Acquisition pre-management method and system based on digital twinning

Abstract

The invention discloses an acquisition pre-management method and system based on digital twinning, and relates to the related technical field of data acquisition processing, and the method comprises the steps: constructing a terminal digital twinning body, and collecting terminal archive information, communication interaction data and task execution records in real time; a front-end operation and maintenance body is established, and operation state information of a front-end processor is monitored in real time; configuring an acquisition task execution strategy, wherein the acquisition task execution strategy comprises an acquisition task type and a binding relationship between the acquisition task type and an acquisition data item; and according to real-time monitoring data of the terminal digital twinborn body and the front operation and maintenance body, an acquisition task execution strategy is optimized, execution parameters of an acquisition task are intelligently deployed, and a task instruction is generated and sent to the terminal. The technical problems that in the prior art, terminal equipment management real-time performance and front-end processor running state monitoring are insufficient, and the flexibility of a task collection strategy is poor, so that data collection accuracy and efficiency are low are solved, and the technical effect of improving data collection accuracy, integrity and collection efficiency is achieved.

Description

Technical Field

[0001] This application relates to the technical field of data acquisition and processing, and specifically relates to a pre-collection management method and system based on digital twins. Background Art

[0002] With the development of technologies such as the Internet of Things, big data, and cloud computing, the number of devices and the scale of data have grown explosively. Traditional data acquisition management faces many challenges. The management of terminal devices often lacks systematicness and real-time nature. Problems such as untimely update of terminal device file information, difficulty in comprehensively monitoring communication interaction data, and lack of effective records of task execution situations lead to difficulties in ensuring the accuracy and integrity of data acquisition. At the same time, as a key hub for data acquisition, the front-end machine has limited means for monitoring its operating status, and once a failure or performance degradation occurs, it cannot be discovered and processed in a timely manner, thus affecting the stability and efficiency of the entire data acquisition system. In addition, the management of acquisition tasks usually adopts fixed acquisition strategies, which are difficult to adapt to complex and changing business requirements, resulting in data redundancy or untimely acquisition, and affecting the accuracy of data analysis and decision-making.

[0003] Therefore, in the related technologies at the present stage, there are technical problems such as insufficient real-time nature in the management of terminal devices and insufficient monitoring of the operating status of the front-end machine, and poor flexibility of acquisition task strategies, resulting in low accuracy and efficiency of data acquisition. Summary of the Invention

[0004] This application provides a pre-collection management method and system based on digital twins, solves the technical problems in the prior art of insufficient real-time nature in the management of terminal devices and insufficient monitoring of the operating status of the front-end machine, and poor flexibility of acquisition task strategies, resulting in low accuracy and efficiency of data acquisition, and achieves the technical effect of improving the accuracy, integrity, and acquisition efficiency of data acquisition.

[0005] This application provides a pre-collection management method based on digital twins. The method includes: constructing a terminal digital twin body to collect terminal file information, communication interaction data, and task execution records in real time; establishing a front-end operation and maintenance body to monitor the operating status information of the front-end machine in real time; configuring an acquisition task execution strategy, including the type of acquisition task and its binding relationship with acquisition data items, where the type of acquisition task includes polling tasks, reporting tasks, and supplementary polling tasks, and the acquisition data items at least include daily frozen data, curve data, device clock, terminal version, and event records; optimizing the acquisition task execution strategy according to the real-time monitoring data of the terminal digital twin body and the front-end operation and maintenance body, intelligently allocating the execution parameters of the acquisition tasks, and generating task instructions to be sent to the terminal.

[0006] In a possible implementation, the acquisition pre - management method based on digital twin further performs the following processing: According to the terminal digital twin, multi - dimensional view data of each terminal is collected in real time; According to the multi - dimensional view data, terminal archives, communication interactions, and task status are analyzed to obtain the terminal archive information, communication interaction data, and task execution records.

[0007] In a possible implementation, the acquisition pre - management method based on digital twin further performs the following processing: The multi - dimensional view data includes: working condition view, operation and maintenance view, remote communication view, local communication view, and task view. Among them, the working condition view is used to reflect the terminal model, clock deviation, and HPLC module status; the operation and maintenance view is used to record the software version and parameter distribution history; the remote communication view is used to reflect the terminal signal strength and response duration; the local communication view is used to mark the consistency of the electric meter reading time series; the task view is used to track the task execution status.

[0008] In a possible implementation, the acquisition pre - management method based on digital twin further performs the following processing: Through the pre - operation and maintenance entity, the working state data of the front - end machine is monitored in real time, including terminal interaction data, server data transmission data, and thread task status; According to the terminal interaction data, server data transmission data, and thread task status, operation index - related monitoring data is extracted, and operation index calculations are performed according to the preset calculation granularity of the operation index to obtain the operation status information. Among them, the operation indexes include the online rate of the preset dimension of the terminal, the message parsing rate, the storage delay of each task data, the peak value of the server memory load, the occupancy rate of the thread pool, and the terminal interaction timeout ratio; Among them, when the peak value of the server memory load or the online rate reaches the fuse threshold, a warning status information is generated.

