A method, system, and medium for distributed node data processing with constrained timing priority

Abstract

The application discloses a kind of distributed node data processing method, system and medium of constraint timing priority.It is in the side of edge node to obtain field operation and maintenance feature and physical position information, and local data arrangement sequence is constructed in combination with timing priority constraint mechanism;Based on the time state parameter of communication double end dynamic evaluation network disconnection, accordingly trigger incremental compensation mechanism to preferentially synchronize core high priority data;For multi-node concurrent access scene, introduce centralized scheduling control and physical state isolation anti-lock mechanism, guarantee mutual exclusion scheduling and ordered allocation of underlying resources;In addition, the temporal alignment and geographical space topology inclusion relationship of the fusion multi-source Internet of Things data are crossed, and the abnormal state of equipment and the missing state of work are checked.Effectively avoid channel blockage and data delay false alarm under complex weak network environment, solve the read-write conflict problem of underlying resources in concurrent request, improve the timeliness, consistency and reliability of distributed operation and maintenance data flow.

Description

Technical Field

[0001] This invention relates to the field of distributed data processing, and in particular to a distributed node data processing method, system, and medium that constrains time-series priority. Background Technology

[0002] With the development of the Internet of Things (IoT) and distributed computing technologies, widely distributed industrial facilities (such as water pipe networks and energy plants) commonly adopt distributed node operation and maintenance inspection systems based on mobile terminals. In actual engineering operations, inspection nodes are often located in specific scenarios with remote geographical locations, complex network environments (such as no network, weak network, frequent disconnections), and multiple devices competing for storage resources at high concurrency. Ensuring the time-series consistency of heterogeneous data, preventing missed inspections and false alarms caused by weak networks, and preventing over-issuance of physical inventory under high concurrency have become urgent technical problems to be solved in the field of distributed data processing.

[0003] While some solutions exist in existing technologies, they have shortcomings in application. Regarding data synchronization and network compensation mechanisms for distributed nodes, existing patents such as CN107918816A "Water Affairs Inspection System" disclose a water affairs inspection system based on mobile terminals, which can perform basic inspection data reporting. However, this solution lacks a time-series constraint mechanism based on data importance in physically isolated environments with weak networks or frequent network outages. When the network channel recovers, existing synchronization mechanisms typically use indiscriminate regular polling uploads, which can easily lead to limited bandwidth being instantly occupied by regular data, causing channel congestion. It is also unable to quantify the time-series breaks during network outages and cannot build an incremental compensation channel for breakpoints within the first handshake cycle after link recovery. This results in abnormal data such as equipment failures not receiving first access, delaying the intervention and response of the cloud dispatch center. Secondly, regarding the maintenance material scheduling mechanism in a multi-terminal concurrent environment, the existing patent CN103854115A, "Method for Spare Parts Reservation and Consumption Information Management of Ship Maintenance Work Orders," discloses a reservation process for spare parts associated with maintenance work orders. However, this solution relies on a centralized database form deduction method and does not consider data read / write conflicts caused by network latency in data flow between distributed heterogeneous systems. When multiple mobile terminals apply for the same scarce material from the cloud within a short physical time window, this solution is prone to data conflicts and physical inventory over-issuance. In addition, this solution lacks state interception and deadlock prevention timeout release mechanisms. If the business flow is delayed in the maintenance approval of a certain node, it will cause the global warehouse resources to be unable to be scheduled normally, failing to meet the mutual exclusion and priority fairness of physical resource allocation in a concurrent state.

[0004] Furthermore, regarding the spatiotemporal verification and false alarm prevention mechanism for the physical operation status of distributed nodes, existing patents such as CN118195577A "Method, Device, Medium and Equipment for Charging Station Inspection Using AR" introduce augmented reality and positioning technologies. However, its determination of missed inspection status relies heavily on the real-time data uploaded by the terminal network. In remote areas with weak network, data upload delays caused by network fluctuations often lead the management system to mistakenly record such data as missed inspections. Existing solutions fail to integrate telemetry data from IoT sensors with the discrete trajectory streams of inspection terminals for temporal cross-verification and lack a verification method based on the spatiotemporal trajectory and the spatial topology inclusion relationship of virtual electronic fences, resulting in low accuracy of the system in verifying the physical operation status of maintenance personnel.

[0005] In view of the shortcomings of existing distributed node data processing schemes in terms of disconnection compensation control, concurrent resource lock mutual exclusion, and physical benchmark spatiotemporal verification, there is an urgent need in this field for a data processing method to solve problems such as generating data processing flow with time priority constraints, deadlock prevention resource allocation, and high-precision spatial verification in complex network and concurrent access environments. Summary of the Invention

