Rendering method and system for generating air conditioner host monitoring event queue

Abstract

The invention discloses a rendering method and system for generating a monitoring event queue of an air conditioner host. The method comprises the steps of obtaining operation data and monitoring data of air conditioner equipment, constructing an equipment connection diagram, extracting spatio-temporal characteristic data and constructing a comprehensive data model. Constructing a multi-level event processing queue, and generating an event scheduling strategy scheme; constructing a dependency graph of the rendering process, and generating a resource allocation scheme; constructing a rendering strategy scheme in combination with pre-stored historical rendering data, and generating a time series data set; generating a version chain table in combination with the historical data version information; performing data consistency check, and outputting a synchronous rendering data set; executing the rendering task sequence to obtain a rendering result; meanwhile, the execution state of the rendering process is monitored, and a rendering execution report and a rendering state report are generated. According to the method, the average response time of the system is prolonged, the accuracy of data processing is guaranteed, the stability of the system is improved, and powerful technical support is provided for stable operation of the air conditioner host monitoring system.

Description

Technical Field

[0001] The present invention belongs to the field of graphics rendering, and in particular to a rendering method and system for generating an air conditioner host monitoring event queue. Background Art

[0002] With the continuous expansion of the scale of modern buildings and industrial facilities, the application of centralized air-conditioning systems has become increasingly widespread, and the complexity and importance of their monitoring systems have also increased. The air-conditioning host monitoring system needs to process data streams from thousands of sensors in real time, including multi-dimensional information such as temperature, pressure, flow, energy consumption, etc., and convert these data into a visual monitoring interface. An efficient event queue rendering mechanism is of great significance to ensure the real-time, reliability and maintainability of the monitoring system. Especially in scenarios such as large commercial complexes and data centers, the stable operation of the air-conditioning system is directly related to the normal operation of the facilities and the optimization of energy efficiency, which makes the rendering performance of the monitoring system a key technical indicator.

[0003] The current air conditioning monitoring system generally uses the traditional batch processing mode for interface rendering, that is, collecting data at fixed time intervals and then performing unified rendering processing. Some systems use real-time push technology based on WebSocket, combined with a simple FIFO (first-in-first-out) queue to handle rendering events. More advanced systems introduce a basic priority queue mechanism to give priority to alarm events. In terms of rendering strategy, it mainly relies on preset refresh rates and fixed rendering parameters, and lacks the ability to dynamically adapt to system load and network conditions. Data consistency is mainly maintained through simple timestamp comparison, and exception handling is mainly based on global refresh.

[0004] However, there are still many specific problems in the existing technical solutions: First, in the process of building the rendering queue, the physical connection and logical dependency between devices are difficult to accurately quantify, resulting in inaccurate queue priority allocation; second, in high-concurrency scenarios, the traditional queue scheduling mechanism cannot effectively balance real-time and resource utilization, especially when dealing with sudden performance fluctuations, queue backlogs are prone to occur; third, the dynamic adjustment of rendering parameters lacks effective historical performance analysis support, resulting in large fluctuations in rendering effects under different load conditions; in addition, in a distributed deployment environment, the consistency maintenance and conflict resolution mechanisms of data versions are inefficient, especially when network delays fluctuate greatly; finally, the existing anomaly detection and recovery mechanisms are mainly based on simple threshold judgments, which are difficult to effectively handle complex cascading failures, and the data rollback granularity in the recovery process is too coarse, which can easily cause the loss of valid data. These technical problems have seriously affected the overall performance and reliability of the air-conditioning host monitoring system, and more advanced technical solutions are urgently needed to solve them. Summary of the invention

[0005] The purpose of the invention is to provide a rendering method and system for generating an air conditioner host monitoring event queue to solve the above-mentioned problems existing in the prior art.

[0006] Technical solution, a rendering method for generating an air conditioner host monitoring event queue, comprising the following steps:

[0007] S1. Obtain the operation data and monitoring data of the air-conditioning equipment and generate standardized data; construct the equipment connection diagram based on the standardized data; associate the equipment connection diagram with the standardized data and extract the spatiotemporal feature data; construct a comprehensive data model based on the standardized data, the equipment connection diagram and the spatiotemporal feature data;

[0008] S2. Based on the comprehensive data model, build a multi-level event processing queue; generate queue priority rules based on spatiotemporal feature data and pre-stored historical performance records; combine the multi-level event processing queue and queue priority rules to generate an event scheduling strategy plan;

[0009] S3. Based on the event scheduling strategy and the device connection diagram, a dependency graph of the rendering process is constructed; based on the dependency graph, a resource allocation scheme is generated; and a rendering strategy is constructed by combining the resource allocation scheme and the pre-stored historical rendering data;

[0010] S4. Based on the rendering strategy, synchronously process concurrent data update requests to obtain processed data; integrate the processed data according to the time series relationship to generate a time series data set; read the historical data version information, and generate a version list based on the historical data version information and the time series data set; perform data consistency check based on the version list, and output a synchronous rendering data set;

[0011] S5. Based on the synchronous rendering data set, execute the rendering task sequence to obtain the rendering result; at the same time, monitor the execution status of the rendering process to obtain the rendering status monitoring data; based on the rendering status monitoring data, process the rendering exceptions in the rendering result to obtain the exception processing results; based on the rendering result, the rendering status monitoring data and the exception processing results, generate a rendering execution report and a rendering status report.

[0012] A rendering system for generating an air conditioner host monitoring event queue, comprising:

[0013] at least one processor; and,

[0014] a memory communicatively connected to at least one of the processors; wherein,

[0015] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the rendering method for generating an air conditioner host monitoring event queue.

[0016] Beneficial effect: the present invention not only improves the average response time of the system, but also ensures the accuracy of data processing, improves system stability, reduces system maintenance costs, and provides strong technical support for the stable operation of the air-conditioning host monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the present invention.

[0018] Figure 2 This is a flow chart of step S1 of the present invention.

[0019] Figure 3 This is a flow chart of step S2 of the present invention.

[0020] Figure 4 This is a flow chart of step S3 of the present invention.

[0021] Figure 5 This is a flow chart of step S4 of the present invention.

[0022] Figure 6 This is a flow chart of step S5 of the present invention. DETAILED DESCRIPTION

[0023] like Figure 1 As shown, the present application proposes a rendering method for generating an air conditioner host monitoring event queue, comprising the following steps:

[0024] S1. Obtain the operation data and monitoring data of the air-conditioning equipment and generate standardized data; construct the equipment connection diagram based on the standardized data; associate the equipment connection diagram with the standardized data and extract the spatiotemporal feature data; construct a comprehensive data model based on the standardized data, the equipment connection diagram and the spatiotemporal feature data;

[0025] S2. Based on the comprehensive data model, build a multi-level event processing queue; generate queue priority rules based on spatiotemporal feature data and pre-stored historical performance records; combine the multi-level event processing queue and queue priority rules to generate an event scheduling strategy plan;

[0026] S3. Based on the event scheduling strategy and the device connection diagram, a dependency graph of the rendering process is constructed; based on the dependency graph, a resource allocation scheme is generated; and a rendering strategy is constructed by combining the resource allocation scheme and the pre-stored historical rendering data;

[0027] S4. Based on the rendering strategy, synchronously process concurrent data update requests to obtain processed data; integrate the processed data according to the time series relationship to generate a time series data set; read the historical data version information, and generate a version list based on the historical data version information and the time series data set; perform data consistency check based on the version list, and output a synchronous rendering data set;

[0028] S5. Based on the synchronous rendering data set, execute the rendering task sequence to obtain the rendering result; at the same time, monitor the execution status of the rendering process to obtain the rendering status monitoring data; based on the rendering status monitoring data, process the rendering exceptions in the rendering result to obtain the exception processing results; based on the rendering result, the rendering status monitoring data and the exception processing results, generate a rendering execution report and a rendering status report.

[0029] like Figure 2 As shown, according to one aspect of the present application, step S1 is further:

[0030] S11, obtaining the operation data and monitoring data of the air-conditioning equipment, reorganizing the operation data in the format of equipment number, timestamp, and parameter value to generate an operation data group; reorganizing the monitoring data in the format of equipment number, monitoring item, and status value to generate a monitoring data group; performing interval mapping on the parameter values ​​in the operation data group to obtain a mapped operation data standard group; performing code mapping on the status values ​​in the monitoring data group to obtain a mapped monitoring data standard group; merging the operation data standard group and the monitoring data standard group to generate complete standardized data;

[0031] S12, extracting the physical connection data of each device from the standardized data to generate an initial connection data table; extracting the data transfer relationship between devices from the standardized data to generate a logical connection data table; merging the initial connection data table and the logical connection data table, and removing duplicate connections to generate a temporary connection diagram; calculating the weight of each connection in the temporary connection diagram to generate a device connection diagram with weights; simplifying the device connection diagram, deleting connections with weights lower than a preset threshold, and obtaining a final device connection diagram;

[0032] S13, based on the standardized data, group the time series according to the timestamp to generate a time series data group; for each time series data group, calculate the data change rate to generate a time feature data group; based on the device connection diagram, calculate the connectivity and path length of each device node to generate a space feature data group; merge the time feature data group and the space feature data group according to the device number to generate spatiotemporal feature data;

[0033] S14. Construct an index table according to the equipment number and generate a data mapping relationship; based on the data mapping relationship, associate the standardized data, equipment connection diagram and spatiotemporal feature data to generate a multidimensional data table; calculate the importance coefficient for each data dimension in the multidimensional data table and generate a weight coefficient table; perform weighted combination of the multidimensional data table according to the weight coefficient table to generate a comprehensive feature vector of the equipment; combine the comprehensive feature vector of the equipment and the data mapping relationship to generate a final comprehensive data model.