[0009] In a possible implementation, the acquisition pre - management method based on digital twin further performs the following processing: Execution parameters of the reporting task are configured according to the user type, where the execution parameters include the reporting execution frequency, the collected data items, the priority of the data items, and the reporting data requirements.

[0010] In a possible implementation, the acquisition pre - management method based on digital twin further performs the following processing: acquiring view data of the terminal according to the terminal digital twin, identifying abnormal view data, and configuring the task scheduling priority according to the abnormal data characteristics; predicting the idle window of the front - end machine based on the operation status data of the front - end machine monitored by the front - end operation and maintenance entity in combination with historical task data; obtaining resource constraint conditions based on the view data of the terminal acquired by the terminal digital twin and the abnormal data characteristics; optimizing the acquisition task parameters and task issuing time according to the resource constraint conditions, the task scheduling priority and the idle window, and obtaining the execution parameters for dispatching the acquisition tasks, where the execution parameters include the acquisition period, the number of points acquired each time, the number of task batches, and the issuing time.

[0011] In a possible implementation, the acquisition pre - management method based on digital twin further performs the following processing: performing load fusing verification according to the task instruction in combination with the real - time operation status data of the front - end operation and maintenance entity, and issuing the task instruction when the fusing threshold is not met; tracking the task instruction based on the task execution, obtaining the terminal task response status view through the terminal digital twin, and calculating the acquisition success rate; when the success rate does not meet the acquisition success rate threshold, backtracking the task response status view to obtain the failure record; analyzing the failure reason according to the failure record, dynamically adjusting the resource constraint conditions and priority rules, and iteratively optimizing the execution parameters.

[0012] This application also provides an acquisition pre - management system based on digital twin, including: an information acquisition module, used to construct a terminal digital twin, and real - time acquire terminal file information, communication interaction data and task execution records; an operation status monitoring module, used to establish a front - end operation and maintenance entity and real - time monitor the operation status information of the front - end machine; an execution policy configuration module, used to configure the acquisition task execution policy, including the acquisition task type and its binding relationship with the acquisition data items, where the acquisition task type includes polling tasks, reporting tasks, and supplementary polling tasks, and the acquisition data items at least include daily frozen data, curve data, device clock, terminal version, and event records; an execution parameter allocation module, used to optimize the acquisition task execution policy according to the real - time monitoring data of the terminal digital twin and the front - end operation and maintenance entity, intelligently allocate the execution parameters of the acquisition tasks, and generate a task instruction to send to the terminal.

[0013] The method and system for pre - acquisition management based on digital twin proposed in this application are intended to construct a terminal digital twin body to collect terminal file information, communication interaction data, and task execution records in real - time; establish a pre - operation and maintenance body to monitor the operation status information of the front - end machine in real - time; configure the execution strategy of the acquisition task, including the type of acquisition task and its binding relationship with the acquisition data items; optimize the execution strategy of the acquisition task according to the real - time monitoring data of the terminal digital twin body and the pre - operation and maintenance body, intelligently allocate the execution parameters of the acquisition task, and generate a task instruction to be sent to the terminal. This solves the technical problems existing in the prior art, such as insufficient real - time performance in terminal device management, insufficient monitoring of the operation status of the front - end machine, and poor flexibility of the acquisition task strategy, resulting in low accuracy and efficiency of data acquisition, and achieves the technical effect of improving the accuracy, integrity, and acquisition efficiency of data acquisition. Brief Description of the Drawings

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in sequence. On the contrary, according to the need, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0015] Figure 1 It is a schematic flowchart of the method for pre - acquisition management based on digital twin provided by the embodiment of the present application.

[0016] Figure 2 It is a schematic structural diagram of the system for pre - acquisition management based on digital twin provided by the embodiment of the present application.

[0017] Explanation of the reference numerals: Information acquisition module 10, operation status monitoring module 20, execution strategy configuration module 30, execution parameter allocation module 40. Detailed Embodiments

[0018] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above - mentioned and other purposes, features, and advantages of this application more obvious and understandable, the following specifically presents the detailed embodiments of this application.

[0019] In order to make the purpose, technical solution, and advantages of this application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The embodiment of the present application provides a collection pre-management method based on digital twins, such as Figure 1 As shown, the method includes: Step S100: Build a digital twin of the terminal to collect terminal profile information, communication interaction data, and task execution records in real time.

[0022] Step S100 further includes step S110, collecting multi-dimensional view data of each terminal in real time based on the terminal digital twin; step S120, performing terminal file, communication interaction, and task status analysis based on the multi-dimensional view data to obtain the terminal file information, communication interaction data and task execution records.