[0006] The main objective of this invention is to provide a distributed node data processing method, system, and medium that constrains time priority, thereby solving the problems of existing technologies in complex network and concurrent access environments, such as lack of time priority constraints, easy occurrence of high-concurrency resource conflicts, and missed inspections and false alarms.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a distributed node data processing method with constrained temporal priority, comprising the following steps: S1. Obtain the device coding data from the optical scan of the target wastewater treatment facility site by the mobile inspection terminal. To extract physical preset coordinates Combined with real-time spatial coordinates Calculate the actual physical distance on the sphere When the actual physical distance of the sphere Less than or equal to the effective distance threshold The time-based verification is valid; the abnormal text features at the scene are mapped to the text classification table. Match and extract data transmission priority tags Combined with the absolute timestamp of the basic hardware clock The abnormal image and text features at the scene are encapsulated into standard data blocks. According to the data transmission priority label with absolute timestamp Perform double sorting to generate asynchronous data sequences waiting to be sent. ; S2, based on the last synchronization timestamp of the mobile inspection terminal's reconnection handshake history. Last received timestamp from the cloud dispatch center Calculate the generation time tomographic parameters When time tomographic parameters Greater than the time synchronization error threshold At that time, a full polling freeze command is triggered. And allocate bandwidth to activate incremental compensation channels via incremental compensation channel Asynchronous waiting data sequence The data is pushed to the cloud dispatch center, and a dequeue operation is performed based on the confirmation receipt to generate synchronized data items. ; S3, regarding synchronized data items Analysis of wastewater maintenance material requirements parameters During the concurrent monitoring time window The internal activation scheduler is based on data transmission priority tags in sequence. with absolute timestamp Execution sorting establishes the first maintenance work order In material requirements parameters Not exceeding the real-time physical inventory of the material storage platform Time-based generation of thresholds including lock duration Pre-occupied lock data Real-time physical inventory Additional dirty state parameters To intercept concurrent requests and generate inventory alert commands Perform inventory subtraction or exceed the lockout duration threshold. After deadlock is released, cancel the pre-held lock data. ; S4, Wastewater IoT telemetry data sequence The middle is adjacent to the absolute timestamp Pre-sampling points With subsequent sampling points Perform linear interpolation to generate theoretical estimates The on-site water meter readings differed from the theoretical estimates. When the difference exceeds the limit, a hardware fault flag is generated. ; Calculate the difference between the historical records and the set of sites to be inspected within the physical inspection cycle to generate a list of suspected uninspected sites. Based on its physical preset coordinates Delineate virtual electronic fences When the spatiotemporal trajectory coordinate stream of the operation and maintenance terminal To physical preset coordinates The spatial distances are all greater than the radius of the physical electronic fence. At that time, a true list of missed inspections is generated. .

[0008] In the preferred embodiment, step S1 involves performing hierarchical sorting to generate an asynchronous data sequence waiting to be sent. Specifically, it includes: Extract characteristic characters from abnormal text data at the scene and map them to the text classification table of the local terminal. Perform string matching to determine data transmission priority tags. ; standard data blocks Store in local cache space and compare data transfer priority tags. The hierarchy moves high-priority standard data blocks to the front of the queue; in the data transmission priority label If they are the same, compare the absolute timestamps. Perform a secondary sort to generate an asynchronous data sequence waiting to be sent. .

[0009] In the preferred embodiment, step S2 generates time-to-time parameters. And activate the incremental compensation channel Specifically, it includes: Send detection data packets to obtain network responses and generate network channel recovery status. Initiate a reconnection handshake request to obtain the last received timestamp from the cloud. ; Get the last received timestamp from the cloud Last synchronized timestamp in history Time difference to generate time tomography parameters ; Time tomographic parameters Exceeding the time synchronization error threshold At this time, the regular full-state polling operation is suspended, and transmission bandwidth resources are independently allocated to activate the incremental compensation channel. .

[0010] In the preferred embodiment, step S2 involves using an incremental compensation channel. Data items are pushed to the cloud dispatch center to generate synchronized data items. Specifically, it includes: Read the asynchronous data sequence waiting to be sent from the local cache. High-priority standard data blocks are extracted sequentially using memory pointers and pushed to the cloud scheduling center. Upon receiving a successful confirmation from the cloud scheduling center, execute the queue dequeue operation and move the corresponding standard data block into the synchronized data item. ; Asynchronous waiting sequence of data to be sent When clearing, close the incremental compensation channel. And release the full polling freeze command. .

[0011] In the preferred scheme, step S3 establishes the first maintenance work order data under concurrent request status. Specifically, it includes: For synchronized data items Perform unpacking and convert to maintenance work order data ; Within the set concurrent monitoring time window Internally, when multiple parameters pointing to the same material requirement are obtained... Maintenance work order data Establish concurrent resource contention conditions and activate the external scheduling script program. Establish a transaction-level communication connection; According to the data transmission priority label High and low order and absolute timestamp The order of multiple maintenance work order data Execute the sorting algorithm to determine the first maintenance work order data at the beginning of the sequence. .

[0012] In the preferred embodiment, a dirty state parameter is added in step S3. Intercepting concurrent requests and revoking pre-held lock data Specifically, it includes: dirty state parameters When concurrent access requests for the same target material are received from other terminals during the product's lifespan, the system will reject the numerical deduction calculation based on the interception rules and return an inventory alert command. ; Enable timeout monitoring mechanism to compare the current system time with the lock duration threshold. Within the lock duration threshold If no approval completion signal is received before the expiration date, the deadlock prevention forced release procedure is triggered to stop the inventory value calculation; Clear real-time physical inventory dirty state parameters And revoke the distributed pre-occupied lock data .

[0013] In the preferred embodiment, step S4 involves performing interpolation fitting to generate theoretical estimates. and generate hardware fault flag bits Specifically, it includes: With absolute timestamp Defining the time retrieval window based on the baseline, in the wastewater IoT telemetry data sequence Extracting preceding sampling points With subsequent sampling points Sensor values ​​and sampling time; Time series interpolation alignment is performed based on a linear interpolation algorithm to generate the target device at its absolute timestamp. Theoretical estimate of time ; Obtain theoretical estimates Compared with the on-site water meter reading The absolute difference, when the absolute difference exceeds the physically reasonable threshold Establish the physical damage status of the hardware and generate hardware fault flag bits. .

