A university science and technology park campus abnormal early warning method and system based on wireless networking

Abstract

The embodiment of the application provides a university science and technology park area abnormal early warning method and system based on wireless networking, relates to the technical field of wireless networking, and comprises the following steps: acquiring transmission delay information of each sensor node and collecting environment parameters; generating a data packet according to the transmission delay information and the environment parameters; periodically updating link transmission characteristics of each sensor node according to transmission state information in the data packet; setting a differentiated data recombination waiting boundary according to the link transmission characteristics; according to a real collection cycle identifier of the data packet, merging the data packet into a corresponding cycle cache slot; when the data packet reaches the condition of the waiting boundary, performing recombination on data in the cycle cache slot, and generating an aggregation frame; performing data availability verification according to the aggregation frame to obtain a verification result, and performing inter-building correlation anomaly analysis or taking supplementary processing measures based on the verification result. The application can improve the accuracy and reliability of inter-building correlation anomaly judgment of the early warning system.

Description

Technical Field

[0001] This application relates to the field of wireless networking technology, and more specifically, to a method and system for early warning of anomalies in a university science park based on wireless networking. Background Technology

[0002] In university science park anomaly early warning systems, wireless networking technology is typically used as the physical and link layer support for data transmission. A multi-hop self-organizing network aggregates environmental parameters and device status collected by various sensor nodes to a building aggregation node. To provide a complete monitoring view to the advanced management platform, the aggregation node usually sets a frame reassembly waiting timer. Within the upper limit of the waiting time, it collects and reassembles data from all sub-nodes in the same collection period. However, university science parks typically contain multiple buildings with varying structures, such as physics buildings with thick load-bearing walls and chemical engineering buildings with glass curtain walls. When the system enters a high-frequency, full-park anomaly early warning mode, the different building structures cause significant differences in the attenuation of wireless signals, resulting in significant dispersion in the end-to-end multi-hop delay of each sensor node. In this case, the fixed upper limit of the waiting time setting at the aggregation node for starting the frame reassembly waiting timer to collect data from sub-nodes in the same period is insufficient to accommodate the arrival time differences of multi-source heterogeneous data. Specifically, in high-frequency early warning scanning across multiple heterogeneous buildings in a university science park, data packets within the physics building often arrive later than the globally set reassembly timer timeout due to high penetration loss, long multi-hop paths, and frequent retransmissions. In contrast, data packets from the chemical engineering building arrive completely and earlier. Existing systems typically employ a simplified, uniform waiting limit configuration. After the timer expires, the aggregation node reassembles the received complete data and some incomplete data and submits it upwards. Late data packets arriving in the next cycle are either incorrectly reassembled or discarded due to sequence number mismatches. This results in incomplete and out-of-sequence aggregated frame data received by the early warning system, making it impossible to accurately determine whether cross-building correlation anomalies occur in areas such as precision laboratories. The early warning system cannot reliably perform cross-building correlation anomaly detection due to the compromised data integrity and timing consistency. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method and system for early warning of anomalies in university science parks based on wireless networking. This aims to solve the problem in existing early warning systems for university science parks that use a uniform waiting limit configuration, resulting in incomplete data reconstruction, disordered timing, and an inability to accurately determine cross-building correlation anomalies.

[0004] In a first aspect, embodiments of this application provide a method for early warning of anomalies in a university science park based on wireless networking, including:

[0005] Acquire transmission delay information for each sensor node and collect environmental parameters;

[0006] Generate a data packet containing a real acquisition cycle identifier, a building area identifier, and transmission status information based on the transmission delay information and the environmental parameters;

[0007] Based on the transmission status information in the data packet, the link transmission characteristics of each sensor node are periodically updated, wherein the link transmission characteristics include actual transmission delay, retransmission status, and hop count information.

[0008] Based on the link transmission characteristics, differentiated data reassembly waiting boundaries are set;

[0009] Based on the actual collection cycle identifier of the data packet, the data packet is merged into the corresponding cycle buffer slot, wherein the cycle buffer slot corresponds one-to-one with the actual collection cycle identifier;

[0010] When the data packet reaches the waiting boundary condition, the data in the periodic buffer slot is reassembled and an aggregate frame is generated;

[0011] Data availability is verified based on the aggregated frame, and the verification result is obtained. Based on the verification result, cross-building correlation anomaly analysis or supplementary processing measures are taken.

[0012] According to some embodiments of this application, the collected environmental parameters include:

[0013] Synchronization information is broadcast to each of the sensor nodes, and the local acquisition clock of each sensor node is calibrated according to the synchronization information to ensure that the data acquisition cycle of each sensor node is consistent, thereby obtaining the calibrated local acquisition clock;

[0014] Environmental parameters are collected based on the calibrated local acquisition clock.

[0015] According to some embodiments of this application, setting differentiated data reassembly waiting boundaries based on the link transmission characteristics includes:

[0016] Based on the link transmission characteristics, determine whether there is a stratification phenomenon in the transmission delay of the current set of sensor nodes participating in data reassembly, and obtain the judgment result;

[0017] Based on the judgment result, the sensor node set is divided into node groups with different delay characteristics;

[0018] Based on the latency characteristics of the node groups, differentiated data reassembly waiting boundaries are set, wherein the waiting boundaries include the base waiting time and the extended waiting time for different node groups.

[0019] According to some embodiments of this application, the step of periodically updating the link transmission characteristics of each sensor node based on the transmission status information in the data packet includes:

[0020] Obtain the absolute timestamp during the data packet transmission process;

[0021] Based on the actual collection cycle identifier of the data packet and the preset database, the expected timestamp is obtained, wherein the preset database contains timestamps that correspond one-to-one with the cycle identifier;

[0022] The transmission delay time is obtained by calculating the difference between the absolute timestamp and the expected timestamp.

[0023] The link transmission characteristics of each sensor node are periodically updated based on the transmission status information in the data packet and the transmission delay time.

[0024] According to some embodiments of this application, merging the data packets into the corresponding periodic buffer slots based on the actual collection period identifier of the data packets includes:

[0025] Get the cycle sequence number of the current processing cycle;

[0026] The difference between the actual collection period identifier and the period sequence number of the data packet is calculated to obtain the calculation result;

[0027] If the calculation result is 0, the data packet is merged into the periodic cache slot corresponding to the current processing cycle;

[0028] If the calculation result is negative, the data packet is merged into the periodic cache slot corresponding to the historical processing period according to the actual collection period identifier of the data packet.