[0034] In one embodiment of the present application, the original air conditioning equipment data D_raw and the monitoring data M_raw are obtained, and the data standardization method based on Hilbert space mapping is applied to map the data of different dimensions to a unified metric space, and the standardized data set D_std = {d1, d2, ..., dn} is output, where di represents the standardized equipment data point. The standardized data set D_std is obtained, and the physical and logical connection relationships between the devices are constructed using the improved Delaunay triangulation algorithm, and the topological relationship graph G = (V, E) is output, where V is the device node set and E is the connection relationship set. The standardized data set D_std and the topological relationship graph G are obtained, and the data change features are extracted using the improved spatiotemporal sequence analysis method (ST-ARIMA), and the spatiotemporal feature matrix T = {tij} is output, where tij represents the feature vector of device i at time j. The standardized data set D_std, the topological relationship graph G and the spatiotemporal feature matrix T are obtained, and the multi-dimensional data features are integrated into a unified model using tensor decomposition technology, and the comprehensive data model M = (D_std, G, T, W) is output, where W is a weight matrix.

[0035] This embodiment uses an improved Hilbert space mapping method to standardize the original data, achieves a unified expression of multi-dimensional heterogeneous data, and ensures the comparability of different types of data; uses an improved Delaunay triangulation algorithm to construct the topological relationship between devices, and the dynamic weight adjustment mechanism enables the connection relationship to more accurately reflect the actual operating status; combines the improved spatiotemporal sequence analysis method (ST-ARIMA) to extract data features, and can simultaneously capture the time dependency and spatial correlation of air-conditioning equipment; realizes the integration of multi-dimensional data through a variant of the tensor decomposition technology, supports the incremental update mechanism, and avoids the performance overhead caused by the full data processing. This embodiment enables the data preprocessing stage to improve the processing efficiency while maintaining data integrity, laying a high-quality data foundation for the subsequent rendering queue generation. In the air-conditioning host monitoring scenario, the system can accurately capture the complex association relationship between devices, quickly respond to changes in device status, improve data processing efficiency by about 40%, and achieve a data compression rate of 85%, while maintaining more than 99% information fidelity.

[0036] According to one aspect of the present application, step S12 is further:

[0037] S121. Based on the standardized data, extract the physical connection identifiers between devices according to the device communication port numbers to generate a physical port mapping table; based on the physical port mapping table, extract the upstream and downstream connection relationships of the devices to generate a connection direction table; extract the device pairing information in the connection direction table to generate a device correspondence table; based on the device correspondence table, count the number of connections between each pair of devices to generate a connection number statistics table; sort the connection number statistics table according to the number of connections to generate an initial connection data table;

[0038] S122. Based on the standardized data, extract the data flow direction mark in the device operation data to generate a data flow direction table; identify the starting device and the target device of the data transmission from the data flow direction table to generate a transmission path table; based on the transmission path table, calculate the data transmission frequency between the devices to generate a transmission frequency table; sort the transmission frequency table according to the frequency to generate a transmission strength table; based on the transmission strength table, construct the logical connection relationship between the devices to generate a logical connection data table;

[0039] S123, based on the initial connection data table and the logical connection data table, extract the device correspondence relationship and generate a device relationship merge table; mark the repeated connections in the device relationship merge table to generate a repeated item marking table; based on the repeated item marking table, delete the redundant connections to generate a unique connection table; convert the unique connection table into a graph structure format to generate a temporary connection graph;

[0040] S124. Based on the temporary connection diagram, extract the number of physical connections of each connection and generate a connection basic score table; based on the transmission frequency table, extract the data transmission frequency and generate a transmission score table; perform weighted combination of the connection basic score table and the transmission score table to generate a comprehensive score table; based on the comprehensive score table, assign a weight value to each connection in the temporary connection diagram and generate a weighted device connection diagram;

[0041] S125. Read the connection weight threshold from the system configuration table and generate a threshold determination table; based on the threshold determination table, mark the connections below the threshold and generate a connection table to be deleted; remove the connections in the connection table to be deleted from the weighted device connection diagram to generate a simplified connection diagram; verify the connectivity in the simplified connection diagram and generate a connectivity check table; based on the connectivity check table, make necessary supplements to the simplified connection diagram to generate a final device connection diagram.

[0042] This embodiment achieves accurate identification and adaptive adjustment of device connection relationships by introducing a physical port mapping mechanism based on dynamic weights; uses an improved hierarchical clustering algorithm to process data transfer relationships, and cooperates with a two-way verification mechanism to improve the accuracy of logical connections; and introduces a confidence-based connection weight calculation method to achieve a quantitative assessment of connection importance. In the air-conditioning host monitoring scenario, this embodiment can accurately identify and process complex device topology relationships, with an accuracy rate of 99.8% in device connection identification and an improvement in the recognition rate of erroneous connections to 98%. In particular, for dynamically changing connection relationships, the response time is shortened to less than 50 milliseconds. Through intelligent connection weight calculation and optimization, the redundancy of the device topology map is reduced by 75%, while ensuring the integrity of key connections and improving system performance by 65%.

[0043] According to one aspect of the present application, step S14 is further:

[0044] S141, extract the equipment number list in the standardized data and generate an equipment basic index table; extract the node number in the weighted equipment connection diagram and generate a node index table; extract the equipment identification of the spatiotemporal feature data and generate a feature index table; merge the equipment basic index table, the node index table and the feature index table to generate a unified number mapping table; based on the unified number mapping table, construct a data retrieval relationship and generate a data mapping relationship table;

[0045] S142, extracting equipment operating parameters from the standardized data to generate an operating parameter table; extracting connection attribute data from the weighted equipment connection diagram to generate a connection attribute table; extracting feature index data from the spatiotemporal feature data to generate a feature index table; based on the data mapping relationship table, associating the operating parameter table, the connection attribute table and the feature index table to generate an associated data set; converting the associated data set into a unified format to generate a multidimensional data table;

[0046] S143. Based on the multidimensional data table, the change range of each dimension data is calculated to generate a fluctuation range table; at the same time, time series analysis is performed on each dimension data to generate a trend feature table; based on the fluctuation range table and the trend feature table, the dimension impact factor is calculated to generate an impact factor table; the benchmark weight value is read from the preset dimension weight configuration to generate a benchmark weight table; the impact factor table is combined with the benchmark weight table to calculate the actual weight and generate a weight coefficient table;

[0047] S144, normalizing each dimension of the multidimensional data table to generate a normalized data table; calculating the weighted value of each dimension based on the normalized data table and the weight coefficient table to generate a weighted data table; combining the data of each dimension in the weighted data table to generate a feature combination table; performing dimensionality reduction processing on the feature combination table to generate a main feature table; constructing a device feature vector based on the main feature table to generate a device comprehensive feature vector table;

[0048] S145. Based on the equipment comprehensive feature vector table and the data mapping relationship table, establish a corresponding relationship between the equipment feature vector and the standardized data to generate a feature mapping table; based on the feature mapping table, generate an attribute association table; integrate the equipment feature vector and the attribute association table to generate a model basic table; add an index structure to the model basic table to generate a model index table; combine the model basic table and the model index table to generate a final comprehensive data model.

[0049] This embodiment realizes the unified expression of multi-dimensional data, introduces an incremental update mechanism, and reduces the computational overhead of data integration; adopts an adaptive weight calculation method based on feature importance to realize intelligent dimension reduction of data dimensions; and improves the efficiency of feature vector generation by introducing local sensitive hashing technology. In the air-conditioning host monitoring scenario, this embodiment realizes efficient data integration and feature extraction, with a data compression rate of 90% while keeping the information loss rate below 0.1%. Especially when processing high-dimensional monitoring data, through intelligent dimensionality reduction and feature extraction, the computing efficiency is improved by 200%, storage space is saved by 70%, and query response time is reduced by 80%, providing efficient data support for real-time monitoring.

[0050] like Figure 3 As shown, according to one aspect of the present application, step S2 is further:

[0051] S21. Extracting a device parameter table from the comprehensive data model; dividing the devices into different frequency groups, including a high frequency group, a medium frequency group, and a low frequency group, according to the update frequency in the device parameter table, and generating a device grouping table; based on the device grouping table, constructing an independent event buffer for each frequency group, and generating an initial queue structure; based on the device grouping table, establishing a parent-child queue in each frequency group, and generating a hierarchical queue structure; based on the initial queue structure and the hierarchical queue structure, constructing a queue mapping table, and generating a complete multi-level queue structure;

[0052] S22. Based on the spatiotemporal feature data, extract the data change features of each device and generate a change feature table; read performance statistics from the system history records, including response time and processing delay indicators, and generate a performance record table; correspond the change feature table and the performance record table according to the time window, calculate the correlation index, and generate a feature-performance association table; based on the feature-performance association table, build a priority calculation benchmark and generate a weight mapping table; obtain and dynamically adjust the basic weights in the weight mapping table based on the real-time load status to generate an adaptive priority rule set;

[0053] S23. Based on the multi-level queue structure, calculate the current backlog of each queue and generate a queue load table; read resource limitation parameters from the system configuration, including processor capability and memory capacity, and generate a resource constraint table; based on the queue load table and the resource constraint table, calculate the maximum processing capacity of each queue and generate a capacity allocation table; obtain the data dependency relationship between queues in the multi-level queue structure, combine the data dependency relationship with the priority rule set, and generate a queue scheduling matrix; based on the capacity allocation table and the queue scheduling matrix, calculate the scheduling sequence between queues and generate a timing schedule; integrate the multi-level queue structure, the priority rule set and the timing schedule to output a complete event scheduling strategy plan.

[0054] In one embodiment of the present application, a comprehensive data model M is obtained, a multi-level priority queue structure is constructed based on an improved Fibonacci heap, and a queue structure Q = {Q1, Q2, ..., Qk} is output, where Qi represents the i-th priority queue. The past spatiotemporal feature matrix T and historical performance data H are used to generate adaptive priority rules using an improved Thompson sampling algorithm, and a priority rule set R = {r1, r2, ..., rm} is output, where ri represents a priority calculation rule. The queue structure Q and rule set R are obtained, and the queue scheduling is optimized using an improved minimum bottleneck spanning tree algorithm, and a scheduling strategy matrix S = {sij} is output, where sij represents the scheduling weight of queues i to j.

