a potential risk of disrupted collaborative structures. Conversely, when the entropy value continuously decreases and tends towards uniformity, the system can output a warning of behavioral solidification tendencies, indicating a potential decline in organizational innovation capabilities.

[0053] Example 2: In the internal operation system of a typical mid-sized SaaS service provider, the company manages several cross-regional outsourced development teams based on an agile delivery model, responsible for the parallel development and continuous iteration of multiple customer interface customization modules. During the project process, the management has long faced the following problems: response delays at some service nodes occur frequently without advance warning; when project tasks are frequently delayed or requirements change, the team's response is lagging; at certain stages, team behavior tends to be consistent, leading to a decline in innovation capabilities and collaboration efficiency. In response to these practical problems that traditional evaluation methods are difficult to achieve early quality risk identification, the company deployed the outsourced service quality evaluation system based on deep learning of this invention, aiming to verify the feasibility, accuracy, and application effect of the proposed micro-habit capture-dynamic modeling-disturbance detection-entropy analysis mechanism in actual outsourced service scenarios.

[0054] The trial lasted six weeks, covering the entire outsourcing service process, including requirements gathering, development and implementation, system testing, and deployment. Data collection frequency: task status change logs were sampled every 10 minutes; API call logs every 5 minutes; code commit logs every 15 minutes. A 30-minute sliding time window was used for extracting micro-habit representation data, which effectively covered the operational rhythm changes in typical outsourcing tasks and captured representative micro-behavioral fluctuations. Contextual beacon setting logic: key project stages were determined by progress tags in the task management platform; peak service periods were calculated based on the moving average of historical task concurrency; task density levels were represented by the number of tasks allocated per unit time. The model was built using a reference normal model construction method: the first 5 days of service data were selected as the normal training period during the initial sampling phase; a multi-dimensional spatial distribution of micro-habit features was constructed using a self-organizing map network (SOM); the model was iteratively updated every 24 hours to adapt to dynamic changes in the service environment; deviation from the benchmark value T was used. ref Set as 95% confidence boundary, deviation from calibration factor f context Dynamically generated based on the beacon context.

[0055] During the period from 9:00 AM to 11:00 AM on day 17, the system detected the following disturbance signals for the outsourced team A: the interface call success rate decreased by more than 25%, the short-term volatility standard deviation was 0.073, significantly higher than the standard deviation of 0.021 in the historical normal range; the median task processing interval increased from 7 minutes to 23 minutes, exceeding the scenario-adjusted deviation threshold T. adj The concentration of code submission behavior (measured by the Gini coefficient) dropped to 0.32, indicating an abnormal shift in the distribution of task execution among personnel. The system aggregated these disturbance signals based on temporal overlap and service process chain affiliation, and matched them to the risk map of collaborative division of labor imbalance-response bottleneck-resource concentration decline defined in the disturbance pattern library, ultimately triggering a moderate-level service quality warning. Meanwhile, the spectral entropy monitoring of similar micro-habit behavior sequences of Team B over a continuous period showed that the entropy value was consistently lower than 1.5 standard deviations below the historical mean between days 20 and 24 (see table for details).

[0056] date Spectral entropy value (normalized) Day 20 0.428 Day 21 0.403 Day 22 0.392 Day 23 0.385 Day 24 0.377

[0057] Based on this, the system determined that the behavioral pattern exhibited significant convergence characteristics, indicating a potential rigidity in the group's innovation capabilities and collaborative structure. A low-level warning was issued for management's reference. During high-task-load phases (such as the project entering its critical delivery period on day 28), the system's monitoring of micro-habit deviations did not produce false alarms, demonstrating that the contextual calibration mechanism effectively adapted to phased behavioral changes, improving identification accuracy and reducing the false positive rate. Regarding the moderate warning triggered on day 17 for team A, subsequent management verification revealed that the failure of the middleman scheduling mechanism indeed led to slow task response and uneven resource allocation, verifying the high consistency between the system's warning results and actual service obstacles. After receiving a low-entropy warning, team B underwent task restructuring and communication mechanism optimization on day 25. Subsequently, the spectral entropy value gradually recovered, and system records show that the entropy value stabilized above 0.45 from day 27 onwards, verifying the practical reference value of spectral entropy as a dynamic characteristic indicator of group behavior in quality monitoring.

