23results about How to "Avoid accidental deletion" patented technology

The invention relates to the technical field of big data storage, in particular to a storage control method and device based on big data. The method comprises the following steps: acquiring an original data stream and application demand information; performing data dynamic correlation analysis on the original data flow according to the application demand information to obtain a dynamic update data correlation graph; performing feature vector generation on the dynamic update data association graph to obtain a data feature vector; performing intelligent quality evaluation on the data feature vector to obtain a data quality label; performing timeliness evaluation according to the data feature vector, and performing access popularity evaluation to obtain a timeliness score and an access popularity score; and acquiring system resource load condition information. The storage resource utilization rate, the data access performance and the overall efficiency of the system are improved by solving the limitation of a traditional method in the aspects of static data management and resource allocation.

The invention provides a non-intrusive java type application log desensitization method, and relates to the technical field of data desensitization, a java framework log module method is rewritten and desensitized through a desensitization program, and increased data information is obtained; obtaining log record information, performing preliminary judgment on the log record information, and obtaining judgment passing data according to the preliminary judgment information; analyzing the judgment passing information, performing cyclic judgment on the k value to obtain cyclic judgment information, and performing desensitization processing according to a cyclic judgment result to obtain a final processing result; calculating a desensitization efficiency coefficient, judging the desensitization efficiency, further calculating a desensitization precision coefficient, judging the desensitization precision, and updating the desensitization process data in real time according to a judgment result; and after all cycle judgments are desensitized, obtaining a desensitization completion result, and outputting desensitized or non-desensitized log record information.

This invention discloses a clutter discrimination and suppression method based on multiple echo features, comprising: acquiring radar front-end clutter and target echo detection video data and dividing it into several connected regions; extracting multi-dimensional features and labeling prior information for each connected region; using the Relief feature selection algorithm to calculate and filter the weights of the multi-dimensional features, removing redundant features to obtain the optimal feature vector; using this feature vector to train a support vector machine classifier, and obtaining the optimal parameter model through cross-validation; acquiring measured clutter data, extracting corresponding features and inputting them into the model; and determining whether to remove clutter and retain targets based on the output. This invention effectively filters out dynamic clutter spots, avoids false deletion of weak targets, significantly reduces the false alarm rate of the system, and alleviates the computational burden of subsequent track processing.

The invention discloses a cross-border logistics information management system capable of integrating data, which relates to the technical field of cross-border logistics and comprises a central processing module, a data integration unit, a loss query unit, a data storage library and a data cleaning unit. According to the cross-border logistics information management system capable of integrating the data, the data is analyzed through the logistics distribution module, so that the logistics information is calculated through the pre-estimation distribution module, the detection completion module and the time recording module, and the obtained logistics information can be detected through the connected data detection module; according to the invention, the system enables the predicted delivery time and completion time of logistics to be obtained, integrates the obtained data through the data integration module, and displays the obtained data through the connected terminal display unit, thereby enabling a user to check the integrated data, enabling the user to know the time of logistics distribution more clearly, and improving the user experience. Therefore, when the cross-border logistics information management system is used, the problem that data integration is not easy can be solved.

The invention discloses a file type determination method and device, equipment, a medium and a program product, and belongs to the technical field of big data. The method comprises the following steps: acquiring file identification information of each file in a target project directory; matching each piece of file identification information with configuration file identification information in a preset configuration file information base and module file identification information in a preset module file information base to obtain a matching result; in a database, obtaining generation record information corresponding to the target file identification information; in the first page, displaying the target file identification information and the corresponding generation record information; and in response to a first input for the first control in the first page, determining that a file corresponding to the first control in the target item directory is a useless file. According to the embodiment of the invention, the useless file can be quickly and accurately detected, and the cleanliness and maintainability of the project are improved.

The invention relates to the technical field of laser radars, and particularly provides a 2D laser radar point cloud data processing method. The method comprises the following steps: setting a distance threshold value and an echo intensity threshold value of data processing; obtaining a circle of point cloud data, and obtaining a radar actual measurement distance and echo intensity; retaining the point cloud data with the distance smaller than the preset distance, and setting the data points larger than the distance as invalid values to obtain a circle of new data set; judging the boundary of the target object according to the invalid value to obtain an index value of the boundary; according to echo intensity characteristics, setting sliding windows and sliding times, comparing echo intensity mean values in adjacent windows, if a difference value is greater than a set threshold value or reaches the set sliding times, retaining the point cloud and quitting data processing, otherwise, deleting the edge broadening points. According to the method, the broadening problem of the edge of the target object is effectively eliminated, and the real contour of the scanned target object is restored.

