Wearable intelligent collaborative management and control method for power customer service risk

Abstract

The application discloses a wearable intelligent collaborative management and control method for power customer service risk, and relates to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring real-time interaction of a wearable device and synchronizing power business data; separating style and emotion feature vectors by using an adversarial network; constructing a dynamic reference vector based on the style feature and calculating an emotion deviation degree to generate real-time risk representation; predicting a risk evolution track according to the real-time risk representation; when a trigger condition is met, simulating the effect of a candidate strategy based on counterfactual reasoning and determining a target strategy in combination with a business state. The application is used to solve the problems of high risk identification false alarm rate, intervention feedback lag and difficulty in balancing service benefits and operation safety under the complex environment of power operation.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and data processing technology, and more specifically, to a wearable intelligent collaborative management and control method for power customer service risks. Background Technology

[0002] As a fundamental pillar of the national economy, the power industry places great emphasis on customer service quality and on-site safety. With the deepening of digital transformation in power companies, wearable smart terminals are widely used in frontline power repair, mobile showrooms, and counter service scenarios. Real-time monitoring of risk status during interactions via wearable devices is crucial for preventing escalation of customer service disputes and ensuring the safety of power operations.

[0003] Existing customer service risk intervention solutions are mainly based on a linear logic of "voice acquisition - feature extraction - threshold determination". Typically, microphone arrays are used to collect audio, extract features such as acoustic energy, speech rate, or semantic keywords, and compare these features with preset risk thresholds. Once a feature value exceeds the threshold, the system sends a vibration or voice alert to the operator via a wearable device to achieve the purpose of risk intervention.

[0004] However, the aforementioned technical solutions have significant limitations in complex power operation scenarios. First, acoustic features suffer from severe "environment-emotion" entanglement. High background noise at power repair sites and the increased volume of workers to overcome environmental noise can easily be misinterpreted by the system as negative emotions, leading to a very high false alarm rate. Second, most existing technologies rely on retrospective threshold triggering, lacking forward-looking prediction of risk evolution trends, resulting in delayed intervention. Finally, existing intervention mechanisms ignore the special characteristics of the power operation environment, failing to consider the potential interference risks that intervention actions (such as strong vibrations or voice announcements) may cause to workers performing high-risk operations (such as live-line maintenance). This "invisible" conflict of intervention costs not only fails to effectively mitigate emotions but may even induce secondary production safety accidents. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a wearable intelligent collaborative management and control method for power customer service risks. By utilizing adversarial networks to achieve deep decoupling of emotional characteristics and environmental background noise, and combining risk trajectory prediction under dynamic benchmarks with counterfactual intervention simulation, this method addresses the problems of high false alarm rate in customer service risk identification, delayed feedback, and difficulty in balancing intervention benefits and operational safety in complex power operation environments.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A wearable intelligent collaborative management and control method for power customer service risks includes the following steps: acquiring real-time interactive data collected by wearable devices and synchronized power business operation status data; using an adversarial feature decoupling network to perform feature separation on the real-time interactive data to obtain a steady-state feature vector representing the steady state of the environment and an emotional feature vector representing semantic emotion; constructing a dynamic benchmark vector based on the steady-state feature vector, adjusting the update window length of the dynamic benchmark vector according to the fluctuation degree of the steady-state feature vector, and calculating the deviation of the emotional feature vector relative to the dynamic benchmark vector to generate a real-time risk representation; generating a risk evolution trajectory within a future time window based on the real-time risk representation; when the risk evolution trajectory meets the intervention triggering conditions, simulating the execution effect of different candidate strategies based on counterfactual reasoning, and determining the target intervention strategy in conjunction with the power business operation status data.

[0007] In a preferred embodiment, the feature separation of the real-time interactive data using an adversarial feature decoupling network includes: inputting the real-time interactive data into an encoder and mapping it to steady-state encoding and emotion encoding, respectively; constructing a mutual information minimization constraint to reduce the feature correlation between the steady-state encoding and the emotion encoding; and using a discriminator and a classifier to perform adversarial training and supervised training on the steady-state encoding and the emotion encoding, respectively, so that the steady-state feature vector and the emotion feature vector are decoupled in feature distribution.

[0008] In a preferred embodiment, the smoothing function based on the sliding window employs an adaptive window length. The logic for determining the adaptive window length is as follows: calculate the variance of the steady-state feature vector within a preset historical time period to characterize the degree of fluctuation in the steady state of the environment; the adaptive window length is negatively correlated with the variance value, and when the variance value increases, the adaptive window length is shortened, and when the variance value decreases, the adaptive window length is extended.

