38 results about "Blacklisting" patented technology

The invention relates to the technical field of live broadcast, and provides a live broadcast bullet screen real-time interaction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring bullet screens sent by audiences in a live broadcast room in real time, and matching the bullet screens to be processed with a white list and a black list respectively; if the to-be-processed bullet screen is matched with the compliance keyword in the white list, judging that the to-be-processed bullet screen is a compliance bullet screen, obtaining corresponding reply content based on verbal skills in the white list, and replying; if the to-be-processed bullet screen is matched with the violation keyword in the blacklist, judging that the to-be-processed bullet screen is a violation bullet screen, and not replying the to-be-processed bullet screen; and if the to-be-processed bullet screen is not matched with both the white list and the black list, generating corresponding reply content by using a preset large language model and replying. According to the invention, the bullet screens are processed through the white list, the black list and the large language model, so that flexible processing of diversified bullet screens is realized, targeted replies are made to the bullet screens of audiences, and the interactivity of live broadcast is improved.

The invention provides a risk determination method and device, equipment, a storage medium and a program product. Relates to the technical field of information security. The method comprises the following steps: acquiring a target face recognition video, and acquiring at least one background picture in the video; identifying the illumination state corresponding to each background picture, and judging whether the illumination state meets the natural light state at the current time; if not, background features corresponding to the background pictures are extracted, whether the background picture with the most background features exists or not is judged, and if not, the background features corresponding to the background pictures are determined as a background feature set; and judging whether background features in a blacklist exist in the set, if not, inputting the set into a reinforcement learning model, thereby calculating a risk coefficient corresponding to the set according to a current dynamic calculation strategy, and determining the risk degree of the video based on the coefficient. And the accuracy of risk determination results is improved.

The invention discloses a commercial bank blacklist management method based on a big data platform and Elasticsearch, and the method comprises the steps: building a blacklist data center based on the big data platform, integrating multi-source heterogeneous data, and carrying out the real-time data cleaning and standardization processing; establishing a distributed real-time retrieval engine by utilizing Elasticsearch, and constructing a reverse index for the processed data; establishing an associated risk map model, dynamically analyzing the association degree between the client and the blacklist main body through a map calculation engine, generating a risk conduction coefficient, and storing the risk conduction coefficient; during business handling, a multi-condition combination query request is initiated to Elasticsearch through an ESB (Enterprise Service Bus) real-time calling interface; a dynamic updating mechanism is adopted, and the blacklist state is automatically updated when a risk threshold value is triggered. According to the method, by integrating multi-source heterogeneous data, millisecond risk interception is realized, a dynamic association graph is constructed, the active defense capability of a bank on risks such as fraudulent transactions, credit default and money laundering behaviors is improved, and the method is suitable for risk management and control of core business scenes such as pre-loan auditing, transaction monitoring and anti-money laundering.

The present invention provides an integrated low-altitude control and counter-drone command vehicle system and control method, belonging to the field of vehicle-mounted drone prevention and control. The method includes: preparing a prevention and control plan, setting up protected areas, denied areas, and early warning areas; demarcating the flight airspace of the vehicle-mounted drone, planning the flight route, preparing a flight plan, receiving flight plan approval, controlling the vehicle-mounted drone to take off and execute the mission, and adding it to a whitelist; detecting and identifying drones entering the early warning area, generating target early warning information, and adding them to a blacklist; when a drone on the blacklist enters the denied area, determining whether the drone model on the blacklist is in a spectrum feature library, determining whether the drone on the blacklist and the drone on the whitelist are in the same frequency band and within the same interference beam range, and performing interference treatment based on the determination result; and after the mission is completed, replaying and statistically analyzing the data transmission and reception instructions and the control scene. The present invention is comprehensive in function and practical in method.

