Deep learning-based outsourcing service quality evaluation method

Abstract

The invention relates to the technical field of machine learning, and discloses an outsourcing service quality evaluation method based on deep learning, which comprises the following steps: capturing operational micro habit dynamics of a service subject through lightweight representation data, establishing a self-adaptively updated reference normal state model, monitoring deviation in real time, generating an early disturbance signal, and determining the outsourcing service quality according to the early disturbance signal; and performing correlation analysis on the multiple signals to identify a predefined disturbance mode, and finally dynamically evaluating the service quality based on mode matching. According to the method, early-stage risk early warning which cannot be captured by a traditional macroscopic index is realized through fine-grained perception and modeled association of micro habits, and meanwhile, a judgment threshold is dynamically calibrated in combination with a situation beacon, so that the early-warning precision and timeliness are remarkably improved, and the early-warning effect is improved. And the multi-dimensional monitoring capability from individual anomaly to collective out-of-order is expanded by means of group behavior entropy change analysis.

Description

Technical Field

[0001] The present invention relates to an outsourcing service quality assessment method based on deep learning, and belongs to the technical field of machine learning. Background Art

[0002] Currently, mainstream technical solutions in this field usually build evaluation models based on a predefined service quality indicator system. By collecting key performance data within the service cycle, deep learning algorithms are used for post-quality scoring and anomaly detection. The typical implementation path includes: building a multi-dimensional service quality indicator system, collecting historical service data to train supervised learning models, generating quality scores based on real-time data input and triggering threshold alarms. This type of method has basic applicability in standardized service scenarios, but faces essential technical bottlenecks when dealing with complex outsourced service ecosystems.

[0003] As outsourcing service scenarios evolve towards diversified and distributed collaborative models, traditional static indicator systems and a posteriori analysis mechanisms have gradually exposed systemic defects. Taking software outsourcing development as an example, although existing technologies can monitor explicit indicators such as code submission frequency and defect repair cycle, it is difficult to capture process signals such as subtle changes in developers' coding habits and atypical fluctuations in team communication rhythm. When service entities encounter hidden quality risks due to skill bottlenecks or collaborative imbalances, existing methods often have to wait for the problems to become explicit (such as project delays and customer complaints) before triggering alarms, missing the best time for intervention. More importantly, traditional models rely on massive historical data to establish evaluation benchmarks, which makes it difficult to adapt to dynamic changes in the service environment and easily misjudge reasonable behavioral fluctuations caused by context switching as anomalies. Although the industry has tried to introduce incremental learning to optimize model adaptability, it is limited by complex feature engineering and computing resource consumption, making it difficult to achieve real-time dynamic calibration.

[0004] Existing technologies suffer from three fundamental technical contradictions: 1. The static indicator system with fixed evaluation dimensions conflicts with the essential laws of the dynamic evolution of service quality, resulting in systematic underdetection of early risk signals; 2. The centralized data processing architecture is incompatible with the real-time requirements of distributed service scenarios, resulting in significant delays in risk warnings; 3. There is a cognitive gap between the isolated nature of individual behavior monitoring and the emergent nature of group collaboration, making it impossible to identify systemic quality decline caused by micro-behavioral disorders. Although the industry has sought improvements by increasing data collection density and model complexity, it has fallen into a new dilemma of increased data privacy risks and soaring computing 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 achieve an upgrade in the service quality control paradigm. Summary of the Invention

[0005] The present invention provides an outsourcing service quality assessment method based on deep learning, the main purpose of which is to solve the problems of insufficient early risk identification ability, low adaptability to dynamic environment and lack of early warning of group behavior disorder caused by traditional quality assessment methods relying on static indicator systems and hysteresis analysis mechanisms.

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

[0007] Step 1: Obtain lightweight representation data of at least one operational micro-habit of a service subject or multiple service subjects during the outsourcing service process, where the lightweight representation data reflects the dynamic changes of the operational micro-habit over time;

[0008] Step 2: For each operational micro-habit, based on its corresponding lightweight representation data, a reference normal model is established and adaptively maintained through machine learning methods. The reference normal 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 from the reference normal model. When the degree of deviation meets the statistical significance judgment conditions pre-set by the method, an early atypical disturbance signal is generated;

[0010] Step 4: performing correlation analysis on the received one or more early atypical disturbance signals to identify a disturbance pattern corresponding to the service risk scenario and predefined by the method;

[0011] Step 5: Based on the identified disturbance pattern, evaluate the current quality status of the outsourced service, or output warning information, and dynamically adjust the focus parameters for subsequent quality evaluation.