[0029] According to some embodiments of this application, the step of reorganizing the data in the periodic cache slot and generating an aggregate frame when the waiting boundary condition is reached includes:

[0030] Based on the building area identifier and transmission status information in the data packet, data packets from different buildings are initially grouped;

[0031] For each data packet within a group, identify the internal data items contained in the data packet and the preset logical dependencies;

[0032] Based on the logical dependencies, time-series calibration and correlation checks are performed on each of the internal data items to obtain the time-series calibration and correlation check results;

[0033] Based on the timing calibration and correlation check results, the data in the periodic cache slot is reassembled, and an aggregate frame is generated.

[0034] According to some embodiments of this application, the step of performing data availability verification based on the aggregated frame, obtaining verification results, and performing cross-building association anomaly analysis or taking supplementary processing measures based on the verification results includes:

[0035] Extract the data integrity identifier, late insertion identifier, and timing reliability identifier from the aggregated frame;

[0036] According to the preset verification rules, the data integrity identifier, the late addition identifier, and the temporal reliability identifier are cross-verified to identify whether there is a contradiction in the aggregated frame and obtain the verification result. The verification result includes high temporal reliability but low integrity, or low temporal reliability but high integrity.

[0037] Based on the verification results, perform cross-building correlation anomaly analysis or take supplementary processing measures.

[0038] According to some embodiments of this application, the step of performing cross-building association anomaly analysis or taking supplementary processing measures based on the verification results includes:

[0039] When the verification result is high temporal reliability but low integrity, the availability score of the aggregated frame is adjusted to assess the criticality of the missing data;

[0040] Based on the criticality of the missing data, targeted supplementation processing is triggered to obtain the supplementation results.

[0041] When the verification result is low temporal reliability but high integrity, the availability score of the aggregated frame is adjusted to assess the impact of temporal deviation on the association analysis.

[0042] Based on the degree of impact, a timing correction process is triggered to obtain the timing correction result;

[0043] Based on the adjusted availability score of the aggregated frame and the supplementary processing result or the timing correction processing result, cross-building association anomaly analysis or supplementary processing measures are performed.

[0044] According to some embodiments of this application, the step of performing cross-building association anomaly analysis or taking supplementary processing measures based on the adjusted availability score of the aggregated frame and the supplementary processing result or the timing correction processing result includes:

[0045] The adjusted availability score of the aggregated frame and the supplementary processing result or the time-series correction processing result are matched with preset building type features to determine the building type to which the current analysis targets.

[0046] Based on the building type, load the corresponding anomaly judgment logic and threshold set from the preset building-specific anomaly pattern library;

[0047] Based on the anomaly judgment logic and the threshold set, cross-building association anomaly analysis is performed on the availability score of the adjusted aggregated frame and the supplementary processing result or the time-series correction processing result to identify specific cross-building association anomalies.

[0048] Based on the cross-building association anomalies, an early warning instruction is generated for the building type.

[0049] Secondly, this application also discloses an anomaly early warning system for a university science park based on wireless networking, including:

[0050] The module acquires and establishes data to obtain transmission delay information from each sensor node and collects environmental parameters.

[0051] The generation module is used to generate a data packet containing a real acquisition cycle identifier, a building area identifier, and transmission status information based on the transmission delay information and the environmental parameters.

[0052] The update module is used to periodically update the link transmission characteristics of each sensor node according to the transmission status information in the data packet, wherein the link transmission characteristics include actual transmission delay, retransmission status and hop count information.

[0053] The setting module is used to set differentiated data reassembly waiting boundaries based on the link transmission characteristics;

[0054] The merging module is used to merge the data packets into the corresponding periodic buffer slots according to the actual collection period identifier of the data packets, wherein the periodic buffer slots correspond one-to-one with the actual collection period identifiers;

[0055] The reassembly module is used to reassemble the data in the periodic buffer slot and generate an aggregate frame when the data packet reaches the waiting boundary condition;

[0056] The analysis module is used to perform data availability verification based on the aggregated frame, obtain the verification result, and perform cross-building association anomaly analysis or take supplementary processing measures based on the verification result.

[0057] The technical solution according to the embodiments of this application has at least the following beneficial effects: This application provides a method for early warning of anomalies in a university science park based on wireless networking. It acquires the transmission delay information and environmental parameters of each sensor node, and generates data packets containing the actual collection period identifier, building area identifier, and transmission status information based on this information. On this basis, this application periodically updates the link transmission characteristics of each sensor node and sets differentiated data reassembly waiting boundaries based on these characteristics. When the data packet reaches the waiting boundary condition, the data in the periodic buffer slot is reassembled, and an aggregate frame is generated. Finally, data availability is verified based on the aggregate frame, and cross-building correlation anomaly analysis or supplementary processing measures are taken based on the verification results. This method effectively solves the problem in the prior art where the attenuation of wireless signals from multiple heterogeneous buildings in a university science park varies greatly, resulting in significant dispersion of end-to-end multi-hop delays for each sensor node. Existing systems use a unified waiting upper limit configuration, causing late data packets to be incorrectly combined or discarded after the timer expires at the aggregation node. This results in incomplete and out-of-sequence aggregate frame data received by the early warning system, making it impossible to accurately determine cross-building correlation anomalies. The technical solution of this application enables the dynamic setting of differentiated data reassembly waiting boundaries based on link transmission characteristics such as actual transmission delay, retransmission status, and hop count information, thereby better adapting to the arrival time differences of multi-source heterogeneous data. This ensures that data packets can be reassembled more completely and accurately under different building structures and transmission conditions, avoiding data loss and timing discrepancies caused by fixed waiting time limits. Therefore, this application can provide complete and time-consistent aggregated frame data, significantly improving the accuracy and reliability of the early warning system in judging cross-building correlation anomalies.

[0058] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0059] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0060] Figure 1 A flowchart illustrating an anomaly warning method for a university science park based on wireless networking, provided as an embodiment of this application;

[0061] Figure 2 A flowchart illustrating the process of collecting environmental parameters according to one embodiment of this application;

[0062] Figure 3 A flowchart illustrating the setting of differentiated data reassembly waiting boundaries is provided for one embodiment of this application;

[0063] Figure 4 This is a schematic diagram of an anomaly early warning system for a university science park based on wireless networking, provided as an embodiment of this application. Detailed Implementation

[0064] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0065] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0066] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: the existence of a alone, the existence of b alone, the existence of c alone, the simultaneous existence of a and b, the simultaneous existence of a and c, the simultaneous existence of b and c, or the simultaneous existence of a, b, and c, where a, b, and c can be single or multiple.

[0067] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0068] The method for early warning of campus anomalies in university science parks based on wireless networking provided in this application can be applied to terminals, servers, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application that implements the method for early warning of campus anomalies in university science parks based on wireless networking, but is not limited to the above forms.