The invention discloses a risk detection method and system based on a network order, and the method comprises the steps: obtaining order data of a network transaction platform, carrying out the data preprocessing of the order data, obtaining an order data set, determining a blacklist data set corresponding to the order data set according to a preset data index, and carrying out the detection of the blacklist data set. Performing feature extraction on the blacklist data set to obtain blacklist features, inputting the blacklist features into a trained blacklist model to perform risk prediction analysis to obtain a risk prediction result of the order data, and constructing a plurality of judgment indexes of the order data according to a preset data index and the blacklist data set, sample data and business maintenance data are enriched, the multi-dimensional risk detection accuracy of order data is improved, the security level of the SKU can be corrected in time according to the risk detection result, the accuracy and efficiency of risk user detection are improved, and the security of shop operation is effectively ensured.

The invention provides a report writing content-based wrongly written character detection reminding method, which comprises the following steps of: obtaining a preliminary report statement needing to be detected, if characters and / or words in the preliminary report statement are characters and / or words in a blacklist medical wrongly written character database, judging that the characters and / or words are wrongly written characters and defining as first wrongly written characters, and at the moment, judging that the characters and / or words are wrongly written characters and defining as second wrongly written characters; extracting correct content corresponding to the first wrongly written character from the blacklist medical wrongly written character database, and replacing the first wrongly written character with the correct content; based on a white list NLP word library, performing white list word segmentation on the modified preliminary report statement, and if a single character which cannot be divided appears, determining that the single character is a wrongly written character, and defining the single character as a second wrongly written character; and extracting the word with the highest probability from the white list NLP word bank according to the pinyin as the modification content, and returning the modification content to the report. The invention further discloses a system for detecting and reminding wrongly written characters based on the written content of the report. According to the method, the wrong content of the report text is recognized, a doctor is reminded to correct wrongly written characters, and the problems of false report and missing report of the wrongly written characters in the text report are avoided.

The invention discloses a multivariate integrated identity authentication system and method. The system comprises an identity authentication module, a white list module, a black list module, a risk verification module and an early warning module. Wherein the identity authentication module is used for performing self-adaptive identity authentication according to risk level information of a target user; the white list module is used for storing the target user in a preset white list when the identity authentication of the target user is passed; the blacklist module is used for storing the target user in a preset blacklist when the identity authentication of the target user is not passed; the risk verification module is used for carrying out risk verification on the target user according to the behavior habit of the target user, a preset white list and a preset black list, and determining a risk level corresponding to the target user; and the early warning module is used for giving an early warning to the target user when the identity of the target user is not authenticated and / or the risk level is greater than a preset risk level. By adopting the system scheme, the user experience is improved.

The invention discloses an anti-crawler method based on energy management and related equipment, and belongs to the technical field of communication.The method comprises the steps that when a target request is received, whether the login environment of the target request passes verification or not is judged; if yes, obtaining identification information of the target request, and judging whether the identification information of the target request is in a blacklist; if yes, refusing to respond to the target request; if not, judging whether the identification information of the target request is in a blacklist cache list or not; wherein the blacklist and the blacklist cache list are lists created based on a machine learning algorithm and according to historical logs; identification information of illegal users is stored in the blacklist, and identification information of abnormal users is stored in the blacklist cache list; the historical log is a log corresponding to a historical request for accessing the gateway; and if yes, responding to the target request, and prompting abnormal information. Through the method, crawler behaviors on the network can be effectively limited, and the security of the network is ensured.

The application relates to a user portrait-based account security monitoring method and device and electronic equipment, the method comprising: acquiring user behavior data; determining a clustering gang according to the user behavior data and a preset clustering model; determining a clustering gang score according to a preset clustering gang scoring rule and the clustering gang; determining a risk score according to the user behavior data and a real-time prediction model; acquiring user login data, a preset blacklist and a third-party risk control score; and determining whether to intercept according to the clustering gang score, the risk score, the third-party risk control score, the user login data, the preset blacklist and a preset risk control rule. The application has the effect of improving the real-time performance of an account security monitoring system.