[0009] In a preferred embodiment, generating a risk evolution trajectory within a future time window based on the real-time risk representation includes: constructing a time series graph containing the current real-time risk representation and a sequence of historical risk states; performing convolution operations on the time series graph using a graph neural network to capture the dependencies of risk states in the time dimension, and outputting a sequence of risk probability distributions for multiple future time steps as the risk evolution trajectory.

[0010] In a preferred embodiment, the step of simulating the execution effects of different candidate strategies based on counterfactual reasoning and determining the target intervention strategy in conjunction with the power business operation status data includes: for each candidate strategy in the preset strategy library, deduce the counterfactual risk trajectory after executing the candidate strategy; calculate the benefit value of the counterfactual risk trajectory relative to the original risk evolution trajectory; evaluate the disturbance cost of executing the candidate strategy based on the power business operation status data; calculate the difference between the benefit value and the disturbance cost, and select the candidate strategy with the largest difference as the target intervention strategy.

[0011] In a preferred embodiment, the evaluation logic for the disturbance cost is as follows: parse the power business operation status data to determine the danger level of the current business scenario; if the danger level is higher than a preset threshold, increase the disturbance cost weight of tactile feedback and auditory blocking strategies; if the danger level is lower than the preset threshold, decrease the disturbance cost weight of voice guidance strategies.

[0012] In a preferred embodiment, after generating the intervention strategy, the method further includes: continuously monitoring the actual risk changes after implementing the target intervention strategy; calculating the residual between the actual risk changes and the predicted counterfactual risk trajectory; and updating the model parameters used to generate the risk evolution trajectory online when the residual exceeds a preset threshold.

[0013] In a preferred embodiment, the online update of the model parameters used to generate the risk evolution trajectory includes: keeping the encoder weights and graph neural network weights in the adversarial feature decoupling network unchanged, and only performing gradient correction on the subsequent prediction output layer parameters of the graph neural network.

[0014] This invention provides a wearable intelligent collaborative management and control system for power customer service risks, comprising: a data acquisition module for acquiring real-time interactive data collected by wearable devices and synchronized power business operation status data; a feature decoupling module for using an adversarial feature decoupling network to perform feature separation on the real-time interactive data, obtaining a steady-state feature vector representing the steady state of the environment and an emotional feature vector representing semantic emotion; a risk characterization module for constructing a dynamic benchmark vector based on the steady-state feature vector and calculating the deviation of the emotional feature vector relative to the dynamic benchmark vector to generate a real-time risk characterization; a risk evolution module for generating a risk evolution trajectory within a future time window based on the real-time risk characterization; and a strategy determination module for simulating the execution effects of different candidate strategies based on counterfactual reasoning when the risk evolution trajectory meets the intervention triggering conditions, and determining the target intervention strategy in conjunction with the power business operation status data.

[0015] The technical effects and advantages of this invention's wearable intelligent collaborative management method for power customer service risks are as follows: This invention achieves deep separation between environmental steady state and semantic sentiment by utilizing adversarial feature decoupling networks, effectively eliminating the coupling interference of background noise on risk judgment in complex power environments and significantly improving the accuracy of risk identification. By constructing a dynamic benchmark vector based on style features, it achieves adaptive adaptation of risk measurement to different operating scenarios and speaker styles, greatly reducing the system's false alarm rate. Combining risk evolution trajectory prediction and counterfactual reasoning simulation, it transforms traditional passive response into forward-looking predictive intervention, and can dynamically weigh the benefits of intervention against operational interference in conjunction with power business status data, ensuring that intervention decisions safeguard customer service quality while also taking into account the safety and reliability of power production operations. Attached Figure Description

[0016] Figure 1 A schematic diagram of the wearable intelligent collaborative management and control method for power customer service risks provided in an embodiment of the present invention; Figure 2 A physical image of the wearable smart badge provided in an embodiment of the present invention; Figure 3 This is a physical image of a desktop microphone provided in an embodiment of the present invention; Figure 4 The simulation waveform diagram for risk calculation under strong noise environment provided in the embodiments of the present invention; Figure 5 A comparative diagram of counterfactual risk trajectory projections for different intervention strategies provided in embodiments of the present invention; Figure 6 This is a curve showing the convergence of the prediction residual under the line learning mechanism provided in this embodiment of the invention. Figure 7 This is a block diagram of a wearable intelligent collaborative management and control system for power customer service risks, provided as an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, Figure 1 This invention presents a wearable intelligent collaborative management and control method for power customer service risks, comprising the following steps: S1 acquires real-time interactive data collected by wearable devices and synchronized power business operation status data.

[0019] In this embodiment, the acquisition step aims to establish temporal correlations between multi-source heterogeneous data, providing standardized input for subsequent feature decoupling.

