43 results about "Call volume" patented technology

A method for detecting and preventing bot activity in a protected environment by leveraging API behavior analysis is disclosed. The method includes identifying target APIs vulnerable to bot attacks through the analysis of parameters such as user credentials and session tokens. It determines the importance of target APIs based on call volume and context, generating correlation keys from headers, cookies, or payloads to track API flows. The method identifies mandatory and probable pre-APIs using historical patterns, assigns risk scores based on criticality, and validates API request sequences in real-time by analyzing integrity and sequence likelihood. Caching mechanisms store data related to APIs, correlation keys, and invocation paths for real-time lookups. Incoming requests are assessed for missing pre-APIs or sequence deviations, with substitute APIs validated against thresholds. Requests flagged as bot traffic are blocked, ensuring secure API workflows and mitigating automated threats effectively.

An emergency management system (EMS) generates a likelihood of a 911 service outage for a public safety access point (PSAP). The EMS receives supplemental call data for mobile devices that initiated 911 calls to a public safety answering point (PSAP). The EMS generates a 911 call volume metric for the 911 calls. The EMS receives a number of queries from the PSAP for a portion of the supplemental call data that corresponds to the 911 call volume metric. The EMS tracks the number of queries received from the PSAP. The EMS determines the likelihood of a 911 service outage for the PSAP based on the 911 call volume metric and the number of queries received. The EMS notifies the PSAP of the likelihood of the 911 service outage (e.g., with an outage score).

The invention provides an incoming call processing method and device, electronic equipment and a storage medium, and the incoming call processing method is applied to a called terminal, and comprises the steps: executing first incoming call reminding under the condition that a call request is detected, and carrying out incoming call processing on the called terminal under the condition that a called user inputs a paid answering operation for the first incoming call reminding. The paid answering process comprises the steps that the incoming call is answered or the second incoming call reminding is triggered after the payment confirmation information corresponding to the calling number is detected, the second incoming call reminding or the incoming call answering can be triggered only when the calling user pays the fee, and the second incoming call reminding or the incoming call answering cannot be triggered if the calling user is not willing to pay the fee; therefore, part of calls can be intercepted, and the incoming call reminding amount and the actual call volume of crank calls are effectively reduced; and the answering of the crank call can bring additional economic compensation to the called user, so that the negative emotion of the called user for answering the crank call can be effectively relieved, and the user experience is improved.

The invention relates to the technical field of server deployment and capacity expansion, in particular to a service dynamic capacity expansion method and device, equipment and a storage medium. Comprising the following steps: acquiring page view data of a plurality of target pages and call volume data of services in a preset time period, and calculating an average page view of each back-end service in the preset time period based on the call volume data; according to the page view data and the call volume data, calculating a current conversion rate from each target page to the corresponding back-end service, and calculating a current service load value of the corresponding back-end service after routing conversion; judging whether the current service load value is greater than a preset threshold, and if so, calling a historical data set corresponding to a preset time period in a historical database; and based on the historical data set, the comprehensive average access amount and the current conversion rate, judging whether a capacity expansion triggering condition is met, and if yes, triggering dynamic capacity expansion of the corresponding back-end service. The resource utilization rate and the better cost effectiveness can be improved, and the user experience is improved.

The invention relates to the technical field of artificial intelligence, can be applied to service system platforms of medical health, financial science and technology and the like, and discloses an agent personnel processing method and device, computer equipment and a storage medium. Carrying out cleaning and normalization preprocessing on the obtained historical job performance data, and carrying out feature extraction on the preprocessed historical job performance data to obtain a feature vector; based on the feature vector, generating a call volume prediction result of the seat queue through a pre-trained call volume prediction model; determining a task distribution strategy according to the call volume prediction result and the current working state of each seat person in the seat queue, and automatically distributing a task to each seat person according to the task distribution strategy; therefore, task distribution and personnel management of the seat personnel can be optimized.

Aspects of the present disclosure relate to coordinating conversations with an "active talk bar" displayed on an XR device for an artificial reality (XR) environment, whose control bar for voice conversations continuously exists in multiple XR experiences. A user can create a voice call with other users of a plurality of XR devices, end the call, manage participants of the call, adjust call volume, and the like through the active call bar. Users may also view which XR experiences other users within the group are accessing and may invite them to join their XR experience. The active talk bar may continuously exist on the user interface as the user moves between the experience and the application. In addition, no matter where other users are located, the users can continue voice communication in different experiences, that is, the users can participate in the voice communication without being located in the same experience.