[0058] Experimental results show that the deep learning-based service quality assessment method of this invention has the following technical advantages in real outsourcing scenarios: it can realize real-time perception of fine-grained behavioral deviations in the service process and has high sensitivity; the contextual beacon mechanism effectively improves the adaptability and robustness of the model in different service stages; the dynamic monitoring mechanism of spectral entropy sequence can help identify abnormal trends in group behavior and expand the dimensions of quality management; the assessment system can be deployed at low cost in edge computing architecture, is suitable for distributed service systems, and is especially suitable for the service quality management needs of small and medium-sized technology outsourcing enterprises.

[0059] Example 3: This example combines Figures 1 to 3 This paper describes the implementation of a deep learning-based outsourcing service quality assessment method, such as... Figure 1 As shown, Figure 1 The comparison chart of disturbance signal and spectral entropy trends shows a comparative analysis of the changes in the success rate of group A interfaces (expressed as a percentage) and the spectral entropy of group B (normalized spectral entropy value) during the period from day 17 to day 25. In the chart, black dots represent the success rate of group A interfaces, and boxes represent the spectral entropy of group B. The left vertical axis (reduction in interface call success rate %) and the right vertical axis (normalized spectral entropy value) are used for scale marking. Two reference threshold lines are also plotted in the chart: T... ref The baseline threshold represents the deviation judgment benchmark value set by the system based on the normal model of the service subject; the 1.5σ lower limit threshold is the lower bound warning threshold calculated based on the mean and standard deviation of historical entropy values.

[0060] like Figure 2As shown in the diagram, the data acquisition layer is responsible for collecting raw data such as operational micro-habits and contextual beacons, forming lightweight representation data as input. This data then enters the modeling and analysis layer, where a reference normal model is constructed and deviation detection is performed, thereby generating early disturbance signals. These disturbance signals are transmitted as input to the decision output layer, where service status judgment and risk response are achieved through disturbance pattern recognition and quality assessment. Simultaneously, a dynamic calibration mechanism is used to generate context-adaptive thresholds and feed them back to the modeling and analysis layer for threshold adjustment, ensuring that the model has real-time response capabilities to changes in service stages. In addition, this module also includes a group entropy change analysis function, which can generate spectral entropy sequences to evaluate collaborative efficiency and behavioral anomalies, thereby issuing early warnings of collaborative disorder and feeding them back to the decision layer for comprehensive judgment. Finally, the entire system achieves self-adjustment and closed-loop optimization through parameter optimization instructions, ensuring the real-time performance, adaptability, and efficiency of the overall model in complex outsourcing environments.

[0061] like Figure 3 As shown, firstly, the data acquisition layer obtains raw information such as operational micro-habits and contextual beacons, and converts it into lightweight representational data as input for subsequent analysis. Then, this data is sent to the modeling and analysis layer, where a reference normal model is built and deviation detection is performed to determine whether abnormal changes have occurred in current service behavior. If an anomaly is detected, an early disturbance signal is generated and transmitted to the decision output layer. The decision output layer undertakes the tasks of disturbance pattern recognition and quality assessment, outputting a service quality status judgment based on model rules. The diagram also includes a core component: a dynamic calibration mechanism with a context-adaptive threshold. This module interacts with the modeling layer through a threshold adjustment interface, optimizes the deviation judgment criteria based on contextual changes, and feeds back to the system as a whole through parameter optimization. In addition, the system includes a group entropy change analysis and collaborative disorder early warning module. This module, based on the input spectral entropy sequence, identifies abnormal evolution trends in group behavior and feeds back relevant risk warnings to the decision layer through an auxiliary path, enhancing the system perspective and accuracy of disturbance identification.