[0012] Preferably, operational micro-habits include: changing trends in the average interval time for responding to customer interactions in manual services, or fluctuations in the time taken to process tasks, or changes in the sequence of commonly used operations, or increases or decreases in the frequency of communication interactions; operational micro-habits also include: fluctuations in the success rate of application program interface calls in automated services, or changes in the frequency of occurrence of specific categories of error logs, or peak occurrence patterns of system resource utilization.

[0013] Preferably, the lightweight characterization data is formed by extracting statistical features from the original data of operational micro-habits within a preset sliding time window, and 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, which can learn and periodically update the behavior pattern based on the historical data of operational micro-habits to adapt to the baseline state changes of the service environment.

[0015] Preferably, 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 whose occurrence frequency reaches a predetermined threshold within a preset time window, 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 later optimization through machine learning, to identify disturbance patterns associated with service risk scenarios from the aggregated early atypical disturbance signal combination.

[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 the lightweight representation data and the reference normal model: capturing at least one context beacon that represents the current macro-operation context of the outsourced service and is predefined by the method; based on the captured context beacon and in accordance with the 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 f context ; and the application context adjustment factor f context Adjust the deviation detection threshold to obtain the context-calibrated deviation detection threshold T adj , calculated as:

[0017] T adj =T ref ×(1+f context ),

[0018] Among them, T ref is the baseline deviation detection threshold determined by referring to the normal model when there is no contextual beacon influence; furthermore, when determining whether the degree of deviation meets the statistical significance judgment condition preset by the method, the context-calibrated deviation detection threshold T is used. adj As a judgment criterion, the determination of the degree of deviation can be adapted to the macro-operational situation.

[0019] Preferably, the method also includes: for a predefined service subject group, selecting at least one similar micro-habit that is commonly shared by members of the group; obtaining lightweight characterization data of each member of the group on the similar micro-habit within a continuous time window, and constructing a group micro-habit spectral entropy sequence that characterizes the diversity of the group's behavioral performance on the similar micro-habit based on the lightweight characterization data, and the group micro-habit spectral entropy is used to quantify the degree of discreteness of the behavioral performance of similar micro-habits within the group; and monitoring the dynamic changes of the group micro-habit spectral entropy sequence, when the degree of its deviation from its historical baseline or its change trend meets the atypical entropy change judgment conditions pre-set by the method, generating a warning signal indicating that the service subject group is at risk of collaborative disorder or behavioral solidification, and using the warning signal as a supplementary basis for evaluating the current quality status of the outsourced service.

[0020] Preferably, the step of dynamically adjusting the focus parameters for subsequent quality evaluation includes: based on the currently identified disturbance pattern, increasing the evaluation weight corresponding to the service quality dimension directly related to the disturbance pattern in the subsequent evaluation model, or improving the collection precision and analysis frequency of the monitoring data related to the service quality dimension.

[0021] Preferably, the step of obtaining lightweight representation data of operational micro-habits gives priority to using existing and easily accessible digital records in the outsourced service process as the data source. The digital records include timestamp information of the email system, code submission log information of the version control system, status update data of the task management tool, or call log data of the application interface.

[0022] Preferably, the mapping relationship between disturbance patterns and service risk scenarios predefined in the method, as well as the rules for dynamically adjusting the focus parameters for subsequent quality assessment, are initially configured based on the domain expert knowledge base. During the operation of the method, the mapping relationship and the parameters of the rules 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 warning and the effectiveness of the risk judgment.

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

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

[0025] 2. By introducing situational beacons that represent macro-operational situations, the system can dynamically adjust the judgment thresholds for micro-habit deviations in different service stages. When the service enters a critical task cycle, it automatically increases its sensitivity to micro-habits related to response efficiency; during the routine maintenance stage, it relaxes its tolerance for innovative and exploratory behaviors. This situation-based dynamic calibration mechanism enables the reference normal model to reflect the individual's historical behavior patterns and actively adapt to the current task goals, effectively avoiding misjudgments and missed judgments caused by environmental switching, and significantly improving the signal-to-noise ratio and decision-making value of the warning signal.