The embodiment of the invention provides a code testing method and device, electronic equipment and a readable medium, and relates to the technical field of computers.The method comprises the steps that in response to a code submission request of a target program, a to-be-submitted code indicated by the code submission request is tested based on a first test case currently recorded in a pre-created blacklist, and the first test case is used for testing the to-be-submitted code; and obtaining a blacklist test result. And testing the to-be-submitted code based on a second test case currently recorded in a pre-created white list to obtain a white list test result. And if the white list test result represents that the test is passed, submitting a to-be-submitted code to the target program, and under the condition that a first test case passing the test exists in the black list test result, respectively updating the black list and the white list according to the black list test result so as to obtain the updated black list and white list. In this way, by automatically testing the code indicated by the code submission request, the testing efficiency can be improved.

The invention belongs to the technical field of computers, and provides a route ticket order processing method and device, a storage medium and equipment, and the method comprises the steps: determining the user attribute of an ordering user based on the historical ticket refunding data of the ordering user in a second preset time period; when determining that the user attribute of the order placing user is a blacklist user, intercepting a current ticket business order; and under the condition that the user attribute of the order placing user is determined to be the white list user, processing the current ticket business order according to the target execution strategy to obtain a corresponding processing result. Through the processing method, the current ticket business order can be intercepted in time under the condition that the order placing user is determined to be the blacklist user; and under the condition that the order placing user is determined to be the white list user, the current ticket business order is flexibly processed according to different target execution strategies.

The invention discloses a multi-dimensional feature analysis-based paper submission automatic selection model construction method, and relates to the technical field of paper submission automatic selection model construction. The method comprises the following steps: acquiring data such as comprehensive ranking, category ranking and primary and secondary subject ranking of colleges and universities from various authoritative channels to construct portraits, then performing vector representation on colleges and universities to be examined and submitted, converting related features of the colleges into a vector form through a specific mapping function, and then calculating score weighting factors of the colleges to be examined and submitted; according to the method, the number of times of reviewing, the total reviewing time and the number of blacklist experts are comprehensively considered, a weight coefficient is determined through a scientific method, and then the similarity between reviewing and reviewing college vectors is calculated through cosine similarity. According to the method, the paper submission selection efficiency and precision can be improved, proper colleges and universities are screened by integrating multiple factors, data changes can be dynamically adapted, academic fairness is guaranteed, and self-optimization can be achieved.

The invention relates to a method (100) for acquiring data from a vehicle (50), comprising the following steps:-ascertaining (101) a data set (40) of at least one respective sensor (11) of a plurality of vehicles (50),-ascertaining (102) a respective feature vector (30) of the data set (40),-ascertaining (103) a respective compressed representation (32) of the feature vector (30), and-comparing (104) the compressed representation with a blacklist and / or a whitelist.

A neural network apparatus for generating a new chemical structure may receive a structure input of a chemical structure; generate, based on the structure input, a negative attention vector that indicates a respective probability of presence of each of a plurality of blacklists in the structure input; generate a structure expression by encoding the structure input; generate a final reverse blacklist vector that does not include the plurality of blacklists, based on the negative attention vector and the structure expression; and generate the new chemical structure by decoding the final reverse blacklist vector.

The invention discloses a multi-dimensional customer identification method and system based on online voice. The method comprises the following steps: pre-establishing a blacklist word library database; reading call content and collecting audio data during a call; directly converting the audio data into text data, and segmenting the text data into words and word units; converting the audio data into a character format based on the audio features and segmenting the audio data into words and word units; comparing the words with the word units based on a blacklist word library database, and obtaining blacklist incoming call numbers based on a comparison result; on-line voices of the identified non-blacklist incoming call number are screened, emotion analysis is carried out, and positive, negative and neutral emotions are identified; according to the method and the device, the number of the crank call and the number of the junk information are added into the blacklist, so that unnecessary interference and disturbance can be effectively reduced, and the working efficiency is improved.