[0023] Preferably, a mirror model is constructed in a virtual space based on physical terminal devices (such as smart meters, sensors, industrial controllers, etc.) to obtain a terminal digital twin, that is, through the Internet of Things, sensor networks, etc., the physical status, operating parameters, historical data, etc. of the terminal device are mapped to the virtual model to form a virtual entity that is synchronized with the physical device in real time and dynamically interacts; then, multi-dimensional view data of each terminal is collected in real time based on the terminal digital twin. Specifically, terminal device-related data is collected from different dimensions, including terminal files, communication interactions, and task status, and then the terminal files, communication interactions, and task status are parsed to obtain terminal file information, communication interaction data, and task execution records. Among them, the terminal file information includes device manufacturer, model, unique identifier (such as IMEI / UUID), installation location, region, current running version (such as firmware version number), cumulative running time, battery life remaining rate, historical fault count, etc., reflecting the "identity characteristics" and "health status" of the terminal device, which is used for equipment asset management and full life cycle traceability.

[0024] Preferably, the communication interaction data is real-time status data describing the data transmission process between the terminal and the front-end machine (or server), and is used to diagnose communication link problems. Specifically, it includes link status such as communication protocol type (such as MQTT, CoAP), connection success rate, number of interruptions, retransmission rate, etc., and data interaction details such as request-response delay (such as the time taken for a polling task from initiation to receiving data), data verification result (whether CRC verification passes), and abnormal data frame type (such as timeout, format error). The task execution record refers to the record of the execution process and result of the acquisition task, and is used to evaluate the task execution efficiency and data quality. Specifically, it includes task types such as distinguishing polling tasks (actively obtaining data), reporting tasks (passively receiving data), and supplementary polling tasks (reissuing missing data), execution status such as task trigger time, completion time, whether it times out, data item integrity (such as whether daily frozen data is collected in full), and abnormal logs such as task failure reasons (such as device offline, permission denied), number of retries, and manual intervention records.

[0025] Further, step S110 further includes that the multi-dimensional view data includes a working condition view, an operation and maintenance view, a remote communication view, a local communication view, and a task view. Among them, the working condition view is used to reflect the terminal model, clock deviation, and HPLC module status. The operation and maintenance view is used to record the software version and parameter distribution history. The remote communication view is used to reflect the terminal signal strength and response duration. The local communication view is used to mark the consistency of the electricity meter reading time series. The task view is used to track the task execution status.

[0026] Preferably, the multi-dimensional view data includes a working condition view, an operation and maintenance view, a remote communication view, a local communication view, and a task view. Among them, the working condition view reflects the physical operating state and basic attributes of the terminal device, and is used to monitor the device health and working condition compliance in real time. The key data items include the terminal model, clock deviation, and HPLC module status, as shown in Table 1: Table 1 Key Data Table of Working Condition View Data item Description Typical application Terminal model Device manufacturer's custom model (such as DDZY123-Z type smart meter) Distinguish the compatibility differences of devices from different manufacturers and adapt to specific protocol parsing rules Clock deviation The difference between the terminal clock and the standard time (unit / second) Detect abnormal clock synchronization of the device to avoid data acquisition timing chaos caused by time deviation (such as incorrect calculation of substation line loss) Clock deviation Operating status of the high-speed power line carrier communication module (online / offline, signal strength, bit error rate) Diagnose the quality of the power line communication network and locate the nodes where carrier communication is interrupted (such as the HPLC modules of a certain building going offline collectively) Preferably, the operation and maintenance view records the terminal equipment software version iteration, parameter configuration history and maintenance operations, which are used to trace the operation and maintenance process and fault root cause analysis, wherein the software version may be the terminal firmware version number (such as V3.2.1) and the patch version number, and the parameter issuance history is a record of remote configuration parameters, such as meter reading cycle, rate period, and alarm threshold; the remote communication view is the real-time status data describing the communication between the terminal and the front-end / master station, which is used to evaluate the remote link quality, including the terminal signal strength and response time, wherein the terminal signal strength is the strength value of the signal received by the wireless communication module, and the response time is the time interval from the master station initiating a request to receiving the terminal data; the local communication view is used It is used to mark the timing and consistency data of the interaction between the terminal and local devices (such as electricity meters, charging piles, and sensors) through short-distance communication (such as RS485, Zigbee, and Bluetooth). This includes the consistency of the meter reading time series, that is, the timestamp difference between the terminal collecting data from multiple meters. The task view records the execution status of various collection tasks, including the entire chain of data from task creation, issuance, execution to completion. It is used to track the task execution status and quantify task efficiency and quality. It may include task type (call task, reporting task, and supplementary call task), execution status (pending, executing, success, failure, timeout), and exception cause (device offline, parameter error, channel conflict).