[0069] The embodiments of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0070] See Figure 1 , Figure 1 This is a flowchart illustrating an anomaly warning method for a university science park based on wireless networking, provided in one embodiment of this application. The anomaly warning method for a university science park based on wireless networking provided in this embodiment includes, but is not limited to, steps S110 to S170, which are described in detail below.

[0071] Step S110: Obtain the transmission delay information of each sensor node and collect environmental parameters;

[0072] Step S120: Generate a data packet containing the actual collection cycle identifier, building area identifier, and transmission status information based on the transmission delay information and environmental parameters;

[0073] Step S130: Based on the transmission status information in the data packet, periodically update the link transmission characteristics of each sensor node, wherein the link transmission characteristics include actual transmission delay, retransmission status and hop count information.

[0074] Step S140: Based on the link transmission characteristics, set differentiated data reassembly waiting boundaries;

[0075] Step S150: According to the actual collection cycle identifier of the data packet, merge the data packet into the corresponding cycle buffer slot, wherein the cycle buffer slot corresponds one-to-one with the actual collection cycle identifier.

[0076] Step S160: When the data packet reaches the waiting boundary condition, the data in the periodic buffer slot is reassembled and an aggregate frame is generated;

[0077] Step S170: Perform data availability verification based on the aggregated frame, obtain the verification result, and perform cross-building association anomaly analysis or take supplementary processing measures based on the verification result.

[0078] It should be noted that sensor nodes refer to physical devices deployed within university science parks to collect environmental parameters (such as temperature, humidity, air quality, equipment status, etc.). These devices transmit the collected data to the aggregation node via wireless networking technology. Transmission latency information refers to the time required for data to travel from the sensor node to the receiving end, reflecting the transmission efficiency and congestion status of the wireless link. Environmental parameters are various physical quantities or status information collected by the sensor nodes. Data packets are the basic units carrying this information for network transmission, containing the actual collection period identifier, building area identifier, and transmission status information. These identifiers and information are crucial for subsequent data processing and anomaly warning. Link transmission characteristics describe the quality of data transmission in more detail, including actual transmission latency, retransmission status (indicating whether a data packet needs to be sent multiple times to arrive successfully), and hop count information (the number of intermediate nodes a data packet passes through from the source node to the destination node). Differentiated data reassembly waiting boundaries refer to the data reassembly waiting time dynamically set according to the transmission characteristics of different sensor nodes, rather than a uniform fixed time. Periodic buffer slots are logical storage units used to temporarily store data packets from different sensor nodes within the same collection period. An aggregated frame is a reconstructed complete data frame containing data from all relevant sensor nodes within the same period. Data availability verification checks the completeness and timing reliability of the aggregated frame to ensure data reliability. Cross-building correlation anomaly analysis, based on the verification results, conducts in-depth analysis and judgment of potential correlation anomalies between different buildings.

[0079] In one embodiment, the transmission delay information of each sensor node is first acquired, and environmental parameters are collected. Preliminary network performance testing can be performed during sensor node deployment to record the time required for data packets to travel from the sensor node to the aggregation node, thus obtaining transmission delay information. Simultaneously, the sensor nodes periodically collect environmental parameters of their area, such as temperature, humidity, and PM2.5 concentration. The collection of these environmental parameters can be controlled by a microcontroller within the sensor node at a preset frequency. After collecting the environmental parameters, the sensor node encapsulates them with the current collection period identifier (e.g., a timestamp or sequence number), the building area identifier (e.g., "Physics Building A," "Chemical Building B"), and the transmission status information (e.g., initial transmission, retransmission count, etc.) into a data packet. This data packet is then sent out via the wireless network. Upon receiving the data packet, the aggregation node parses the transmission status information. If the data packet indicates a retransmission, the retransmission statistics for that sensor node are updated. Simultaneously, the actual transmission delay can be calculated by recording the difference between the arrival time and transmission time of the data packet. Hop count information can be obtained from the routing information carried in the data packet header. The updates to these link transmission characteristics can be periodic, such as updating at regular intervals or after receiving a certain number of data packets, to reflect dynamic changes in network conditions. If the link transmission characteristics of a sensor node indicate a long transmission latency and a high retransmission rate, a longer reassembly wait boundary can be set for it to ensure that its data has sufficient time to arrive. Conversely, for sensor nodes with short transmission latency and low retransmission rates, a shorter wait boundary can be set. This differentiated setting can avoid data loss or incompleteness caused by a fixed wait time limit. The aggregation node maintains a series of periodic buffer slots, each corresponding to a specific acquisition period. When a data packet is received, the system reads the actual acquisition period identifier from the data packet and places it into the corresponding periodic buffer slot. If a periodic buffer slot has not yet been created, a new slot will be created based on the actual acquisition period identifier of the data packet. When the number of data packets in a periodic buffer slot reaches a preset threshold, or the wait boundary time corresponding to that slot has expired, the system will trigger a data reassembly operation. During the reassembly process, the system integrates all data packets belonging to the same acquisition period in the slot, removes redundant information, and generates a complete aggregate frame according to a preset format. After generating the aggregate frame, the system performs availability checks such as integrity checks and timing consistency checks. The check results indicate whether the aggregate frame has issues such as missing data or timing errors. Based on these check results, the system can decide whether to directly perform cross-building correlation anomaly analysis or take supplementary processing measures, such as requesting retransmission of missing data or correcting timing, to ensure the accuracy of subsequent analysis.

[0080] It should be noted that this application employs a differentiated data reassembly waiting boundary setting mechanism, combined with periodic link transmission characteristic updates and data availability verification. Compared to the uniform fixed waiting time upper limit used in the closest prior art, this application significantly improves the integrity and timing consistency of data aggregation. By periodically updating the link transmission characteristics of each sensor node, this application can perceive and quantify the differences in transmission performance between different nodes in real time. Based on this, personalized data reassembly waiting boundaries are set according to these differences, ensuring that even in areas with poor transmission conditions, data packets have sufficient time to arrive and be incorporated into the reassembly, thereby avoiding data loss and timing discrepancies. Furthermore, the data availability verification step further guarantees the quality of the aggregated frames, providing a reliable data foundation for subsequent cross-building correlation anomaly analysis. This dynamic adaptation and refined management strategy enables this application to provide more accurate and reliable anomaly early warning capabilities in the complex and heterogeneous environment of university science parks.