[0014] In the preferred embodiment, step S4 involves defining a virtual electronic fence. And compare spatial intersection features, specifically including: Extract physical inspection cycle Historical inspection record data set Collection of sites to be patrolled Perform set difference operations to identify nodes not included in historical inspection records and generate a list of suspected uninspected sites. ; Using physical preset coordinates Construct a physical electronic fence radius for the geometric center. A buffer zone was established to define the virtual electronic fence area. ; Extracting the spatiotemporal trajectory coordinate stream From discrete trajectory points to physical preset coordinates The spherical distance is used to determine the spatial topological inclusion relationship and remove areas falling within the virtual electronic fence region. Site records within; All spherical distances extracted are greater than the radius of the physical electronic fence. The site records generate a detailed list of uninspected sites. .

[0015] In a preferred embodiment, the present invention also provides a distributed node data processing system with constrained temporal priorities for performing the above-described method. The system includes: The edge state machine initialization unit is used to obtain the real-time spatial coordinates of the mobile inspection terminal. With physical preset coordinates Perform distance verification; acquire on-site anomaly image and text features, based on data transmission priority tags. with absolute timestamp For standard data blocks Perform hierarchical sorting to generate asynchronous data sequences waiting to be sent. ; The breakpoint incremental synchronization unit is used to calculate the synchronization timestamp difference to generate time tomography parameters. Trigger a full polling freeze command Activate incremental compensation channel via incremental compensation channel Push data sequence generates synchronized data items ; The scheduling unit is used to parse the wastewater maintenance material requirement parameters. Establish the first maintenance work order data Generate pre-occupied lock data Real-time physical inventory Additional dirty state parameters Intercept concurrent requests and revoke pre-held lock data after value updates or deadlock prevention release. ; The verification unit is used to extract preceding and subsequent sampling points, perform interpolation fitting, and generate theoretical estimates. To generate hardware fault flags A list of suspected entities is established based on set difference operations, and virtual electronic fences are demarcated. Perform spatial topology relationship determination, filter out delayed data, and generate a true list of uninspected sites. .

[0016] In a preferred embodiment, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0017] This invention provides a distributed node data processing method, system, and medium with constrained time priority. Addressing the shortcomings of existing mobile terminal inspection systems in weak network or frequent network outage environments, where indiscriminate polling uploads lead to congestion of limited channels by routine data and delayed transmission of high-risk abnormal data, this invention proposes a disconnection compensation control mechanism based on comparing time discontinuity parameters and error thresholds. By calculating the difference in timestamps between the mobile terminal and the cloud, the network outage span is quantified. Upon determining a valid discontinuity, the full polling operation is automatically suspended, and bandwidth is independently allocated to activate a dedicated incremental compensation channel. Combined with the terminal's local dual sorting logic based on data importance and absolute timestamps, this avoids channel congestion in the initial network recovery phase, ensuring that high-priority data, such as core equipment failures, receive the highest transmission priority in the first cycle of link recovery, thus improving the cloud's response speed to physical emergencies. To address the problems of existing maintenance material scheduling solutions relying on centralized databases for direct deduction, which can easily lead to over-issuance of inventory and global scheduling paralysis due to approval delays when multiple terminals compete for scarce resources during high-concurrency periods, this invention designs a concurrent resource scheduling process with mutual exclusion and deadlock prevention capabilities. Within a set concurrency monitoring window, the first maintenance work order is established, and pre-occupied lock data is generated on the material storage platform. Subsequent concurrent request execution status is intercepted by adding dirty status parameters. Furthermore, this solution introduces a timeout monitoring mechanism based on a lock duration threshold. When the approval process stalls, a deadlock-preventing forced release procedure can be triggered, ensuring mutual exclusion and fairness in physical resource allocation under concurrent conditions and preventing scheduling paralysis caused by underlying data read / write conflicts.

[0018] To address the issues of false alarms caused by data upload delays due to network fluctuations in existing inspection systems, and the low accuracy of physical operation status verification, this invention proposes a false alarm prevention and verification mechanism that integrates spatiotemporal topological constraints and IoT data alignment. The missing data detection operation is transformed into a pre-processing step of spatial verification. After identifying suspected targets through set difference operations, a spatial topological inclusion relationship comparison is performed between the spatiotemporal trajectory coordinate stream and a virtual electronic fence constructed based on physical coordinates. Geographical spatial constraints are used to cross-validate missing digital records, filtering out data artifacts caused by network latency. Simultaneously, time series interpolation alignment is performed by extracting neighboring IoT sampling points to suppress time misalignment caused by asynchronous sampling, thereby improving the accuracy of edge hardware fault identification and physical operation verification. Attached Figure Description

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of a distributed node data processing method with constrained temporal priority according to the present invention; Figure 2 This is a schematic diagram of a distributed node data processing system with constrained timing priority according to the present invention. Detailed Implementation

