Unified Management Method and System for Provincial and Regional Station Dispatch

Abstract

This invention discloses a unified management method and system for provincial and municipal station dispatching, relating to the field of integrated operation and management technology. The method includes: receiving data from provincial and municipal station monitoring equipment, including timestamps, event codes, and associated parameters; analyzing the data to identify data fluctuations; and filtering potential fault signals by comparing historical patterns to obtain initial fault screening results. The unified management method for provincial and municipal station dispatching provided by this invention achieves early identification of fault signals and accurate classification of abnormal patterns by receiving and analyzing data from monitoring equipment. It improves the efficiency and accuracy of data analysis by comparing real-time and historical data, accelerates fault detection and enhances the handling capability of complex abnormal situations by identifying data changes in different time periods, sets alarm response times and processing priorities for different anomaly types to improve emergency response efficiency, optimizes network traffic management strategies and data transmission paths, reduces processing latency, improves network performance and stability, and effectively enhances operational efficiency and user experience.

Description

Technical Field

[0001] This invention relates to the field of integrated operation and management technology, specifically to a unified management method and system for provincial and local station dispatching. Background Technology

[0002] Integrated transportation management technology involves unifying the management and scheduling of various transportation modes and methods through an integrated technology platform to improve overall transportation efficiency and service quality. This technology is primarily applied to large-scale transportation networks, including rail, road, air, and water transport, aiming to achieve optimal resource allocation and operational automation through high-level information integration. The core of this field lies in the application of information technology, including real-time data acquisition, processing, and analysis, as well as the development of advanced scheduling algorithms and decision support systems. Integrated transportation management technology enables operations managers to quickly respond to various operational situations, optimize transportation processes, and enhance user experience by providing a unified user interface and decision-making tools.

[0003] The unified management method for provincial and municipal station scheduling is a strategy for optimizing the management of transportation hubs or stations. Its main purpose is to rationally allocate station resources through efficient scheduling methods, reduce the space required by stations, and improve station operational efficiency. This method is applied to railway, subway, or large bus stations. Through integrated information systems and intelligent scheduling algorithms, it precisely controls and schedules vehicles entering and leaving the station. Its purpose is to reduce physical space requirements, accelerate vehicle turnaround speed, reduce congestion, improve passenger experience, and enhance station operational efficiency. The key theme is to achieve the rapid, orderly, and safe flow of transportation vehicles, enabling each node in the transportation network to operate at its best, thereby improving the overall efficiency and sustainability of transportation. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that while existing unified management methods for provincial and regional station dispatching have advantages in information integration and resource allocation, they still have limitations in rapidly identifying and handling emergencies. Especially in large-scale transportation networks, traditional data processing methods rely on preset rules and models, lacking sufficient flexibility to cope with complex and ever-changing realities. For example, when faced with unconventional data fluctuations or sudden network congestion, existing technologies cannot quickly locate the problem or perform effective dispatching, leading to prolonged response times and decreased processing efficiency. The lack of targeted alarm configurations and priority handling mechanisms results in the omission or incorrect response to critical events, causing a decline in service quality in emergencies, increased operating costs, and even affecting passenger safety and satisfaction.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a unified management method for provincial and regional station scheduling, comprising:

[0007] Receive data from provincial and municipal monitoring equipment, including timestamps, event codes, and associated parameters; analyze the data to identify data fluctuations; and filter potential fault signals by comparing with historical patterns to obtain preliminary fault screening results.

[0008] Using the initial fault screening results, the data is analyzed, key data points are extracted, data changes are compared across different time periods, abnormal patterns are identified, abnormal situations are automatically classified, and abnormal pattern classification results are obtained.

[0009] Based on the abnormal pattern classification results, set alarm configuration, configure alarm response time and processing priority, record alarm triggering and processing process, and establish provincial and municipal station management alarm execution records;

[0010] By utilizing the provincial and municipal station management alarm execution records, the timeliness and effectiveness of alarm responses are analyzed, network traffic management strategies are adjusted, data transmission paths are optimized, and optimized scheduling commands are generated.