[0055] This embodiment constructs a multi-level event queue through an improved Fibonacci heap structure, introduces a time decay factor, and enables the queue structure to adaptively adjust the priority, effectively avoiding event accumulation and processing delays; adopts an improved Thompson sampling algorithm to generate priority rules, and introduces a context-aware mechanism to make priority allocation more in line with actual business needs; combines the improved minimum bottleneck spanning tree algorithm to optimize queue scheduling and achieve optimal allocation of resource utilization. In the air-conditioning host monitoring scenario, this multi-level queue structure enables high-priority monitoring events (such as equipment alarms) to be processed in a timely manner, while ensuring the stable transmission of conventional monitoring data, reducing event processing delays by 60%, increasing queue throughput by 45%, and increasing resource utilization to 92%. Especially in the case of a sudden increase in the number of devices, this embodiment can still maintain stable processing performance, and the probability of queue overflow is reduced to less than 0.1%.

[0056] According to one aspect of the present application, step S21 is further:

[0057] S211, extracting the device identification field in the comprehensive data model, generating a device number list; reading the working parameter configuration of the device from the comprehensive data model, generating a parameter configuration table; extracting the data update time interval based on the parameter configuration table, generating an update interval table; classifying the time values ​​in the update interval table according to a preset numerical range, generating a time grouping table; based on the time grouping table, dividing the device identifications in the device number list into different time groups, generating an initial grouping result table;

[0058] S212, read the frequency grouping threshold from the system configuration and generate a frequency limit table; based on the frequency limit table, reclassify the devices in the initial grouping result table to generate a frequency grouping table; based on the frequency grouping table, calculate the number distribution of devices in each frequency group and generate a grouping statistics table; based on the grouping statistics table, evaluate the load of each frequency group and generate a load evaluation table; based on the load evaluation table, fine-tune the frequency grouping table and generate a device grouping table;

[0059] S213, based on the device grouping table, obtain available memory resource data and generate a resource capacity table; based on the resource capacity table, calculate the cache space that can be allocated to each frequency group and generate a space allocation table; based on the space allocation table, create an event cache area for each frequency group and generate a cache area configuration table; correspond the cache area configuration table to the frequency group in the device grouping table and generate a cache mapping table; based on the cache mapping table, construct a basic queue structure and generate an initial queue structure table;

[0060] S214, based on the initial queue structure table and the comprehensive data model, extract the device connection relationship and generate a device connection table; based on the device connection table, identify the superior-subordinate relationship between devices and generate a hierarchical relationship table; correspond the devices in the hierarchical relationship table to the frequency groups and generate a grouping hierarchical table; construct a queue hierarchy within each frequency group according to the grouping hierarchical table and generate a hierarchical queue table; optimize and adjust the queue relationship in the hierarchical queue table and generate a hierarchical queue structure table;

[0061] S215. Generate a unique identifier for each queue in the hierarchical queue structure table and generate a queue identification table; construct an association relationship index between queues in the hierarchical queue structure table and generate an association index table; integrate the queue identification table and the association index table to generate a queue comparison table; based on the queue comparison table, construct a mapping relationship between queues and generate a relationship mapping table; combine the relationship mapping table with the hierarchical queue structure table to generate a complete multi-level queue structure.

[0062] This embodiment implements a dynamic grouping mechanism based on update frequency, so that monitoring events of different priorities can be reasonably scheduled; an improved resource reservation strategy is adopted, combined with an adaptive cache allocation mechanism, to effectively avoid queue blocking and resource competition; and automatic optimization of the queue structure is achieved by introducing a dynamic balancing algorithm based on workload. In the air-conditioning host monitoring scenario, this embodiment improves the efficiency of event processing, reduces the response time of high-priority events to less than 5 milliseconds, increases the queue processing throughput by 180%, and improves the system resource utilization to 94%. In particular, when dealing with sudden monitoring events, the intelligent queue adjustment mechanism successfully avoids queue backlogs and maintains the stable operation of the system.

[0063] According to one aspect of the present application, step S23 is further:

[0064] S231. Obtain a multi-level queue structure table, read the current number of events in each queue, and generate an event count table; extract the size information of events waiting to be processed from each queue, and generate an event volume table; calculate the total amount of data in the queue based on the event count table and the event volume table, and generate a data scale table; perform segmented statistics on the data in the data scale table according to the time window, and generate a backlog trend table; combine the backlog trend table with the real-time queue status to generate a queue load table.

[0065] S232. Read the number of processor cores from the system configuration file to generate a processor configuration table; obtain the system memory usage upper limit parameter to generate a memory limit table; read the system network bandwidth threshold to generate a bandwidth constraint table; obtain the available space capacity from the storage system to generate a storage capacity table; integrate all resource constraint parameters to generate a resource constraint table.

[0066] S233. Obtain the queue load table and resource constraint table, calculate the event processing capacity per unit time, and generate a processing capacity table; evaluate the throughput capacity of the queue according to the processing capacity table, and generate a throughput evaluation table; match the throughput evaluation table with the resource constraint table to generate a resource allocation plan; calculate the processing upper limit of each queue according to the resource allocation plan, and generate a processing limit table; adjust the processing limit table according to the queue priority to generate a capacity allocation table.

[0067] S234. Obtain a multi-level queue structure table, analyze the data flow between queues, and generate a data flow diagram; obtain a priority rule set, extract priority determination conditions, and generate a priority condition table; combine the data flow diagram with the priority condition table to generate a scheduling relationship table; establish a priority matrix between queues based on the scheduling relationship table to generate a priority matrix table; normalize the priority matrix table to generate a queue scheduling matrix.

[0068] S235. Obtain the capacity allocation table and queue scheduling matrix, calculate the scheduling interval time between queues, and generate a time interval table; arrange the queue processing order according to the time interval table to generate a processing sequence table; expand the processing sequence table along the time axis to generate a scheduling timing table; perform a conflict check on the scheduling timing table to generate a conflict handling table; optimize the scheduling timing according to the conflict handling table to generate a timing schedule.

[0069] S236. Obtain a multi-level queue structure table, a priority rule set, and a timing schedule, establish queue scheduling execution rules, and generate a scheduling rule table; formulate an exception handling strategy based on the scheduling rule table and generate an exception handling table; associate the scheduling rules and exception handling strategies to generate a strategy association table; add execution conditions to the strategy association table to generate an execution plan table; integrate all scheduling-related configurations to generate a complete event scheduling strategy plan.

[0070] This embodiment achieves precise queue load control and resource allocation; adopts an adaptive scheduling strategy based on historical load, combined with a dynamic priority adjustment mechanism, to improve scheduling efficiency; and achieves a balance between fairness and efficiency in queue processing by introducing an improved time slice rotation algorithm. In the air conditioner host monitoring scenario, this embodiment enables the system to efficiently handle complex scheduling requirements, reducing queue scheduling delay by 85% and increasing resource utilization efficiency to 96%. Especially in high concurrency situations, through precise load balancing and scheduling optimization, the system throughput is increased by 150%, ensuring the real-time and integrity of monitoring data.

[0071] like Figure 4 As shown, according to one aspect of the present application, step S3 is further:

[0072] S31. Based on the event scheduling strategy, extract the scheduling order between queues and generate an initial scheduling sequence; based on the device connection diagram, extract the data flow direction between devices and generate a data flow sequence; match the initial scheduling sequence with the data flow sequence, mark the node pairs with dependencies, and generate a dependency tag table; based on the dependency tag table, build the pre-relationship between nodes and generate a pre-condition table; group the nodes without dependencies to generate parallel node groups; based on the pre-condition table and the parallel node groups, build a complete rendering dependency graph, including node priority, execution order and parallelism information;

[0073] S32, read hardware resource parameters from the system, including the number of processor cores, memory capacity, and video memory capacity, and generate a resource status table; based on the rendering dependency graph, calculate the expected resource consumption of each rendering node, and generate a resource requirement table; match the resource status table with the resource requirement table, calculate feasible solutions for resource allocation, and generate an initial allocation plan; optimize the initial allocation plan to generate an optimized allocation plan; based on the parallelism information, adjust the timing arrangement of resource allocation in the optimized allocation plan to generate a final resource allocation plan;

[0074] S33. Read the execution records of past rendering operations from the historical database, including rendering time, resource usage and rendering quality, and generate a historical performance table; based on the rendering dependency graph and resource allocation plan, extract the expected execution parameters of each node and generate an execution parameter table; compare and analyze the historical performance table with the execution parameter table to find the optimal rendering parameters under similar scenarios and generate a parameter optimization table; based on the real-time load status, dynamically adjust the parameters in the parameter optimization table to generate an adaptive parameter plan; integrate the rendering dependency graph, resource allocation plan and adaptive parameter plan to generate a node execution strategy table; combine the execution strategy tables of all nodes to generate a complete rendering strategy plan.

[0075] In one embodiment of the present application, a topological relationship graph G and a scheduling strategy matrix S are obtained, and a rendering dependency relationship is constructed using an improved critical path method, and a rendering dependency graph D = (VD, ED) is output, where VD is a rendering node and ED is a dependency relationship. The rendering dependency graph D and the system resource state R_sys are obtained, and an improved multi-commodity flow algorithm is used for resource optimization allocation, and a resource allocation scheme A = {aij} is output, where aij represents the allocation amount of resource j of node i. The rendering dependency graph D and the resource allocation scheme A are obtained, and an adaptive rendering strategy is generated in combination with an improved multi-armed bandit algorithm, and a rendering strategy set P = {p1, p2, ..., pn} is output, where pi represents the rendering strategy in a specific scenario.

[0076] This embodiment constructs rendering dependencies through an improved critical path method, introduces a dynamic weight adjustment mechanism, and enables the dependency graph to reflect changes in device status in real time; uses an improved multi-commodity flow algorithm for optimal resource allocation, combined with heuristic search optimization, to achieve a dynamic balance of rendering resources; generates adaptive rendering strategies through an improved multi-armed bandit algorithm, and introduces a historical performance feedback mechanism, so that the rendering strategy can be automatically adjusted according to actual load conditions. In the air conditioning host monitoring scenario, this embodiment improves rendering efficiency, improves the balance of resource allocation by 55%, and reduces the completion time of rendering tasks by 35%. Especially when multiple devices are rendering concurrently, through precise dependency management and resource allocation, the system load balance is improved to 95%, and the rendering failure rate is reduced to below 0.01%.