[0026] 3. In view of 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 changes that are difficult to detect through individual monitoring. When the diversity of group behavior increases abnormally, it will warn of potential risks of collaborative disorder; and when the entropy value continues to decline, it will identify early signs of innovation rigidity. This mechanism that 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 model library and assessment weight configuration through a reinforcement learning mechanism. After each alert is triggered, the mapping relationship between the disturbance pattern and service risk is automatically corrected based on reverse verification of actual quality events, and the focus dimensions and analysis granularity of subsequent monitoring are dynamically adjusted. This closed-loop optimization mechanism enables the assessment model to adapt to the specificity of different service scenarios. While maintaining the lightweight core algorithm, it gradually builds an intelligent assessment system with domain adaptability, effectively avoiding the generalization bottleneck of traditional static models in new scenarios. By prioritizing the use of existing digital footprints to construct micro-habit representations, the system significantly reduces the deployment cost and privacy risks of data collection. At the same time, reference normal modeling and deviation detection are distributed and executed on service terminals, and only lightweight disturbance signals are uploaded, which not only ensures real-time performance but also avoids computational overload of central nodes. This edge 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a comparison diagram of the disturbance signal and spectrum entropy change trends of the present invention;

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

[0030] Figure 3 This is a flow chart of the service quality evaluation based on dynamic threshold and spectrum entropy feedback of the present invention.

[0031] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0032] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

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

[0034] Step 1: Obtain lightweight representation data of at least one operational micro-habit of a service subject or multiple service subjects during the outsourcing service process, where the lightweight representation data reflects the dynamic changes of the operational micro-habit over time;

[0035] Step 2: For each operational micro-habit, based on its corresponding lightweight representation data, a reference normal model is established and adaptively maintained through machine learning methods. The reference normal model is used to characterize 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 from the reference normal model. When the degree of deviation meets the statistical significance judgment conditions pre-set by the method, an early atypical disturbance signal is generated;

[0037] Step 4: performing correlation analysis on the received one or more early atypical disturbance signals to identify a disturbance pattern corresponding to the service risk scenario and predefined by the method;

[0038] Step 5: Based on the identified disturbance pattern, evaluate the current quality status of the outsourced service, or output warning information, and dynamically adjust the focus parameters for subsequent quality evaluation.

[0039] Preferably, operational micro-habits include: changing trends in the average interval time for responding to customer interactions in manual services, or fluctuations in the time taken to process tasks, or changes in the sequence of commonly used operations, or increases or decreases in the frequency of communication interactions; operational micro-habits also include: fluctuations in the success rate of application program interface calls in automated services, or changes in the frequency of occurrence of specific categories of error logs, or peak occurrence patterns of system resource utilization.

[0040] Preferably, the lightweight characterization data is formed by extracting statistical features from the original data of operational micro-habits within a preset sliding time window, and 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, which can learn and periodically update the behavior pattern based on the historical data of operational micro-habits to adapt to the baseline state changes of the service environment.

[0042] Preferably, 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 whose occurrence frequency reaches a predetermined threshold within a preset time window, 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 later optimization through machine learning, to identify disturbance patterns associated with service risk scenarios from the aggregated early atypical disturbance signal combination.

[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 the lightweight representation data and the reference normal model: capturing at least one context beacon that represents the current macro-operation context of the outsourced service and is predefined by the method; based on the captured context beacon and in accordance with the 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 f context ; and the application context adjustment factor f context Adjust the deviation detection threshold to obtain the context-calibrated deviation detection threshold T adj , calculated as:

[0044] T adj =T ref ×(1+f context ),

[0045] Among them, T ref is the baseline deviation detection threshold determined by referring to the normal model when there is no contextual beacon influence; furthermore, when determining whether the degree of deviation meets the statistical significance judgment condition preset by the method, the context-calibrated deviation detection threshold T is used. adj As a judgment criterion, the determination of the degree of deviation can be adapted to the macro-operational situation.