[0020] Example 1 like Figures 1 to 2 As shown, a distributed node data processing method with constrained temporal priority is presented. This method specifically includes the following steps: S1. Obtain the device coding data from the optical scan of the target wastewater treatment facility site by the mobile inspection terminal. To extract physical preset coordinates Combined with real-time spatial coordinates Calculate the actual physical distance on the sphere When the actual physical distance of the sphere Less than or equal to the effective distance threshold The time-based verification is valid; the abnormal text features at the scene are mapped to the text classification table. Match and extract data transmission priority tags Combined with the absolute timestamp of the basic hardware clock The abnormal image and text features at the scene are encapsulated into standard data blocks. According to the data transmission priority label with absolute timestamp Perform double sorting to generate asynchronous data sequences waiting to be sent. ; S2, based on the last synchronization timestamp of the mobile inspection terminal's reconnection handshake history. Last received timestamp from the cloud dispatch center Calculate the generation time tomographic parameters When time tomographic parameters Greater than the time synchronization error threshold At that time, a full polling freeze command is triggered. And allocate bandwidth to activate incremental compensation channels via incremental compensation channel Asynchronous waiting data sequence The data is pushed to the cloud dispatch center, and a dequeue operation is performed based on the confirmation receipt to generate synchronized data items. ; S3, regarding synchronized data items Analysis of wastewater maintenance material requirements parameters During the concurrent monitoring time window The internal activation scheduler is based on data transmission priority tags in sequence. with absolute timestamp Execution sorting establishes the first maintenance work order In material requirements parameters Not exceeding the real-time physical inventory of the material storage platform Time-based generation of thresholds including lock duration Pre-occupied lock data Real-time physical inventory Additional dirty state parameters To intercept concurrent requests and generate inventory alert commands Perform inventory subtraction or exceed the lockout duration threshold. After deadlock is released, cancel the pre-held lock data. ; S4, Wastewater IoT telemetry data sequence The middle is adjacent to the absolute timestamp Pre-sampling points With subsequent sampling points Perform linear interpolation to generate theoretical estimates The on-site water meter readings differed from the theoretical estimates. When the difference exceeds the limit, a hardware fault flag is generated. ; Calculate the difference between the historical records and the set of sites to be inspected within the physical inspection cycle to generate a list of suspected uninspected sites. Based on its physical preset coordinates Delineate virtual electronic fences When the spatiotemporal trajectory coordinate stream of the operation and maintenance terminal To physical preset coordinates The spatial distances are all greater than the radius of the physical electronic fence. At that time, a true list of missed inspections is generated. .

[0021] In the preferred embodiment, step S1 specifically includes the following steps: S11. Collect physical location and verify its validity. During the edge node perception phase, the mobile terminal uses a hardware camera component to perform optical scanning on the target wastewater treatment facility site and acquire equipment coding data. Then, the system reads the locally stored site ledger database and extracts the data coded for the device. Uniquely bound physical preset coordinates containing reference latitude and longitude parameters Simultaneously, the system sends an activation command to the underlying hardware layer, invoking the mobile terminal's satellite positioning module to obtain real-time spatial coordinates. ; Based on real-time spatial coordinates With physical preset coordinates The difference in latitude and longitude between the two points is used to calculate the actual spherical physical distance between them. Subsequently, the system will calculate the actual physical distance on the spherical surface. effective distance threshold preset by the system Numerical comparison and verification were performed; if the actual physical distance of the sphere... The system determines that the current physical space is within the valid operating range and generates a valid physical location verification result flag. If the actual physical distance on the sphere The system refuses to generate a valid flag bit; S12, Local assessment of on-site operation and maintenance characteristics When the system receives a valid physical location verification result flag bit Then, the data entry channel is opened; mobile terminals collect on-site anomaly text data for the target site through an interactive interface. With standardized image features In a physically isolated state with no or weak network signal, the system extracts abnormal text data from the site. The characteristic characters in the text are mapped to a pre-built text classification mapping table within the local terminal. Perform string matching one by one; The system uses a text classification mapping table. The system uses pre-defined hardware fault level rules, such as mapping terms representing physical pump shutdowns to high urgency and terms representing paint peeling to low urgency; it also calculates and assigns corresponding data transmission priority tags to current anomaly records. Simultaneously, the basic hardware clock is invoked to obtain the absolute timestamp of the moment the data processing is completed. ; Finally, the acquired text data of on-site anomalies will be... Standardized image features Data transmission priority label and absolute timestamp Perform a combined mapping to generate record entries carrying priority identifiers. ; S13. Asynchronous waiting transmission queue arrangement based on priority characteristics The system receives record items carrying priority identifiers. Then, it is packaged and converted into standard data blocks suitable for channel transmission through low-level encoding. Subsequently, the system allocates an independent memory structure in the mobile terminal's local cache space to initialize and build an asynchronous waiting queue for sending. The system will generate standard data blocks Continuously store into the asynchronous waiting-to-send queue middle; During the storage process, the system extracts standard data blocks. Internally carried data transmission priority label Perform queue-level sorting management; specifically, compare the data transmission priority tags of all data blocks in the current queue. The system moves high-priority standard data blocks to the front of the queue; when multiple standard data blocks with the same priority are identified, the system extracts their corresponding absolute timestamps. Perform secondary sorting of sub-levels according to time sequence to generate an asynchronous waiting queue for transmission. ; To prevent channel congestion caused by blindly sending packets in a network physical environment with frequent disconnections, and to protect the first-release right of core equipment fault data during the link recovery phase.