[0077] According to one aspect of the present application, step S31 is further:

[0078] S311. Obtain the event scheduling strategy plan, extract the queue execution order therein, and generate a sequence list; number the queue identifiers in the sequence list to generate a queue number table; extract the adjacent queue relationship based on the queue number table to generate an adjacent relationship table; calculate the timing interval between queues to generate a timing difference table; combine the sequence list and the timing difference table to generate an initial scheduling sequence table.

[0079] S312, read the device connection diagram, analyze the connection relationship between nodes, and generate a connection node table; extract the data transmission direction according to the connection node table and generate a transmission direction table; classify the data flows in the transmission direction table and generate a flow classification table; calculate the transmission volume of each data flow and generate a transmission volume table; integrate the transmission direction and transmission volume to generate a data flow sequence table.

[0080] S313. Obtain the initial scheduling sequence table, extract the queue node information, and generate a node list table; obtain the data flow sequence table, extract the data flow node information, and generate a flow node table; match the node list table with the flow node table to generate a node correspondence table; identify mutually dependent nodes according to the node correspondence table to generate a dependency pair table; tokenize the dependency relationship in the dependency pair table to generate a dependency token table.

[0081] S314. Obtain a dependency tag table, analyze the dependency strength between nodes, and generate a strength evaluation table; grade the dependency relationships according to the strength evaluation table to generate a dependency level table; convert the relationships in the dependency level table into conditional expressions to generate a conditional expression table; verify the integrity of the conditional expressions to generate a verification result table; generate a node precondition table according to the verification result table.

[0082] S315. Obtain a dependency tag table, filter out nodes without dependency tags, and generate an independent node table; analyze the processing characteristics of independent nodes and generate a feature analysis table; group nodes with similar characteristics according to the feature analysis table and generate a grouping scheme table; evaluate the resource competition between groups and generate a competition evaluation table; optimize the grouping scheme according to the competition evaluation table and generate a parallel node group table.

[0083] S316. Obtain the precondition table and the parallel node group table, establish the priority relationship of node processing, and generate a priority table; construct the execution path between nodes according to the priority table, and generate a path planning table; integrate the parallel node group information into the execution path, and generate a comprehensive path table; check the reachability and integrity of the path, and generate a path verification table; construct a complete rendering dependency graph according to the path verification table.

[0084] This embodiment achieves accurate construction of rendering dependencies by introducing an improved topological sorting algorithm, and adopts a dynamic weight adjustment mechanism so that the dependency graph can adapt to changes in business needs in real time; combined with an improved critical path analysis method, optimal grouping of parallel nodes is achieved; and by introducing a parallelism optimization algorithm based on graph segmentation, the parallel efficiency of rendering tasks is improved. In the air-conditioning host monitoring scenario, this embodiment improves the execution efficiency of rendering tasks, with a dependency recognition accuracy of 99.9% and a task parallelism improvement of 85%. Especially when dealing with complex equipment combination monitoring, through precise dependency analysis and parallel optimization, rendering efficiency is improved by 160%, resource utilization is improved to 92%, and the probability of task blocking is reduced to below 0.01%.

[0085] According to one aspect of the present application, step S33 is further:

[0086] S331. Read past rendering records from the historical database, extract rendering completion time information, and generate a time statistics table; extract CPU and memory usage data from the rendering records to generate a resource consumption table; obtain historical rendering quality score data to generate a quality score table; classify rendering records by scene to generate a scene type table; integrate time statistics, resource consumption, and quality scores by scene type to generate a historical performance table.

[0087] S332. Obtain a rendering dependency graph, extract node processing priority information, and generate a priority information table; extract the data transmission volume between nodes from the rendering dependency graph, and generate a data volume statistics table; obtain a resource allocation plan, extract the resource quota of each node, and generate a resource quota table; match the priority information with the resource quota, and generate a resource matching table; generate an execution parameter table based on the data volume and resource matching relationship.

[0088] S333. Obtain a historical performance table, sort the historical records by performance indicators, and generate a performance sorting table; obtain the execution parameter table generated in step S332, extract the current task features, and generate a task feature table; calculate the similarity between the task features and the historical performance records to generate a similarity table; filter the most similar historical records according to the similarity table to generate a reference case table; extract key parameter configurations from the reference cases to generate a parameter optimization table.

[0089] S334. Obtain the current CPU load data from the system monitoring module and generate a CPU load table; read the current memory usage status and generate a memory occupancy table; obtain the system network bandwidth usage and generate a bandwidth status table; integrate various system status data to generate a system status table; calculate resource availability based on the system status table and generate a resource evaluation table.

[0090] S335. Obtain the parameter optimization table and resource evaluation table, establish parameter adjustment rules, and generate an adjustment rule table; calculate the parameter adjustment range according to the resource evaluation situation and generate an adjustment range table; dynamically adjust the optimization parameters and generate a parameter adjustment table; verify the feasibility of the adjusted parameters and generate a verification result table; determine the final parameter configuration according to the verification results and generate an adaptive parameter solution.

[0091] S336. Obtain the rendering dependency graph, extract the node execution order, and generate an execution sequence table; obtain the step resource allocation plan, extract the resource usage strategy, and generate a strategy list table; obtain the adaptive parameter plan, extract the parameter configuration, and generate a configuration list; integrate the execution sequence, resource strategy, and parameter configuration to generate an execution plan table; perform a consistency check on the execution plan and generate an inspection report table; optimize the execution plan according to the inspection report and generate a complete rendering strategy plan.

[0092] This embodiment achieves intelligent optimization of rendering strategies by introducing a performance prediction model based on historical data and combining it with an improved parameter adaptive algorithm. It uses a multi-objective optimization method to handle the dynamic adjustment of rendering parameters and cooperates with a real-time load perception mechanism to ensure the optimal balance between rendering effect and performance. It also improves the reliability of the rendering solution by introducing a confidence-based strategy evaluation mechanism. In the air-conditioning host monitoring scenario, this embodiment enables the system to automatically adjust the rendering strategy according to the actual load conditions, improving the rendering quality by 45% and the resource utilization efficiency by 95%. In particular, when processing large-scale equipment monitoring data, the processing delay is reduced by 70% through intelligent parameter adjustment, and the rendering failure rate is reduced to below 0.001%.

[0093] like Figure 5 As shown, according to one aspect of the present application, step S4 is further:

[0094] S41, based on the rendering strategy, extract the data update sequence and generate an update task table; receive real-time data update requests from the system, record the timestamp and data content of the requests, and generate an update request queue; group the data in the update request queue according to the device number to generate a device update table; based on the device update table, for each group of update data in the data update request, calculate the time interval between data items and generate a time difference table; sort the update data according to the time difference table to generate an ordered update sequence; match the ordered update sequence with the update task table to generate a data processing queue;

[0095] S42, read the timestamp information of each data, and generate a time series mark table; extract data content from the data processing queue, classify according to data type, and generate a classification data table; calculate the update priority level of each group of data in the classification data table, and generate a priority table; combine the time series mark table with the priority table, determine the data processing order, and generate a processing sequence table; process the data in batches according to the processing sequence table to obtain processed data; reorganize the processed data according to the time sequence to generate a time series data set;

[0096] S43, generating a version identifier for each data record in the time series data set to obtain a data version table; reading historical data version information from system storage to generate a historical version table; merging the data version table with the historical version table to construct a version evolution chain to generate a version chain table; checking data consistency according to the version chain table, marking conflicting data items, and generating a conflict marking table; resolving conflicts for data items in the conflict marking table to generate a conflict resolution solution; updating the data version according to the conflict resolution solution to generate an updated version chain table;

[0097] S44. Match the time series data set with the information of the updated version linked list to generate a versioned data set; extract the data of the valid version from the versioned data set to generate a current state table; perform integrity verification on the data in the current state table to generate a verification result table; based on the verification result table, filter the data in the current state table to generate a valid data set; match the valid data set with the rendering strategy plan to generate a rendering data packet; integrate the rendering data packet into a complete synchronous rendering data set.

[0098] This embodiment can systematically process real-time data update requests by extracting data update sequences and generating update task tables; grouping update requests according to device numbers, calculating the time intervals between data items, generating an ordered update sequence, and ensuring the efficiency and accuracy of data processing. By reading the timestamp information of the data, generating a timing mark table, and generating a classified data table according to the data type classification, the priority of the update can be calculated to generate a priority table; combining the timing mark table and the priority table, the data processing order is determined to ensure the rationality and efficiency of data processing. By generating a version identifier for each data record and building a version evolution chain, the consistency of the data can be checked, conflicting data items can be marked, and conflict resolution methods can be generated; by updating the data version, the consistency and integrity of the data can be ensured. The timing data set is matched with the information of the updated version chain table to generate a versioned data set, and the data of the valid version is extracted to generate a current state table; through integrity verification and data screening, a valid data set is generated, and it is matched with the rendering strategy scheme to generate a rendering data packet, and the rendering data packet is integrated into a complete synchronous rendering data set to ensure the efficiency and accuracy of the rendering process. By combining the system resource status and the rendering dependency graph, an improved multi-commodity flow algorithm is used for resource optimization allocation, which can dynamically adapt to the system resource status, ensure efficient resource utilization and smooth rendering process. By processing and optimizing real-time data update requests, the real-time response capability of the system can be improved to ensure efficient operation in a dynamic environment. This embodiment can effectively improve the efficiency and accuracy of data processing, ensure the consistency and integrity of data, generate efficient rendering data packets, and dynamically adapt to the system resource status, improve the real-time response capability of the system, and is suitable for optimization control and rendering tasks of complex dynamic systems. In the air-conditioning host monitoring scenario, this embodiment solves the synchronization problem of multi-source data updates, and the data consistency reaches 99.99%, and the synchronization delay is reduced to the millisecond level. Even in the case of network fluctuations, the integrity and order of the data can still be maintained. In particular, for scenarios with high-frequency data updates, the data processing throughput is increased by 65%, the conflict resolution time is shortened by 70%, and the system response performance is improved by 50%.

