10 results about "Dynamic data structures" patented technology

This invention discloses a method, system, device, and storage medium for rendering form items based on dynamic data. The method includes: determining the dynamic data structure of the form items; rendering and concatenating the required form item types with the dynamic data structure according to business needs through a front-end page to obtain a data rendering template; and rendering the actual form data using the data rendering template through the front-end page to obtain the corresponding dynamic form items. In this invention, all form item rendering type judgment logic is processed on the front-end, which improves the data purity of the development interface and effectively meets the form item rendering requirements of dynamic data.

The application discloses a diabetes foot closed loop model construction method and system based on multi-source data, and belongs to the technical field of medical information technology and intelligent health management technology. Through a sensor terminal deployed on the patient side, a clinical information integration interface and a mobile detection device, multi-source heterogeneous data is synchronously collected. On this basis, a dynamic data structure body containing a main node and multiple sub-nodes is constructed, multi-source data is mapped to the corresponding sub-nodes according to a timestamp alignment rule, and a time sequence correlation graph is established in each sub-node. The system listens to the data stream in real time, adopts an incremental learning logic with a time decay factor to update the graph, and adjusts the weight parameters of other sub-nodes through an intervention response matrix according to a cross-node influence rule. The application provides support for risk assessment and pathological evolution analysis of diabetes foot by constructing a dynamically evolving data structure and a cross-dimension correlation mechanism.

The invention relates to the technical field of greenbelt management and maintenance, in particular to a greenbelt intelligent management and maintenance system based on cloud computing, which comprises a cloud multi-source integration module, a time sequence disturbance analysis module, a disturbance chain association module, a disease trend identification module and a management and maintenance regulation and control linkage module. And judging the online state of the sensing equipment and the plot numbering accuracy. According to the method, a cloud-oriented data integration system is constructed, multi-source environment parameters and spatial attributes are collected into a dynamic data structure, global association and time sequence connection of information among land parcels are achieved, data are automatically sorted through a cloud platform to form a parameter evolution link, multi-level feature criteria such as coverage, time sequence and trend are established for monitoring objects, and the monitoring efficiency is improved. Abnormal evolution performance of a disease state is directly fed back to a management and maintenance regulation and control process through linkage judgment, and system output regulation and control configuration is generated according to a real-time state, so that the consistency of risk research and judgment and the pertinence of a regulation and control decision are improved.

The invention provides a data processing method and device, a computer program product and a computer readable storage medium. According to the method, a corresponding domain-specific language is defined for a universal dynamic data structure of a page, so that transmission of page data from a rear end to a front end is realized by using a nested structure of a page module described by the domain-specific language and domain-specific language data of parameters of each page module; and the page rendering is completed by analyzing the domain specific language data. Furthermore, under the condition that the user modifies the data of the page, the method utilizes the data, in the target data format, of the modified page returned from the front end to update the domain-specific language data before modification, so that the consistency of the data, especially the consistency of the sequence and the structure, is ensured when the page data is processed. Finally, the updated domain-specific language data of the page is stored in a database for subsequent reading.

The invention discloses a data processing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the steps of obtaining to-be-processed data, wherein the to-be-processed data comprises at least one data field; performing serialization processing on each data field to generate a field byte sequence of each data field so as to obtain a data byte sequence of the to-be-processed data; generating field metadata of each data field according to the field byte sequence of each data field to obtain a metadata mapping file of the to-be-processed data; serializing the metadata mapping file of the to-be-processed data to obtain a metadata byte sequence; and placing the metadata byte sequence in front of the data byte sequence to obtain a serialization result of the to-be-processed data. According to the embodiment, data serialization and analysis based on dynamic metadata driving are achieved, the dynamic data structure can be processed, limitation to the data structure is reduced, the processing mode is more flexible, implementation is easy, and the processing efficiency is high.

This invention relates to the field of green space management technology, specifically a cloud-based intelligent green space management system. The system includes a cloud-based multi-source integration module, a temporal disturbance analysis module, a disturbance chain association module, a disease trend identification module, and a management and control linkage module. Based on various green space on-site monitoring points, it determines the online status of sensing devices and the accuracy of plot numbers. This invention constructs a cloud-oriented data integration system, aggregating multi-source environmental parameters and spatial attributes into a dynamic data structure. This achieves global correlation and temporal continuity of information between plots. Data is automatically processed by the cloud platform to form parameter evolution chains, establishing multi-level characteristic criteria for monitoring objects, including coverage, temporal sequence, and trend. Abnormal evolution of disease status is directly fed back to the management and control process through linkage judgment. The system outputs control configurations based on real-time status, thereby improving the consistency of risk assessment and the pertinence of control decisions.

Ingesting data from one or more data sources, wherein the data is associated with a tenant of a multi-tenant platform. Generating a machine learning model input based on the ingested data. Providing the generated machine learning model input to a machine learning model. Inferring, using the machine learning model, an inferred dynamic data structure, wherein the inferred dynamic data structure includes a subset of entity attributes inferred by the machine learning model from a set of the entity attributes, wherein at least a portion of the entity attributes include conditional entity attributes, wherein the conditional entity attributes depend on the values of one or more of the other entity attributes of the set of the entity attributes. Presenting, via a graphical user interface (GUI), a visual representation of the inferred dynamic data structure. Tracking user interactions received through the GUI associated with the visual representation of the inferred dynamic data structure. Dynamically adjusting, using one or more other machine learning models, the inferred dynamic data structure based on the tracked user interactions. Presenting, via the GUI, the dynamically adjusted inferred dynamic data structure.

A computerized method, system, and computer program providing a dynamic data structure transformation pipeline are presented. This is achieved by receiving transformation information relating to a source data structure and a target data structure, generating a transformation model for transforming data items from the source data structure to the target data structure based on the received transformation information, integrating, in a transaction environment, an automatic data structure transformation based on the transformation model for transforming data items from the source data structure into the target data structure, testing the automatic data structure transformation elementwise for elements included in the data items, validating the automatic data structure transformation in a sandbox of the transaction environment, and, in response to unexpected answers and / or errors during testing and / or validating of the automatic data structure transformation were received, enriching the transformation information, and repeating at least a part of the process.

Ingesting data from one or more data sources, wherein the data is associated with a tenant of a multi-tenant platform. Generating a machine learning model input based on the ingested data. Providing the generated machine learning model input to a machine learning model. Inferring, using the machine learning model, an inferred dynamic data structure, wherein the inferred dynamic data structure includes a subset of entity attributes inferred by the machine learning model from a set of the entity attributes, wherein at least a portion of the entity attributes include conditional entity attributes, wherein the conditional entity attributes depend on the values of one or more of the other entity attributes of the set of the entity attributes. Presenting, via a graphical user interface (GUI), a visual representation of the inferred dynamic data structure. Tracking user interactions received through the GUI associated with the visual representation of the inferred dynamic data structure. Dynamically adjusting, using one or more other machine learning models, the inferred dynamic data structure based on the tracked user interactions. Presenting, via the GUI, the dynamically adjusted inferred dynamic data structure.

A computer system is provided for retrospectively processing a data structure that includes a plurality of entries. The computer system determines if certain data transactions requests that have been recorded in the data structure could have been executed differently (e.g., by being processed at an earlier point in time). For a given entry in the data structure, the system determines if data transaction request could have at least partly succeeded against a prior recorded state of at least one of two ordered lists of pending data transaction requests. Another entry is then found that caused the initial entry to fail in execution and a time delta is stored between the timestamp of the another entry and the timestamp of the initial entry.