[0046] Preferably, the method also includes: for a predefined service subject group, selecting at least one similar micro-habit that is commonly shared by members of the group; obtaining lightweight characterization data of each member of the group on the similar micro-habit within a continuous time window, and constructing a group micro-habit spectral entropy sequence that characterizes the diversity of the group's behavioral performance on the similar micro-habit based on the lightweight characterization data, and the group micro-habit spectral entropy is used to quantify the degree of discreteness of the behavioral performance of similar micro-habits within the group; and monitoring the dynamic changes of the group micro-habit spectral entropy sequence, when the degree of its deviation from its historical baseline or its change trend meets the atypical entropy change judgment conditions pre-set by the method, generating a warning signal indicating that the service subject group is at risk of collaborative disorder or behavioral solidification, and using the warning signal as a supplementary basis for evaluating the current quality status of the outsourced service.

[0047] Preferably, the step of dynamically adjusting the focus parameters for subsequent quality evaluation includes: based on the currently identified disturbance pattern, increasing the evaluation weight corresponding to the service quality dimension directly related to the disturbance pattern in the subsequent evaluation model, or improving the collection precision and analysis frequency of the monitoring data related to the service quality dimension.

[0048] Preferably, the step of obtaining lightweight representation data of operational micro-habits gives priority to using existing and easily accessible digital records in the outsourced service process as the data source. The digital records include timestamp information of the email system, code submission log information of the version control system, status update data of the task management tool, or call log data of the application interface.

[0049] Preferably, the mapping relationship between disturbance patterns and service risk scenarios predefined in the method, as well as the rules for dynamically adjusting the focus parameters for subsequent quality assessment, are initially configured based on the domain expert knowledge base. During the operation of the method, the mapping relationship and the parameters of the rules 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 warning and the effectiveness of the risk judgment.

[0050] Example 1: In a typical implementation scenario, a medium-sized technical service enterprise manages multiple geographically dispersed outsourced development teams through remote collaboration. The service content covers requirements analysis, coding implementation, unit testing, and delivery acceptance. The collaboration between teams mainly relies on tools such as task management tools, version control systems, instant messaging software, and interface debugging platforms for interaction. To achieve effective control of overall service quality, the enterprise deploys the method of the present 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, and preferentially selects timestamp data from the email system, task status change logs in the task management tool, code submission records of the version control platform, and call logs of the interface service platform as raw data sources. The system extracts statistical features that reflect 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, with common settings such as five minutes to one hour). These statistical features include the mean and fluctuation of task processing intervals, the short-term variation of interface call failure rates, and the deviation trend of the time distribution of code submission behavior. These statistical features constitute lightweight representation data with high information density and no privacy implications, ensuring the security of data processing and the controllability of computing load.

[0051] Subsequently, the system constructs a reference normal model of the service subject based on an unsupervised deep learning algorithm. Specifically, at the beginning of the service or within a specified period without abnormalities, the system uses a self-organizing map network to form a multi-dimensional spatial distribution map of various micro-habit features, and updates the model in a periodic manner to adapt to the dynamic changes of the service environment. When the 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 the basis for deviation, and combines the preset warning conditions to determine whether a deviation signal is triggered; in order to further improve the contextual adaptability of deviation judgment, the system parses a set of situational beacon information reflecting the current macro service status in parallel, including It includes task density level (which can be reflected by the number of tasks assigned per unit time), current service stage (determined by project management progress labels), and service peak prompt signals (predicted based on historical concurrent trends), etc., and automatically calculates calibration factors based on the mapping rules between preset situations and micro-habit behaviors to dynamically adjust the currently applicable deviation judgment threshold. For example, when delivery is approaching, the system will appropriately increase the monitoring sensitivity of task response micro-habits to enhance the 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 situation calibration, it will generate an early disturbance signal. To further enhance the accuracy and explanatory power 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 correlation relationships: one is disturbance signals with a high degree of overlap in occurrence time, and the other is abnormal signal nodes that logically belong to the same service process chain; the system combines the decision tree algorithm with the Bayesian causal reasoning method to comprehensively evaluate the possibility that aggregated signals pose risks, and matches the analysis results with a predefined disturbance pattern library. When a risk pattern that meets the conditions is identified, the corresponding service quality warning information is output.