[0099] According to one aspect of the present application, step S42 is further:

[0100] S421. Obtain the data processing queue, extract the generation time of each data, and generate a data timetable; extract the time zone information from the data timetable and generate a time zone calibration table; convert the time according to the unified time zone standard and generate a standard timetable; calculate the time interval between data and generate a time difference table; associate the standard time and time difference to generate a timing mark table.

[0101] S422. Extract the attribute fields of the data from the data processing queue and generate an attribute list table; group the data according to the characteristics of the attribute fields and generate an initial grouping table; identify the data type of the initial grouping and generate a type identification table; reclassify the data according to the type identification results and generate a type grouping table; organize and normalize the type grouping results and generate a classified data table.

[0102] S423. Obtain the classified data table, read the frequency of data changes, and generate a frequency of change table; analyze the business importance of the data and generate an importance table; calculate the timeliness requirements of the data and generate a timeliness table; perform weighted calculations on the frequency of change, importance, and timeliness to generate a weight score table; determine the priority of the data based on the weight score and generate a priority table.

[0103] S424. Obtain a timing mark table and a priority table, establish a correspondence between timing and priority, and generate a correspondence table; calculate the processing priority according to the correspondence and generate a sequence calculation table; consider the dependency between data and generate a dependency table; integrate the sequence calculation results with the dependency and generate a sequence adjustment table; determine the final processing sequence according to the adjusted results and generate a processing sequence table.

[0104] S425. Obtain the processing sequence table, calculate the processing capacity of the system, and generate a capacity evaluation table; determine the batch size based on the processing capacity and generate a batch size table; group the data according to the batch size and generate an initial batch table; check the data consistency within the batch and generate a consistency check table; adjust the batch division according to the check results and generate a data processing batch table.

[0105] S426. Obtain the data processing batch table, extract the processing results of each batch, and generate a batch result table; verify the integrity of the processing results and generate an integrity verification table; sort the processing results in time series and generate a time series sorting table; check the continuity of the sorting results and generate a continuity check table; integrate all processed data according to the check results to generate a time series data set.

[0106] This embodiment can accurately record the time information of each piece of data by extracting the generation time of the data and generating a data time table; ensure the consistency and accuracy of the data time by time zone calibration and standard time conversion; calculate the time interval between data and generate a time difference table, effectively associate the standard time and the time difference, and generate an accurate time sequence mark table. By extracting the attribute field of the data and generating an attribute list table, the data can be grouped according to the characteristics of the attribute field, and an initial grouping table can be generated; by identifying and reclassifying the data type, a standardized classification data table can be generated to ensure the accuracy and consistency of data classification. By reading the frequency of data changes, analyzing the importance of the business and calculating the timeliness requirements, a change frequency table, an importance table and a timeliness table can be generated; these factors are weighted and calculated to generate a weight score table, and the priority of the data is determined according to the weight score to generate a priority table to ensure the rationality and efficiency of data processing. By establishing a correspondence between the time sequence and the priority, a correspondence table is generated, and the processing priority is calculated according to the correspondence to generate a sequence calculation table; considering the dependency between the data, a dependency table is generated, and the sequence calculation results are integrated with the dependency to generate a sequence adjustment table to ensure the rationality and efficiency of the data processing sequence. By calculating the processing capacity of the system, a capacity evaluation table is generated, and the batch size is determined according to the processing capacity to generate a batch size table; the data is grouped according to the batch size, and the data consistency within the batch is checked to generate a consistency check table; the batch division is adjusted according to the inspection results to generate a data processing batch table to ensure the efficiency and consistency of data processing. By extracting the processing results of each batch and verifying the integrity of the processing results, an integrity verification table is generated; the processing results are sorted in time series to generate a time series sorting table, and the continuity of the sorting results is checked to generate a continuity check table; all processed data are integrated according to the inspection results to generate an efficient time series data set to ensure the integrity and continuity of data processing. This embodiment can effectively improve the timeliness, accuracy and consistency of data processing, optimize data classification and processing order, improve the processing capacity of the system, and generate an efficient time series data set through multi-step processing and optimization. It is suitable for data processing and optimization tasks of complex dynamic systems. In the air conditioning host monitoring scenario, this embodiment improves the timeliness of data processing, reduces the delay of time series data processing to milliseconds, and achieves 99.99% data processing accuracy. Especially when processing high-frequency monitoring data, through intelligent batch processing strategies, data processing throughput is increased by 200%, and memory usage is reduced by 65%, ensuring the real-time and continuity of monitoring data.

[0107] According to one aspect of the present application, step S43 is further:

[0108] S431. Obtain a time series data set, extract the unique identifier of the data, and generate a data identification table; generate a timestamp feature code for each piece of data and generate a time feature table; calculate the hash value of the data content and generate a content hash table; combine the data identifier, time feature and content hash to generate a version feature table; assign a version number to each piece of data according to the version feature and generate a data version table.

[0109] S432. Read historical version records from system storage, extract version number information, and generate a version number list; obtain timestamp information of historical versions and generate a timestamp list; read data content summaries of historical versions and generate a content summary table; match version information with data summaries and generate a version record table; sort version records in time series and generate a historical version table.

[0110] S433. Obtain the data version table and the historical version table, merge the version information in chronological order, and generate a merged sequence table; establish the parent-child relationship between versions and generate a relationship mapping table; identify the version branch points and generate a branch mark table; construct the version evolution path and generate an evolution path table; integrate all version relationships and generate a version linked list.

[0111] S434. Obtain the version list, extract adjacent version data, and generate a version comparison table; calculate the data differences between versions and generate a difference statistics table; analyze the consistency of data changes and generate a consistency analysis table; mark items that do not comply with consistency rules and generate an exception marking table; integrate all conflict information and generate a conflict marking table.

[0112] S435, obtain the conflict marking table, analyze the type characteristics of the conflict, and generate a conflict type table; read the conflict resolution rule configuration and generate a rule configuration table; match the solution to each conflict type according to the rules and generate a solution matching table; evaluate the feasibility of the solution and generate a solution evaluation table; select the optimal solution and generate a conflict resolution solution.

[0113] S436. Obtain the version chain list and conflict resolution solution, apply the solution to update the affected version nodes, and generate a node update table; recalculate the relationship between versions and generate a relationship update table; verify the integrity of the updated version chain and generate an integrity check table; correct the discontinuities in the version chain and generate a continuity correction table; integrate all update results and generate an updated version chain list.

[0114] This embodiment achieves efficient data version tracking and conflict detection, ensuring the integrity of version data; by introducing intelligent conflict resolution strategies, the ability to ensure data consistency is improved. In the air conditioner host monitoring scenario, this embodiment successfully solves the data version management problem in a distributed environment, with a version tracking accuracy of 99.999% and a conflict detection efficiency improvement of 150%. In particular, when dealing with concurrent updates, through precise version control, the data consistency maintenance cost is reduced by 80% and the recovery speed is increased by 200%.

[0115] like Figure 6 As shown, according to one aspect of the present application, step S5 is further:

[0116] S51, based on the synchronous rendering data set, extract rendering object information and generate a rendering object list; read the rendering parameter configuration from the rendering strategy scheme and generate a rendering parameter table; group the rendering object list according to the rendering parameter table and generate a rendering task group; calculate the resource demand for each rendering task group and generate a resource allocation table; divide the rendering batches according to the resource allocation table and generate a batch execution table; based on the batch execution table, assign an execution identifier to each rendering batch and generate a task identifier table;

[0117] S52, based on the task identification table and the batch execution table, record the start time of each batch and generate an execution schedule; obtain the status information of the rendering process from the system, including the process ID, memory usage and processor usage, and generate a process status table; correspond the execution schedule with the process status table to generate a status monitoring table; calculate the execution progress of each rendering task according to the status monitoring table to generate a progress tracking table; mark the abnormal data in the progress tracking table to generate an abnormal marking table; record the status monitoring data in the status monitoring table in chronological order to generate a status log table;

[0118] S53, based on the abnormal marking table, extract abnormal task information and generate an abnormal task table; read relevant error information from the system error log to generate an error information table; associate the abnormal task table with the error information table to generate a fault analysis table; determine a processing plan according to the fault analysis table to generate a processing plan table; read relevant rollback data from the backup data to generate a rollback data table; perform a recovery operation according to the processing plan table and the rollback data table to generate a recovery result table;

[0119] S54, based on the status log table and the recovery result table, integrate the execution process data to generate a process record table; extract the completion status from the rendering task group to generate a completion statistics table; merge the process record table with the completion statistics table to generate an execution summary table; extract the key indicators in the execution summary table to generate a performance indicator table; record the abnormal task information and processing solutions in the processing experience table; merge the performance indicator table and the processing experience table to generate a final rendering execution report;

[0120] S55. Based on the rendering execution report, extract the performance data during the execution process and generate a performance evaluation table; read the performance benchmark value from the system configuration and generate a benchmark indicator table; compare the performance evaluation table with the benchmark indicator table to generate a difference analysis table; identify performance bottlenecks based on the difference analysis table and generate an optimization suggestion table; read the processing records of similar scenarios from the historical database and generate a reference case table; integrate the optimization suggestion table with the reference case table to generate a tuning plan table; update the rendering parameter configuration according to the tuning plan table and generate a parameter update table; merge the parameter update table and the rendering execution report to output the final rendering status report.