[0081] See Figure 2 , Figure 2 This is a flowchart illustrating the process of collecting environmental parameters according to one embodiment of this application. This application further proposes steps for collecting environmental parameters, including:

[0082] Step S210: Broadcast synchronization information to each sensor node, calibrate the local acquisition clock of the sensor node according to the synchronization information to ensure that the data acquisition cycle of each sensor node is consistent, and obtain the calibrated local acquisition clock.

[0083] Step S220: Collect environmental parameters according to the calibrated local acquisition clock.

[0084] Specifically, synchronization information can be understood as a timestamp or synchronization signal, its purpose being to provide a unified time reference for all sensor nodes participating in data acquisition. For example, synchronization information can be periodically broadcast by a central server or a specific coordinating node to ensure that all sensor nodes receive the latest time synchronization instructions. Calibrating the local acquisition clock of a sensor node refers to the process where, upon receiving synchronization information, the sensor node compares and adjusts its internal clock with the time reference carried in the synchronization information, thereby eliminating or reducing potential clock drift or initial deviations between different sensor nodes. Its purpose is to ensure that each sensor node starts or ends its data acquisition cycle at the same time, resulting in a high degree of consistency in the time dimension of environmental parameters collected from different nodes. This ensures that the data acquisition cycles of all sensor nodes are consistent, meaning that all sensor nodes acquire data at the same frequency and starting point, thus obtaining a calibrated local acquisition clock. After the local acquisition clock is calibrated, each sensor node will strictly follow this unified and calibrated clock when acquiring environmental parameters. Through the above technical solution, the temporal consistency and accuracy of the acquired environmental parameters can be significantly improved. This consistency is crucial for subsequent data processing. For example, during data reassembly, it allows for more accurate merging of data belonging to the same acquisition cycle into the corresponding cycle buffer slots, preventing data misalignment or loss due to clock skew. Furthermore, highly consistent data provides more reliable input for subsequent cross-building anomaly analysis, making anomaly pattern identification more accurate and thus improving the reliability and effectiveness of the entire early warning system.

[0085] In one embodiment, it is assumed that hundreds of sensor nodes are deployed within a university science park, distributed across different buildings and areas, to monitor environmental parameters such as temperature, humidity, and PM2.5. To ensure that the data collected by these sensor nodes is synchronized in time, a central management server broadcasts a synchronization message containing the current precise timestamp to all sensor nodes every minute. When a sensor node receives this synchronization message, it compares its internal real-time clock with the timestamp in the synchronization message. If a discrepancy is found between the local clock and the synchronization timestamp (e.g., the local clock is 50 milliseconds ahead), the sensor node immediately adjusts its local clock to match the central server's time. After calibration, all sensor nodes will strictly follow this unified, calibrated local acquisition clock to synchronously collect environmental parameters at a preset acquisition cycle (e.g., every 30 seconds). For example, when all nodes are calibrated to Beijing time 10:00:00, they will simultaneously collect data at times such as 10:00:00, 10:00:30, and 10:01:00, thereby ensuring a high degree of consistency of all collected environmental parameters in the time dimension.

[0086] See Figure 3 , Figure 3 This application provides a flowchart illustrating the process of setting differentiated data reassembly waiting boundaries according to one embodiment of the present application. Furthermore, the present application proposes that the steps for setting differentiated data reassembly waiting boundaries based on link transmission characteristics include:

[0087] Step S310: Based on the link transmission characteristics, determine whether there is a layering phenomenon in the transmission delay of the current set of sensor nodes participating in data reassembly, and obtain the judgment result;

[0088] Step S320: Based on the judgment result, divide the sensor node set into node groups with different delay characteristics;

[0089] Step S330: Based on the latency characteristics of the node group, set differentiated data reassembly waiting boundaries, wherein the waiting boundaries include the basic waiting time and extended waiting time for different node groups.

[0090] Specifically, determining whether the transmission delay of the current set of sensor nodes participating in data reassembly exhibits a stratified phenomenon refers to statistically analyzing the link transmission characteristics (including actual transmission delay, retransmission status, and hop count) collected by each sensor node over a period of time. This analysis can be achieved using clustering algorithms or statistical significance tests to identify whether there are distinct, differentiated subgroups within the transmission delay distribution. Based on the determination results, the sensor node set is divided into node groups with different delay characteristics. This can be understood as grouping sensor nodes with similar transmission delay characteristics into the same node group based on the identified stratification. For example, nodes with stable, low transmission delays can be grouped into a "low-latency group," while nodes with high and fluctuating transmission delays can be grouped into a "high-latency group." Differentiated data reassembly waiting boundaries are set based on the delay characteristics of the node groups. For instance, a shorter base waiting time can be set for the "low-latency group," with a smaller extended waiting time based on its historical fluctuations; while a longer base waiting time can be set for the "high-latency group," with a larger extended waiting time based on its retransmission rate and hop count. The waiting boundary includes a base waiting time and an extended waiting time for different node groups. The base waiting time is the minimum waiting duration to ensure that most data packets arrive, while the extended waiting time is used to cope with instantaneous network fluctuations or anomalies, allowing for flexible extension of the waiting time within a certain range to improve data integrity. This technical solution enables refined management of the waiting boundary for sensor data reassembly within a university science park. By identifying the hierarchical phenomenon of transmission delay and dividing node groups accordingly, more reasonable and adaptive waiting boundaries can be set for nodes with different delay characteristics, significantly improving the efficiency and accuracy of data reassembly. This not only effectively reduces additional delays caused by inappropriate waiting times but also maximizes data integrity in complex network environments, providing high-quality, high-reliability aggregated data for subsequent anomaly early warning analysis, thereby improving the response speed and accuracy of the entire early warning system.

[0091] In this regard, this application further proposes the following steps for periodically updating the link transmission characteristics of each sensor node based on the transmission status information in the data packet:

[0092] Obtain the absolute timestamp during the data packet transmission process;

[0093] Based on the actual collection cycle identifier of the data packet and the preset database, the expected timestamp is obtained. The preset database contains timestamps that correspond one-to-one with the cycle identifier.

[0094] The transmission delay time is obtained by calculating the difference between the absolute timestamp and the expected timestamp.

[0095] The link transmission characteristics of each sensor node are periodically updated based on the transmission status information and transmission delay time in the data packets.