[0052] Taking a specific example, within 24 hours after a service team entered the system test preparation stage, the frequency of code submission dropped significantly, the status of multiple tasks in the task update record switched frequently, and the interface service call log showed that the success rate of the test interface call continued to decline. Although the above signals have not yet reached the abnormal threshold in a single dimension, the system has identified the typical compound disturbance pattern of functional integration obstruction caused by collaboration disharmony through comprehensive analysis of these disturbance signals, and generated a medium-level quality warning based on this, prompting managers to pay close attention to the status of the team and make necessary interventions; after each disturbance pattern is identified, the system will further dynamically adjust the evaluation strategy parameters, give priority to increasing the monitoring weight of the micro-habit features related to the disturbance pattern, and shorten the analysis refresh frequency accordingly. For example, in the above case, the system The system sets the service team's operational micro-habits in interface calls as key focus objects, shortens its data collection cycle from every hour to every ten minutes, thereby improving the response speed and monitoring granularity of potential problems; at the same time, the system also implements a spectrum entropy analysis mechanism based on the overall behavioral characteristics of the service group. In multiple consecutive time windows, the system collects lightweight representation data of group members on a certain type of homogeneous micro-habit dimension, and calculates the spectrum entropy change sequence of behavioral performance within the group to reflect the dynamic changes in the level of behavioral diversity. When the entropy value rises significantly, the system will judge it as a signal of abnormally increased group behavior dispersion, indicating that there may be a risk of disorder in the collaborative structure; on the contrary, when the entropy value continues to decline and tends to be consistent, the system can output a behavioral solidification tendency warning to indicate the potential risk of a decline in the organization's innovation ability.

[0053] Example 2: In the internal operation system of a typical medium-sized SaaS service provider, the enterprise manages several cross-regional outsourcing development teams based on the agile delivery model, which are responsible for the parallel development and continuous iteration of multiple customer interface customization modules. During the project advancement process, the management has long faced the following problems: response delays of some service nodes occur from time to time but there is no advance warning; when project tasks are frequently postponed or requirements change, the team's response lags; team behavior tends to be consistent at certain stages, resulting in a decline in innovation ability and collaboration efficiency; in response to the practical problem that these traditional evaluation methods are difficult to achieve early quality risk identification, the enterprise deploys the outsourcing service quality evaluation system based on deep learning of the present 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 outsourcing service scenarios.

[0054] The trial cycle lasted for six weeks, covering the entire process of outsourcing services, including demand collation, development and implementation, system testing, and online delivery stages; data collection frequency: the sampling interval for task status change logs was every 10 minutes; the sampling interval for interface call logs was every 5 minutes; the sampling interval for code submission logs was every 15 minutes, and the sliding time window for the extraction of micro-habit characterization data was based on a 30-minute sliding time window setting. This time length can fully cover the changes in operating rhythm in typical outsourcing tasks and can capture representative micro-behavioral fluctuations; situational beacon setting logic: the key stages of the project are determined by the progress labels in the task management platform; the service peak period is calculated based on the moving average of the historical task concurrency; the task density level is expressed as the number of tasks assigned per unit time; reference to the normal model construction method: the first 5 days of service data are selected as the normal training period in the initial sampling stage; the self-organizing map network (SOM) is used to construct the multi-dimensional spatial distribution of micro-habit characteristics; the model is iteratively updated every 24 hours to adapt to the dynamic changes in the service environment; the deviation judgment benchmark value T ref Set as 95% confidence bounds, the deviation calibration factor f context Dynamically generated based on the beacon context.

[0055] Disturbance Signal Identification and Aggregation Processing: During the morning period of 9:00 to 11:00 on the 17th day, the system detected the following disturbance signals for the outsourcing team in Group A: the interface call success rate dropped by more than 25%, the standard deviation of the short-term volatility was 0.073, significantly higher than the standard deviation of the historical normal range of 0.021; the median task processing interval increased from 7 minutes to 23 minutes, exceeding the deviation threshold T after context adjustment. 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 imbalanced collaboration, response bottleneck, and decreased resource concentration defined in the disturbance pattern library, ultimately triggering a moderate service quality warning. Spectral entropy monitoring of similar micro-habit behavior sequences in Group B over a continuous period of time revealed that between the 20th and 24th days, the entropy value remained consistently below 1.5 standard deviations of the historical mean (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 behavioral convergence, suggesting a potential trend of rigidification in the group's innovation capabilities and collaborative structure. It then issued a low-level warning for management's reference. During periods of high task load (such as the project entering its critical delivery period on the 28th), the system's monitoring of micro-habit deviations did not produce false positives, demonstrating that the contextual calibration mechanism effectively adapted to phased behavioral changes, improving recognition accuracy and reducing false positives. Regarding the moderate warning triggered by Team A on the 17th day, subsequent management verification revealed that the failure of the intermediary scheduling mechanism led to slow task response and uneven resource allocation, verifying the high consistency between the system's warning results and actual service barriers. After Team B received the low-entropy warning, the company reorganized its tasks and optimized its communication mechanisms on the 25th day. Subsequently, the spectral entropy value gradually recovered, and system records show that the entropy value has stabilized above 0.45 since the 27th day, validating the practical reference value of spectral entropy as an indicator of the dynamic characteristics of group behavior in quality monitoring.