[0027] Step S200: Establish a front-end operation and maintenance body to monitor the operating status information of the front-end processor in real time.

[0028] Step S200 further includes step S210, which monitors the working status data of the front-end machine in real time through the front-end operation and maintenance body, including terminal interaction data, server data transmission data, and thread task status; step S220, extracts operation indicator-related monitoring data based on the terminal interaction data, server data transmission data, and thread task status, calculates the operation indicator according to the preset calculation granularity of the operation indicator, and obtains the operation status information, wherein the operation indicator includes the online rate of the terminal preset dimension, the message parsing rate, the storage delay of each task data, the server memory load peak, the thread pool occupancy rate, and the terminal interaction timeout ratio; wherein, when the server memory load peak or the online rate reaches the fuse threshold, an early warning status information is generated.

[0029] Preferably, a pre - operation and maintenance system is established, that is, virtual monitoring entities are deployed on the front - end machine. Through tools such as embedded probes and log collectors, the underlying operation data of the front - end machine is captured in real - time to achieve full - link monitoring of the front - end machine, mainly including real - time obtaining of interaction data and internal thread status between the front - end machine and terminals and servers; converting the original data into quantifiable operation metrics to reflect system performance and health; triggering alarms based on preset thresholds to identify system risks in advance. Through the pre - operation and maintenance system, the working state data of the front - end machine is monitored in real - time, including terminal interaction data, server data transmission data, and thread task status. Specifically, terminal online / offline events, connection duration, number of received / sent packets, number of bytes, number of packet parsing errors, number of interaction timeouts, etc. are collected through network packet capture tools (such as Wireshark) or communication middleware (such as Kafka) to obtain communication data (i.e., terminal interaction data) between the front - end machine and terminal devices (such as smart meters, sensors).

[0030] Preferably, daily freeze data sent to the server, total amount and time consumption of curve data, data storage delay in the database (time difference from receiving terminal data to writing to the database), batch writing success rate, uplink / downlink bandwidth utilization rate are obtained through database audit logs to collect data flow status data (i.e., server data transmission data) between the front - end machine and the master server. Active thread count, maximum thread count, number of tasks waiting in the queue, task priority (such as real - time measurement and control task > supplementary measurement task), execution time consumption distribution, number of lock competitions (such as lock conflicts when multiple threads write to the cache), peak CPU / memory occupancy, etc. are collected through JVM monitoring tools to obtain the operation status data of the internal thread pool and task queue of the front - end machine, reflecting the system resource scheduling ability.

[0031] Preferably, based on terminal interaction data, server data transmission data, and thread task status, operation metric - related monitoring data is extracted, and then operation metrics are calculated according to the online rate, packet parsing rate, data storage delay of each task, peak server memory load, thread pool occupancy rate, and terminal interaction timeout ratio of the preset dimensions of the terminal according to their corresponding preset calculation granularity to obtain operation status information, as shown in Table 2: Table 2 Mapping Table of Operation Metrics and Operation Status Information Operating index Calculation method Operating status information Online rate of the preset dimension of the terminal (Number of online terminals / Total number of terminals) × 100%, statistically calculated by dimensions such as region / manufacturer / model, etc. Terminal access capability Message parsing rate Number of messages successfully parsed per unit time (Parsing rate = Number of successful parses / minute) Data processing efficiency Inbound delay of task data Time difference between the terminal data reception time and the database write time, statistically calculated by task type (polling / reporting) Data storage quality Peak value of server memory load Maximum proportion of server memory occupied by the front-end process, statistically calculated by hour / day Server load pressure ThreadPool occupancy rate (Number of active threads / Maximum number of threads) × 100%, statistically calculated by stratifying according to task priority Thread resource utilization rate Proportion of terminal interaction timeouts (Number of interaction timeouts / Total number of interactions) × 100%, statistically classified by communication protocol (4G / HPLC) Interaction reliability Preferably, when the pre-operation and maintenance system detects that an indicator reaches the fuse threshold, hierarchical warning and automatic response are triggered. Specifically, when the memory load peak of the server fuses, that is, the memory occupancy continuously exceeds 85% (such as for 5 consecutive minutes), it is regarded that the system is about to crash, a red warning message is generated, and the memory recovery policy is automatically executed, including cleaning invalid cache data and terminating low-priority tasks (such as delaying non-real-time supplementary call tasks); at the same time, the server expansion plan is triggered, that is, requesting temporary memory resources from the cloud platform or switching to a standby front-end machine. When the terminal online rate fuses, that is, the terminal online rate in a certain area is lower than 90% (such as a large number of power distribution station devices going offline), it is regarded as a major communication network failure, then an orange warning message is generated, the failure area is marked, and the standby communication link is automatically switched.