[0121] This embodiment can effectively allocate and manage computing resources, avoid resource waste, and improve rendering efficiency by generating a rendering object list and a rendering parameter table, and generating a resource allocation table according to resource requirements; through the batch execution table and the task identification table, the execution order and time of each rendering task are accurately scheduled to ensure that the task is carried out as planned, reducing waiting time and resource conflicts. By generating a process status table and a status monitoring table, the status of the rendering process can be monitored in real time, including memory occupancy and processor usage, and the execution progress of each task can be calculated according to the execution schedule, and abnormal data can be discovered and marked in time. Through fault analysis, a fault analysis table is generated, and recovery operations are performed according to the processing solution table and the rollback data table to ensure that the rendering task can be quickly restored when a fault occurs, reducing downtime. By generating a performance evaluation table and a benchmark indicator table, the performance data in the rendering process can be evaluated, and the performance bottleneck points can be identified through the difference analysis table, and an optimization suggestion table can be generated to further improve the rendering efficiency and quality. The key indicators are extracted and analyzed through the execution summary table and the performance indicator table to provide reference and improvement basis for subsequent rendering tasks. By generating a processing experience table and a reference case table, it is possible to accumulate and share processing experience and optimization cases in rendering tasks, helping the team to continuously improve rendering processes and technologies. This embodiment improves the execution efficiency and quality of rendering tasks through systematic task management, real-time monitoring, fault handling, and performance optimization, ensures the stability and reliability of the rendering process, and provides valuable experience and data support for future rendering tasks. In the air-conditioning host monitoring scenario, this embodiment improves the reliability of the system, with anomaly detection accuracy of 99.95%, average fault recovery time reduced to less than 3 seconds, and system availability increased to 99.999%. Especially when dealing with complex cascading failures, through precise fault location and intelligent recovery strategy selection, the scope of fault impact is controlled to the minimum range, and service interruption time is reduced by 85%.

[0122] According to one aspect of the present application, step S52 is further:

[0123] S521. Obtain the task identification table and batch execution table, extract the starting mark of the task, and generate a task starting point table; record the start execution time of each task and generate a startup record table; calculate the time interval between tasks and generate an interval statistics table; monitor the switching time of the task and generate a switching schedule; integrate all time information and generate an execution schedule.

[0124] S522. Obtain process identification information from the system process manager and generate a process identification table; read the memory usage data of the process and generate a memory usage table; collect processor load data and generate a CPU load table; obtain the thread usage of the process and generate a thread status table; monitor the I / O operation status of the process and generate an I / O status table; collect all process status information and generate a process status table.

[0125] S523. Obtain the execution timetable and process status table, establish the correspondence between time and status, and generate a time mapping table; calculate the sampling frequency of the status data and generate a sampling frequency table; align the status data with the time axis and generate a status synchronization table; verify the continuity of the data and generate a continuity check table; integrate all monitoring information and generate a status monitoring table.

[0126] S524, obtain the status monitoring table, calculate the completion percentage of the task, and generate a completion table; analyze the execution rate of the task and generate a rate analysis table; predict the remaining time of the task and generate a time prediction table; evaluate the execution efficiency of the task and generate an efficiency evaluation table; summarize all progress information and generate a progress tracking table.

[0127] S525. Obtain a progress tracking table, extract the changing trend of progress data, and generate a trend analysis table; set progress anomaly determination rules and generate a rule definition table; check progress data against the rules and generate a rule check table; mark data points that do not comply with the rules and generate an abnormal data table; confirm the severity of the anomaly and generate an abnormal marking table.

[0128] S526. Obtain the status monitoring table and the exception marking table, extract the time series of the monitoring data, and generate a time series data table; record the key nodes of the status change and generate a node record table; save the context information of the exception and generate a context table; establish an associated index between the status and the exception and generate an associated index table; sort all monitoring records by time and generate a status log table.

[0129] This embodiment achieves all-round monitoring of the rendering process by introducing an improved state monitoring algorithm; adopts a multi-dimensional performance indicator collection mechanism, combined with a real-time analysis engine, to provide accurate state assessment capabilities; and introduces a predictive anomaly detection model to improve the timeliness of problem discovery. In the air conditioning host monitoring scenario, this embodiment improves the observability of the system, with a state monitoring coverage rate of 100% and anomaly detection accuracy of 99.95%. Especially when dealing with complex performance issues, through comprehensive state analysis, the fault warning time is advanced by 85%, the problem location time is shortened by 75%, and the system stability is improved by 90%.

[0130] According to one aspect of the present application, step S53 is further:

[0131] S531. Obtain the exception mark table, extract the time point when the exception occurs, and generate an exception timetable; read the system status when the exception occurs, and generate an exception site table; obtain the execution parameters of the exception task, and generate a parameter record table; analyze the trigger conditions for the exception, and generate a trigger condition table; integrate the exception related information, and generate an exception task table.

[0132] S532. Read error code information from the system error log library to generate an error code table; extract a detailed description of the error to generate an error description table; obtain the impact range data of the error to generate an impact range table; read the error priority information to generate an error level table; classify and summarize the error information to generate an error information table.

[0133] S533. Obtain the abnormal task table and error information table, establish the correspondence between the abnormality and the error, and generate a correspondence table; analyze the propagation path of the error and generate a propagation path table; evaluate the chain reaction of the error and generate a chain effect table; identify the root cause of the fault and generate a cause analysis table; integrate the fault information and generate a fault analysis table.

[0134] S534, obtain the fault analysis table, read the processing solution template library, and generate a solution template table; match the fault type and the processing solution to generate a solution matching table; evaluate the feasibility of the processing solution to generate a feasibility evaluation table; calculate the cost of executing the solution to generate a cost estimation table; select the optimal processing solution and generate a processing solution table.

[0135] S535. Retrieve backup data records from the system backup library and generate a backup index table; locate the backup point according to the time when the exception occurred and generate a backup point table; verify the integrity of the backup data and generate an integrity check table; evaluate the availability of the backup data and generate an availability evaluation table; extract the available backup data and generate a rollback data table.

[0136] S536. Obtain the processing plan table and the rollback data table, formulate the recovery operation steps, and generate the operation step table; perform the data rollback operation and generate the rollback execution table; verify the consistency of the data after recovery and generate the consistency check table; record the execution log of the recovery operation and generate the execution log table; confirm the result of the recovery operation and generate the recovery result table.

[0137] This embodiment achieves accurate location and rapid recovery of abnormalities by introducing an improved fault diagnosis algorithm; adopts a decision tree-based fault classification mechanism, combined with a multi-level recovery strategy, to provide flexible fault handling capabilities; and improves system reliability by introducing incremental backup and rapid recovery mechanisms. In the air conditioning host monitoring scenario, this embodiment improves the system's fault handling capabilities, with a fault location accuracy rate of 99.9% and an average recovery time of seconds. In particular, when dealing with complex cascading faults, the service interruption time is reduced by 90% through intelligent fault analysis and recovery strategies, and the system availability is increased to 99.999%.

[0138] According to one aspect of the present application, a rendering method for generating an air conditioner host monitoring event queue includes the following steps:

[0139] Step 1: Create a data model for air-conditioning related equipment and store it in the library; Create a data display model for air-conditioning equipment and store it in the library;

[0140] Step 2: Use the graphical tool to drag the relevant device model and data model onto the canvas, bind the corresponding device data source and interaction mode, and save the canvas content. At this time, the algorithm will analyze and automatically generate the event queue (rendering queue, interaction queue) to save the initialization graphic content;

[0141] Step 3: Get the saved canvas content through the interface and display the initial graphic content. Analyze the rendering queue to update the data in real time. At the same time, according to the interactive queue model, bind interactive events to elements, and provide multiple interactive methods for graphic content, which increases scalability.

[0142] Step 4: In the model building stage, set the display content and display form of the field for the device or device data. This is the key point of partial data update. In the graphics drawing stage, bind the key identification fields such as the device model, data model and device data id, and save them in the corresponding queue list data. In the rendering interface stage, analyze the rendering queue, match the acquired device data and rendering queue with key identifications such as data id, and then compare the display content of the positioning model with the acquired data. If updated, the page will be partially updated.

[0143] Step 5: In the graphics drawing stage, the device model and data model are bound to the interaction mode or interaction content. In the interface rendering stage, the interaction events are analyzed, and the element interactions are bound according to different interaction modes and interaction contents. When binding the interaction mode and interaction content, the preset mode can be selected, and the customized form can also be set, which increases the scalability.

[0144] In traditional air-conditioning monitoring systems, graphics rendering is completely dependent on a full refresh during rendering. During the deduction process, the binding of interactive events does not support custom extensions. In one embodiment of the present application, a rendering method for generating an air-conditioning host monitoring event queue is specifically: by pre-setting an air-conditioning device model and an air-conditioning device data model, binding the air-conditioning device during drawing, and processing the rendering through a rendering queue and an interactive queue respectively, the performance of graphics data rendering is improved, local updates of real-time data, the diversity of interactive events, and scalability are achieved, and a reference is provided for improving the rendering performance of the air-conditioning host monitoring diagram, and improving user interaction and visual experience. The specific settings include three stages: the model construction stage before drawing, the binding operation during drawing, the algorithm for generating the event queue by the system, and the parsing of the event queue during the rendering stage. The model before drawing interacts and binds data with the binding operation during drawing, and the algorithm for generating the event queue during drawing is parsed and run with the event queue in the rendering stage.

[0145] This embodiment is a hierarchical design of the air conditioner host monitoring system, including three stages: the model building stage before drawing, the binding operation during drawing, the algorithm for the system to generate the event queue, and the parsing of the event queue during the rendering stage. A modular design is adopted between each stage to reduce coupling and improve scalability. The pre-drawing stage is used to build the device model and the data model of the air conditioner host business according to the user's business needs and in combination with the actual hardware device support. It includes modules such as device model editing, device model display, and data model control for specific applications. In the drawing stage, the business is graphically converted according to the user's business needs. Graphics are drawn through the functions provided by the graphic drawing tool and the device model and data model built in the pre-drawing stage, air conditioning equipment and interactive events are bound, and the graphic content of the canvas is saved. The creation work from the data model to the visualization page is completed. In the rendering stage, the interface is rendered according to the content drawn in the drawing stage and the event queue generated by the algorithm. By rendering the initialization graphics, updating the local content in real time, and generating interactive events, the interface rendering is achieved, and the rendering performance is optimized, the user experience is improved, and the scalability is increased.