[0020] S101, regarding the acquisition of real-time interactive data, this data specifically includes audio streams containing voice and environmental components, as well as environmental perception data, received through a standard data interface and output by the front-end acquisition terminal. The front-end acquisition terminal includes wearable smart badges (such as...). Figure 2 (as shown) and desktop microphones (such as) Figure 3 (As shown). The smart badge is an existing intelligent audio acquisition terminal with an integrated microphone array, which has beamforming and sound source localization modules embedded inside. The system establishes a communication connection based on a common transmission protocol (such as Bluetooth / Wi-Fi) and directly acquires multi-channel PCM audio data (e.g., 16kHz sampling rate, 16-bit quantization depth) through the standard SDK interface or application layer protocol (such as MQTT / HTTP / Socket) provided by the device manufacturer. Specifically, based on the device's preset channel definition logic or adaptive recognition logic based on energy envelope analysis, the system marks the audio stream in the main beam direction (usually the 0-degree angle direction of the device's vertical axis) as the "wearer channel" and the background sound field or secondary audio stream processed by the blind source separation (BSS) module as the "interaction object channel". It should be noted that in order to ensure that subsequent steps can extract style features that characterize the ambient noise, the acquired data stream should contain at least one original signal that has not been excessively noise-reduced or a signal that retains the residual ambient background.

[0021] The desktop microphone is a pickup device with array signal processing capabilities. The system reads its output independent vocal track and ambient background track via USB or network interface to adapt to fixed interaction scenarios such as counters. In addition, the real-time interactive data also includes synchronously received triaxial acceleration data output by the device's built-in inertial measurement unit (IMU) and ambient background noise decibel values ​​output by the sound level meter module. Before inputting into subsequent models, the system performs a multimodal alignment operation: using an interpolation algorithm, the low-frequency sampled IMU data and decibel data are upsampled to be synchronized with the audio frame rate and spliced ​​in the channel dimension to form a composite input tensor containing acoustic information and physical environment information, which serves as the "real-time interactive data".

[0022] S102, regarding the acquisition of power business operation status data, the system polls or subscribes to the data message queues of the power company's Production Management System (PMS) and dispatching command platform through a predefined application programming interface (API). The power business operation status data specifically covers the digital work order information associated with the current moment, including work order type (such as "10kV line live-line maintenance," "low-voltage meter fault reporting," and "electricity bill dispute consultation"), the geospatial coordinates of the work location (GPS / BeiDou positioning data), and the system's pre-set business risk level (e.g., marking live-line work as Level 1 high risk and routine consultation as Level 3 low risk).

[0023] S103, regarding data synchronization, given that real-time interactive data and business data originate from heterogeneous systems, the system is configured with a unified Network Time Protocol (NTP) server to ensure that all terminal data packets carry a unified high-precision timestamp. The data processing center does not use physical encapsulation but instead employs a time-indexed mapping-based logic for soft synchronization: using the audio stream's timeline as the primary key, an index mapping relationship is established between business operation logs and audio time segments. Specifically, within an allowable time tolerance range (e.g., ±200ms to ±500ms), the system associates the current business risk level and job status label with the corresponding audio data segment, thereby constructing a time-aligned "voice-environment-business" multimodal data index table, ensuring that each frame of acoustic features has accurate business context constraints during subsequent analysis.

[0024] S2, using an adversarial feature decoupling network to perform feature separation on the real-time interactive data, to obtain a steady-state feature vector representing the steady state of the environment and an emotion feature vector representing semantic emotion.

[0025] In this embodiment, the use of an adversarial feature decoupling network to perform feature separation on the real-time interactive data aims to extract pure emotional components strongly correlated with business risks from the mixed raw signals. The specific execution process includes: S201, the real-time interactive data is input into the encoder, which is mapped to steady-state encoding and emotion encoding, respectively. Specifically, the encoder adopts a layered architecture: the front end contains a multi-layer one-dimensional convolutional neural network (1D-CNN) to perform convolution operations on the spectrogram features of the input audio (e.g., treating the frequency dimension of the Fbank spectrogram as the feature channel dimension) to extract local frequency domain features; the back end connects to a bidirectional long short-term memory network (Bi-LSTM) to capture the temporal dependencies of the speech sequence. To achieve physical feature separation, the encoder's end is connected to two independent feature projection layers, which map the hidden states output by the Bi-LSTM to a dimension of... steady-state eigenvectors and dimension are Emotion feature vector .