[0062] Example 4: In a medium-sized service platform that provides technical outsourcing services to multiple industries, in order to solve the problem of service quality fluctuations caused by factors such as frequent staff turnover and unstable service processes, the platform integrated the outsourcing service quality evaluation method based on deep learning proposed in this invention into its task collaboration system. This application scenario has the characteristics of parallel collaboration of multiple service entities, frequent task switching, and diverse forms of original data records, and has the typicality and representativeness of verifying the key mechanisms involved in this invention under actual operating conditions. In the initial stage of system deployment, the platform administrator set a seven-day initial training period before service launch as a reference normal model. Typical service samples, including development and testing tasks, were selected, and micro-habit characteristic behavior data of service subjects during this period were extracted as the basis for modeling. Raw data was collected from code commit logs in the version control system, task status change records in the task collaboration platform, call logs in the interface service platform, and communication records reflecting changes in interaction frequency in team collaboration tools. The data collection frequency was dynamically configured based on the interface call intervals of each information system, typically controlled between five and fifteen minutes. The system set the length of each sliding time window to thirty minutes, with a window overlap rate of fifty percent. Within each time window period, the system extracted the mean and standard deviation of task response intervals, the local fluctuation range of interface call success rates, and the time concentration index of code commit behavior from the raw data. The system identifies four core statistical features, including the entropy change trend of internal team communication and interaction behaviors. These features, after normalization, constitute lightweight behavior vectors of the service subjects, with timestamps and task identifiers attached as data labels for subsequent modeling and analysis. The system uses a self-organizing mapping network based on unsupervised learning to model these lightweight behavior vectors. During the training period, the behavior vectors of each service subject are mapped to a two-dimensional topology through continuous input, forming their respective normal behavior pattern distribution areas. After training, the system records the normal distribution boundary vectors of each subject as their reference behavior contours. During actual service operation, whenever the system receives a new behavior input, it compares it with the spatial distance of the reference behavior contour. If the distance exceeds the mean of the historical deviations of the service subject during training plus one and a half standard deviations, and this deviation condition is met for two consecutive time windows, the early disturbance signal generation mechanism is triggered.

[0063] During this deployment, between 9:00 and 10:00 on the 16th day, the system detected the following anomalies in the operational behavior of an interface development engineer in Group A: the median task response interval of this engineer increased from eight minutes to nineteen minutes; the success rate fluctuation shown in the interface call log was more than twice the historical average; and the distribution of his code submission time periods significantly changed from centralized to highly discrete, reflected in a sharp drop in the behavioral concentration index. These three behavioral characteristics, deviating from the normal range, were recorded by the system as independent disturbance signals. Through a cross-module aggregation mechanism, they were merged into an aggregated signal set within the same time period. The system identified this combination of characteristics as conforming to a resource allocation imbalance—task congestion and concurrency disturbance pattern, and subsequently automatically output a medium-level service quality warning message, marking the interface function module currently managed by the engineer as a high-concern service node. The system further invoked the contextual beacon module to analyze information such as service load intensity, service stage, and concurrent task density within the current time period. It identified this period as a peak development phase in the middle of the project. Based on the beacon-factor mapping relationship, a 10% positive adjustment factor was generated to correct the deviation detection threshold. This made the system more sensitive to micro-habitual features such as response efficiency during periods of significant service load increase. Subsequent manual verification revealed that the engineer's delayed response to this task was due to assisting with other project tasks. The team management had not promptly noticed the impact on the main task chain. The service quality warning generated by the system prompted the team to restructure the task structure, effectively mitigating the service response delay issue. During this period, the system also conducted a group spectral entropy analysis on the behavior of other members in the same group in two micro-habit dimensions: task response interval time and code submission time distribution. The spectral entropy sequence of the group was constructed and monitored continuously. From the 18th to the 22nd day, the entropy values ​​of the group in the above two dimensions were significantly lower than the lower limit of one standard deviation of the historical mean, showing a trend of behavioral convergence. Based on this, the system judged that the service group may have the risk of declining innovation ability and rigid collaboration structure, output a mild level behavioral pattern solidification warning, and suggested that the manager introduce operational path diversity in subsequent task allocation to stimulate the improvement of system behavioral diversity. These are all extended implementation methods known to those skilled in the art.