This invention belongs to the field of Internet technology and relates to a distributed task monitoring method and system based on state transition and targeted push. It includes receiving subscription requests initiated by clients, adding clients to communication groups associated with the task status corresponding to a tab, and maintaining the group affiliation information of each client in real time; based on the group affiliation information, when a task status transitions from its original state to a new state, locating the original state group and the new state group, pushing a removal notification to the original state group and a move-in notification to the new state group; when a task in the execution state experiences a progress update, locating the execution state group and pushing a progress update message only to that group; and using the removal notification, move-in notification, and progress update message as outputs to trigger clients to perform removal, addition, and progress refresh operations on the corresponding tasks in their local task lists, respectively. This solves problems such as list synchronization issues and numerous invalid pushes, achieving real-time list consistency and accurate push notifications.

The invention provides a data cleaning method and system based on AI, and belongs to the technical field of data cleaning. The method comprises the following steps: constructing a data importance judgment standard and a data cleaning model; collecting data in real time, classifying the data in real time according to the data importance judgment standard, and pasting a mark convenient for subsequent identification and analysis on the data; and obtaining the heat change of the data, and cooling and degrading or warming the data step by step into hot data, cold data, ice data and sealed data according to the data heat classification standard and the mark. The problems that in the prior art, in the data cleaning process, judgment on data importance is rigid and inaccurate, and mistakenly deleted data cannot be reproduced are solved.

This invention relates to the field of file cleanup technology, and in particular to a data lake file timely cleanup system and method based on storage-compute separation. The timely cleanup method includes: scanning each file in the data lake, determining the files to be cleaned based on the scan results to obtain a number of isolated files; accumulating the isolated files to obtain the actual number of markings, and performing batch cleanup on the data lake according to the actual number of markings or the initial cleanup cycle; for any cleanup batch, generating a cleanup set from the isolated files corresponding to the batch cleanup, and arranging the files to be cleaned in the cleanup set in a cleanup order; during cleanup based on the cleanup order arrangement, monitoring the cleanup process in real time, sequentially determining the consistency of the cleanup target data, cleanup process data, and cleanup operations, and determining the corresponding process control mode based on the determination results; periodically acquiring cleanup results, and adjusting the cleanup conditions based on the cleanup results.

The invention relates to a mail system single copy storage system and method based on object storage. The system comprises a mail index service module, an object storage service module, a single copy storage management service module and a mail client. When a mail is sent, the hash value of a mail body file is calculated, only a unique copy is stored in the object storage, and the reference count of the unique copy is managed through the metadata table; when the mail is read, the file in the object storage is directly accessed through the mail index; when the mail is deleted, the reference count is decreased progressively, and the file is physically deleted only when the count is zero. According to the method and the system, single copy storage of the mail attachments is realized, the storage space occupation in a mass mail sending scene is remarkably reduced, and meanwhile, the consistency and the security of data are ensured through a reference counting mechanism.

This invention belongs to the field of communication technology and discloses a low-power data communication method and system for electricity meters based on a CAT1 module. The electricity meter collects metering data and event data, encapsulates them into data frames with serial numbers, and writes them into a transmission queue. When a queue threshold, alarm threshold, or time threshold is met, a communication session is established, waking up the CAT1 module to send batch uplink data containing a session identifier and batch sequence start and end numbers. The platform returns confirmation information, including a set of confirmed sequence intervals, missing data frame identifiers, and downlink pending transmission identifiers. The terminal retransmits missing frames within the session based on this information, opens a listening window only when a downlink command exists, deletes confirmed frames after the session ends, enters low-power mode, and persists the queue state to support power-off resume transmission.

The invention relates to a mirror image layer cleaning method and device, computer equipment and a readable storage medium. The method comprises the steps that multiple to-be-cleaned mirror image layers on a target user side are obtained, and the to-be-cleaned mirror image layers are unused mirror image layers in all mirror image layers stored on the target user side; obtaining target associated information and target weight information of each to-be-cleaned mirror image layer from a pre-constructed target user-level mirror image layer information base; and cleaning the to-be-cleaned mirror image layer according to the target associated information and the target weight information. The cleaning effect of the method is good.

This application provides an operation processing method, apparatus, device, and readable storage medium. In the technical solution provided by this application, a dual-level processing mechanism of message level and point level is constructed. At the message level, permission conflict detection based on user identity is introduced to effectively prevent non-creator users from accidentally deleting or tampering with other people's content, thus achieving fine-grained operation isolation. At the point level, conflict detection is pushed down to the smallest data unit coordinate point. By comparing the point position sets of the current operation and the historical operation, the spatial overlapping area is accurately identified, and the operation type corresponding to the current operation message is combined to dynamically determine whether execution is allowed, ensuring that the operation result is certain and predictable. This achieves hierarchical deterministic operation conflict processing, improving the smoothness and user experience of the online collaborative whiteboard system.