[0026] S202, Construct a mutual information minimization constraint to reduce the feature correlation between the steady-state encoding and the emotion encoding. To ensure... and To ensure orthogonality within the latent space, this embodiment introduces a mutual information estimator (such as the CLUB algorithm) as a loss function term. Specifically, the mutual information estimator internally contains a variational approximation network. The network consists of two fully connected neural network (MLP) layers, with the concatenated feature vector as input. The output is a log-likelihood estimate of the conditional probability. The system minimizes the upper bound of mutual information, forcibly widening the distance between two feature vectors in their distributions to ensure that their information is mutually exclusive. The target value of minimizing this upper bound of mutual information is defined as the mutual information loss. .

[0027] S203, the discriminator and classifier are used to perform adversarial training and supervised training on the steady-state encoding and the emotion encoding, respectively, so that the steady-state feature vector and the emotion feature vector are decoupled in terms of feature distribution. Specifically, the style discriminator includes two parallel classification heads: one is an environment classifier, used to identify environmental scene labels (such as "strong wind noise" and "indoor quiet"); the other is a speaker classifier, used to identify the speaker's identity ID (if the ID cannot be obtained, triplets are used to cluster and distinguish different speakers).

[0028] To prevent steady-state eigenvectors Degenerate into invalid noise and ensure that the emotion feature vector To ensure network purity, the network is trained using a "two-stream constraint mechanism": S204, Supervised Stream: This stream converts the steady-state feature vector... The style discriminator is directly input, and the environmental classification loss and speaker classification loss (denoted as...) are minimized. ), forced It includes realistic ambient noise and speaker attribute information; S205, Adversarial Stream: This involves transferring the emotional feature vector... The same style discriminator is input via a gradient inversion layer (GRL). At this point, the discriminator's goal is to detect... Whether style information remains in the code, while the encoder's goal is to maximize the discriminator's classification error (i.e., deceive the discriminator), the two form an adversarial game (denoted as...). ).

[0029] Meanwhile, the emotion classifier uses As input, minimize the cross-entropy loss (denoted as ). To ensure the accuracy of emotion recognition. Overall optimization objective function. for: (1) In a preferred embodiment, to balance the degree of decoupling and classification accuracy, the range of values ​​for the weight hyperparameter is set to... , The specific values ​​are adaptively adjusted based on the model's convergence. Through this mechanism, the system can ensure... Accurately characterize environmental benchmarks, while It only represents the pure, real-time semantic emotional state.

[0030] S3. Construct a dynamic baseline vector based on the steady-state feature vector, and calculate the deviation of the emotion feature vector from the dynamic baseline vector to generate a real-time risk representation.

[0031] In this embodiment, the core logic of calculating real-time risk characterization lies in establishing an "emotional baseline vector" that can adaptively adjust with the steady state of the environment, and assessing risk by quantifying the difference between the current emotional state and this baseline vector. Unlike existing technologies that use a fixed threshold, this method utilizes the volatility of decoupled style features to control the update rate of the emotional baseline vector, thereby achieving robust adaptation to different work scenarios. The deviation of the emotional feature vector from the dynamic baseline vector is specifically calculated using the following formula: (2) The physical meaning and calculation logic of each variable in the formula are as follows: For a moment The real-time risk representation value is a dimensionless scalar. for The emotion feature vector output by the adversarial network at each moment; for The dynamic baseline vector at any given moment represents the "normal emotional baseline" in the current context; The Euclidean norm is used to calculate the deviation of the current mood from the normal baseline. This is the mutation sensitivity coefficient, which typically takes values ​​ranging from 1 to 2. to This is used to balance the weights of long-term deviations and transient outbreaks in risk scoring; The time rate of change of the emotion feature vector (i.e., the Euclidean norm of the first difference) is used to capture dramatic fluctuations in emotion.

[0032] Wherein, the dynamic reference vector It is not a static constant, but rather utilizes an adaptive window length. It is obtained by applying a moving average to the historical sentiment feature vector sequence. The specific calculation method is as follows: (3) To address the challenge of distinguishing between "environmental disturbances" and "real risks," this embodiment utilizes steady-state feature vectors. The statistical properties are used to dynamically control the window length in the above formula. Instead of directly involving the style vector in the deviation subtraction operation, it adapts the window length. The determination logic is as follows: S301, Calculate the variance of the steady-state feature vector over a preset historical time period. This characterizes the environmental steady state and the degree of fluctuation in the speaker's fundamental frequency. In a preferred embodiment, the system maintains a length of... A historical style feature queue (e.g., set to 30 to 60 seconds) is generated, and the average variance of each dimension of the vector sequence in the queue is calculated.

[0033] S302, Set the adaptive window length With the variance value They are negatively correlated, and the calculation formula is: (4) In the formula, The base window length (e.g., the number of frames corresponding to 10 seconds). As an adjustment constant, This indicates a round-down operation. Additionally, the system has a minimum window threshold. (For example, 3 frames), if the calculation result is less than Then take This is to prevent the reference plane from being distorted due to an excessively small window size.

