66 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.

The present application discloses a voice call management method, device, equipment, storage medium and product, which relates to the field of voice call management technology, and discloses a voice call management method, including: when monitoring multi-user calls, collecting call content; extracting semantic features of the call content based on a preset semantic analysis model, analyzing the importance of each call content according to the extracted semantic features, the preset semantic analysis model is a model obtained by training the training model after semantically weighting the dialogue content of different discussion issues in each construction environment; adjusting the call volume of each user based on the importance. That is, when analyzing the importance of each call content, combined with the weight of each semantic, the output importance is made more in line with the current construction environment, so as to ensure accurate adjustment of the call volume of each user, so that the user can hear the key points of the call.

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 discloses a service flow data early warning method, system and device, a medium and a program product. The method comprises the following steps: monitoring service flow data in real time and sending the service flow data to a distributed Redis cluster; performing real-time processing on the service flow data by using a distributed Redis cluster to obtain a multi-dimensional real-time interface call amount; setting early warning thresholds of a plurality of gears and corresponding early warning lines based on the business rules; and performing early warning judgment on the real-time interface calling amount by utilizing the early warning lines of the multiple gears, and performing early warning notification on the real-time interface calling amount exceeding the early warning lines. According to the method and the device, the flow statistics of the API calling amount, the multi-dimensional statistics of the API interface flow and the accurate early warning of the data flow abnormity are realized.

To reduce call volume and improve efficiency of operator training by automatically summarizing, analyzing and reconstructing log of a call center.SOLUTION: A system includes means for automatically summarizing, analyzing and reconstructing logs of a call center, means for identifying thoughts of customers and causes of calls, and means for reducing call volume and improving efficiency of operator training by expanding FAQ documents.SELECTED DRAWING: Figure 1

The embodiment of the present application discloses a method, device, storage medium and computer equipment for adjusting a concurrent resource pool, the method comprising: obtaining the number of pending outbound calls of each outbound call scenario in the current multiple outbound call scenarios, and obtaining the scenario weight coefficient of each outbound call scenario; calculating the weighted average total outbound call duration of each outbound call scenario based on the number of pending outbound calls of each outbound call scenario and the scenario weight coefficient; determining the outbound call concurrency of each outbound call scenario within the next adjustment time interval according to the weighted average total outbound call duration of each outbound call scenario and the reference total outbound call duration of each outbound call scenario; allocating outbound call resources of the outbound call concurrent resource pool to each outbound call scenario according to the concurrency of each outbound call scenario. The outbound call concurrency is obtained by calculating the weighted average total outbound call duration corresponding to the outbound call scenario, thereby dynamically allocating outbound call resources to avoid the situation where the outbound call list cannot be dialed on the same day and does not cause waste of the list.

The invention discloses a multi-node simultaneous concurrent calling method for an intelligent customer service calling robot, and belongs to the technical field of customer service calling control, and the method comprises the following steps: S1, building a node cluster service according to each existing calling node, and monitoring the calling state and load rate of each node after the node cluster service is connected with each node; and S2, after the call task starts, the node cluster service obtains the current call volume and the corresponding load rate of each node, and distributes the call task to the node of which the current load rate meets a preset condition. According to the method, a calling strategy based on load balancing is adopted, the calling task of the original service is independent of one node cluster service, the calling tasks of multiple nodes are distributed, the method of multi-node simultaneous concurrent calling is achieved, the calling tasks are reasonably distributed to all the nodes according to the load rates of all the nodes before calling, and therefore the calling efficiency 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.

The invention provides a service system for a call center, and the system comprises an active-active center which at least comprises a first central platform, a second central platform and a network call management server, the network call management server is used for carrying out resource sharing, load balancing, disaster recovery application and implementation unified control between the first central platform and the second central platform; the service control and data synchronizer is used for instruction control and bidirectional data synchronization between the active-active center and the disaster recovery center; the first digital relay group, the second digital relay group and the third digital relay group are respectively connected with the public telephone network; the first digital relay group inputs a preset first proportion of access call volume into the first central platform; the second digital relay group inputs a preset second proportion of access call volume into a second central platform; and the third digital relay group controls the corresponding access call volume to be input into the disaster recovery center. The invention ensures that the hotline service is not interrupted and the service function is normal, and protects the data and service security.

The present invention relates to the field of intelligent outbound call technology, and discloses an intelligent outbound call system applying big data technology, comprising a task management unit, an outbound call unit, a processing unit, a recording unit, a central unit, a collection unit and a sorting unit, wherein the task management unit is used for selecting an outbound call type, the outbound call unit is used for providing customer outbound call services, the recording unit is used for recording outbound call records within a week and calculating an outbound call volume W, and the processing unit receives the outbound call volume W and sends an analysis instruction to the central unit after processing; the present invention first sets a selection module, which has six types, and the six types can cover most of the existing industries requiring outbound calls, thereby preliminarily meeting the use requirements of different industries, and when used in different industries, data support can be provided through a database to ensure that different industries have corresponding voices to use when making outbound calls, making it more intelligent and adaptable.

The embodiment of the invention provides a call volume adjustment method and device, call equipment and a storage medium, and the method comprises the steps: when a communication request for establishing a communication connection between first call equipment and target call equipment is received, adjusting the call volume of the target call equipment; outputting a first audio frequency of a target frequency in a target cable, connected with the target call device, of the switch; in the process of transmitting the first audio in the target cable, collecting the audio in the target cable to obtain a second audio; determining a volume gain level of the target call device based on the target amplitude of the second audio; and sending the volume gain level to the target call device to indicate the target call device to adjust the call volume between the target call device and the first call device according to the volume gain level. According to the invention, the problem of low audio quality in the prior art is solved, and the effect of audio gain compensation is further achieved.

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 present application provides a method for adjusting call volume, an electronic device and a storage medium, and relates to the field of terminal technology. The method includes: the electronic device receives a first operation of the user, and the first operation is used to trigger the electronic device to initiate a call; the electronic device detects noise data of the ambient sound in response to the first operation; the electronic device determines that the noise data is in the first noise interval, and outputs a sound signal through the first sound unit; the electronic device determines that the noise data switches from the first noise interval to the second noise interval, and outputs a sound signal through the first sound unit and the second sound unit; wherein the minimum value of the noise data in the second noise interval is greater than the maximum value of the noise data in the first noise interval.