[0058] Experimental results show that the service quality evaluation method based on deep learning in the present 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 with high sensitivity; the contextual beacon mechanism effectively improves the adaptability and robustness of the model in different service stages; the dynamic monitoring mechanism of the spectral entropy sequence can assist in identifying abnormal trends in group behavior and expand the quality management dimension; the evaluation system can be deployed at low cost in the 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 companies.

[0059] Example 3: This example combines Figures 1 to 3 , an outsourcing service quality assessment method based on deep learning is implemented, such as Figure 1 As shown, Figure 1 The disturbance signal and spectrum entropy change trend comparison chart shows the system's comparative analysis of the change trend between the decline in the interface success rate of Group A (expressed in percentage) and the spectrum entropy of Group B (normalized spectrum entropy value) from the 17th to the 25th day. In the figure, the interface success rate of Group A is represented by black dots, and the spectrum entropy of Group B is represented by squares. The left vertical axis (the decline in the interface call success rate%) and the right vertical axis (normalized spectrum entropy value) are used for scale marking respectively. Two reference threshold lines are also drawn in the figure: T ref The benchmark threshold represents the deviation judgment benchmark value set by the system based on the service subject's reference to the normal model; the 1.5σ lower limit threshold is the lower warning threshold calculated based on the mean and standard deviation of the historical entropy value.

[0060] like Figure 2As shown in the figure, the data acquisition layer is responsible for collecting raw data such as operational micro-habits and situational beacons to form lightweight representation data as input; the data then enters the modeling and analysis layer, where a reference normal model is constructed and deviation detection is performed to produce an early disturbance signal; the disturbance signal is passed as input to the decision output layer, where service status judgment and risk response are realized through disturbance pattern recognition and quality assessment; at the same time, the dynamic calibration mechanism is used to generate situational adaptive thresholds and feed them back to the modeling and analysis layer for threshold adjustment to ensure that the model has real-time response capabilities to changes in service stages; in addition, the module also includes a group entropy change analysis function, which can generate spectral entropy sequences for evaluating collaborative efficiency and behavioral changes, thereby issuing collaborative disorder warnings 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 to ensure the real-time, adaptability and efficiency of the overall model in a complex outsourcing environment.