[0034] The specific technical effects brought about by this logic are as follows: when When the value is large (e.g., during a power repair operation in wind and rain or with a noisy background), the denominator increases, leading to a longer window length. Significantly shortened. This means the sentiment baseline is... Calculations based solely on a very short recent history mean the reference surface rapidly adapts to changes in the current input signal. In this case, signal fluctuations from the environment are quickly absorbed into the reference surface, causing... Keep the value low to avoid misjudging high-energy speech in harsh environments as high-risk.

[0035] Conversely, when At smaller times (e.g., in a quiet dispatch room). Approximately the base length This means that the reference surface is calculated based on a long historical period and has extremely high stability. At this point, if the speaker displays suppressed anger or a slight change in tone, the current vector... It will deviate significantly from the stable reference surface This allows them to keenly detect hidden risks.

[0036] To visually demonstrate the anti-interference effect of the "adaptive window" in a noisy environment, this embodiment provides a set of simulation waveform comparisons, such as... Figure 4 As shown in the figure. Curve A (variance of environmental style) ): Within the 10s to 25s interval, a sudden rainstorm occurred at the simulated work site, causing a sharp increase in style variance; Curve B (adaptive window length) ): In response to the increase in curve A, the window length rapidly decreases from the default 500 frames to 50 frames; Curve C (Dynamic Reference Vector) Due to the shortened window, the reference plane rapidly follows the rise in environmental noise energy in the input signal; Curve D (risk value of the prior art): using a fixed threshold, the false alarm risk spikes to over 0.9 after 10 seconds; Curve E: risk characterization of the present invention. Thanks to the following effect of the benchmark, the relative deviation calculated by this scheme remained within a safe range of below 0.2 until the simulated real argument occurred at 28 seconds, at which point curve E accurately identified the risk jump. Figure 4 As can be seen, this embodiment effectively solves the problem of false positives caused by sudden environmental changes in outdoor power operation scenarios.

[0037] Regarding the specific implementation details of parameter settings, if the feature vector has already undergone L2 normalization, the adjustment constant... The value is usually set to around 100 to ensure... The value remains within the effective adjustment range of 0.5 to 5.0 under normal noise conditions, ensuring that the dynamic range of window scaling meets business requirements.

[0038] S4, Based on the real-time risk characterization, generate the risk evolution trajectory within the future time window.

[0039] In this embodiment, the steps aim to construct a forward-looking risk prediction model, transforming passive "post-event response" into proactive "pre-event prevention." Specifically, the step of generating the risk evolution trajectory within a future time window based on the real-time risk characterization follows the following execution logic: S401, construct a time-series graph containing the current real-time risk representation and historical risk state sequences. Specifically, the system maintains a graph of length in memory. Time sliding window (preferred setting) (The time steps are 10 to 20, corresponding to approximately 5 to 10 seconds of historical data), and each discrete time point within this window is instantiated as a graph node, thereby constructing a dynamic time series graph. The node set includes historical nodes. and the current time node Each node's feature vector not only contains the high-dimensional emotion feature vector at that moment. It also splices together the real-time risk representation after being mapped to dimension d (consistent with the dimension of the sentiment feature vector) via a fully connected layer. In terms of edge set construction, in addition to establishing temporal edges that conform to the natural flow of time, the system also calculates the semantic relevance weights between nodes based on a self-attention mechanism. To ensure computational efficiency and eliminate redundant interference, the system implements a sparsity processing strategy for semantically related edges: using the cosine similarity formula. Computation node features The correlation between them is determined by retaining all temporal edges, but only the top semantic correlation weights are retained. (For example By connecting these connections, a sparse adjacency matrix can be constructed that can accurately capture long-distance dependencies.

[0040] S402, a graph neural network is used to perform convolution operations on the time-series graph to capture the dependencies of risk states in the time dimension, and outputs a risk probability distribution sequence for multiple future time steps as the risk evolution trajectory. In this preferred embodiment, to adapt to efficient computation of dynamic graphs, spatial graph convolution or a graph message passing mechanism is used. During the operation, the GCN layer uses the sparse adjacency matrix to aggregate the risk patterns and sentiment evolution features of historical time step nodes to the current time step node. In the high-dimensional feature representation, the nodes The final embedding vector encapsulates the spatiotemporal context information within the entire observation window.