[0032] Step S300, configure the execution policy of the acquisition task, including the type of acquisition task and its binding relationship with the acquisition data item. The type of acquisition task includes the call task, the reporting task, and the supplementary call task. The acquisition data item includes at least daily frozen data, curve data, device clock, terminal version, and event record.

[0033] Step S300 further includes that the reporting task includes: configuring the execution parameters of the reporting task according to the user type, where the execution parameters include the reporting execution frequency, the acquisition data item, the data item priority, and the reporting data requirement.

[0034] Preferably, configure the execution policy of the acquisition task, including the type of acquisition task and its binding relationship with the acquisition data item. Specifically, the type of acquisition task includes the call task, the reporting task, and the supplementary call task. The call task refers to the task that the front-end machine actively requests data from the terminal (similar to the pull mode). The reporting task refers to the task that the terminal actively sends data to the front-end machine according to the preset rules (similar to the push mode). The supplementary call task refers to the supplementary acquisition task initiated when the front-end machine finds that the data is missing or abnormal. The acquisition data item includes at least daily frozen data, curve data, device clock, terminal version, and event record. The binding relationship between the type of acquisition task and the acquisition data item is shown in Table 3: Table 3 Task type - Acquisition data item binding relationship table Preferably, for reporting tasks, refined execution parameters need to be configured according to user types (such as residents, industrial and commercial users, substations), including reporting execution frequency, collected data items, data item priorities, and reporting data requirements. For example, since the load fluctuations of residential users are small, a low reporting execution frequency (such as once per hour) is configured to reduce terminal energy consumption; for industrial and commercial users with fast-changing loads, a high reporting execution frequency (such as once every 15 minutes) is configured to support real-time electricity price calculation; for key equipment in substations, an extremely high reporting execution frequency (such as once every 5 minutes) is configured to ensure grid safety monitoring. Residential users only collect basic data items such as active power and reactive power; industrial and commercial users add advanced data items such as demand data and harmonic data to support power quality analysis; substations add key data items such as equipment temperature and switch status to support equipment status monitoring. The daily frozen data items have a high priority; the curve data has a lower priority. For reporting data requirements, the first 8 curve points are packed and reported in each cycle, and the first 4 curve points are reported in each cycle; important data items (such as daily frozen data) need to be accompanied by a CRC32 checksum to ensure data integrity.

[0035] Step S400: Optimize the execution strategy of the acquisition task according to the real-time monitoring data of the terminal digital twin and the pre-operation and maintenance entity, intelligently allocate the execution parameters of the acquisition task, and generate a task instruction to be sent to the terminal.

[0036] Step S400 further includes step S410: Collect the view data of the terminal by the terminal digital twin, identify abnormal view data, and configure the task scheduling priority according to the abnormal data characteristics; step S420: Predict the idle window of the front-end computer according to the operation status data of the front-end computer monitored by the pre-operation and maintenance entity combined with historical task data; step S430: Obtain resource constraint conditions based on the view data of the terminal collected by the terminal digital twin and the abnormal data characteristics; step S440: Optimize the acquisition task parameters and task issuance time according to the resource constraint conditions, the task scheduling priority, and the idle window, and obtain the execution parameters for allocating the acquisition task. The execution parameters include the acquisition cycle, the number of points collected at one time, the number of batches of tasks, and the issuance time.

[0037] Preferably, the abnormal view data is identified and classified according to the view data collected by the terminal digital twin of the terminal. For example, a remote signal strength < 20 dBm or a local meter reading delay > 1 second is a communication anomaly, a curve acquisition failure rate > 30% or a daily freeze consecutive failure > 2 times is a task anomaly, and a terminal clock deviation > 5 seconds is a clock anomaly. The task scheduling priority is configured according to the abnormal data characteristics. When there is a clock anomaly, the time calibration task priority is the highest level. When there is a task anomaly, the supplementary call task priority > the regular acquisition priority. Then, based on the operation status data of the front-end machine monitored by the front-end operation and maintenance entity combined with the historical task data, that is, obtaining the real-time operation status data (CPU load rate, message parsing queue length) from the front-end operation and maintenance entity, and then training a load prediction model in combination with the historical task data to output the idle window of the front-end machine in the next 1 hour, and ensuring that the CPU load rate < 70% and the message queue length < 50 under the idle window condition.