[0061] like Figure 3 As shown in the figure, first, the data acquisition layer obtains the original information such as operational micro-habits and situational beacons, and converts it into lightweight representation data as input for subsequent analysis; then, the data is sent to the modeling and analysis layer, where a reference normal model is constructed and deviation detection is performed to determine whether there is any abnormal change in the current service behavior. If an abnormality is detected, an early disturbance signal is generated and transmitted to the decision output layer; the decision output layer undertakes the task of disturbance pattern recognition and quality assessment, and outputs the service quality status judgment according to the model rules. The figure also includes a core link dynamic calibration mechanism situational adaptive threshold. This module interacts with the modeling layer through the threshold adjustment interface, optimizes the deviation judgment standard in combination with situational changes, and feeds back to the entire system in the form of parameter optimization; in addition, the system also includes a group entropy change analysis collaborative disorder warning module. This module is based on the input spectral entropy sequence to identify the abnormal evolution trend of group behavior and feed back relevant risk prompts to the decision layer through auxiliary paths to enhance 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 personnel turnover and unstable service processes, the platform integrates the outsourcing service quality assessment method based on deep learning proposed in the present invention into its task collaboration system. This application scenario has the characteristics of parallel collaboration of multiple service entities, frequent task switching, and various forms of original data records. It is typical and representative for verifying the key mechanisms involved in the present invention under actual operating conditions. At the beginning of system deployment, the platform management party sets the initial training period of seven consecutive days before the service goes online as the reference normal model, selects typical service samples including development and testing tasks, and extracts the micro-habit characteristic behavior data of the service subject in this stage as the basis for modeling. The original data is collected from the code submission log in the version management system, the task status change record in the task collaboration platform, the call log in the interface service platform, and the communication record reflecting the change of interaction frequency in the team collaboration communication tool. The data collection frequency is dynamically configured according to the interface call interval of each information system, usually controlled between every five and fifteen minutes; the system sets the length of each sliding time window to thirty minutes, and the window overlap rate is fifty percent. In each time window period, the system extracts the mean and standard deviation of the task response interval, the local fluctuation amplitude of the interface call success rate, the time concentration index of the code submission behavior, and the time concentration index of the code submission behavior from the original data. and the entropy change trend of communication and interaction behaviors within the team. These features constitute the lightweight behavior vector of the service subject after normalization, and are accompanied by timestamps and task identifiers as data labels for subsequent modeling and analysis; the system uses a self-organizing mapping network based on unsupervised learning to model the above lightweight behavior vectors. During the training period, the behavior vector of each service subject is mapped to a two-dimensional topological structure through continuous input to form its own normal behavior pattern distribution area. After the training is completed, the system records the normal distribution boundary vector of each subject as its reference behavior profile. During the actual operation of the service, whenever the system receives new behavior input, it compares and judges it by the spatial distance with the reference behavior profile. If the distance exceeds the mean of the historical deviation value of the service subject during the training period plus one and a half standard deviations, and the deviation condition is met in two consecutive time windows, the early disturbance signal generation mechanism is triggered.

[0063] During this implementation and deployment process, 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 from Team A: the median interval between the engineer's task responses increased from eight minutes to nineteen minutes, the success rate fluctuations shown in the interface call log were more than twice the historical average, and the distribution of his code submission time periods shifted significantly from centralized to highly discrete, reflected in a sharp drop in the behavioral concentration index. The three behavioral characteristics mentioned above were recorded as independent disturbance signals by the system after they deviated from the normal range. These signals were then merged into a set of aggregated signals within the same time period through a cross-module aggregation mechanism. The system identified this combined feature as consistent with the resource allocation imbalance - task congestion and concurrency disturbance pattern, and automatically output a medium-level service quality warning message, marking the interface function module currently under the engineer's responsibility as a high-attention service node. The system further calls the situational beacon module to analyze information such as the service load intensity, service stage, and concurrent task density in the current time period, and identifies this period as the peak period of functional development in the mid-term of the project. Based on the beacon-factor mapping relationship, it generates a 10% positive adjustment of the deviation detection threshold correction factor, so that the system has a higher sensitivity to the micro-habit characteristics of response efficiency during the period of significant increase in service load. After subsequent manual verification, the engineer had to assist in handling other project tasks, which led to a delay in his response to this task, and the team management did not promptly notice the impact on the main task chain. The service quality warning information generated by the system prompted the team to reorganize the task structure, effectively alleviating the problem of service response delay. During this period, the system also conducted a group spectral entropy analysis on the behavioral performance of other members of the same group in the two micro-habit dimensions of task response interval time and code submission time distribution, constructed a spectral entropy sequence for the group and conducted continuous window monitoring. 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 be at risk of declining innovation ability and rigid collaborative structure, output a mild level of behavioral pattern solidification warning, and suggested that managers introduce operational path diversity in subsequent task allocation to stimulate the improvement of system behavioral diversity. These are all extended implementation methods known to ordinary technicians in this field.

[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 limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for evaluating outsourcing service quality based on deep learning, characterized in that: The method comprises the following steps: Step 1: Obtain lightweight representation data of at least one operational micro-habit of a service subject or multiple service subjects during the outsourcing service process, where the lightweight representation data reflects the dynamic changes of the operational micro-habit over time; Step 2: For each operational micro-habit, based on its corresponding lightweight representation data, a reference normal model is established and adaptively maintained through machine learning methods. The reference normal model is used to characterize 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 from the reference normal model. When the degree of deviation meets the statistical significance judgment conditions pre-set by the method, an early atypical disturbance signal is generated; Step 4: performing correlation analysis on the received one or more early atypical disturbance signals to identify a disturbance pattern corresponding to the service risk scenario and predefined by the method; Step 5: Based on the identified disturbance pattern, evaluate the current quality status of the outsourced service, or output warning information, and dynamically adjust the focus parameters for subsequent quality evaluation.