The present invention discloses a method and device for screening information to be recommended. The invention includes: the server where the recommendation engine is located receives the recommendation request information transmitted by the client, recalls one or more items according to the recommendation request information, extracts the first ID of the target object in the recommendation request information, obtains the item blacklist corresponding to the first ID, and the item blacklist is pre-configured on the server by the operator, performs difference set processing on the item blacklist and the first item to obtain one or more second items, sorts the second items using a sorting model, and transmits the one or more second items to the client according to the sorting result. With the above scheme, the staff configures the item blacklist for different IDs on the server, and subsequently screens the information recommended to the client according to the item blacklist. The item blacklist can be flexibly configured, which solves the problem of low flexibility and low efficiency in screening information to be recommended in the related technology.

The application provides a black list processing method, device, equipment, storage medium and program product, relates to the technical field of electronic commerce, and the method comprises the steps that a query task request is received, a to-be-checked merchant list and a preset hit threshold are obtained based on the query task request; a thread is configured for each to-be-checked merchant in the to-be-checked merchant list; based on the query task request, the to-be-checked merchant list is processed by using at least one thread to obtain a query result, and the query result is added to a queue; the query result is the result of whether the to-be-checked merchant hits the black list; based on the adding order of the query result in the queue, the number of merchants hitting the black list in the query result is counted in sequence, and when it is determined that the number of merchants is greater than the preset hit threshold, a return result is generated; the return result is used for indicating that all the to-be-checked merchants in the to-be-checked merchant list are black list merchants; in this way, the queuing mode of processing the query result in the first return first processing mode is adopted, the response time is reduced, and the query rate is accelerated.

The present invention discloses a method for constructing an automatic selection model for paper submission based on multi-dimensional feature analysis, and relates to the technical field of automatic selection model construction for paper submission. First, data collection and university subject portrait construction are carried out, and data such as comprehensive rankings, category rankings, and first- and second-level subject rankings of universities are obtained from multiple authoritative channels to construct portraits. Then, the universities to be reviewed and submitted for review are represented by vectors, and the relevant features of the universities are converted into vector form through a specific mapping function. Then, the scoring weighting factors of the universities to be reviewed are calculated, and the number of reviews, total review time, and the number of blacklist experts are comprehensively considered. The weight coefficients are determined by scientific methods, and then the cosine similarity is used to calculate the similarity between the vectors of the submitted and received universities. The present invention can improve the efficiency and accuracy of paper submission and selection, comprehensively select suitable universities based on multiple factors, dynamically adapt to data changes, ensure academic fairness, and can be self-optimized.

The invention discloses a monitoring method and device for a breeding place, equipment and a storage medium. Comprising the following steps: acquiring a to-be-monitored sound signal of a breeding place, and extracting a target voiceprint feature in the to-be-monitored sound signal; based on a pre-established blacklist sample library, performing comparison processing on the target voiceprint features and voiceprint features in the blacklist sample library to obtain a comparison result; and according to a comparison result, determining an alarm result monitored by the farm. According to the invention, after the target voiceprint feature in the to-be-monitored sound signal is compared with the voiceprint feature in the blacklist sample library, whether the farm needs to give an alarm is judged, so that the situation that the monitoring equipment of the existing farm is shielded by other objects, so that a visual blind area is caused, and the monitoring efficiency is improved is avoided. Subsequent observation and rapid treatment of harmful organisms which are hidden in positions and influence livestock growth are achieved, the working efficiency is greatly improved, and resources are saved.

The application provides a black list confusion method and device based on a secret sharing network and a judgment system. The method comprises the following steps: a confusion node receives secret shares sent by N bank nodes; wherein, the black list data of each bank node is regarded as a secret, the secret is split into N secret shares, and the N secret shares are sent to N confusion nodes respectively, N is a natural number greater than or equal to 3; the confusion node sums and encrypts the received secret shares to obtain confusion data; and the confusion node sends the confusion data to a judgment node; wherein, the confusion data is used by the judgment node to judge whether the secret is a black list. Based on the secret sharing network, the secret splitting algorithm and the secret confusion, the application can prevent brute force cracking, so that the black list customer data of the shared banks can be shared under the premise of ensuring data security and confidentiality, and the interference of the random value of the local differential privacy algorithm is avoided, and the judgment result is more accurate.