[0041] S403, the system performs a read operation to extract the current node after graph convolution update. The feature vector is then fed into a fully connected prediction head. This prediction head outputs a value of length [length missing]. Vector sequence (preferably with a prediction step size set) (3 to 5 time steps). Each element in the sequence Representation model for the future The numerical prediction results of the real-time risk representation values ​​at each time step (the magnitude of which reflects the probability distribution of the risk occurring at that moment). This sequence of predicted values ​​constitutes the risk evolution trajectory, providing a quantitative basis for subsequent counterfactual inference. To ensure the predictive ability of the model, during the offline training phase, the system uses the risk value sequence at historical moments as input and the ground truth value at future moments as the label, updating the weight parameters of the graph neural network and the prediction head by minimizing the mean squared error loss (MSE Loss).

[0042] S5. When the risk evolution trajectory meets the intervention triggering conditions, the execution effect of different candidate strategies is simulated based on counterfactual reasoning, and the target intervention strategy is determined in combination with the power business operation status data.

[0043] In this embodiment, the "intervention trigger condition" is specifically set as follows: when the original risk evolution trajectory output in step S4 shows that the future continuous Risk probability value of a frame (e.g., 3 frames) When the preset warning threshold (e.g., 0.75) is exceeded, the decision logic for this step is activated.

[0044] To achieve counterfactual inference, it is important to note that the temporal evolution prediction model described in step S4 is designed as a conditional generative model during construction or has control variables introduced during the training phase. Specifically, the historical dataset used by this model during training includes not only risk state sequences and emotional feature sequences, but also markers of intervention actions that actually occurred at historical moments. Therefore, this model has the ability to learn the causal mapping between "action-state" and can output differentiated prediction results based on different input control variables.

[0045] The specific execution process of this step includes: S501, for each candidate strategy in the preset strategy library, deduce the counterfactual risk trajectory after executing that candidate strategy. The system constructs a virtual intervention simulation space. When simulating the original risk evolution trajectory, the strategy vector of the input model is set to an all-zero vector ( This represents maintaining a "no intervention" state; while simulating counterfactual risk trajectories, the current context feature vector is combined with the representative candidate strategy. The one-hot encoded vector or embedding vector is concatenated and then input into the time-series evolution prediction model. Based on the learned causal relationships, the model outputs a hypothesis execution strategy. The sequence of probability distributions of future risks.

[0046] S502, calculate the payoff value of the counterfactual risk trajectory relative to the original risk evolution trajectory. This payoff value... The ability of intervention actions to suppress risk was quantified by calculating the difference in area under the curve (DAUC) between the original high-risk trajectory and the simulated low-risk trajectory within a future time window.

[0047] S503, assess the disruption cost of executing the candidate strategy based on the power service operation status data.

[0048] S504, calculate the difference between the benefit value and the disturbance cost (represented as a weighted difference in the specific algorithm), and select the candidate strategy with the largest difference as the target intervention strategy. The comprehensive evaluation score... The following formula is used for calculation: (5) In the formula, For the first in the preset strategy library One candidate strategy; To reduce the expected value of the normalized risk, the value range is: ; Adjustment coefficients to balance user experience and security; In order to be in the current power business operation status The cost of the disturbance is also normalized to .

[0049] Regarding the disturbance cost in the formula The evaluation logic is as follows: The power business operation status data is analyzed to determine the danger level of the current business scenario.

[0050] If the danger level is greater than or equal to a preset threshold (e.g., in scenarios like "live-line work on a 10kV line" or "high-altitude emergency repair"), the disturbance cost weighting of tactile feedback and auditory blocking strategies is significantly increased. To ensure absolute safety, the system will then reduce the cost of strong vibration or high-decibel alarm strategies. Forced to be set to a blocking value (e.g.) Far exceeding the normal range ), which makes its overall score The value becomes negative, thus being automatically removed from the decision-making process to prevent electric shock accidents caused by startling workers.

[0051] If the danger level is less than a preset threshold (e.g., in "work order entry" or "rest" scenarios), the disturbance cost weight of voice guidance strategies is reduced. At this point, the cost... Take the smaller value (e.g.) The system will prioritize selecting the profit value. A sophisticated voice guidance strategy is employed to calm the workers down as quickly as possible.

[0052] To facilitate understanding of the decision-making logic, a high-risk dialogue segment in an inspection scenario is used as an example. The decision matrix generated by the system is shown in Table 1.

[0053] Table 1

[0054] As shown in Table 2, although Strategy 1 offers the highest risk-reward ratio, its high disruption cost during the ongoing inspection process results in a lower overall score. Conversely, Strategy 2, while offering slightly lower returns, incurs minimal costs, ultimately achieving the highest overall score of 0.45 and thus becoming the target intervention strategy.