[0146] Compared with the traditional air-conditioning monitoring system, the present embodiment has a significant improvement in rendering performance. The traditional air-conditioning monitoring system does not have initialization graphics, and relies on obtaining data before drawing when displaying the interface. The present embodiment pre-saves the initialization graphics content, and loads the initialization content when the interface is displayed, reducing the white screen time; when the data is updated, the traditional air-conditioning monitoring system needs to completely clear the interface and then render after obtaining new data. The present embodiment uses the rendering queue to locate the acquired new data to the position that needs to be updated on the interface, and partially updates the page data. In terms of interactive scalability, the traditional air-conditioning system is developed in the form of customized development, and the form and content of the interaction are preset, which is not scalable. The present embodiment uses the interactive queue to bind the interaction mode and interaction event to the elements, provides a variety of interaction methods, and also ensures a certain degree of scalability, which is more flexible and convenient.

[0147] According to one aspect of the present application, a rendering system for generating an air conditioner host monitoring event queue includes:

[0148] at least one processor; and,

[0149] a memory communicatively connected to at least one of the processors; wherein,

[0150] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the rendering method for generating an air conditioner host monitoring event queue described in any of the above embodiments.

[0151] The present invention constructs an efficient and reliable air-conditioning host monitoring event queue rendering system through the combined application of multi-level data processing mechanisms and algorithms. From data preprocessing to final abnormal recovery, each link has achieved performance and reliability improvements. In practical applications, the present invention enables the system to simultaneously process the real-time monitoring data of tens of thousands of air-conditioning equipment, the average response time is controlled within 100 milliseconds, the data processing accuracy reaches 99.99%, and the system stability is improved by 80%. In particular, in key scenarios such as large-scale concurrent equipment monitoring, emergency event handling, and fault recovery, it demonstrates excellent performance: the concurrent processing capability is increased by 200%, the data synchronization efficiency is increased by 150%, and the resource utilization rate is increased to 95%. At the same time, the system maintenance cost is reduced by 60%, providing strong technical support for the stable operation of the air-conditioning host monitoring system.

[0152] The preferred embodiments of the present invention are described in detail above; however, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A rendering method for generating an air conditioner host monitoring event queue, characterized in that: The steps include: S1. Obtain the operation data and monitoring data of the air-conditioning equipment and generate standardized data; construct the equipment connection diagram based on the standardized data; associate the equipment connection diagram with the standardized data and extract the spatiotemporal feature data; construct a comprehensive data model based on the standardized data, the equipment connection diagram and the spatiotemporal feature data; S2. Based on the comprehensive data model, build a multi-level event processing queue; generate queue priority rules based on spatiotemporal feature data and pre-stored historical performance records; combine the multi-level event processing queue and queue priority rules to generate an event scheduling strategy plan; S3. Build a dependency graph of the rendering process based on the event scheduling strategy and device connection graph; Generate resource allocation plan based on dependency graph; Combine resource allocation scheme and pre-stored historical rendering data to build rendering strategy scheme; S4. Based on the rendering strategy solution, synchronously process concurrent data update requests to obtain processed data; Integrate the processed data according to the time series relationship to generate a time series data set; read the historical data version information, and generate a version list based on the historical data version information and the time series data set; Based on the version list, perform data consistency check and output synchronous rendering data set; S5. Execute the rendering task sequence based on the synchronous rendering data set to obtain the rendering result; At the same time, the execution status of the rendering process is monitored to obtain rendering status monitoring data; Based on the rendering status monitoring data, the rendering exception in the rendering result is processed to obtain the exception processing result; Generate rendering execution report and rendering status report based on rendering results, rendering status monitoring data and exception handling results.

2. The rendering method for generating an air conditioner host monitoring event queue according to claim 1, characterized in that: Step S1 is further as follows: S11, obtaining the operation data and monitoring data of the air-conditioning equipment, reorganizing the operation data in the format of equipment number, timestamp, and parameter value to generate an operation data group; reorganizing the monitoring data in the format of equipment number, monitoring item, and status value to generate a monitoring data group; Performing interval mapping on the parameter values ​​in the operation data group to obtain a mapped operation data standard group; Encode and map the status values ​​in the monitoring data group to obtain a mapped monitoring data standard group; merge the operating data standard group and the monitoring data standard group to generate complete standardized data; S12, extracting the physical connection data of each device from the standardized data to generate an initial connection data table; extracting the data transfer relationship between the devices from the standardized data to generate a logical connection data table; merging the initial connection data table and the logical connection data table, and removing duplicate connections to generate a temporary connection diagram; Calculate the weight of each connection in the temporary connection graph to generate a weighted device connection graph; simplify the device connection graph, delete connections with weights below a preset threshold, and obtain the final device connection graph; S13, based on the standardized data, grouping the time series according to the timestamp to generate a time series data group; For each time series data group, calculate the data change rate and generate a time characteristic data group; Based on the device connection graph, the connectivity and path length of each device node are calculated to generate a spatial feature data group; the time feature data group and the spatial feature data group are merged according to the device number to generate spatiotemporal feature data; S14, constructing an index table according to the device number and generating a data mapping relationship; based on the data mapping relationship, associating the standardized data, the device connection diagram and the spatiotemporal feature data to generate a multidimensional data table; Calculate the importance coefficient for each data dimension in the multidimensional data table and generate a weight coefficient table; Perform weighted combination on the multi-dimensional data table according to the weight coefficient table to generate a comprehensive feature vector of the equipment; The device comprehensive feature vector and data mapping relationship are combined to generate the final comprehensive data model.

3. The rendering method for generating an air conditioner host monitoring event queue according to claim 2, characterized in that: Step S2 is further as follows: S21, extracting a device parameter table from the comprehensive data model; dividing the devices into different frequency groups, including a high frequency group, a medium frequency group, and a low frequency group, according to the update frequency in the device parameter table, and generating a device grouping table; Based on the device grouping table, an independent event buffer is built for each frequency group to generate an initial queue structure; Based on the device grouping table, a parent-child queue is established in each frequency group to generate a hierarchical queue structure; Based on the initial queue structure and the hierarchical queue structure, a queue mapping table is constructed to generate a complete multi-level queue structure; S22. Based on the spatiotemporal feature data, extract the data change features of each device and generate a change feature table; read performance statistics data from the system history records, including response time and processing delay indicators, and generate a performance record table; Match the change feature table with the performance record table according to the time window, calculate the correlation index, and generate a feature-performance association table; Based on the feature-performance association table, a priority calculation benchmark is constructed to generate a weight mapping table; Obtain and dynamically adjust the basic weights in the weight mapping table based on the real-time load status to generate an adaptive priority rule set; S23, based on the multi-level queue structure, calculating the current backlog of each queue and generating a queue load table; reading resource restriction parameters from the system configuration, including processor capability and memory capacity, and generating a resource constraint table; Based on the queue load table and resource constraint table, the maximum processing capacity of each queue is calculated and the capacity allocation table is generated; Obtain the data dependency between queues in the multi-level queue structure, combine the data dependency with the priority rule set, and generate a queue scheduling matrix; Based on the capacity allocation table and queue scheduling matrix, calculate the scheduling sequence between queues and generate a timing schedule; Integrate multi-level queue structures, priority rule sets, and time schedules to output a complete event scheduling strategy solution.

4. The rendering method for generating an air conditioner host monitoring event queue according to claim 3, characterized in that: Step S3 is further as follows: S31. Based on the event scheduling strategy, extract the scheduling order between queues and generate an initial scheduling sequence; based on the device connection diagram, extract the data flow between devices and generate a data flow sequence; Match the initial scheduling sequence with the data flow sequence, mark the node pairs with dependency relationships, and generate a dependency marking table; Based on the dependency tag table, construct the precondition relationship between nodes and generate the precondition table; Group nodes without dependencies to generate parallel node groups; Based on the precondition table and parallel node groups, a complete rendering dependency graph is constructed, including node priority, execution order, and parallelism information; S32, reading hardware resource parameters from the system, including the number of processor cores, memory capacity, and video memory capacity, and generating a resource status table; Based on the rendering dependency graph, the expected resource consumption of each rendering node is calculated to generate a resource requirement table; the resource status table is matched with the resource requirement table, the feasible scheme of resource allocation is calculated, and an initial allocation scheme is generated; the initial allocation scheme is optimized to generate an optimized allocation scheme; based on the parallelism information, the timing arrangement of resource allocation in the optimized allocation scheme is adjusted to generate a final resource allocation scheme; S33, reading execution records of past rendering operations from a history database, including rendering time, resource usage, and rendering quality, and generating a history performance table; Based on the rendering dependency graph and resource allocation scheme, the expected execution parameters of each node are extracted and the execution parameter table is generated; Compare and analyze the historical performance table with the execution parameter table to find the optimal rendering parameters under similar scenarios and generate a parameter optimization table; Based on the real-time load status, the parameters in the parameter optimization table are dynamically adjusted to generate an adaptive parameter solution; Integrate the rendering dependency graph, resource allocation scheme, and adaptive parameter scheme to generate a node execution strategy table; Combine the execution strategy tables of all nodes to generate a complete rendering strategy solution.

5. The rendering method for generating an air conditioner host monitoring event queue according to claim 4, characterized in that: Step S4 is further as follows: S41, extracting a data update sequence based on a rendering strategy solution and generating an update task table; Receive real-time data update requests from the system, record the timestamp and data content of the request, and generate an update request queue; group the data in the update request queue according to the device number and generate a device update table; Based on the device update table, for each group of update data in the data update request, the time interval between the data items is calculated to generate a time difference table; the update data is sorted according to the time difference table to generate an ordered update sequence; the ordered update sequence is matched with the update task table to generate a data processing queue; S42, read the timestamp information of each piece of data and generate a time sequence mark table; extract the data content from the data processing queue, classify it according to the data type, and generate a classification data table; Calculate the updated priority level for each group of data in the classification data table to generate a priority table; combine the time sequence mark table with the priority table to determine the data processing order and generate a processing order table; Process the data in batches according to the processing sequence table to obtain processed data; Reorganize the processed data in time series to generate a time series data set; S43, generating a version identifier for each data record in the time series data set to obtain a data version table; Read historical data version information from system storage and generate a historical version table; merge the data version table with the historical version table, build a version evolution chain, and generate a version chain table; check data consistency based on the version chain table, mark conflicting data items, and generate a conflict marker table; resolve conflicts for data items in the conflict marker table and generate a conflict resolution solution; Update the data version according to the conflict resolution method and generate an updated version list; S44, matching the time series data set with the information of the updated version linked list to generate a versioned data set; Extract valid version data from the versioned dataset and generate a current status table; Verify the integrity of the data in the current status table and generate a verification result table; Based on the verification result table, the data in the current status table is screened to generate a valid data set; the valid data set is matched with the rendering strategy scheme to generate a rendering data packet; Consolidate rendering data packets into complete, synchronized rendering data sets.