[0038] Preferably, based on the view data and abnormal data characteristics collected by the terminal digital twin of the terminal, resource constraint conditions are obtained, specifically including that the number of single-point acquisitions of a communication anomaly terminal ≤ 4 points, the acquisition batches of areas with more than 300 households ≥ 2 groups, and the execution interval of the time calibration task of a clock anomaly terminal < 10 minutes; then, according to the resource constraint conditions, task scheduling priorities, and idle windows, the acquisition task parameters and task distribution time are optimized. Specifically, based on the priority, idle window, and constraint conditions, the optimal execution parameters are generated through a multi-objective optimization algorithm (such as genetic algorithm, simulated annealing algorithm), that is, the execution parameters of the acquisition task are allocated, including the acquisition cycle, the number of single-point acquisitions, the number of task batches, and the distribution time. Among them, high-priority tasks compress the acquisition cycle to ensure data timeliness, and low-priority tasks extend the acquisition cycle to release resources; in a bandwidth-limited scenario, the number of single-point acquisition data is reduced to reduce transmission traffic, and in a high-precision demand scenario, the number of points is increased to meet the load prediction requirements; during high-load periods of the front-end machine, the number of batches is increased to reduce concurrent pressure, and during idle periods, the number of batches is reduced to improve task execution efficiency; the idle window is preferentially matched: emergency tasks are inserted at the start point of the predicted idle window to ensure timely execution, and ordinary tasks are postponed to idle periods at night or on weekends. When multiple high-priority tasks compete for the same window, they are sorted according to the scope of influence; finally, the optimized acquisition cycle, single-point number, task batch, and distribution time are output.

[0039] Further, step S400 further includes step S450, performing load fuse verification according to the task instruction in combination with the real-time operation status data of the pre-operation and maintenance entity. When the fuse threshold is not met, the task instruction is issued; step S460, tracking the task instruction based on the task execution, obtaining the terminal task response status view through the terminal digital twin, and calculating the collection success rate; step S470, when the success rate does not meet the collection success rate threshold, backtracking the task response status view to obtain the failure record; step S480, analyzing the failure cause according to the failure record, dynamically adjusting the resource constraint conditions and priority rules, and iteratively optimizing the execution parameters.

[0040] Preferably, load fuse verification is performed according to the task instruction in combination with the real-time operation status data of the pre-operation and maintenance entity, that is, before issuing the task instruction, the resources required by the task instruction (such as computing resources, network bandwidth, storage write volume) are comprehensively evaluated with the current real-time operation status data of the front-end machine. If none of the operation indicators reach the preset fuse threshold (for example, the server memory load peak is lower than 85%, and the thread pool occupancy rate is lower than 90%), it is determined that the front-end machine has sufficient resources to process the task, and the task instruction is allowed to be issued; if one or more indicators reach or exceed the fuse threshold, it means that the front-end machine has too high a load. At this time, the task issuance is suspended to avoid system collapse due to overload and ensure the stable operation of the front-end machine.

[0041] Preferably, after the task instruction is issued, the entire process of task execution is tracked, and the terminal task response status view is obtained in real time through the terminal digital twin. This view contains detailed information such as task reception time, start execution time, execution completion time, data return status, etc., and the collection success rate is calculated through the formula collection success rate = number of successfully completed tasks / total number of tasks × 100%. When the calculated collection success rate does not reach the preset collection success rate threshold (such as lower than 99%), the system backtracks the terminal task response status view and extracts the records of all failed tasks, including various detailed information during the task execution, such as task timeout, data verification error, terminal offline, etc.

[0042] Preferably, by deeply analyzing the failure records, the reasons for the failures are analyzed. Then, based on the analyzed reasons for the failures, the resource constraint conditions and priority rules are dynamically adjusted. For example, if the failure is caused by insufficient terminal communication capabilities, the task priority of this type of terminal can be reduced, or the number of data points collected per time can be reduced, or the collection period can be extended, etc., to adjust the resource constraint conditions. If it is found that some high-priority tasks frequently fail due to tight resources of the front-end machine, the priority rules are re-evaluated, and the priorities of some non-critical high-priority tasks are appropriately reduced to free up resources for more important tasks. Finally, based on the adjusted resource constraint conditions and priority rules, the execution parameters of the collection tasks (such as collection period, number of data points collected per time, number of task batches, sending time) are iteratively optimized to generate a new task execution strategy to improve the collection success rate of the tasks and ensure the accuracy of data collection.

[0043] In the foregoing, with reference to Figure 1 the acquisition preprocessing management method based on digital twin according to an embodiment of the present invention has been described in detail. Next, with reference to Figure 2 the acquisition preprocessing management system based on digital twin according to an embodiment of the present invention will be described.

[0044] The acquisition preprocessing management system based on digital twin according to an embodiment of the present invention is used to solve the technical problems in the prior art, such as insufficient real-time management of terminal devices and monitoring of the operating status of the front-end machine, and poor flexibility of the acquisition task strategy, resulting in low accuracy and efficiency of data acquisition, and achieves the technical effects of improving the accuracy, integrity, and acquisition efficiency of data acquisition. As Figure 2 shown, the acquisition preprocessing management system based on digital twin includes: an information acquisition module 10, an operating status monitoring module 20, an execution strategy configuration module 30, and an execution parameter allocation module 40.