The invention discloses a file deletion method, a server, computer equipment, a medium and a program product, and the method comprises the steps: executing a file deletion process according to a preset time interval, and sequentially reading file information of to-be-deleted files in a file database constructed by a file synchronization process, the file information comprises a file name, a storage path and file modification time, and the file modification time is the last modification time before synchronization; querying the latest modification time of the to-be-deleted file according to the file name and the storage path of the to-be-deleted file; comparing the file modification time of the to-be-deleted file with the latest modification time; and under the condition that the file modification time is consistent with the latest modification time, deleting the to-be-deleted file. The file deletion process can be executed for multiple times according to the preset time interval, the file updating condition is judged at the same time, unsynchronized files after updating are prevented from being deleted by mistake, the file deletion success rate can be improved, and the mistaken deletion risk is reduced.

The present invention discloses a large-scale fine-grained classification algorithm for SNI information of mobile applications, and belongs to the field of network security technology. The problem that the traditional domain name classification method in the prior art is difficult to eliminate invalid SNI information and extract relevant characteristic SNI in large-scale SNI results is solved; the present invention deletes the second-level domain name determined as invalid information in the SNI extraction result based on statistical characteristics, and de-duplicates the characteristic character string based on the preset threshold condition according to the number of times the invalid second-level domain name appears in the APP obtained by the initial screening, obtains the first de-duplicate result and uses K-Means clustering to de-duplicate it twice, and obtains the third de-duplicate result; traverses the repeated SNI data in the third de-duplicate result, de-duplicates the SNI of the second-level domain name of the dissimilar APP, cleans the obtained result data, and obtains the final characteristic SNI result. The present invention effectively extracts the characteristic SNI of the APP, and can be applied to the identification of encrypted traffic features.

The application relates to the technical field of concrete ultrasonic flaw detection image processing, and discloses a concrete flaw detection ultrasonic image speckle noise suppression method and system based on multi-modal signal processing, which is characterized in that the original ultrasonic signal is subjected to adaptive white noise injection and EMD decomposition, non-aliasing IMF components are obtained through modal alignment and averaging, and high-frequency noise dominant components and medium-low-frequency effective signal components are divided according to the kurtosis criterion; subsequently, the high-frequency components are subjected to VMD optimization, the frequency band is accurately divided by constructing a variational constraint model and iteratively solving; the improved adaptive wavelet threshold function and the hierarchical hybrid threshold strategy are adopted for the medium-low-frequency components, so that the noise is accurately filtered out and the effective signal is retained; finally, the de-noised components of various frequency bands are fused to generate a one-dimensional time domain signal, the residual noise points are removed through median filtering after being restored into a two-dimensional image, and the de-noised enhanced image is output. The application realizes accurate separation of the noise and the effective signal, and completely retains the fine defect features in the concrete.

The invention provides a point cloud data processing and refined calculation method and device and a readable storage medium thereof, and provides an innovative solution in view of poor point cloud generation precision, insufficient refinement and multiple noisy points in the prior art. According to the method, through a unique noisy point removing step, noisy points are accurately removed by means of expansion grid and self-defined feature growth discrimination; through a point cloud segmentation identification step, accurate segmentation is realized through calculation according to a local normal vector and the like; the point cloud is optimized through a parallelization algorithm by utilizing the steps of linear fitting, segmentation merging and resampling; and recombining the point cloud according to the scene through a point cloud recombining step. The algorithm process has remarkable advantages in the fields of robots, surveying and mapping and the like, the point cloud quality can be greatly improved, the actual application effect is enhanced, an efficient, universal and feasible strategy is provided for data processing of related industries, and development and practice deepening of the point cloud technology are powerfully promoted.

This invention relates to the field of speech data processing technology, and discloses a data processing method and system based on a language function detection system. The method includes: acquiring raw detection audio streams and task event logs through the language function detection system; constructing a task-level unified time index sequence; constructing a dynamic silence threshold; performing task-level coarse segmentation on the raw detection audio stream; constructing effective response scores; identifying effective response segments and secondary correction segments; reconstructing task-level structured results; and outputting aphasia language ability assessment data. Compared to existing technologies that primarily process entire recordings uniformly, especially in aphasia rehabilitation assessment scenarios with long pauses, repetitive pronunciations, and intrusive prompts, this invention addresses the technical problem of accurately extracting effective response segments. By using task event-driven temporal alignment and dynamic silence threshold segmentation, this method improves the reliability of aphasia language function assessment data.