[0022] In the preferred embodiment, step S2 specifically includes the following steps: S21. Compare the reconnection handshake sequence. The mobile terminal's communication module sends detection data packets to the cloud event management module at set time intervals to obtain network responses. When it continuously receives stable network response packets, it confirms that the current physical network channel is active and generates a network channel recovery status. Subsequently, the mobile terminal initiates a reconnection handshake request to the cloud, and carries the last synchronization timestamp stored locally on the mobile terminal in the handshake data packet. ; After receiving the reconnection handshake request, the cloud event management module extracts the last received timestamp from the cloud-side record. It then sends the data back to the mobile terminal, which receives it. Afterwards, and Perform interpolation to calculate and generate time series tomographic parameters. This is used to quantify the data time span during a network outage; S22, Full-scale state polling freeze and incremental compensation channel activation After confirming the network channel recovery status After activation, the system will acquire time-series tomographic parameters. Time synchronization error threshold pre-installed in the local terminal Perform numerical comparison; if If the system determines that no effective communication interruption has occurred, it maintains normal communication status; if The system identified a serial number gap. In the event of a sequence number gap, the system immediately issues a full polling freeze command to the underlying communication hardware interface. The system suspends the routine full-state polling operation between the mobile terminal and the cloud; subsequently, it invokes the bandwidth allocation protocol to independently allocate transmission bandwidth resources and activates a dedicated incremental compensation channel. ; By blocking the regular polling operation, sufficient transmission bandwidth is ensured for the backhaul of core tomographic data; S23. Targeted push of high-concurrency backlogged data based on priority tags. System verification confirms incremental compensation channel In active state and full polling freeze command After taking effect, read the sorted asynchronous waiting-to-send queue from the local cache. ,rely on Internally based on data transmission priority tags with absolute timestamp Based on the established order, the system sequentially extracts high-priority standard data blocks located at the front of the queue using memory pointers. ; The system will extract standard data blocks via a dedicated incremental compensation channel The system pushes data to the cloud-based event management module. During communication, each time the mobile terminal receives a successful confirmation from the cloud, the system performs a dequeue operation, removing the corresponding standard data block. From asynchronous waiting send queue Remove from the set and store in the set of standard data blocks that have been successfully synchronized. until Data cleared, system closes incremental compensation channel And release the full polling freeze command. Generate an updated asynchronous wait-to-send queue. ; By relying on the local queue sequential push mechanism, the number of concurrent connections is limited, ensuring that the limited network bandwidth prioritizes the transmission of core fault data, so that the cloud system can receive the physical operation and maintenance status earlier.