[0055] For the predicted high-risk trends, the system simulates the effects of different intervention strategies in a simulation space, such as... Figure 5 As shown. Figure 5 The simulation results demonstrate the system's performance under three counterfactual paths—"no intervention," "tactile vibration intervention," and "voice-guided intervention"—in a high-voltage live-line working scenario. Through analysis... Figure 5 It can be seen that although vibration intervention (path 2) can reduce the risk value the fastest, it is in... Disturbance in this state is extremely costly, resulting in a negative overall benefit ratio; therefore, the system ultimately ruled out this path and chose the voice guidance path (path 3), which has a gentler risk reduction curve but is safer.

[0056] In this embodiment, after generating the intervention strategy, the method further includes: continuously monitoring the actual risk changes after implementing the target intervention strategy.

[0057] This step establishes a closed-loop optimization mechanism of "prediction-intervention-feedback," aiming to eliminate the discrepancy between model projections and the real world, and to enable the system to adapt to the personalized behavioral patterns of specific operators. In practice, the system continues to calculate the real risk representation sequence under controlled conditions in real time within a preset observation window after sending the intervention command (e.g., 5 to 10 seconds after intervention). .

[0058] Specifically, the residual between the actual risk change and the predicted counterfactual risk trajectory is calculated. This is taken into account the true risk representation output in step S3. The risk is a physical quantity based on Euclidean distance (its range may be greater than 1), while the counterfactual risk trajectory output in step S5 is a probability distribution value (its range is [0, 1]). There is a mismatch in the dimensions between the two. Therefore, before calculating the residuals, the system first maps the measured real risk representation sequence to the probability space using a mapping function. The specific mapping formula is as follows: (6) In the formula, This represents the normalized measured risk probability. This is a scaling factor used to adjust the steepness of the mapping function; The bias coefficient is used to calibrate the probability center; and These are preset calibration parameters to ensure that the transformed values ​​have the same physical meaning as the model output.

[0059] Subsequently, the system calculates the prediction residuals using the following formula. : (7) In the formula, The number of time steps for the observation window; The moment the intervention instruction was issued; For time series evolution prediction models in The risk probability value predicted at any given time, assuming the execution of the target strategy.

[0060] When the residual exceeds a preset threshold (e.g., 0.15), the model parameters used to generate the risk evolution trajectory are updated online. To ensure the system's engineering stability and prevent the model's general capabilities from degrading due to overfitting to single-interaction data, the system adopts a layered update strategy: freezing the encoder weights in the bottom-level graph neural network (GCN) and adversarial feature decoupling network, and only updating the gradients of the weights in the top-level fully connected prediction head or user-personalized embedding layer. This selective parameter update strategy ensures that the model can quickly model specific user behaviors while maintaining robustness to the acoustic environment of the power scenario by utilizing the frozen bottom-level network, effectively preventing model degradation or oscillations during online learning and ensuring the engineering reliability of the risk intervention system.

[0061] The specific update process employs an experience replay mechanism: the system stores the current interaction samples (including state, intervention actions, measured results, and residuals) in a first-in-first-out experience buffer. When the update trigger condition is met, the system does not directly use a single sample for backpropagation. Instead, it randomly samples a mini-batch of mixed samples from the buffer and fine-tunes the parameters of the unfrozen layer using stochastic gradient descent (SGD) or the Adam optimizer. Through this mechanism, the system can achieve personalized adaptation to the psychological feedback characteristics of specific users and effectively suppress model parameter oscillations caused by a single abnormal sample.

[0062] like Figure 6 As shown, this demonstrates the trend of online learning mechanisms improving the prediction accuracy of the system. Figure 6 The residual convergence of the system was recorded during its initial operation and after 50 online updates. With the experience replay mechanism continuously fine-tuning the fully connected prediction head, the prediction residual gradually converged from an initial average of 0.22 to below 0.05. This demonstrates that the system can continuously correct its understanding of specific operators' psychological feedback patterns through closed-loop adaptive learning, resulting in a significant increase in the accuracy of counterfactual inference over time.

[0063] Example 2, Figure 7 A wearable intelligent collaborative management and control system for power customer service risks is presented, including: The data acquisition module is used to acquire real-time interactive data collected by wearable devices and synchronized power business operation status data; The feature decoupling module is used to perform feature separation on the real-time interactive data using an adversarial feature decoupling network to obtain a steady-state feature vector representing the steady state of the environment and an emotion feature vector representing semantic emotion. The risk characterization module is used to construct a dynamic baseline vector based on the steady-state feature vector, and calculate the deviation of the emotion feature vector from the dynamic baseline vector to generate a real-time risk characterization. The risk evolution module is used to generate a risk evolution trajectory within a future time window based on the real-time risk representation. The strategy determination module is used to simulate the execution effects of different candidate strategies based on counterfactual reasoning when the risk evolution trajectory meets the intervention triggering conditions, and to determine the target intervention strategy in combination with the power business operation status data.