[0045] The information acquisition module 10 is used to construct a terminal digital twin body, and to collect terminal profile information, communication interaction data, and task execution records in real time; the operating status monitoring module 20 is used to establish a front-end operation and maintenance body, and to monitor the operating status information of the front-end machine in real time; the execution strategy configuration module 30 is used to configure the acquisition task execution strategy, including the acquisition task type and its binding relationship with the acquisition data items, where the acquisition task type includes a polling task, a reporting task, and a supplementary polling task, and the acquisition data items at least include daily frozen data, curve data, device clock, terminal version, and event records; the execution parameter allocation module 40 is used to optimize the acquisition task execution strategy according to the real-time monitoring data of the terminal digital twin body and the front-end operation and maintenance body, and to intelligently allocate the execution parameters of the acquisition tasks, and to generate a task instruction to be sent to the terminal.

[0046] Next, the specific configuration of the information collection module 10 will be described in detail. The information collection module 10 further includes: collecting multi-dimensional view data of each terminal in real time according to the terminal digital twin; parsing the terminal profile, communication interaction, and task status based on the multi-dimensional view data to obtain the terminal profile information, communication interaction data, and task execution records.

[0047] Next, the specific configuration of the information collection module 10 will be further described in detail. The information collection module 10 further includes: the multi-dimensional view data includes: working condition view, operation and maintenance view, remote communication view, local communication view, task view, where the working condition view is used to reflect the terminal model, clock deviation, and HPLC module status, the operation and maintenance view is used to record the software version and parameter distribution history, the remote communication view is used to reflect the terminal signal strength and response duration, the local communication view is used to mark the consistency of the electricity meter reading time series, and the task view is used to track the task execution status.

[0048] Next, the specific configuration of the operation status monitoring module 20 will be described in detail. The operation status monitoring module 20 further includes: monitoring the working status data of the front-end machine in real time through the front-end operation and maintenance entity, including terminal interaction data, server data transmission data, and thread task status; extracting operation index correlation monitoring data based on the terminal interaction data, server data transmission data, and thread task status, and performing operation index calculations according to the preset calculation granularity of the operation index to obtain the operation status information, where the operation index includes the online rate of the preset dimension of the terminal, the message parsing rate, the storage delay of each task data, the peak value of the server memory load, the occupancy rate of the thread pool, and the terminal interaction timeout ratio; when the peak value of the server memory load or the online rate reaches the fuse threshold, a warning status information is generated.

[0049] Next, the specific configuration of the execution policy configuration module 30 will be described in detail. The execution policy configuration module 30 further includes: configuring the execution parameters of the reported task according to the user type, where the execution parameters include the reported execution frequency, the collected data items, the priority of the data items, and the requirements for the reported data.

[0050] Next, the specific configuration of the execution parameter allocation module 40 will be described in detail. The execution parameter allocation module 40 further includes: collecting view data of the terminal according to the terminal digital twin, identifying abnormal view data, and configuring task scheduling priorities according to abnormal data characteristics; predicting the idle window of the front-end machine based on the operation status data of the front-end machine monitored by the front-end operation and maintenance entity in combination with historical task data; obtaining resource constraint conditions based on the view data of the terminal collected by the terminal digital twin and abnormal data characteristics; optimizing the collection task parameters and task distribution time according to the resource constraint conditions, the task scheduling priorities, and the idle window, and obtaining the execution parameters for allocating the collection tasks, where the execution parameters include the collection period, the number of points collected each time, the number of task batches, and the distribution time.

[0051] Next, the specific configuration of the execution parameter allocation module 40 will be described in detail. The execution parameter allocation module 40 further includes: performing load fuse verification according to the task instruction in combination with the real-time operation status data of the front-end operation and maintenance entity, and issuing the task instruction when the fuse threshold is not met; tracking the task instruction based on the task execution, obtaining the terminal task response status view through the terminal digital twin, and calculating the collection success rate; when the success rate does not meet the collection success rate threshold, backtracking the task response status view to obtain the failure record; analyzing the failure cause according to the failure record, dynamically adjusting the resource constraint conditions and priority rules, and iteratively optimizing the execution parameters.

[0052] The acquisition front-end management system based on digital twin provided by the embodiments of the present invention can execute the method for acquiring front-end management based on digital twin provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0053] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The included individual units and modules are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0054] The above specific implementation manners do not constitute a limitation to the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A data acquisition pre - management method based on digital twin, characterized in that, Including: Construct a terminal digital twin to collect terminal file information, communication interaction data, and task execution records in real time; Establish a pre-operation and maintenance entity to monitor the operating status information of the front-end computer in real time; Configure the acquisition task execution strategy, including the acquisition task type and its binding relationship with the acquisition data items. The acquisition task types include call measurement tasks, reporting tasks, and supplementary call tasks. The acquisition data items at least include daily frozen data, curve data, device clock, terminal version, and event records; Optimize the acquisition task execution strategy according to the real-time monitoring data of the terminal digital twin and the pre-operation and maintenance entity, intelligently allocate the execution parameters of the acquisition tasks, and generate task instructions to be sent to the terminal.