[0096] Specifically, obtaining the absolute timestamp during data packet transmission refers to recording the precise point in time when a data packet is sent from a sensor node or arrives at the receiver. This absolute timestamp is typically provided by the system clock, possessing high precision and global consistency, and its purpose is to accurately measure the transmission time of data packets in the network. The expected timestamp is obtained based on the actual acquisition cycle identifier of the data packet and a preset database. This can be understood as the system maintaining a preset database that stores the expected arrival timestamps of data packets corresponding to each actual acquisition cycle identifier. The actual acquisition cycle identifier is a periodic marker attached to the data collected by the sensor node at a predetermined frequency, such as "collected every 5 seconds" or "collected every minute." By querying this preset database, the expected arrival time of the data packet under ideal network conditions can be determined based on the actual acquisition cycle identifier. The transmission delay time is obtained by calculating the difference between the absolute timestamp and the expected timestamp, which involves comparing the actual arrival timestamp of the data packet with the expected timestamp obtained from the preset database based on its actual acquisition cycle identifier and calculating the time difference between the two. This time difference represents the delay experienced by data packets during transmission in the network. Its purpose is to reflect the impact of factors such as network congestion and degraded link quality on the real-time performance of data transmission. Periodically updating the link transmission characteristics of each sensor node based on the transmission status information and transmission delay time in the data packets involves combining the calculated transmission delay time with existing transmission status information in the data packets (such as retransmission count, signal strength, packet loss rate, etc.) to comprehensively evaluate and update the current link transmission characteristics of the sensor node. This comprehensive evaluation provides a more complete and accurate view of the link health status, thus providing a more reliable foundation for subsequent data reassembly and anomaly early warning.

[0097] In one embodiment, the solution of this application solves the problem that relying solely on transmission status information may result in insufficiently refined link feature updates by introducing precise calculation of data packet transmission delay time. Specifically, by obtaining the absolute timestamp during data packet transmission and combining it with a preset real collection period identifier and expected timestamp, the actual transmission delay of each data packet can be accurately quantified. This delay information directly reflects the real-time performance of the network link, such as the degree of congestion or the stability of the transmission path. Combining this transmission delay time with the original transmission status information in the data packet (such as retransmission status, hop count information, etc.) can form a more comprehensive and dynamic view of the link transmission characteristics. It is precisely because of this refined consideration of transmission delay that the system can more sensitively capture subtle changes in the network link, thereby providing more accurate and reliable input for subsequently setting differentiated data reassembly waiting boundaries and conducting cross-building correlation anomaly analysis.

[0098] In response, this application further proposes the aforementioned step of merging data packets into the corresponding periodic buffer slots based on the actual data packet acquisition period identifier. By introducing a periodic sequence number comparison mechanism, accurate merging of data packets is achieved, effectively distinguishing and processing data packets belonging to the current period and historical periods. The aforementioned step of merging data packets into the corresponding periodic buffer slots based on the actual data packet acquisition period identifier specifically includes:

[0099] Get the cycle sequence number of the current processing cycle;

[0100] The difference between the actual collection period identifier and the period sequence number of the data packet is calculated to obtain the calculation result;

[0101] If the calculation result is 0, the data packet will be merged into the periodic buffer slot corresponding to the current processing cycle.

[0102] If the calculation result is negative, the data packet is merged into the periodic buffer slot corresponding to the historical processing period based on the actual data packet acquisition period identifier.

[0103] Specifically, obtaining the cycle sequence number of the current processing cycle refers to the unique identifier maintained by the system during data processing to represent the data cycle currently being processed. This cycle sequence number is typically an incrementing integer used to mark the time window for data acquisition and processing. For example, the system can increment the cycle sequence number every certain period of time (e.g., 1 minute) to distinguish data acquired within different time periods. The difference calculation between the actual acquisition cycle identifier and the cycle sequence number of the data packet yields a result. This can be understood as comparing the acquisition timestamp (converted to a cycle identifier) ​​carried by the data packet itself with the cycle identifier currently being processed by the system to determine whether the data packet belongs to the current cycle, a future cycle, or a historical cycle. This difference calculation can quickly and quantitatively reflect the "freshness" or "time sequence position" of the data packet. When the calculation result is 0, the data packet is merged into the cycle buffer slot corresponding to the current processing cycle, meaning that the data packet's acquisition cycle is completely consistent with the cycle currently being processed by the system. At this time, the data packet is considered currently valid data and is placed in the current cycle buffer slot corresponding to the actual acquisition cycle identifier for subsequent reassembly and analysis. When the calculation result is negative, the data packet is merged into the corresponding periodic cache slot of the historical processing period based on its actual acquisition period identifier. This means that when the acquisition period identifier of the data packet is less than the period sequence number of the current processing period, it indicates that the data packet is "late" data and belongs to a past processing period. To ensure the integrity and traceability of historical data, these late data packets are not discarded, but are accurately merged into the corresponding historical periodic cache slot according to their actual acquisition period identifier. Through the above technical solution, this application can effectively solve the problem of improper handling of late data in the traditional data merging process, and significantly improve the accuracy and robustness of data management. Specifically, by introducing the comparison of periodic sequence numbers, the system can intelligently distinguish between current data and historical data, ensuring that all data packets, regardless of their arrival time, can be merged into their correct periodic cache slot, thereby improving the accuracy and reliability of anomaly warning.

[0104] In this regard, this application further proposes the following steps for reassembling the data in the periodic buffer slot and generating an aggregate frame when the data packet reaches the waiting boundary condition:

[0105] Based on the building area identifier and transmission status information in the data packets, data packets from different buildings are initially grouped.

[0106] For each data packet within a group, identify the internal data items contained in the data packet and the preset logical dependencies;

[0107] Based on logical dependencies, time-series calibration and correlation checks are performed on each internal data item to obtain the results of time-series calibration and correlation checks.

[0108] Based on the timing calibration and correlation check results, the data in the periodic cache slots is reassembled, and an aggregate frame is generated.

[0109] Specifically, during the initial grouping of data packets, building area identifiers carried in the packets can be used to distinguish data sources. For example, packets from teaching buildings can be grouped together, and packets from laboratories can be grouped into another group. Simultaneously, transmission status information, such as packet transmission delay and retransmission count, can also serve as a basis for auxiliary grouping, ensuring that packets with similar transmission characteristics within the same physical area are prioritized. This aims to lay the foundation for subsequent refined processing and avoid confusion between data from different areas. Identifying the internal data items and preset logical dependencies within the data packets refers to parsing the payload of each packet and extracting various environmental parameters (such as temperature, humidity, PM2.5 concentration, etc.) or other sensor data. The system will then identify potential logical dependencies between these internal data items based on a preset rule base or configuration. For example, within a certain building area, temperature sensor data may be strongly correlated with air conditioning operating status data, or data from multiple sensors may collectively reflect the operating status of a device. These logical dependencies are crucial for time-series calibration and correlation checks. Timing calibration and correlation checks based on logical dependencies for internal data items refer to aligning timestamps and verifying consistency of internal data items within the same data packet after identifying the dependencies between data items. Timing calibration aims to eliminate time deviations caused by transmission delays or sensor clock drift, ensuring that all related data items remain synchronized on the timeline. Correlation checks, based on preset logical dependencies, verify whether there are contradictions or anomalies between data items. For example, if the temperature in a certain area rises sharply, but the air conditioning system shows normal operation, this may be a potential anomaly. These checks improve the internal consistency and reliability of the data. The aggregation process can either merge multiple related data packets into a larger data structure or extract related data items from different data packets and organize them into a unified aggregation frame according to a preset format.