[0023] In the preferred embodiment, step S3 specifically includes the following steps: S31, Parse concurrent maintenance tasks The cloud-based event management module receives the set of standard data blocks that have been successfully synchronized. Then, the data is unpacked to extract hardware fault characteristic characters; the system then converts these characteristic characters into structured maintenance work order data. Regarding maintenance work order data The system retrieves and calls upon the pre-defined bill of materials mapping table based on the specified equipment component types. Obtain the corresponding material requirement parameters through key-value matching. ; The system sets a fixed-length concurrent monitoring time window in memory. Multiple maintenance work orders are generated by continuously collecting data uploaded from different mobile terminals within the window. The system executes a number of tasks and compares them with the attributes. When it is verified that they are within the same concurrent monitoring time window... The memory contains two or more maintenance work order data. They all point to the same material requirement parameters When high concurrency resource contention conditions are met, overlapping maintenance work order data will occur. The data is collected, packaged, and used to generate a maintenance work order data set. ; When the conditions for high-concurrency resource contention are met, the system activates a separately deployed external scheduling script program through the asynchronous message queue inside the server. And collect maintenance work order data Material Requirements Parameters This external scheduling script is passed as an initial call parameter. ;This enables the system to have a control mechanism for capturing and centrally managing instantaneous high-concurrency requests; S32, Pre-occupied data lock write External scheduling script program After receiving the input parameters, a transaction-level communication connection is established with the material data storage platform via the application programming interface, and a read command is sent to the platform to obtain the material requirement parameters. Matching target material real-time physical inventory ; Subsequently, the external scheduling script program Maintenance work order data set It contains multiple maintenance work order data. Execute the sorting algorithm; The system first extracts data from each maintenance work order. Data transmission priority tag carried The data is initially sorted from highest to lowest priority according to the numerical settings; for data transmission priority tags with the same priority... Maintenance work order data The system further extracts its bound absolute timestamp. The data is sorted twice according to the chronological order of the records; the first maintenance work order data at the very beginning of the sequence is then identified. The remaining maintenance work orders that are not ranked first in the set will be added to the remaining maintenance work order set. ; External scheduling script program Verify the data of the first maintenance work order. Required material requirements parameters Does it meet the requirement of being less than or equal to the real-time physical inventory? The numerical requirements; if the verification passes, the external scheduling script program... Based on this record, generate a corresponding unique equipment identification code containing the target material and the first maintenance work order data. Tracking identifiers and set lock duration thresholds Distributed pre-occupied lock data and the distributed pre-occupied lock data Write the control field corresponding to the target material into the material data storage platform; Ensure the mutual exclusion and efficiency of priority allocation of physical resources under high concurrency conditions; S33. Dirty state parameter marking and deadlock prevention value delay deduction The material data storage platform receives distributed pre-occupancy lock data. Then, the system changes the storage status attribute of the target material to the current real-time physical inventory level. Additional dirty state parameters ; In dirty state parameters During the duration of the activation period, when the system receives a set of remaining maintenance work orders pointing to the same target material... When making an application, the dirty state parameters are used. The interception rules refuse to execute the direct deduction calculation and generate an inventory alert command. And return to each requesting terminal, thereby constraining concurrent repeated reads on other nodes; For the first maintenance work order data that has been locked The system continuously monitors the status of its associated physical maintenance approval process. Once the system receives a confirmation signal indicating that the approval is complete, it will then process the approval based on the material requirement parameters. The value of the attached dirty state parameter Real-time physical inventory Perform the subtraction operation to obtain the updated actual inventory data. ; During computation, the system concurrently activates a timeout monitoring mechanism to compare the distributed pre-occupied lock data. Lock duration threshold recorded in the middle Compared to the current system time; if within the lock duration threshold No maintenance work order data received before the deadline Upon receiving the approval completion signal, the system triggers the deadlock prevention and forced release procedure, and the subtraction operation is not performed. After the subtraction operation is successfully completed or the deadlock prevention mechanism is triggered, the system clears the dirty status parameters on the target material field. Revoke distributed pre-occupied lock data and utilize updated actual inventory data. Overwrite the existing stored values, complete the resource update, and avoid global resource scheduling paralysis caused by approval delays; In the preferred embodiment, step S4 specifically includes the following steps: S41. Timing Alignment and Deviation Calculation of Physical Reference for Multi-Source Heterogeneous Data The cloud system receives on-site water meter readings uploaded by mobile terminals. and the corresponding absolute timestamp Query the cloud database to extract the telemetry data sequences periodically reported by IoT sensors associated with the target device. ; The system uses absolute timestamps Based on the telemetry data sequence Define forward and backward time retrieval windows, scan and extract data distributed across absolute timestamps. The effective physical sampling points are located adjacent to both sides; the preceding sampling points are locked respectively. and subsequent sampling points Among them, the preceding sampling points Includes sampling time With sensor values Subsequent sampling points Includes sampling time With sensor values ; The system extracts the above values ​​and uses a linear interpolation algorithm to calculate the target device at the absolute timestamp. Theoretical estimate at this instant Specifically, theoretical estimates The formula for linear interpolation is: (1); in, Absolute timestamps generated for alignment Theoretical estimate of time; These are the sensor values ​​from the previous sampling points; These are the sensor values ​​for subsequent sampling points; For absolute timestamps collected on-site by mobile terminals; The sampling time of the preceding sampling point; This refers to the sampling time of subsequent sampling points; The system obtains theoretical estimates. Then, calculate the on-site water meter readings. Compared with theoretical estimates absolute difference in water volume Read the preset physical reasonable threshold and the absolute difference in water volume With physical reasonable threshold The system compares the values ​​and determines the absolute difference in water volume. If physical damage is confirmed to be present in the remote communication module or mechanical dial, a hardware fault flag will be generated. ; S42. Preliminary investigation of suspected uninspected sites based on set operations. The system reads the preset physical inspection cycle. And retrieve the data falling within that physical inspection cycle from the cloud database. Historical inspection record data set and the corresponding list of sites to be patrolled. ; Analyzing historical inspection record data sets Extract all the inspection site identification codes carried within, and construct a set of inspection sites in hash mapping format. Similarly, the system parses the list of sites to be patrolled. Extract the identification codes of all sites that are required to be inspected, and construct a target site set. ; The system targets a set of sites. Combined with inspected stations Perform set difference operations and traverse the target site set. For each element in the set, check whether the element exists in the set of inspected stations. In, it will exist But does not exist The data nodes are separated and combined to generate a list of suspected uninspected sites. After generating the list, enable the internal geographic information service interface to add the list of suspected uninspected sites. Load the spatial verification queue and wait to be assigned to the spatial intersection determination algorithm module; By using set operations to identify suspected targets and removing meaningless full-space comparisons, the computational scope is reduced for subsequent high-computation-power-consuming physical space trajectory verification. S43. Verification of uninspected tracks and electronic fences to prevent false alarms. The system analyzes the list of suspected uninspected sites one by one. For each target station, extract the physical preset coordinates containing the reference latitude and longitude values. The extracted physical preset coordinates With the center as the radius of the preset physical electronic fence Using a radius, define the virtual electronic fence area of ​​the site in the two-dimensional coordinate system of the geographic information system. ; The system is based on the physical inspection cycle. Extract the spatiotemporal trajectory coordinate stream set of all associated operation and maintenance terminals assigned to the area where the site is located from the trajectory database. ; Initiate the spatial intersection determination algorithm and traverse the set of spatiotemporal trajectory coordinate streams. The discrete GPS trajectory points are used to calculate the distance between each GPS trajectory point and the preset physical coordinates using the spherical distance formula. The actual spatial distance between ; The system compares the actual spatial distance With physical electronic fence radius The numerical value, when the set of spatiotemporal trajectory coordinates is obtained. There exists any trajectory point that satisfies At that time, it was determined that the maintenance terminal had previously entered the virtual electronic fence area within the specified period. It was confirmed that the current record gaps were due to data upload delays caused by a weak network environment, and the site was removed from the list of suspected uninspected sites. Remove from the middle; When the system has traversed the entire set of spatiotemporal trajectory coordinate streams And all trajectory points satisfy When a missed inspection event is confirmed at a site, the site is marked, and the corresponding information is written to generate a detailed list of uninspected sites. ; By combining pure software-level data filtering with physical GPS trajectory constraints, the electronic fence spatial algorithm is used to filter out data artifacts caused by network fluctuations, thereby improving the accuracy of the system's verification of the physical operation status of edge-side maintenance personnel.