The invention discloses an application log recording method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: evaluating the importance score of each application according to the business association degree, the system coupling degree, the fault influence degree and the safety sensitivity of each application; obtaining the calling amount of each application through a preset time sliding window, and predicting the future calling amount of the application according to the calling amount in the time sliding window; according to the importance score and the future call amount of each application, a log collection strategy of each application is determined, and the log collection strategies comprise full-amount log collection and key log collection; according to the future call amount, the log collection strategy and the log query requirement of each application, a log storage system of each application is determined, and the log storage system comprises a distributed log system, a local file log system and a database log system; and collecting the log of the application according to the log collection strategy, and storing the collected log to a corresponding log storage system.

A machine learning model (e.g., including a deep learning neural network) with learned embeddings is applied to time series data with associated metadata to obtain predictions of the time series value. For example, a call volume in a period of time may be predicted based on call volume data for a sequence of time bins in a window of preceding time. Time bins may be associated with respective metadata, such as day of week, hour of day, day of month, holiday, part of business cycle, weather, and / or tide. These pieces of metadata may be mapped to embedding vectors using trained embedding functions. The resulting embedding vectors may be input to a neural network along with the corresponding time series data (e.g., call volumes) to make a prediction for future time bin. For example, the prediction may be used to provision servers in a network infrastructure.

A method for synchronizing seating personnel configuration and customer call volume within a contact center estimates the number of seating personnel provisioning the contact center during a future time, and uses historical data to estimate the number of customer calls expected during the future time. The method performs a first comparison of the number of seats to an expected customer call amount at the future time, and then determines at least one preferred contact center callback window. The method communicates with the customer to request at least one preferred customer to select a callback time, and performs a second comparison to provide the customer with a suggested callback time, the suggested callback time is selected to synchronize the customer call volume with the number of seats, the preferred contact center callback window, and the preferred customer selected callback time to improve customer experience and contact center efficiency.

This application provides a method, apparatus, and computer-readable storage medium for constructing a call volume prediction model. The method includes: obtaining the total call volume before the transfer from a first call center and the total call volume before the transfer from a second call center; obtaining the transfer ratio of the first call center within a first time period, where the transfer ratio is the ratio of the target call volume to the total call volume before the transfer from the first call center; determining call transfer information from the first call center to the second call center within the first time period based on the total call volume before the transfer from the first call center and the transfer ratio; summing the total call volume before the transfer from the second call center and the call transfer information to reconstruct the first historical call volume information of the second call center, obtaining the reconstructed target historical call volume data; and constructing a target call volume prediction model based on the target historical call volume data. The embodiments of this application result in higher prediction accuracy for the reconstructed call volume prediction model.

Various methods and processes, apparatuses or systems, and media for forecasting of service level indicator (SLI) metrics using deep learning algorithms in order to detect anomalies and to provide accurate alerts to users are disclosed. The method includes: receiving information that relates to an incoming call volume for a particular queue from among a set of queues; analyzing, by using a model, the first information in order to determine a set of SLI metrics that relate to the queue; generating a forecast of one or more of the SLI metrics for the queue; and displaying, via a graphical user interface, information that relates to the forecast.

The embodiments of the present application are applicable to the technical field of R & D management, and provide a method, device, terminal device and storage medium for calculating interface test coverage. The method includes: obtaining the application interfaces used by each application in the target system, the number of test cases and the call volume of each application interface; determining the number of tests of the tested application interfaces from multiple application interfaces based on the number of test cases; and determining the target application interfaces and the target number of the target application interfaces based on the call volume; the target application interfaces are the interfaces actually used by the target system; adjusting a preset correction coefficient according to the ratio of the target number to the total number of application interfaces to obtain an adjusted target correction coefficient; calculating the ratio of the number of tests to the target number and the target correction coefficient to obtain the actual interface test coverage of the target system. By adopting the above method, the accuracy of calculating the interface test coverage of the target system can be improved.

This application provides an API service management method, apparatus, device, medium, and program product, relating to the field of big data technology. The method comprises: processing API requests corresponding to multiple subsets based on an API service architecture to obtain task execution results; the API service architecture including at least one of an access layer, a control layer, and an execution layer; collecting business data and traffic volume data corresponding to the API requests; obtaining a comprehensive health score and API call volume prediction value for the subsets based on historical data, the business data, and the traffic volume data; and performing differentiated traffic volume allocation for the multiple subsets based on the comprehensive health score and the predicted API call volume value. This application improves API service resource utilization and response efficiency.