2. The acquisition pre - management method based on digital twin according to claim 1, wherein, Construct a terminal digital twin to collect terminal file information, communication interaction data, and task execution records in real time, including: Collect multi-dimensional view data of each terminal in real time according to the terminal digital twin; Analyze the terminal file, communication interaction, and task status based on the multi-dimensional view data to obtain the terminal file information, communication interaction data, and task execution records.

3. The acquisition pre-management method based on digital twin according to claim 2, characterized in that The multi-dimensional view data includes: working condition view, operation and maintenance view, remote communication view, local communication view, and task view. Among them, the working condition view is used to reflect the terminal model, clock deviation, and HPLC module status. The operation and maintenance view is used to record the software version and parameter distribution history. The remote communication view is used to reflect the terminal signal strength and response duration. The local communication view is used to mark the consistency of the meter reading time series. The task view is used to track the task execution status.

4. The acquisition pre - management method based on digital twin according to claim 2, wherein, The establishment of the pre-operation and maintenance entity to monitor the operating status information of the front-end computer in real time includes: Monitor the working status data of the front-end computer in real time through the pre-operation and maintenance entity, including terminal interaction data, server data transmission data, and thread task status; Extract the operation index-related monitoring data according to the terminal interaction data, server data transmission data, and thread task status, calculate the operation index according to the preset calculation granularity of the operation index, and obtain the operating status information. Among them, the operation indexes include the online rate of the preset dimension of the terminal, the message parsing rate, the storage delay of each task data, the peak value of the server memory load, the occupancy rate of the thread pool, and the terminal interaction timeout ratio; Among them, when the peak value of the server memory load or the online rate reaches the fuse threshold, generate a warning status information.

5. The acquisition pre-management method based on digital twin according to claim 1, wherein, The configuration of the acquisition task execution strategy, where the reporting task includes: Configure the execution parameters of the reporting task according to the user type. Among them, the execution parameters include the reporting execution frequency, acquisition data items, data item priority, and reporting data requirements.

6. The acquisition pre-management method based on digital twin according to claim 4, wherein Optimize the acquisition task execution strategy according to the real-time monitoring data of the terminal digital twin and the pre-operation and maintenance entity, and intelligently allocate the execution parameters of the acquisition tasks, including: Collect the view data of the terminal according to the terminal digital twin, identify the abnormal view data, and configure the task scheduling priority according to the abnormal data characteristics; Predict the idle window of the front-end computer according to the operating status data of the front-end computer monitored by the pre-operation and maintenance entity combined with the historical task data; Based on the view data and abnormal data characteristics of the terminal collected by the terminal digital twin, resource constraint conditions are obtained; According to the resource constraint conditions, the task scheduling priority, and the idle window, the collection task parameters and the task issuing time are optimized to obtain the execution parameters of the allocated collection task, where the execution parameters include the collection period, the number of points collected each time, the number of task batches, and the issuing time.

7. The acquisition pre-management method based on digital twin according to claim 6, characterized in that It further includes: Based on the task instruction and the real-time operation status data of the pre-operation and maintenance entity, load fuse verification is performed. When the fuse threshold is not met, the task instruction is issued; Based on the task execution, task instruction tracking is performed. Through the terminal digital twin, a terminal task response status view is obtained, and the collection success rate is calculated; When the success rate does not meet the collection success rate threshold, the task response status view is traced back to obtain the failure record; Based on the failure record, the failure cause is analyzed, the resource constraint conditions and the priority rules are dynamically adjusted, and the execution parameters are iteratively optimized.

8. The acquisition preposition management system based on digital twin is characterized in that, The system is used to implement the digital twin-based collection pre-management method according to any one of claims 1 to 7. The system includes: An information collection module, used to construct a terminal digital twin, and collect terminal file information, communication interaction data, and task execution records in real time; An operation status monitoring module, used to establish a pre-operation and maintenance entity and monitor the operation status information of the front-end machine in real time; An execution strategy configuration module, used to configure the collection task execution strategy, including the collection task type and its binding relationship with the collection data item. The collection task type includes the call measurement task, the reporting task, and the supplementary call task. The collection data item includes at least daily frozen data, curve data, device clock, terminal version, and event record; An execution parameter allocation module, used to optimize the collection task execution strategy according to the real-time monitoring data of the terminal digital twin and the pre-operation and maintenance entity, intelligently allocate the execution parameters of the collection task, and generate a task instruction to be sent to the terminal.

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