[0024] In a preferred embodiment, the present invention also provides a distributed node data processing system with constrained temporal priority, used to execute a distributed node data processing method with constrained temporal priority, the system comprising: The edge state machine initialization unit is built using a mobile inspection terminal equipped with a Qualcomm Snapdragon 8 series industrial-grade processor. The unit calls the Sony IMX series optical camera component to perform optical scanning on the target sewage facility site to obtain equipment coding data, calls the Beidou-3 dual-frequency satellite positioning module to obtain real-time spatial coordinates, calculates the actual spherical physical distance based on the latitude and longitude difference between the real-time spatial coordinates and the physical preset coordinates, and generates a physical position verification result flag bit by comparing the effective distance threshold. The open data input channel collects abnormal text data and standardized image features on site, extracts feature characters and matches them with the text classification mapping table inside the local terminal, assigns data transmission priority labels, calls the basic hardware clock to obtain the absolute timestamp, generates record items carrying priority identifiers, converts the record items into standard data blocks and stores them in the asynchronous waiting-to-send queue in the local cache space, extracts the data transmission priority label to perform the initial sorting, extracts the absolute timestamp to perform the secondary sorting, and generates the sorted asynchronous waiting-to-send queue. The breakpoint incremental synchronization unit uses Huawei's industrial 5G communication baseband router. According to the set time period, it sends detection data packets to obtain network response and generate network channel recovery status. It initiates a reconnection handshake request to the cloud, extracts the last received timestamp from the cloud and performs difference calculation with the last historical synchronization timestamp to generate time series fault parameters. When the time series fault parameters are determined to be greater than the time synchronization error threshold, it sends a full polling freeze command to the underlying communication hardware interface, independently allocates transmission bandwidth resources to activate the incremental compensation channel, reads the sorted asynchronous waiting to send queue in the local cache, extracts the high-priority standard data blocks at the front of the queue in order, and pushes them to the cloud event management module through the incremental compensation channel. It receives the successful confirmation acknowledgment returned by the cloud, performs dequeue operation, stores the data in the successfully synchronized standard data block set, and generates an updated asynchronous waiting to send queue. The scheduling unit is built using Inspur NF5280M6 dual-path cloud architecture servers and a Redis distributed in-memory database platform. It extracts fault characteristic characters from successfully synchronized standard data block sets to generate maintenance work order data, retrieves material requirement parameters from the bill of materials mapping table, captures multiple maintenance work orders pointing to the same material requirement parameters within the concurrent monitoring time window to generate a maintenance work order data set, and activates the external scheduling script program. The external scheduling script program uses a data transmission priority tag and absolute timestamp to execute an algorithm to determine the first maintenance work order data, generates distributed pre-occupied lock data, and directly writes it to the control field of the material data storage platform. It adds a dirty status parameter to the real-time physical inventory to intercept the remaining maintenance work order set, continuously monitors the physical maintenance approval process, generates updated actual inventory data, activates a timeout monitoring mechanism to trigger a deadlock release procedure, and finally clears the dirty status parameter and cancels the distributed pre-occupied lock data. The verification unit is built using Alibaba Cloud PolarDB relational database nodes and ArcGIS enterprise-level geospatial computing engine. Based on absolute timestamps, it extracts preceding and subsequent sampling points from the telemetry data sequence. It uses linear interpolation algorithms to calculate the theoretical estimated value of the target device at the absolute timestamp. It calculates the absolute difference between the on-site water meter reading and the theoretical estimated value and compares it with a physically reasonable threshold to generate a hardware fault flag. It extracts the historical inspection record data set and the list of sites to be inspected and performs set difference operation to separate the corresponding data nodes to generate a list of suspected uninspected sites. It delineates a virtual electronic fence area in a two-dimensional coordinate system with the physical preset coordinates as the geometric center. It traverses the discrete GPS trajectory points of the spatiotemporal trajectory coordinate stream set to calculate the actual spatial distance, compares it with the physical electronic fence radius, eliminates sites with data delays caused by weak network environments, and generates a detailed list of uninspected sites.

[0025] In a preferred embodiment, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the various steps of a distributed node data processing method with constrained timing priorities. The computer-readable storage medium is constructed using an enterprise-grade high-density Blu-ray disc or magnetic tape array, and persistently stores executable code files, including external scheduling scripts, spatial intersection determination algorithm model instruction sets, and linear interpolation calculation code libraries, for mounting, retrieving, and running at any time by the corresponding server hardware underlying operating system or virtualization platform, ensuring the persistent operation and environmental recovery capabilities of the underlying algorithms of the entire distributed operation and maintenance data processing system.

[0026] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A distributed node data processing method with constrained temporal priority, characterized in that, Including the following step S1. Obtain the real-time spatial coordinates of the mobile inspection terminal at the target wastewater treatment facility site. With physical preset coordinates Perform distance verification; if the verification is successful, acquire the abnormal image and text features of the site, and transmit the abnormal image and text features and data priority tags. with absolute timestamp Combine to generate standard data blocks Based on data transmission priority tags Perform hierarchical sorting to generate asynchronous data sequences waiting to be sent. ; S2. Calculate the time stamp difference between the mobile inspection terminal and the cloud dispatch center to generate time discontinuity parameters. Based on time tomographic parameters Trigger full polling freeze command And activate the incremental compensation channel via incremental compensation channel Asynchronous waiting data sequence Data items are pushed to the cloud dispatch center to generate synchronized data items. ; S3, regarding synchronized data items Analysis of wastewater maintenance material requirements parameters And in concurrent request status, based on data transmission priority tags Establish the first maintenance work order Compare material requirements parameters Real-time physical inventory of materials storage platform Generate pre-occupied lock data Real-time physical inventory Additional dirty state parameters Intercept concurrent requests, update inventory values, and then revoke the pre-held lock data. ; S4. Timestamp Neighboring wastewater IoT telemetry data sequence Perform interpolation fitting to generate theoretical estimates. Compare the on-site water meter readings to generate a hardware fault flag. ; A list of suspected uninspected items is generated based on the difference between inspection records. With a list of suspected uninspected items Virtual electronic fences are defined by binding physical coordinates. Comparison of spatiotemporal trajectory coordinates Spatial intersection features generate true missed patrol details .

2. The distributed node data processing method with constrained temporal priority as described in claim 1, characterized in that, In step S1, hierarchical sorting is performed to generate an asynchronous data sequence waiting to be sent. Specifically, it includes: Extract characteristic characters from abnormal text data at the scene and map them to the text classification table of the local terminal. Perform string matching to determine data transmission priority tags. ; standard data blocks Store in local cache space and compare data transfer priority tags. The hierarchy moves high-priority standard data blocks to the front of the queue; in the data transmission priority label If they are the same, compare the absolute timestamps. Perform a secondary sort to generate an asynchronous data sequence waiting to be sent. .