The invention discloses a blacklist detection method and device based on face verification and a medium, and relates to the technical field of computer vision and intelligent monitoring. The method comprises the following steps: acquiring video stream data from a plurality of image acquisition devices in a multi-thread parallel manner to obtain a video frame set containing task identification and image data, and performing face detection on each frame of image in the video frame set to position a face area in each frame of image in the video frame set; extracting a feature vector of the face region to obtain an original face feature vector, and performing normalization processing on the original feature vector to obtain a standard face feature vector; and based on a preset task number and the current timestamp, constructing a retrieval condition, and based on the retrieval condition, performing similarity retrieval on the standard face feature vector in a preset vector database to obtain a blacklist result. Through multi-thread parallel acquisition, face detection, feature extraction and vector retrieval, accurate recognition of key targets is realized.

This invention discloses a cross-language code vulnerability remediation method based on dual constraints of knowledge graph and large model, comprising: receiving multi-language heterogeneous vulnerability code samples, performing feature standardization processing, and extracting the language context and core vulnerability intent tags of the code; inputting the vulnerability intent tags into a pre-built cross-language security knowledge graph, performing cross-level pathfinding matching based on risk patterns, and dynamically extracting the necessary security interfaces for vulnerability remediation and prohibited dangerous functions; using a context-aware mechanism, fusing and concatenating the original vulnerability code with whitelist rules to construct prompt words as soft constraint intent guidance at the input end of the large model; simultaneously, mapping blacklist rules to a set of underlying lexical indexes, introducing a log-probability interceptor in the decoding and inference stage of the large model, forcibly modifying the generation log-probability of blacklisted lexical elements to negative infinity, and implementing hard constraint interception at the underlying level; finally, combining the soft guidance at the input end and the hard interception at the decoding end, bidirectionally controlling the autoregressive generation process of the pre-trained language model, thereby eliminating model illusion and outputting the optimal security remediation patch that conforms to the target language specification.

The invention relates to the technical field of sales data statistics, in particular to an intelligent conversion and statistical method and system for business sales data, and the method comprises the steps: collecting original sales data in real time, carrying out the data preprocessing of the original sales data, generating processed sales data, and carrying out the statistical processing of the processed sales data. Associating the processed sales data to a corresponding salesman entity; based on the processed sales data, dynamically generating a sales combat containing key performance indicator visualization; according to a preset dynamic evaluation model, in combination with the associated processed sales data, performing comprehensive performance scoring and ranking on the salesman entities, and determining a red list and a black business list based on a ranking result; and comparing and analyzing the behavior data characteristics of the employee entities in the red list and the black list, and generating different sales strategy suggestions. According to the application, closed-loop management from data acquisition to strategy generation is realized, and the automation and intelligence level and decision-making efficiency of sales management are improved.

The application provides a customs declaration analysis method based on hierarchical density clustering and a reference system, historical blacklist data sets are analyzed by using an intelligent unsupervised clustering algorithm to obtain a coordinate sequence of each clustering center point after clustering; a reference system data set is obtained, the minimum Euclidean distance of each sample in the reference system data set to each clustering center point coordinate is calculated, and a reference model is constructed according to a sorting algorithm; a customs declaration sample is received in real time, the minimum Euclidean distance to each clustering center point coordinate is calculated, and a minimum Euclidean distance sequence of the customs declaration sample is obtained; the minimum Euclidean distance sequence is input into the reference model to obtain a sorting score; according to the sorting score and the contribution degree corresponding to the customs declaration sample and a business hit threshold, the proportion of customs declarations that need to be analyzed in the customs declaration sample is determined; the method provided by the application can accurately analyze the number of customs declarations that need to be analyzed and inspected in batches, is fast and effective, reduces the control rate, and improves the acquisition rate.