[0110] In one embodiment, assume a university science park contains two teaching buildings, A and B, and a laboratory building, C. Environmental sensors (such as temperature, humidity, and PM2.5) and equipment operation status sensors (such as air conditioning and lighting) are deployed in each building. When these sensor nodes periodically collect data and generate data packets, and a set waiting boundary condition is reached, the system initiates a data reassembly process. The system categorizes data packets from building A into group A, building B into group B, and building C into group C based on the building area identifiers (e.g., building A, building B, and building C) and transmission status information (e.g., average transmission delay of the data packets). Then, for data packets within group A, the system identifies internal data items such as environmental parameters (temperature, humidity, PM2.5, etc.) and air conditioning operation status. Simultaneously, based on preset logical dependencies, the system identifies a strong correlation between the temperature data and the air conditioning operation status data of building A. Subsequently, based on these logical dependencies, the system performs time-series calibration on the temperature data and air conditioning operation status data in the data packets within Group A, ensuring their timestamps are aligned and performing correlation checks, such as checking whether the temperature drops as expected when the air conditioner is turned on. If a data packet's temperature data is found to be significantly inconsistent with the air conditioning status (e.g., the air conditioner is on for a long time but the temperature continues to rise), the data packet or related data items are flagged as potentially problematic. Finally, based on the results of the time-series calibration and correlation checks, the system aggregates all verified data packets within Group A to generate an aggregate frame representing the current state of Building A. The same process is applied to Groups B and C, generating aggregate frames for Buildings B and C respectively. These high-quality aggregate frames are then used for cross-building correlation anomaly analysis, such as analyzing the correlation between air conditioning energy consumption and outdoor temperature in Buildings A and B to identify potential energy waste or equipment malfunctions.

[0111] Specifically, the steps mentioned above, including performing data availability verification based on aggregated frames, obtaining verification results, and conducting cross-building correlation anomaly analysis or taking supplementary processing measures based on the verification results, include:

[0112] Extract the data integrity identifier, late addition identifier, and timing reliability identifier from the aggregated frame;

[0113] According to the preset verification rules, the data integrity identifier, the late addition identifier, and the temporal reliability identifier are cross-validated to identify whether there are contradictions in the aggregated frames and obtain the verification results. The verification results include high temporal reliability but low integrity, or low temporal reliability but high integrity.

[0114] Based on the verification results, conduct cross-building correlation anomaly analysis or take supplementary processing measures.

[0115] The data integrity flag indicates whether the data items in the aggregated frame are complete. For example, it can be a Boolean value or a percentage representing the degree of matching between the expected and actual received data. The late addition flag marks whether there is data added later due to delay in the aggregated frame. Its purpose is to distinguish between original timely data and later supplemented data so that its timeliness impact can be considered during verification. The timing reliability flag assesses whether the time sequence of the data in the aggregated frame is accurate and reliable. For example, it can be calculated based on the difference between the absolute timestamp of the data packet and the expected timestamp. Furthermore, the preset verification rules can be a series of logical judgment conditions used to comprehensively evaluate the data integrity flag, late addition flag, and timing reliability flag. For example, when the data integrity flag indicates missing data, but the timing reliability flag is high, it may mean that some data was lost during transmission, but the timing of the remaining data is accurate. Conversely, if the data integrity flag is high but the timing reliability flag is low, it may indicate that although the data is complete, its timestamp or transmission order has a large deviation. Specifically, the verification results can include various scenarios such as "high temporal reliability but low integrity" or "low temporal reliability but high integrity." These refined verification results provide a more accurate basis for subsequent anomaly analysis or supplementary processing. The solution in this application, during the data availability verification stage, goes beyond simply determining whether the data is usable. Instead, it deeply extracts data integrity identifiers, late addition identifiers, and temporal reliability identifiers from the aggregated frame and cross-validates these identifiers using preset verification rules. This multi-dimensional, cross-validation mechanism enables the system to identify potentially complex and contradictory situations in the aggregated frame, such as complete data but disordered temporal sequence, or accurate temporal sequence but incomplete data. This results in more refined and accurate verification results, providing more reliable input for subsequent cross-building anomaly analysis or supplementary processing measures.

[0116] In response, this application further proposes the following steps for conducting cross-building correlation anomaly analysis or taking supplementary processing measures based on the verification results:

[0117] When the verification result is high temporal reliability but low completeness, adjust the availability score of the aggregated frame and assess the criticality of the missing data;

[0118] Based on the criticality of the missing data, targeted supplementation processing is triggered to obtain the supplementation results.

[0119] When the verification result is low temporal reliability but high integrity, adjust the availability score of the aggregated frame and evaluate the impact of temporal deviation on the association analysis.

[0120] Based on the degree of impact, timing correction processing is triggered to obtain the timing correction processing results;

[0121] Based on the availability score of the adjusted aggregated frame and the results of supplementary processing or time-series correction processing, conduct cross-building association anomaly analysis or take supplementary processing measures.

[0122] Specifically, when the verification result indicates that the aggregated frame has high temporal reliability but low completeness, it means that the temporal order of the data is reliable, but some data is missing. In this case, the system is configured to first adjust the availability score of the aggregated frame, which reflects the reliability of the data in subsequent analysis. Then, the criticality of the missing data is assessed, that is, the importance of the missing data items for cross-building correlation anomaly analysis is determined. For example, if the missing data is a core environmental parameter, its criticality is high; if the missing data is auxiliary data, its criticality is low. Based on the assessment of the criticality of the missing data, targeted supplementary processing is triggered, such as by interpolation, historical data filling, or requesting retransmission to compensate for the missing data, and the supplementary processing results are obtained. Conversely, when the verification result indicates that the aggregated frame has low temporal reliability but high completeness, it means that all data items have been collected, but their temporal order may be biased or delayed, resulting in unreliable temporal order. The system is configured to adjust the availability score of the aggregated frame and assess the impact of temporal deviations on correlation analysis. For example, slight temporal deviations may have little impact, while significant temporal discrepancies may lead to incorrect correlation judgments. Based on the assessed impact level, time-series correction processing is triggered. This includes techniques such as timestamp alignment, sequence rearrangement, or delay compensation to correct time-series issues and obtain the corrected results. After completing the aforementioned targeted supplementary or time-series correction processing, the final cross-building correlation anomaly analysis or further supplementary processing measures are performed based on the adjusted availability score of the aggregated frame and the corresponding supplementary or time-series correction results. This application can differentiate the availability score adjustment and targeted processing of aggregated frames according to the specific type of data availability verification results. This enables the system to more accurately identify and handle data integrity issues or time-series reliability issues, avoiding misjudgments or insufficient processing due to different types of data quality problems. Therefore, it significantly improves the accuracy and reliability of cross-building correlation anomaly analysis, ensures the timeliness and effectiveness of anomaly warnings, and provides more refined and intelligent support for the security management of university science parks.