A non-extension type remote call notification device is connected to a fire telephone. The non-extension type remote call notification device includes: a notification host, a plurality of remote activation devices, and a variable audio source line terminal impedance unit. Among them, the notification host is connected to the remote activation device network module via the notification host network module; the variable audio source line terminal impedance unit is connected to the activation device audio source module of the remote activation device to adjust the call volume of the microphone.

The invention provides a call method and related equipment, and relates to the technical field of terminal equipment. The call method comprises: in response to a first operation of a first user, displaying a first volume control interface and a second volume control interface, the first volume control interface corresponding to a second user communicating with the first user, the second volume control interface corresponding to a third user communicating with the first user, the first volume control interface comprising a first control, and the second volume control interface comprising a second control; the second volume control interface comprises a second control; and in response to a second operation of the first user on the first control, adjusting the call volume of the second user, and / or in response to a second operation of the first user on the second control, adjusting the call volume of the third user. By means of the method, the electronic device can adjust the call volume of one or more specified users in response to the user operation in the call process, the problem that the volume adjustment mode of an existing electronic device is too single is solved, and the call experience of the users is improved.

The present invention discloses an outbound call method, device, equipment and storage medium. The method includes: obtaining an indicator and outbound call ratio information of a first outbound call scenario, wherein the outbound call ratio information includes at least one first outbound call scenario information and an outbound call ratio corresponding to each first outbound call scenario information; wherein the information of the first outbound call scenario includes at least one indicator of the first outbound call scenario; determining the outbound call ratio according to the indicator of the first outbound call scenario and the outbound call ratio information; determining the outbound call volume according to the indicator of the first outbound call scenario and the outbound call ratio; and making outbound calls according to the outbound call volume. By determining a more appropriate outbound call ratio, a more appropriate outbound call volume can be determined, the call loss rate can be reduced, the seat utilization rate can be increased, and the outbound call efficiency can be improved.

The invention belongs to the technical field of telephone traffic management, and particularly relates to an intelligent telephone traffic management system based on multi-source data analysis, which comprises a data fusion and convergence module, a telephone traffic feature deep analysis module, an intelligent telephone traffic distribution decision module, a telephone traffic dynamic scheduling optimization module, a telephone traffic trend prediction and early warning module, a central data warehouse and a telephone traffic management end. According to the invention, the fused and converged data is deeply analyzed to mine the feature information of the telephone traffic, and the telephone traffic distribution scheme is intelligently generated according to the telephone traffic features and the customer service personnel information and is dynamically adjusted according to the actual situation. The telephone traffic trend prediction and early warning module predicts the future change trend of the telephone traffic and generates early warning information in time, so that the telephone traffic can be quickly and accurately allocated to appropriate customer service personnel, and resource preparation can be made in advance to improve the resource utilization efficiency; and the telephone traffic distribution scheme can be dynamically adjusted to adapt to different telephone traffic scenes and changes, so that intelligent management of the telephone traffic is realized.

The invention relates to the technical field of data transmission, and discloses a service-based wireless Internet of Things terminal adaptive channel competition and scheduling method, which comprises the following steps of: after service data is acquired, matching service priority, processing data volume and resource calling volume; determining a first channel competition coefficient based on the service priority, marking a to-be-selected channel, and calculating a second channel competition coefficient by combining the average data volume and the average call volume of the service data in the to-be-selected channel; and weighting to obtain a comprehensive competition coefficient, selecting a final channel according to the sequence, and selecting a channel of which the transmission frequency is lower than a reference value if the difference is equal. According to the method, service differentiation self-adaptive scheduling is realized, the transmission quality of high-priority services is guaranteed preferentially, the channel conflict probability is effectively reduced, the utilization rate of wireless spectrum and channel resources is improved, and the network transmission efficiency and reliability are optimized.

This application provides a procurement forecasting method, apparatus, device, and computer-readable storage medium. The method includes: determining a first estimated call volume of data resources in the next ordering cycle based on the ordering data, ordering attribute information, and configuration attribute information of each ordering object in the current ordering cycle; determining a second estimated call volume of data resources in the next ordering cycle based on the historical call volume corresponding to each historical ordering cycle within a preset historical time period; determining a target estimated volume based on the first and second estimated call volumes; and determining a predicted procurement volume of data resources based on the target estimated volume, the historical call volume corresponding to each historical ordering cycle, and the already procured amount of data resources. This improves the accuracy of the target estimated volume, thereby improving the accuracy of the predicted procurement volume. Cyclic forecasting according to the ordering cycle avoids waste caused by premature procurement and disruption to normal use by ordering users due to late procurement, thus improving the accuracy of procurement timing.