2. The outsourcing service quality assessment method based on deep learning according to claim 1 is characterized in that: Operational micro-habits include: trends in the average interval between responses to customer interactions in manual services, or fluctuations in the time taken to process tasks, or changes in common operation sequences, or increases or decreases in the frequency of communication interactions; and operational micro-habits also include: fluctuations in the success rate of application interface calls in automated services, or changes in the frequency of occurrence of specific categories of error logs, or peak patterns in system resource utilization.

3. The outsourcing service quality assessment method based on deep learning according to claim 1 is characterized in that: The lightweight characterization data is formed by extracting statistical features from the original data of operational micro-habits within a preset sliding time window, and the statistical features include at least one of the mean, variance, rate of change and information entropy value of the operational micro-habits.

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

5. The outsourcing service quality assessment method based on deep learning according to claim 1 is 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 whose occurrence frequency reaches a predetermined threshold within a preset time window, 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 later optimization through machine learning, to identify disturbance patterns associated with service risk scenarios from the aggregated early atypical disturbance signal combination.

6. The outsourcing service quality assessment method based on deep learning according to claim 1 is characterized in that: 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 the lightweight representation data and the reference normal model: capturing at least one context beacon that represents the current macro-operation context of the outsourced service and is predefined by the method; based on the captured context beacon and in accordance with the 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 f context ; and the application context adjustment factor f context Adjust the deviation detection threshold to obtain the context-calibrated deviation detection threshold T adj , calculated as: T adj =T ref ×(1+f context ), Among them, T ref is the baseline deviation detection threshold determined by referring to the normal model when there is no contextual beacon influence; furthermore, when determining whether the degree of deviation meets the statistical significance judgment condition preset by the method, the context-calibrated deviation detection threshold T is used. adj As a judgment criterion, the determination of the degree of deviation can be adapted to the macro-operational situation.

7. The outsourcing service quality assessment method based on deep learning according to claim 1 is characterized in that: The method also includes: for a predefined service subject group, selecting at least one similar micro-habit that is shared by members of the group; obtaining lightweight representation data of each member of the group on the similar micro-habit within a continuous time window, and constructing a group micro-habit spectral entropy sequence that represents the diversity of the group's behavioral performance in the similar micro-habit based on the lightweight representation data, and the group micro-habit spectral entropy is used to quantify the degree of discreteness of the behavioral performance of similar micro-habits within the group; and monitoring the dynamic changes of the group micro-habit spectral entropy sequence. When the degree of its deviation from its historical baseline or its change trend meets the atypical entropy change judgment conditions pre-set by the method, a warning signal is generated indicating that the service subject group is at risk of collaborative disorder or behavioral solidification, and the warning signal is used as a supplementary basis for evaluating the current quality status of the outsourced service.

8. The outsourcing service quality assessment method based on deep learning according to claim 1 is characterized in that: The steps of dynamically adjusting the focus parameters for subsequent quality evaluation include: based on the currently identified disturbance pattern, increasing the evaluation weight corresponding to the service quality dimension directly related to the disturbance pattern in the subsequent evaluation model, or improving the collection precision and analysis frequency of the monitoring data related to the service quality dimension.

9. The outsourcing service quality assessment method based on deep learning according to claim 1 is characterized in that: The steps to obtain lightweight representation data of operational micro-habits should prioritize the use of existing and easily accessible digital records in the outsourced service process as data sources. Digital records include timestamp information of email systems, code submission log information of version control systems, status update data of task management tools, or call log data of application interfaces.

10. The outsourcing service quality assessment method based on deep learning according to claim 1, characterized in that: The method pre-defines 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 set based on the domain expert knowledge base. During the operation of the method, based on the accumulated quality event cases and the evaluation results of the warning accuracy and risk judgment effectiveness, the mapping relationship and rule parameters are automatically iteratively optimized through the reinforcement learning mechanism.

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