The application provides a security defense method and device, electronic equipment and storage medium. The method is applied to AC, and comprises the following steps: after a current first statistical period arrives, a first total number of HTTP / HTTPS messages received from a same STA in the first statistical period is counted; if all the counted first total numbers contain a first total number not less than a first set number, and the device works in a normal mode, the device is set to work in a blacklist mode, a first STA corresponding to the first total number not less than the first set number is added to a blacklist list, and first blacklist information is sent to a first AP accessed by the first STA; when a second deletion notification message carrying second blacklist information of a second STA is received from a second AP, the second STA is deleted from the current blacklist list; and when the blacklist list is empty after the deletion, the device is set to work in the normal mode. The application can improve user experience.

This invention discloses an optimized access control system for all types of electronic products based on a dual-layer independent authentication element, belonging to the field of device security authentication technology. The system adopts a dual-layer independent architecture: a hardware-fixed first authentication element and a user-created second authentication element. The first authentication element is the factory-fixed baseline information of the device, while the second authentication element is generated after the user's first verification of a custom sub-element. The system generates an authorization credential uniquely bound to the current device and application, which cannot be reused across devices or applications. For consecutive authentication failures, the system implements tiered delay, temporary blacklisting, and time-limited blacklisting strategies, only rejecting authentication requests without modifying user data. Users can choose whether to clear historical authorization credentials when modifying custom sub-elements. Only those with valid first authentication element verification can access the second authentication data. The system supports authorization record management and automatic cleanup upon uninstallation. This invention features a simple architecture, high security, effective prevention of brute-force attacks and theft, strong versatility, and applicability to all types of electronic products.

The invention provides an SDoS defense method, and relates to the technical field of block chains, the SDoS defense method comprises the following steps: according to behavior characteristics of an SDoS attack, honesty nodes adopt a defense strategy to judge blocks conforming to the behavior characteristics of the attack and descendant blocks thereof through a blacklist mechanism and add the blocks and the descendant blocks into a blacklist, the honesty nodes use the blacklist mechanism to screen the blocks, and then the blocks and the descendant blocks of the blocks are added into the blacklist; the black list block works behind the non-black list block; and under the condition that a plurality of longest chains without blacklist blocks exist, the honest node adopting the defense strategy selects to work after receiving the chain of the complete block at the earliest. According to the method, based on an incentive mechanism, the attacker is enabled to actively stop attacking by reducing income of the attacker, more nodes are enabled to actively work, and the SDoS attack can be effectively relieved and defended.

The application discloses a WeChat for enterprise customer loss risk early warning method and system, comprising the following steps: S1, acquiring conversation messages, customer behavior events and staff follow-up states in real time through a WeChat for enterprise interface; S2, cleaning and normalizing the collected data, calculating the feature scores of five dimensions of active degree decline rate, response delay growth, customer loss signal, follow-up quality score and risk history accumulation; S3, weighting and summing up the feature scores of each dimension according to preset weights to obtain a comprehensive risk score, and determining a risk level according to the preset interval in which the comprehensive risk score is located; and S4, executing corresponding early warning information pushing and customer management actions according to the risk level. The application belongs to the technical field of conversation management systems, and the structure can realize real-time and accurate capture of customer active deletion and blacklisting behaviors, effectively reducing the false negative rate and the false positive rate.

The invention relates to a customer financial risk grading method and device, equipment, a medium and a product. The method comprises the steps of performing rule scoring on target customer data based on a preset rule engine and at least one risk index to obtain a first risk score value for evaluating dominant risks; performing model scoring on the feature vector of the target customer data based on a trained machine learning model to obtain a second risk score value for evaluating the hidden risk; the machine learning model is obtained by training historical customer data as a sample, and the historical customer data comprises judicial check and control data, blacklist data, suspicious list data and untrusted licensed pedestrian data; performing weighted fusion on the first risk score value and the second risk score value to obtain a final risk score; and mapping the final risk score into a corresponding customer risk level. According to the method, the defects that a pure rule system is high in subjectivity and poor in generalization ability are effectively overcome, and an objective and comprehensive financial risk grading scheme with prediction ability is provided.