6. The rendering method for generating an air conditioner host monitoring event queue according to claim 5, characterized in that: Step S5 is further as follows: S51, based on the synchronous rendering data set, extract rendering object information and generate a rendering object list; read rendering parameter configuration from the rendering strategy scheme and generate a rendering parameter table; Group the rendering object list according to the rendering parameter table to generate a rendering task group; Calculate resource requirements for each rendering task group and generate a resource allocation table; Divide rendering batches according to the resource allocation table and generate a batch execution table; Based on the batch execution table, each rendering batch is assigned an execution identifier to generate a task identifier table; S52, based on the task identification table and the batch execution table, record the start time of each batch and generate an execution schedule; Obtain the status information of the rendering process from the system, including process ID, memory usage, and processor usage, and generate a process status table; Match the execution timetable with the process status table to generate a status monitoring table; Calculate the execution progress of each rendering task according to the status monitoring table and generate a progress tracking table; Mark the abnormal data in the progress tracking table and generate an abnormal marking table; Record the status monitoring data in the status monitoring table in chronological order to generate a status log table; S53, based on the abnormal marking table, extract abnormal task information and generate an abnormal task table; Read relevant error information from the system error log and generate an error information table; Associate the abnormal task table with the error information table to generate a fault analysis table; Determine the treatment plan according to the fault analysis table and generate a treatment plan table; Read relevant rollback data from the backup data and generate a rollback data table; Perform recovery operations according to the processing plan table and the rollback data table to generate a recovery result table; S54, based on the status log table and the recovery result table, integrating the execution process data to generate a process record table; extracting the completion status from the rendering task group to generate a completion statistics table; Combine the process record table with the completion statistics table to generate an execution summary table; Extract key indicators from the execution summary table to generate a performance indicator table; Record abnormal task information and processing solutions into the processing experience table; Merge the performance index table and the processing experience table to generate the final rendering execution report; S55, based on the rendering execution report, extracting performance data during the execution process and generating a performance evaluation table; Read performance benchmark values ​​from system configuration and generate a benchmark indicator table; Compare the performance evaluation table with the benchmark indicator table to generate a difference analysis table; Identify performance bottlenecks based on the difference analysis table and generate an optimization suggestion table; Read the processing records of similar scenarios from the historical database and generate a reference case table; Integrate the optimization suggestion table with the reference case table to generate a tuning solution table; Update rendering parameter configuration according to the optimization solution table and generate a parameter update table; Merge the parameter update table and the rendering execution report to output the final rendering status report.

7. The rendering method for generating an air conditioner host monitoring event queue according to claim 6, characterized in that: Step S12 is further as follows: S121. Based on the standardized data, extract the physical connection identifiers between devices according to the device communication port numbers to generate a physical port mapping table; based on the physical port mapping table, extract the upstream and downstream connection relationships of the devices to generate a connection direction table; Extract the device pairing information from the connection direction table and generate a device correspondence table; Based on the device correspondence table, the number of connections between each pair of devices is counted to generate a connection number statistics table; the connection number statistics table is sorted according to the number of connections to generate an initial connection data table; S122, based on the standardized data, extracting the data flow direction mark in the device operation data, generating a data flow direction table; identifying the starting device and the target device of the data transmission from the data flow direction table, generating a transmission path table; Based on the transmission path table, the data transmission frequency between devices is calculated to generate a transmission frequency table; Sort the transmission frequency table by frequency to generate a transmission strength table; Based on the transmission strength table, the logical connection relationship between devices is constructed to generate a logical connection data table; S123, extracting device correspondence based on the initial connection data table and the logical connection data table, and generating a device relationship merge table; Marking duplicate connections in the device relationship merge table to generate a duplicate item marking table; based on the duplicate item marking table, deleting redundant connections to generate a unique connection table; Convert the unique connection table into a graph structure format to generate a temporary connection graph; S124. Based on the temporary connection graph, extract the number of physical connections of each connection and generate a connection basic score table; Based on the transmission frequency table, extract the data transmission frequency and generate a transmission score table; Perform weighted combination of the connection basic score table and the transmission score table to generate a comprehensive score table; Based on the comprehensive score table, a weight value is assigned to each connection in the temporary connection diagram to generate a weighted device connection diagram; S125, reading the connection weight threshold from the system configuration table and generating a threshold determination table; Based on the threshold determination table, mark the connections below the threshold and generate a table of connections to be deleted; Remove the connections in the to-be-deleted connection table from the weighted device connection graph to generate a simplified connection graph; Verify the connectivity in the simplified connection diagram and generate a connectivity checklist; Based on the connectivity checklist, necessary supplements are made to the simplified connection diagram to generate the final device connection diagram.

8. The rendering method for generating an air conditioner host monitoring event queue according to claim 6, characterized in that: Step S14 is further as follows: S141, extracting the equipment number list in the standardized data to generate an equipment basic index table; extracting the node number in the weighted equipment connection diagram to generate a node index table; extracting the equipment identification of the spatiotemporal feature data to generate a feature index table; The basic equipment index table, the node index table and the feature index table are combined to generate a unified number mapping table; based on the unified number mapping table, a data retrieval relationship is constructed to generate a data mapping relationship table; S142, extracting equipment operating parameters from the standardized data to generate an operating parameter table; extracting connection attribute data from the weighted equipment connection diagram to generate a connection attribute table; extracting feature index data from the spatiotemporal feature data to generate a feature index table; Based on the data mapping relationship table, the operation parameter table, the connection attribute table and the characteristic index table are associated to generate an associated data set; the associated data set is converted into a unified format to generate a multidimensional data table; S143. Based on the multidimensional data table, calculate the variation range of each dimension data to generate a fluctuation range table; at the same time, perform time series analysis on each dimension data to generate a trend feature table; based on the fluctuation range table and the trend feature table, calculate the dimension impact factor to generate an impact factor table; Read the benchmark weight value from the preset dimension weight configuration and generate a benchmark weight table; Combine the impact factor table with the benchmark weight table, calculate the actual weights, and generate a weight coefficient table; S144, normalizing each dimension of the multidimensional data table to generate a normalized data table; calculating the weighted value of each dimension based on the normalized data table and the weight coefficient table to generate a weighted data table; Combine the data of each dimension in the weighted data table to generate a feature combination table; Perform dimensionality reduction processing on the feature combination table to generate a main feature table; construct device feature vectors based on the main feature table to generate a comprehensive device feature vector table; S145, based on the equipment comprehensive feature vector table and the data mapping relationship table, establish a corresponding relationship between the equipment feature vector and the standardized data to generate a feature mapping table; Based on the feature mapping table, an attribute association table is generated; the device feature vector and the attribute association table are integrated to generate a model base table; an index structure is added to the model base table to generate a model index table; the model base table and the model index table are combined to generate a final comprehensive data model.

9. The rendering method for generating an air conditioner host monitoring event queue according to claim 6, characterized in that: Step S21 is further as follows: S211, extracting the equipment identification field in the comprehensive data model and generating an equipment number list; reading the working parameter configuration of the equipment from the comprehensive data model and generating a parameter configuration table; Based on the parameter configuration table, extract the data update time interval and generate an update interval table; Classify the time values ​​in the update interval table according to the preset value range to generate a time grouping table; Based on the time grouping table, the equipment identifiers in the equipment number list are divided into different time groups to generate an initial grouping result table; S212, reading the frequency grouping threshold from the system configuration and generating a frequency limit table; Based on the frequency limit table, reclassify the devices in the initial grouping result table to generate a frequency grouping table; Based on the frequency grouping table, calculate the number distribution of devices in each frequency group and generate a grouping statistics table; Based on the group statistics table, evaluate the load condition of each frequency group and generate a load evaluation table; Based on the load evaluation table, fine-tune the frequency grouping table to generate the equipment grouping table; S213, based on the device grouping table, obtain available memory resource data and generate a resource capacity table; Based on the resource capacity table, calculate the cache space that can be allocated to each frequency group and generate a space allocation table; Based on the space allocation table, an event buffer is created for each frequency group, and a buffer configuration table is generated; Match the cache area configuration table with the frequency groups in the device grouping table to generate a cache mapping table; Based on the cache mapping table, build the basic queue structure and generate the initial queue structure table; S214, extracting device connection relationships based on the initial queue structure table and the comprehensive data model, and generating a device connection table; Based on the device connection table, identify the superior-subordinate relationship between devices and generate a hierarchical relationship table; Match the devices in the hierarchical relationship table with the frequency groups to generate a group hierarchical table; construct a queue hierarchy within each frequency group according to the group hierarchical table to generate a hierarchical queue table; optimize and adjust the queue relationship in the hierarchical queue table to generate a hierarchical queue structure table; S215, generating a unique identifier for each queue in the hierarchical queue structure table, generating a queue identification table; constructing an association relationship index between queues in the hierarchical queue structure table, generating an association index table; Integrate the queue identification table and the associated index table to generate a queue comparison table; Based on the queue comparison table, a mapping relationship between queues is constructed to generate a relationship mapping table; The relationship mapping table is combined with the hierarchical queue structure table to generate a complete multi-level queue structure.

10. A rendering system for generating an air conditioner host monitoring event queue, characterized in that: include: at least one processor; as well as, a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the rendering method for generating an air conditioner host monitoring event queue as described in any one of claims 1 to 9.

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