[0064] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0065] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0066] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0067] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0068] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0069] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wearable intelligent collaborative management and control method for power customer service risks, characterized in that, Includes the following steps: Acquire real-time interactive data collected by wearable devices and synchronized power business operation status data; An adversarial feature decoupling network is used to separate features from real-time interactive data, resulting in steady-state feature vectors and emotion feature vectors. A dynamic baseline vector is constructed based on the steady-state feature vector, and the adaptive window length of the baseline vector is adjusted according to the fluctuation of the steady-state feature vector. The deviation of the sentiment feature vector from the baseline vector is calculated to generate a real-time risk representation. Predict the trajectory of risk evolution within a specified time window based on real-time risk characterization; When the risk evolution trajectory meets the preset intervention trigger conditions, the target intervention strategy is determined and executed in combination with the current power business operation status data.

2. The wearable intelligent collaborative management and control method for power customer service risks according to claim 1, characterized in that, The method of using an adversarial feature decoupling network to perform feature separation on real-time interactive data includes: Real-time interactive data is mapped into steady-state coding and emotion coding respectively through an encoder; Construct mutual information minimization constraints to reduce feature correlation between steady-state encoding and sentiment encoding; By using discriminators and classifiers to perform adversarial training and supervised training on steady-state encoding and emotion encoding respectively, the steady-state feature vector and emotion feature vector are decoupled in terms of feature distribution.

3. The wearable intelligent collaborative management and control method for power customer service risks according to claim 1, characterized in that, The deviation is calculated using the following formula: In the formula, for Real-time risk representation at any given moment; for The emotional feature vector at any given moment; for The steady-state eigenvector at time step; for The dynamic baseline vector at any given time is obtained by performing a moving average on the historical sentiment feature vector sequence using an adaptive window length; Mutation sensitivity coefficient; This represents the rate of change of the emotion feature vector over time.

4. The wearable intelligent collaborative management and control method for power customer service risks according to claim 3, characterized in that, The logic for determining the adaptive window length is as follows: Calculate the variance of the steady-state eigenvector over a predetermined historical period to characterize the degree of fluctuation in the environmental steady state; The adaptive window length is dynamically determined based on the variance value, wherein the adaptive window length is negatively correlated with the variance value.

5. The wearable intelligent collaborative management and control method for power customer service risks according to claim 1, characterized in that, The prediction of risk evolution trajectory within a specified time window based on real-time risk characterization includes: Construct a time-series graph that includes current real-time risk representations and historical risk status sequences; By using graph neural networks to perform convolution operations on time series graphs, the dependencies of risk states in the time dimension are captured, and the risk probability distribution sequence at multiple future time steps is output as the risk evolution trajectory.

6. The wearable intelligent collaborative management and control method for power customer service risks according to claim 1, characterized in that, The process of determining and executing the target intervention strategy based on current power business operation status data includes: For a given candidate strategy, deduce the counterfactual risk trajectory after executing that candidate strategy; Calculate the payoff of the counterfactual risk trajectory relative to the original risk evolution trajectory; The disruption cost of executing the candidate strategy is assessed based on power business operation status data; Calculate the difference between the benefit value and the disturbance cost, and select the candidate strategy with the largest difference as the target intervention strategy.

7. The wearable intelligent collaborative management and control method for power customer service risks according to claim 6, characterized in that, The difference between the benefit value and the cost of disturbance The following formula is used for calculation: In the formula, For the first One candidate strategy; The stated return value represents the expected reduction in risk. In order to be in the current power business operation status The cost of the disturbance; This is the balance coefficient.

8. The wearable intelligent collaborative management and control method for power customer service risks according to claim 7, characterized in that, The evaluation logic for the cost of disruption is as follows: Analyze power business operation status data to determine the risk level of the current business scenario; If the danger level is greater than or equal to the preset threshold, the disturbance cost weight of tactile feedback and auditory blocking strategies will be increased. If the danger level is less than a preset threshold, the disturbance cost weight of voice guidance strategies is reduced.

9. The wearable intelligent collaborative management and control method for power customer service risks according to claim 6, characterized in that, After generating the intervention strategy, the following is also included: Continuously monitor actual risk changes after implementing the target intervention strategy; Calculate the residual between the actual risk change and the predicted counterfactual risk trajectory; When the residual exceeds a preset threshold, the model parameters used to generate the risk evolution trajectory are updated online.

10. The wearable intelligent collaborative management and control method for power customer service risks according to claim 9, characterized in that, The online updating of model parameters used to generate risk evolution trajectories includes: Keeping the encoder weights and graph neural network weights in the adversarial feature decoupling network unchanged, gradient correction is performed only on the parameters of the subsequent prediction output layer of the graph neural network.

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