[0064] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for evaluating the quality of outsourced services based on deep learning, characterized in that, The method includes the following steps: Step 1: Obtain lightweight representation data of at least one operational micro-habit of one or more service entities during the outsourcing service process. The lightweight representation data is formed by extracting statistical features from the raw data of operational micro-habits within a preset sliding time window. The statistical features include at least one of the mean, variance, rate of change, and information entropy value of the operational micro-habit. Step 2: For each operational micro-habit, based on its corresponding lightweight representation data, a reference normality model is established and adaptively maintained through machine learning. The reference normality model is used to represent the behavioral pattern of the operational micro-habit under the baseline service state. Step 3: Monitor the lightweight characterization data in real time and determine the degree of deviation between it and the reference normal model. When the degree of deviation meets the statistical significance judgment condition set in advance by the method, generate an early atypical disturbance signal. Step 4: Perform correlation analysis on one or more early atypical disturbance signals received to identify disturbance patterns that correspond to the service risk scenario and are predefined by the method. Step 5: Based on the identified disturbance patterns, assess the current quality status of the outsourced services, or output early warning information, and dynamically adjust the focus parameters used for subsequent quality assessments. Furthermore, operational micro-habits include: the changing trend of the average interval time for responding to customer interactions in manual services, or the fluctuation of the time taken to process tasks, or the change of commonly used operation sequences, or the increase or decrease of the frequency of communication interactions; and operational micro-habits also include: the fluctuation of the success rate of application programming interface calls in automated services, or the change in the frequency of occurrence of specific types of error logs, or the peak occurrence pattern of system resource utilization. The method further includes, before or during the step of performing real-time monitoring of lightweight representation data and determining its deviation from a reference normal model: capturing at least one context beacon predefined by the method of the current operational context of the representation outsourcing service; and, based on the captured context beacon and according to a context-micro-habit normal expectation mapping rule predefined by the method, determining a calibration parameter for calibrating the deviation detection threshold corresponding to the reference normal model, wherein the calibration parameter is specifically a context adjustment factor. ; and application context adjustment factors Adjust the deviation detection threshold to obtain the context-calibrated deviation detection threshold. The calculation method is as follows: ， in, To establish a baseline deviation detection threshold based on the normal model in the absence of contextual beacons, a context-calibrated deviation detection threshold is used to determine whether the degree of deviation meets the pre-defined statistical significance criteria of this method. This serves as a standard for judgment, ensuring that the determination of the degree of deviation is adapted to the operational context; The method further includes: for a predefined service subject group, selecting at least one similar micro-habit shared by the members of the group; within a continuous time window, acquiring lightweight representation data of each member in the group on the similar micro-habit, and constructing a group micro-habit spectrum entropy sequence based on the lightweight representation data to represent the diversity of the group's behavioral performance on the similar micro-habit, the group micro-habit spectrum entropy being used to quantify the dispersion of the behavioral performance of the similar micro-habit within the group; and monitoring the dynamic changes of the group micro-habit spectrum entropy sequence, generating an early warning signal indicating that the service subject group has a risk of collaborative disorder or behavioral solidification when the degree of deviation from its historical baseline or the trend of its change meets the atypical entropy change judgment conditions pre-set by the method, and using the early warning signal as a supplementary basis for assessing the current quality status of the outsourced service; Context beacon setting logic: Key project phases are determined by progress tags in the task management platform.

2. The outsourcing service quality assessment method based on deep learning according to claim 1, characterized in that, The establishment and adaptive maintenance of the reference normal model are carried out using an unsupervised learning algorithm. The unsupervised learning algorithm can learn and periodically update behavioral patterns based on historical data of operational micro-habits.

3. The outsourcing service quality assessment method based on deep learning according to claim 1, characterized in that, The steps of performing correlation analysis on one or more received early atypical disturbance signals to identify disturbance patterns predefined by the method include: Step 1, aggregating multiple early atypical disturbance signals that occur at a predetermined frequency within a pre-set time window and reach a predetermined threshold, or multiple early atypical disturbance signals that logically belong to the same service process chain; Step 2, using a pattern recognition algorithm including at least one of the following: a decision tree-based classification algorithm, a Bayesian network inference algorithm, and a heuristic rule set initialized based on historical data and capable of subsequent optimization through machine learning, to identify disturbance patterns associated with service risk scenarios from the aggregated combination of early atypical disturbance signals.

4. The outsourcing service quality assessment method based on deep learning according to claim 1, characterized in that, The steps for dynamically adjusting the parameters of interest for subsequent quality assessments include: increasing the assessment weight of service quality dimensions directly related to the identified disturbance patterns in the subsequent assessment model, or increasing the granularity of the monitoring data collection and the frequency of analysis related to the service quality dimensions, based on the currently identified disturbance patterns.

5. The outsourcing service quality assessment method based on deep learning according to claim 1, characterized in that, The steps for obtaining lightweight representation data of operational micro-habits prioritize the use of existing, easily accessible digital records from the outsourcing process as data sources. These digital records include timestamps from email systems, code commit logs from version control systems, status update data from task management tools, or API call logs.

6. The outsourcing service quality assessment method based on deep learning according to claim 1, characterized in that, The method predefines the mapping relationship between disturbance patterns and service risk scenarios, as well as the rules for dynamically adjusting the focus parameters used for subsequent quality assessment. Its initial configuration is based on the domain expert knowledge base. During the operation of the method, the mapping relationship and rule parameters are automatically iteratively optimized through a reinforcement learning mechanism based on the accumulated quality event cases and the evaluation results of the accuracy of early warning and the effectiveness of risk judgment.

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