A method for synchronizing agent staffing and customer call volume in a contact center estimates the number of agents to staff the contact center during a future time period and uses historical data to estimate the number of customer calls expected during the future time period. The method performs a first comparison of the number of agents to the expected customer call volume for the future time period and then determines at least one preferred contact center callback window. The method communicates with the customer to request at least one preferred customer-selected callback time and performs a second comparison to provide the customer with a suggested callback time selected to synchronize the customer call volume with the number of agents, the preferred contact center callback window, and the preferred customer-selected callback time, thereby improving the customer experience and contact center efficiency.

The invention discloses a built-in call volume increasing method, which comprises the following steps of: 1, performing a simulation test according to preset good and good levels to obtain earphone signal voltages C1 and C2 and earphone microphone signal voltages S1 and S2 corresponding to the good and good levels; step 2, obtaining microphone impedance Z1, earphone impedance Z2, radio station receiving interface circuit impedance Z3, alarm interface circuit impedance Z4, user box microphone interface circuit impedance Z5 and machine communication amplifier audio output end circuit impedance Z6 through simulation test; 3, building a test circuit I, a test circuit II and a test circuit III; and step 4, selecting a corresponding test line for testing based on the maintenance object so as to complete the increase of the internal call volume by adjusting the internal component parameters of the maintenance object. According to the built-in call volume increasing method provided by the invention, by detecting the actual installation performance condition of the set-top box and matching with the adjustment of the internal parameters of the set-top box components, the built-in call mutual listening volume after the set-top box is installed is obviously increased.

The application relates to the field of visual technology, in particular to a visual doorbell based on face recognition, which comprises a doorbell body, the front of the doorbell body is provided with a camera and a doorbell button, the doorbell body is provided with a doorbell system, the doorbell system comprises a face collection module, the face collection module is arranged on an outdoor unit and is used for capturing a visitor's face image in real time, a local recognition engine, a built-in pre-stored face database, feature extraction and identity matching of the collected image, a dynamic response unit, a differentiated response strategy is triggered according to a recognition result, when the recognition result is that the visitor is an authorized user, the dynamic response unit automatically unlocks the access control and pushes personalized welcome information to an indoor terminal, when the recognition result is that the visitor is an unauthorized person, the dynamic response unit starts real-time alarm and records a visitor behavior video, a cloud collaborative module, data synchronization with the local recognition engine, remote update of the face database and the recognition algorithm are supported, the visual doorbell based on face recognition automatically enhances the call volume and the interface display brightness for the old users, and a voice assistant is activated to guide the children.

The embodiment of the invention provides an incoming call resource allocation method and device, electronic equipment and a storage medium, belongs to the technical field of artificial intelligence, and is suitable for financial science and technology scenes. The method comprises the following steps: acquiring a historical incoming call volume; daily incoming call volume prediction is carried out based on the historical incoming call volume to obtain target daily incoming call volume prediction data; based on the historical incoming call volume, performing time period call volume prediction to obtain initial time period incoming call volume prediction data; performing influence duration analysis on a pre-acquired target emergency to obtain an event duration; performing time period division based on the event duration to obtain event time period data; based on the event type of the target emergency and the event time period data, correcting the incoming call volume prediction data in the initial time period to obtain incoming call volume prediction data in the target time period; and carrying out incoming call resource allocation based on the target day incoming call volume prediction data and the target time period incoming call volume prediction data. According to the embodiment of the invention, the accuracy of incoming call resource allocation can be improved.

The invention provides an incoming call processing method, device and system, electronic equipment and a storage medium. Under the condition that the incoming call is detected to accord with the preset intelligent interaction condition, the incoming call is answered, the artificial intelligence voice interaction with the calling user is carried out through the artificial intelligence voice assistant, and whether the incoming call belongs to the crank call is judged according to the artificial intelligence voice interaction content; a paid answering process is executed on the incoming call judged to belong to the crank call, the paid answering process comprises an incoming call reminding step, and the incoming call reminding step comprises the step of triggering incoming call reminding after payment confirmation information corresponding to a calling number is detected, so that the crank call answering threshold can be improved, part of crank calls are intercepted, and the call answering efficiency is improved. The incoming call reminding quantity and the actual call volume of crank calls are effectively reduced; and whether the incoming call belongs to the crank call can be accurately judged according to the artificial intelligence voice interaction content, so that the processing rationality of each incoming call can be improved, and the user experience can be improved.