[0123] The steps described above for performing cross-building correlation anomaly analysis or taking supplementary processing measures based on the adjusted availability score of the aggregated frame and the results of supplementary processing or time-series correction processing specifically include:

[0124] The usability score and supplementary processing results or time-series correction processing results of the adjusted aggregated frame are matched with the preset building type characteristics to determine the building type to which the current analysis is targeting.

[0125] Based on the building type, load the corresponding anomaly judgment logic and threshold set from the preset building-specific anomaly pattern library;

[0126] Based on the anomaly judgment logic and threshold set, cross-building association anomaly analysis is performed on the availability score and supplementary processing results or time-series correction processing results of the adjusted aggregated frame in order to identify specific cross-building association anomalies.

[0127] Based on cross-building association anomalies, generate early warning instructions for specific building types.

[0128] Specifically, the aforementioned "predefined building type characteristics" refers to a predefined and stored set of unique attributes used to describe different types of buildings (such as teaching buildings, laboratory buildings, libraries, dormitories, and office buildings) within the university science park. These characteristics may include the building's function, main activity times, typical pedestrian flow patterns, key equipment types, normal ranges of environmental parameters, and types of historical abnormal events. The purpose is to provide contextual information for subsequent anomaly analysis, ensuring the analysis's relevance. The aforementioned "matching" can be achieved by comparing availability scores and processing results with key indicators or patterns in the predefined building type characteristics. For example, if the data mainly comes from laboratory areas and environmental parameters fluctuate significantly, it may be matched to the "laboratory building" type. Furthermore, the aforementioned "predefined building-specific anomaly pattern library" refers to a database containing pre-trained or defined anomaly patterns, judgment rules, and thresholds for different building types. For example, for laboratory buildings, anomaly patterns might include excessive concentrations of specific gases, ventilation system malfunctions, and abnormally high equipment temperatures; for dormitories, the focus might be on abnormal nighttime pedestrian flow or sudden changes in water and electricity consumption. The aforementioned "anomaly judgment logic and threshold set" refers to the algorithms and parameters used to identify abnormal behavior of specific building types. For example, for a certain building type, the judgment logic could be a machine learning-based model, while the threshold set defines which parameters exceeding what range are considered abnormal. The aforementioned "cross-building associated anomaly analysis" refers to, after identifying the building type, using the anomaly judgment logic and threshold set specific to that building type to conduct in-depth analysis of the adjusted availability score and processing results. This can include a comprehensive evaluation of data from multiple sensors to identify anomaly patterns that cannot be detected by a single sensor but are indicated by multiple sensors. For example, simultaneous anomalies in temperature and smoke sensors in a certain area may indicate a fire risk. Therefore, the aforementioned "specific cross-building associated anomalies" refers to anomaly events with specific types and locations that are clearly identified after the above analysis, such as "an abnormal temperature rise in a chemical laboratory on the third floor of a certain experimental building accompanied by an unidentified gas leak." The aforementioned "early warning instructions for building types" refers to generating highly targeted alarm information and suggested handling measures based on the identified specific anomalies. For example, in the event of a chemical leak in a laboratory building, the warning instructions might include "immediately activate the ventilation system, evacuate relevant personnel, and notify the fire department."

[0129] It should be noted that the solution proposed in this application addresses the problems of excessive generality and insufficient specificity in traditional anomaly analysis by introducing building type feature matching and a building-specific anomaly pattern library. Specifically, firstly, the verified and processed data is matched with preset building type features to accurately identify the building type to which the current data belongs, providing important contextual information for subsequent anomaly analysis. Secondly, based on the identified building type, the corresponding anomaly judgment logic and threshold set are loaded from the preset building-specific anomaly pattern library. This mechanism ensures that anomaly analysis is no longer based on a set of general rules, but rather on customized analysis tailored to the function, environment, and potential risks of a specific building. Therefore, when conducting cross-building correlation anomaly analysis, more precise and realistic judgment logic and thresholds can be used to more effectively identify specific anomalies highly correlated with that building type. Finally, based on the identified cross-building correlation anomalies, early warning instructions are generated for that building type, making the early warning information more accurate and the action suggestions more specific, significantly improving the practicality and effectiveness of the early warning.

[0130] See Figure 4 , Figure 4 This is a schematic diagram of a university science park anomaly early warning system based on wireless networking, provided as an embodiment of this application. The university science park anomaly early warning system 400 based on wireless networking includes:

[0131] The acquisition and establishment module 410 acquires the transmission delay information of each sensor node and collects environmental parameters;

[0132] The generation module 420 is used to generate a data packet containing the real acquisition cycle identifier, building area identifier, and transmission status information based on the transmission delay information and environmental parameters.

[0133] The update module 430 is used to periodically update the link transmission characteristics of each sensor node based on the transmission status information in the data packet. The link transmission characteristics include actual transmission delay, retransmission status, and hop count information.

[0134] The setting module 440 is used to set differentiated data reassembly waiting boundaries based on the link transmission characteristics;

[0135] The merging module 450 is used to merge data packets into the corresponding periodic buffer slots according to the actual collection period identifier of the data packets, wherein the periodic buffer slots correspond one-to-one with the actual collection period identifiers.

[0136] The reassembly module 460 is used to reassemble the data in the periodic buffer slot and generate an aggregate frame when the data packet reaches the waiting boundary condition;

[0137] Analysis module 470 is used to perform data availability verification based on the aggregated frame, obtain the verification results, and perform cross-building association anomaly analysis or take supplementary processing measures based on the verification results.