3. The distributed node data processing method with constrained temporal priority as described in claim 1, characterized in that, In step S2, time-to-time parameters are generated. And activate the incremental compensation channel Specifically, it includes: Send detection data packets to obtain network responses and generate network channel recovery status. Initiate a reconnection handshake request to obtain the last received timestamp from the cloud. ; Get the last received timestamp from the cloud Last synchronized timestamp in history Time difference to generate time tomography parameters ; Time tomographic parameters Exceeding the time synchronization error threshold At this time, the regular full-state polling operation is suspended, and transmission bandwidth resources are independently allocated to activate the incremental compensation channel. .

4. The distributed node data processing method with constrained temporal priority as described in claim 1, characterized in that, In step S2, via the incremental compensation channel Data items are pushed to the cloud dispatch center to generate synchronized data items. Specifically, it includes: Read the asynchronous data sequence waiting to be sent from the local cache. High-priority standard data blocks are extracted sequentially using memory pointers and pushed to the cloud scheduling center. Upon receiving a successful confirmation from the cloud scheduling center, execute the queue dequeue operation and move the corresponding standard data block into the synchronized data item. ; Asynchronous waiting sequence of data to be sent When clearing, close the incremental compensation channel. And release the full polling freeze command. .

5. The distributed node data processing method with constrained temporal priority as described in claim 1, characterized in that, In step S3, the first maintenance work order data is established under concurrent request status. Specifically, it includes: For synchronized data items Perform unpacking and convert to maintenance work order data ; Within the set concurrent monitoring time window Internally, when multiple parameters pointing to the same material requirement are obtained... Maintenance work order data Establish concurrent resource contention conditions and activate the external scheduling script program. Establish a transaction-level communication connection; According to the data transmission priority label High and low order and absolute timestamp The order of multiple maintenance work order data Execute the sorting algorithm to determine the first maintenance work order data at the beginning of the sequence. .

6. The distributed node data processing method with constrained temporal priority as described in claim 1, characterized in that, Add dirty state parameters in step S3 Intercepting concurrent requests and revoking pre-held lock data Specifically, it includes: dirty state parameters When concurrent access requests for the same target material are received from other terminals during the product's lifespan, the system will reject the numerical deduction calculation based on the interception rules and return an inventory alert command. ; Enable timeout monitoring mechanism to compare the current system time with the lock duration threshold. Within the lock duration threshold If no approval completion signal is received before the expiration date, the deadlock prevention forced release procedure is triggered to stop the inventory value calculation; Clear real-time physical inventory dirty state parameters And revoke the distributed pre-occupied lock data .

7. The distributed node data processing method with constrained temporal priority as described in claim 1, characterized in that, In step S4, interpolation fitting is performed to generate theoretical estimates. and generate hardware fault flag bits Specifically, it includes: With absolute timestamp Defining the time retrieval window based on the baseline, in the wastewater IoT telemetry data sequence Extracting preceding sampling points With subsequent sampling points Sensor values ​​and sampling time; Time series interpolation alignment is performed based on a linear interpolation algorithm to generate the target device at its absolute timestamp. Theoretical estimate of time ; Obtain theoretical estimates Compared with the on-site water meter reading The absolute difference, when the absolute difference exceeds the physically reasonable threshold Establish the physical damage status of the hardware and generate hardware fault flag bits. .

8. The distributed node data processing method with constrained temporal priority as described in claim 1, characterized in that, In step S4, a virtual electronic fence is defined. And compare spatial intersection features, specifically including: Extract physical inspection cycle Historical inspection record data set Collection of sites to be patrolled Perform set difference operations to identify nodes not included in historical inspection records and generate a list of suspected uninspected sites. ; Using physical preset coordinates Construct a physical electronic fence radius for the geometric center. A buffer zone was established to define the virtual electronic fence area. ; Extracting the spatiotemporal trajectory coordinate stream From discrete trajectory points to physical preset coordinates The spherical distance is used to determine the spatial topological inclusion relationship and remove areas falling within the virtual electronic fence region. Site records within; All spherical distances extracted are greater than the radius of the physical electronic fence. The site records generate a detailed list of uninspected sites. .

9. A distributed node data processing system with constrained temporal priority, used to execute the method as described in any one of claims 1-8, characterized in that, The system includes: The edge state machine initialization unit is used to obtain the real-time spatial coordinates of the mobile inspection terminal. With physical preset coordinates Perform distance verification; acquire on-site anomaly image and text features, based on data transmission priority tags. with absolute timestamp For standard data blocks Perform hierarchical sorting to generate asynchronous data sequences waiting to be sent. ; The breakpoint incremental synchronization unit is used to calculate the synchronization timestamp difference to generate time tomography parameters. Trigger a full polling freeze command Activate incremental compensation channel via incremental compensation channel Push data sequence generates synchronized data items ; The scheduling unit is used to parse the wastewater maintenance material requirement parameters. Establish the first maintenance work order data Generate pre-occupied lock data Real-time physical inventory Additional dirty state parameters Intercept concurrent requests and revoke pre-held lock data after value updates or deadlock prevention release. ; The verification unit is used to extract preceding and subsequent sampling points, perform interpolation fitting, and generate theoretical estimates. To generate hardware fault flags A list of suspected entities is established based on set difference operations, and virtual electronic fences are demarcated. Perform spatial topology relationship determination, filter out delayed data, and generate a true list of uninspected sites. .

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

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