The invention provides a data management method, a server and a storage medium, and belongs to the technical field of data processing.The method comprises the steps that in response to an operation request for any target attachment, a unique identifier of the target attachment is determined; according to the unique identifier, querying attachment information corresponding to the target attachment from an attachment table; updating a reference count in the attachment information, wherein the reference count is used for representing the number of times that the target attachment is referenced by the business entity; according to the operation request, a reference relation table of the target attachment is updated, and the reference relation table is used for recording the incidence relation between the service entity and the attachment. In this way, for one attachment, the server only stores the data stream in the attachment once, redundant storage caused by repeated copying of the same attachment is avoided, the number of times that the attachment information is referenced by the service entity is recorded through the number of times of reference, so that when deletion operation is executed, the number of times of reference of the attachment is used as a reference, deletion of the currently referenced attachment is avoided, and user experience is improved. Accessories are prevented from being deleted by mistake, and a data management mode is optimized.

The invention discloses a cue word generation method and system based on large language model semantic reflection and multi-objective evolution. The method comprises the following steps: initializing a cue word population; calculating a performance index of each cue word, and performing semantic segmentation by using a large language model to calculate an abstraction degree index; performing comprehensive evaluation based on the performance and the abstraction degree, and screening elite individuals by adopting a hierarchical greedy strategy based on a Pareto level; performing semantic reflection variation or parent fusion crossover on elite individuals by using a large language model to generate a next generation population; and the above process is iterated until a termination condition is met. According to the method, the abstraction degree index and the semantic reflection mechanism are introduced, so that the problems of easy overfitting and low variation efficiency in the traditional cue word optimization are solved, and the cue word with high performance and strong generalization ability can be automatically generated.

The application relates to the field of new energy power generation technology and discloses a wind turbine abnormal data identification method, device, medium and product, which comprises the following steps: obtaining historical standard operation data and actual operation data of a wind turbine; calculating and determining a strong abnormal threshold and a general abnormal threshold according to the mean value and the standard deviation of the first logarithmic density estimation value of the historical standard operation data; calculating the second logarithmic density estimation value of the actual operation data; identifying abnormal data according to the comparison results of the second logarithmic density estimation value and the strong abnormal threshold and / or the general abnormal threshold and the comparison result of the variable observation value and the theoretical value in the actual operation data; the strong abnormal threshold and the general abnormal threshold are calculated from normal data; the bandwidth of each operation data is adaptively determined according to the geometric mean density; abnormal discrimination based on local density is realized; meanwhile, the double constraint judgment conditions of the observation value and the theoretical value are introduced; and the accuracy of abnormal data identification is improved.

The application discloses a shield large data scalable warehousing and processing method, comprising the following steps: based on the multivariate heterogeneous data stream collected on the shield construction site, effective tunneling data in the tunneling stage is extracted; the statistical baseline corresponding to the ring number where the effective tunneling data is located is acquired, and the effective tunneling data is dynamically tolerance detected and the abnormal value is eliminated according to the statistical baseline, so that the cleaned time series data is obtained; the expected data amount of the current ring is calculated based on the ring boundary moment determined by the multivariate heterogeneous data stream, and the data coverage of each physical parameter is calculated in combination with the actual effective data amount of the cleaned time series data; the completeness score of the current ring containing a short board penalty term is calculated; when the preconfigured hierarchical trigger threshold is satisfied, the cleaned time series data is input into a preset construction analysis algorithm model, and a construction state analysis result is obtained. The application breaks the dependence on discrete hardware signals, suppresses the high-frequency jitter of the working condition state, and effectively prevents the false deletion of the physical mutation data of the stratum transition section.

The application discloses a coal mine roadway surrounding rock deformation monitoring method and system, and relates to the technical field of geometric measurement. Original data is acquired, and a sudden component in displacement data is removed according to principal stress increment data, and pure rheological creep deformation data is obtained by calculation with the principal stress increment as an integral path; the pure rheological creep deformation data is operated with macroscopic total subsidence displacement to obtain stratum background rigid body settlement data, and the stratum background rigid body settlement data is operated with axial deformation to obtain local net compression deformation data and cooperative compression ratio data; crack normal seam width and inclination data determined by three-dimensional space strain tensor principal strain plane orientation are obtained by calculation of reflected Bragg wavelength shift data; deformation propagation speed vector data is obtained by constructing a time-space sequence from the pure rheological creep deformation data and performing difference gradient operation; and finally, a dimensionless comprehensive deformation characteristic index is obtained. The application realizes synchronous measurement of multi-dimensional geometric deformation of surrounding rock and reconstruction of deformation field spatial distribution.