[0138] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0139] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0140] The above provides a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A method for early warning of anomalies in a university science park based on wireless networking, characterized in that, include: Acquire transmission delay information for each sensor node and collect environmental parameters; Generate a data packet containing a real acquisition cycle identifier, a building area identifier, and transmission status information based on the transmission delay information and the environmental parameters; Based on the transmission status information in the data packet, the link transmission characteristics of each sensor node are periodically updated, wherein the link transmission characteristics include actual transmission delay, retransmission status, and hop count information. Based on the link transmission characteristics, differentiated data reassembly waiting boundaries are set; Based on the actual collection cycle identifier of the data packet, the data packet is merged into the corresponding cycle buffer slot, wherein the cycle buffer slot corresponds one-to-one with the actual collection cycle identifier; When the data packet reaches the waiting boundary condition, the data in the periodic buffer slot is reassembled and an aggregate frame is generated; Data availability is verified based on the aggregated frame, and the verification result is obtained. Based on the verification result, cross-building correlation anomaly analysis or supplementary processing measures are taken.

2. The method according to claim 1, characterized in that, The collected environmental parameters include: Synchronization information is broadcast to each of the sensor nodes, and the local acquisition clock of each sensor node is calibrated according to the synchronization information to ensure that the data acquisition cycle of each sensor node is consistent, thereby obtaining the calibrated local acquisition clock; Environmental parameters are collected based on the calibrated local acquisition clock.

3. The method according to claim 1, characterized in that, The step of setting differentiated data reassembly waiting boundaries based on the link transmission characteristics includes: Based on the link transmission characteristics, determine whether there is a stratification phenomenon in the transmission delay of the current set of sensor nodes participating in data reassembly, and obtain the judgment result; Based on the judgment result, the sensor node set is divided into node groups with different delay characteristics; Based on the latency characteristics of the node groups, differentiated data reassembly waiting boundaries are set, wherein the waiting boundaries include the base waiting time and the extended waiting time for different node groups.

4. The method according to claim 1, characterized in that, The step of periodically updating the link transmission characteristics of each sensor node based on the transmission status information in the data packet includes: Obtain the absolute timestamp during the data packet transmission process; Based on the actual collection cycle identifier of the data packet and the preset database, the expected timestamp is obtained, wherein the preset database contains timestamps that correspond one-to-one with the cycle identifier; The transmission delay time is obtained by calculating the difference between the absolute timestamp and the expected timestamp. The link transmission characteristics of each sensor node are periodically updated based on the transmission status information in the data packet and the transmission delay time.

5. The method according to claim 1, characterized in that, The step of merging the data packets into the corresponding periodic buffer slots based on the actual collection period identifier of the data packets includes: Get the cycle sequence number of the current processing cycle; The difference between the actual collection period identifier and the period sequence number of the data packet is calculated to obtain the calculation result; If the calculation result is 0, the data packet is merged into the periodic cache slot corresponding to the current processing cycle; If the calculation result is negative, the data packet is merged into the periodic cache slot corresponding to the historical processing period according to the actual collection period identifier of the data packet.

6. The method according to claim 1, characterized in that, When the data packet reaches the waiting boundary condition, the data in the periodic buffer slot is reassembled and an aggregate frame is generated, including: Based on the building area identifier and transmission status information in the data packet, data packets from different buildings are initially grouped; For each data packet within a group, identify the internal data items contained in the data packet and the preset logical dependencies; Based on the logical dependencies, time-series calibration and correlation checks are performed on each of the internal data items to obtain the time-series calibration and correlation check results; Based on the timing calibration and correlation check results, the data in the periodic cache slot is reassembled, and an aggregate frame is generated.

7. The method according to claim 1, characterized in that, The step of performing data availability verification based on the aggregated frame, obtaining verification results, and performing cross-building association anomaly analysis or taking supplementary processing measures based on the verification results includes: Extract the data integrity identifier, late insertion identifier, and timing reliability identifier from the aggregated frame; According to the preset verification rules, the data integrity identifier, the late addition identifier, and the temporal reliability identifier are cross-verified to identify whether there is a contradiction in the aggregated frame and obtain the verification result. The verification result includes high temporal reliability but low integrity, or low temporal reliability but high integrity. Based on the verification results, perform cross-building correlation anomaly analysis or take supplementary processing measures.

8. The method according to claim 7, characterized in that, The step of performing cross-building correlation anomaly analysis or taking supplementary processing measures based on the verification results includes: When the verification result is high temporal reliability but low integrity, the availability score of the aggregated frame is adjusted to assess the criticality of the missing data; Based on the criticality of the missing data, targeted supplementation processing is triggered to obtain the supplementation results. When the verification result is low temporal reliability but high integrity, the availability score of the aggregated frame is adjusted to assess the impact of temporal deviation on the association analysis. Based on the degree of impact, a timing correction process is triggered to obtain the timing correction result; Based on the adjusted availability score of the aggregated frame and the supplementary processing result or the timing correction processing result, cross-building association anomaly analysis or supplementary processing measures are performed.

9. The method according to claim 8, characterized in that, The step of performing cross-building association anomaly analysis or taking supplementary processing measures based on the adjusted availability score of the aggregated frame and the supplementary processing result or the time-series correction processing result includes: The adjusted availability score of the aggregated frame and the supplementary processing result or the time-series correction processing result are matched with preset building type features to determine the building type to which the current analysis targets. Based on the building type, load the corresponding anomaly judgment logic and threshold set from the preset building-specific anomaly pattern library; Based on the anomaly judgment logic and the threshold set, cross-building association anomaly analysis is performed on the availability score of the adjusted aggregated frame and the supplementary processing result or the time-series correction processing result to identify specific cross-building association anomalies. Based on the cross-building association anomalies, an early warning instruction is generated for the building type.

10. A university science park anomaly early warning system based on wireless networking, characterized in that, include: The module acquires and establishes data to obtain transmission delay information from each sensor node and collects environmental parameters. The generation module is used to generate a data packet containing a real acquisition cycle identifier, a building area identifier, and transmission status information based on the transmission delay information and the environmental parameters. The update module is used to periodically update the link transmission characteristics of each sensor node according to the transmission status information in the data packet, wherein the link transmission characteristics include actual transmission delay, retransmission status and hop count information. The setting module is used to set differentiated data reassembly waiting boundaries based on the link transmission characteristics; The merging module is used to merge the data packets into the corresponding periodic buffer slots according to the actual collection period identifier of the data packets, wherein the periodic buffer slots correspond one-to-one with the actual collection period identifiers; The reassembly module is used to reassemble the data in the periodic buffer slot and generate an aggregate frame when the data packet reaches the waiting boundary condition; The analysis module is used to perform data availability verification based on the aggregated frame, obtain the verification result, and perform cross-building association anomaly analysis or take supplementary processing measures based on the verification result.

Images