[0011] Based on the optimized scheduling command, a series of network command tests were performed, the execution time and success rate of each command were recorded, the performance logs were analyzed, inefficient commands were filtered out and optimized, and a summary of the results of the simulated operation was constructed.

[0012] Based on the results of the simulation operation, the performance of the scheduling commands is analyzed, the execution rate of each command is measured, and the results are evaluated against the predetermined performance targets to obtain the evaluation results of the unified scheduling management efficiency.

[0013] As a preferred embodiment of the unified management method for provincial and municipal station scheduling described in this invention, the following are included: the initial fault screening results include fault level and associated parameters; the anomaly pattern classification results include anomaly category, anomaly frequency, and associated impact; the provincial and municipal station management alarm execution records include alarm type, response time, and processing status; the optimized scheduling commands include command optimization objectives and expected adjustment effects; the summary of simulation operation results includes test coverage, optimization success rate, and key performance indicators; and the unified scheduling management effectiveness evaluation results include effectiveness rating and response efficiency.

[0014] As a preferred embodiment of the unified management method for provincial and municipal station scheduling described in this invention, the steps of receiving data from provincial and municipal station monitoring equipment, including timestamps, event codes, and associated parameters, analyzing the data to identify data fluctuations, and filtering potential fault signals by comparing historical patterns to obtain preliminary fault screening results are as follows:

[0015] Receive data from provincial and municipal monitoring equipment, including timestamps, event codes, and associated parameters, and store them in the data buffer in the order they are received. Ensure that each piece of data can be recorded and accessed to obtain the dataset entry.

[0016] Using the timestamps and event codes entered in the dataset, time series analysis is performed on the data. Thresholds are set to distinguish between regular and irregular fluctuations. Irregular fluctuations are marked to obtain a volatility-labeled dataset.

[0017] Using the markers in the volatility marker dataset, data points that match historical fault patterns are filtered from the data. Fault signals are identified by setting matching parameters. By comparing historical data patterns with real-time monitoring data, fault points are identified, and initial fault screening results are obtained.

[0018] As a preferred embodiment of the provincial and regional station scheduling unified management method described in this invention, the steps of using the initial fault screening results to analyze data, extract key data points, compare data changes over different time periods, identify abnormal patterns, automatically classify abnormal situations, and obtain abnormal pattern classification results are as follows:

[0019] Import data from the initial fault screening results, initialize the data reading process, set the filtering conditions including data generation time and data type, filter and analyze the data stream, extract data points associated with the fault through numerical comparison, and generate a set of key data points;

[0020] Based on the set of key data points, a time window is set to compare the data points, sort and mark the data points, identify peak changes and abnormal data points, use time stamps to record the changing trend of each data point, and generate a record of the changing patterns.

[0021] By utilizing the recorded patterns of change, a support vector machine algorithm is used to classify and label the data. Classification parameters, including fluctuation range and duration, are set. The data is automatically classified using the classification parameters. Based on the classification results, the characteristics of multiple abnormal patterns are recorded and analyzed to generate abnormal pattern classification results.

[0022] As a preferred embodiment of the unified management method for provincial and regional station scheduling described in this invention, the support vector machine algorithm is expressed as follows:

[0023]

[0024] Where f(x) represents the decision function, w is the normal vector of the hyperplane, x is the input feature vector, and α i Lagrange multipliers for each support vector, k(x,x) i ) is the kernel function, γ is the weighting coefficient of the kernel function, and b is the bias term.

[0025] As a preferred embodiment of the unified management method for provincial and municipal station scheduling described in this invention, the steps of setting alarm configuration, configuring alarm response time and processing priority, recording alarm triggering and processing processes, and establishing provincial and municipal station management alarm execution records based on the abnormal mode classification results are as follows:

[0026] Using the anomaly pattern classification results, alarm parameters are set for each anomaly pattern, response times are defined for severe anomalies and for minor anomalies, and processing priorities are assigned according to the anomaly type to obtain the alarm parameter configuration;

[0027] Based on the alarm parameter configuration, the alarm triggering conditions are defined and set, an event code is assigned to each type of anomaly, and the response level is determined to obtain the alarm triggering mechanism;

[0028] Using the aforementioned alarm triggering mechanism, the triggering and processing of each alarm are recorded, including the alarm time, type, processing measures, and post-processing results, thus establishing a provincial and municipal station management alarm execution record.