The invention relates to the technical field of data processing, and discloses an incoming line voice volume prediction method, device, equipment and medium, which can be applied to the financial and medical fields, and the method comprises the following steps: obtaining historical incoming line voice volume data, carrying out data formatting processing, carrying out holiday attribute identification and feature expansion processing, and obtaining a voice volume feature data set; training and reasoning an incoming line day volume prediction model by using the volume characteristic data set, and performing date and time period volume modeling preparation processing to obtain a time period volume modeling data set; and performing time period distribution prediction on the time period speech volume modeling data set, performing incoming line time period speech volume reduction calculation, and outputting an incoming line speech volume prediction result. In the invention, aiming at the problem that the existing incoming line volume prediction accuracy is low, incoming line time period volume reduction calculation can be carried out according to the calculated time period proportion prediction result, so that the incoming line volume prediction result is output, and the incoming line volume prediction accuracy is improved.

The present invention relates to the technical field of call volume prediction, and in particular to a data-sharing API call volume prediction method, apparatus and device. In the method, a VMD algorithm is used to decompose a time sequence of an original call volume so as to reduce data instability and random fluctuations and enhance a prediction effect of a model; in view of the problems of weak feature extraction capability and low prediction accuracy of a single model, a convolutional neural network (CNN) is combined with long short-term memory (LSTM) to enhance the capability of extracting sequence features and improve sequence prediction accuracy; and an improved particle swarm optimization algorithm is used to enhance the global and local search capabilities of particles at different stages during iteration and enhance the capability of searching for optimal hyperparameters of a CNN-LSTM prediction model, thereby improving prediction accuracy and generalization. The present invention is suitable for prediction tasks of data-sharing API call volume in large-sample scenarios.

The embodiment of the invention discloses an outbound processing method, device and equipment, a computer readable medium and a program product. A specific embodiment of the method comprises the following steps: in response to determining that an outbound call processing stage corresponding to a future time period is not in a cold start stage, generating a predicted outbound call volume and a to-be-processed outbound call volume according to an outbound call index information set corresponding to a current moment; in response to determining that the average idle duration of the seat is greater than the preset average idle duration of the seat, generating outbound adjustment information according to the average idle duration of the seat and the preset average idle duration of the seat; according to the outbound adjustment information, the predicted outbound volume and the to-be-processed outbound volume, generating an actual outbound volume for the future time period; and according to the actual call-out volume, executing call-out processing for the call-out processing stage in a future time period. The implementation mode is related to intelligent customer service, and the outbound call efficiency of the seat can be improved.

The invention provides a predictive outbound call scheduling method, device and equipment based on double calls, belongs to the technical field of communication, and solves the problem of low outbound call efficiency caused by unreasonable double call communication request scheduling and unreasonable resource utilization. The method comprises the following steps: acquiring target call data of an intelligent voice system; according to the target call data, obtaining the average call completing rate, the call-out time and the average call duration of the intelligent voice system; performing call volume estimation according to the target call data and the average call completing rate, the call-out time and the average call duration of the intelligent voice system to obtain a delay call concurrency volume; according to the delay call concurrency quantity, obtaining an estimated quantity of double call communication requests which need to be initiated at present; and obtaining the pre-estimated quantity of early warning information, and initiating a double-call communication request to a communication network. According to the scheme, the communication request initiating amount is dynamically adjusted by estimating the future communication request amount, so that efficient utilization of resources and remarkable improvement of outbound efficiency are realized.

The invention discloses an emergency call volume prediction method, device and equipment based on an STL algorithm and a Prophet model, and relates to the technical field of big data analysis. According to the method, on one hand, an STL algorithm is used to decompose emergency call volume time sequence data to obtain emergency call trend term component time sequence data, and a first emergency call trend term component prediction value of a unit time period in a future day is calculated based on a dynamic exponential weighting method, and on the other hand, according to the emergency call volume time sequence data, a second emergency call trend term component prediction value of a unit time period in the future day is obtained. Based on a Prophet model, predicting to obtain a second emergency call trend item component predicted value, an emergency call seasonal effect item component predicted value, an emergency call holiday effect item component predicted value and an emergency call error item component predicted value in a unit time period in a future day; and finally, based on all prediction results, a final first-aid call volume prediction value of a unit time period in a future day is obtained through comprehensive calculation, so that the prediction precision can be effectively improved by combining the advantages of a Prophet model and an STL algorithm.