[0029] As a preferred embodiment of the unified management method for provincial and municipal station scheduling described in this invention, the steps of analyzing the timeliness and effectiveness of alarm responses using the provincial and municipal station management alarm execution records, adjusting network traffic management strategies, optimizing data transmission paths, and generating optimized scheduling commands are specifically as follows:

[0030] Response time and processing result data are extracted from the alarm execution records of the provincial and municipal stations. Data integration technology is used to classify and sort the event types and response timestamps. Through time series analysis, the response time differences of different alarm types are compared and efficiency is evaluated to obtain alarm efficiency data.

[0031] Based on the alarm efficiency data, network node traffic analysis is performed to identify congested nodes, adjust the data transmission frequency and routing priority of the nodes, and obtain a network adjustment scheme by dynamically adjusting to alleviate network congestion.

[0032] Using the aforementioned network adjustment scheme, new scheduling commands are written and implemented. The command parameters are adjusted through a software programming interface to match the new network traffic configuration, the command is updated, and the optimized scheduling commands are generated.

[0033] Another objective of this invention is to provide a unified management system for provincial and regional station scheduling. By constructing such a system, it addresses the problem that traditional data processing methods in large-scale transportation networks rely on preset rules and models and lack sufficient flexibility to cope with complex and ever-changing issues.

[0034] To solve the above technical problems, the present invention provides the following technical solution: a unified management system for provincial and municipal station dispatching, comprising: a data receiving module, a data analysis module, an alarm configuration module, a command optimization module, and a performance evaluation module; the data receiving module receives data from provincial and municipal station monitoring equipment, performs data integrity checks, identifies abnormal fluctuations, and filters fault signals based on the characteristics of the fluctuations to obtain a fault dataset; the data analysis module analyzes the changing trend of each data point based on the fault dataset, compares data within different time periods through time series analysis, determines abnormal patterns, classifies the abnormal patterns, and generates an abnormal situation classification result; the alarm configuration module... The module utilizes the anomaly classification results to set alarm parameters for various anomalies managed by provincial and municipal stations, including response time and processing priority, records alarm activities in real time, and establishes alarm activity records. The command optimization module analyzes the timeliness and effectiveness of the response from the alarm activity records, adjusts the data flow and network settings of the provincial and municipal stations to match real-time needs, optimizes scheduling commands, verifies the effects of the adjustments through field testing, and creates scheduling optimization details. The performance evaluation module performs performance tests based on the scheduling optimization details, monitors the execution efficiency and success rate of multiple commands, evaluates the operational efficiency and stability of scheduling at the provincial and municipal stations, and obtains the unified scheduling management effectiveness evaluation results.

[0035] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the provincial and regional station scheduling unified management method as described above.

[0036] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the provincial and regional station scheduling unified management method as described above.

[0037] The beneficial effects of this invention are as follows: The unified management method for provincial and municipal station dispatching provided by this invention receives and analyzes data transmitted from monitoring equipment, enabling early identification of fault signals and accurate classification of abnormal patterns. Utilizing real-time data analysis and historical data comparison, it significantly improves the efficiency and accuracy of data analysis. By comparing data changes over different time periods, it can identify and classify abnormal data patterns, which not only accelerates the fault detection process but also enhances the ability to handle complex abnormal situations. Through automatic classification of abnormal situations, corresponding alarm response times and processing priorities can be set for different anomaly types. This strategy improves the efficiency and effectiveness of emergency response. The optimized network traffic management strategy and data transmission path effectively reduce data processing latency, improve overall network performance and stability, and provide a highly efficient, flexible, and secure management method for provincial and municipal station dispatching, thereby improving overall operational efficiency and user experience. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 The above is an overall flowchart of a unified management method for provincial and regional station scheduling provided in an embodiment of the present invention.

[0040] Figure 2 The flowchart illustrates the data receiving process of the provincial and municipal station monitoring equipment in a unified management method for provincial and municipal station scheduling provided in one embodiment of the present invention.

[0041] Figure 3 A flowchart illustrating the process of obtaining abnormal pattern classification results in a unified management method for provincial and regional station scheduling provided in an embodiment of the present invention.

[0042] Figure 4 The flowchart of the provincial and municipal station management alarm execution record is provided as an embodiment of the present invention.

[0043] Figure 5 The flowchart illustrates the generation of optimized scheduling commands for a unified management method for provincial and regional station scheduling provided in one embodiment of the present invention.

[0044] Figure 6 A flowchart summarizing the results of a simulation operation of a unified management method for provincial and regional station scheduling provided in an embodiment of the present invention.

[0045] Figure 7 This is a flowchart illustrating the evaluation of a provincial and regional station scheduling unified management method against predetermined performance targets, as provided in an embodiment of the present invention.

[0046] Figure 8 This is an overall structural diagram of a unified provincial and regional station scheduling management system provided in one embodiment of the present invention. Detailed Implementation

[0047] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Example 1

[0050] Reference Figures 1-7 As an embodiment of the present invention, a unified management method for provincial and regional station scheduling is provided, comprising:

[0051] S1: Receives data from provincial and municipal monitoring equipment, including timestamps, event codes, and associated parameters. It analyzes the data, identifies indicators of regular and non-regular data fluctuations, and filters potential fault signals by comparing historical data patterns to obtain preliminary fault screening results.

[0052] S2: Using the initial fault screening results, analyze the data, extract key data points, compare data changes over different time periods, identify data anomaly patterns, automatically classify anomalies based on the characteristics of the data anomaly patterns, and obtain anomaly pattern classification results.

[0053] S3: Based on the abnormal pattern classification results, set alarm configurations, configure the corresponding alarm response time and processing priority according to the severity of each type of abnormality, record the alarm triggering and processing process, and establish a provincial and municipal station management alarm execution record;

[0054] S4: Utilize the alarm execution records of provincial and municipal stations to analyze the timeliness and effectiveness of alarm responses, adjust network traffic management strategies based on the analysis results, optimize data transmission paths, update scheduling commands, and generate optimized scheduling commands;

[0055] S5: Based on the optimized scheduling commands, execute a series of network command tests, record the execution time and success rate of each command, analyze the performance logs, filter out inefficient commands, optimize the commands, implement feedback corrections, evaluate the effectiveness of the corrected command configurations, and build a summary of the simulation operation results.

[0056] S6: Based on the results of the simulation operation, the performance of each scheduling command is analyzed, the stability and execution rate of each command are measured, the performance indicators are recorded, and the evaluation is carried out against the predetermined performance targets to obtain the evaluation results of the unified scheduling management efficiency.

[0057] The initial fault screening results include fault level and associated parameters; the anomaly pattern classification results include anomaly category, anomaly frequency, and associated impact; the provincial and municipal station management alarm execution records include alarm type, response time, and processing status; the optimized scheduling commands include command optimization objectives and expected adjustment effects; the simulation operation results summary includes test coverage, optimization success rate, and key performance indicators; and the unified scheduling management efficiency evaluation results include efficiency rating and response efficiency.

[0058] Specifically, such as Figure 2 As shown, the specific steps for receiving data from provincial and municipal monitoring equipment, including timestamps, event codes, and associated parameters, analyzing the data to identify data fluctuations, and filtering potential fault signals by comparing historical patterns to obtain preliminary fault screening results are as follows:

[0059] S101: Receive data from provincial and municipal monitoring equipment, including timestamps, event codes, and associated parameters, and store them in the data buffer according to the receiving order. Ensure that each piece of data can be recorded and accessed. The execution flow for dataset entry is as follows.

[0060] S101: Receives data from provincial and regional monitoring equipment, including timestamps, event codes, and associated parameters. Performs data format verification and time synchronization to ensure the integrity of each data packet. Data packets are sorted according to timestamps and stored in the data cache. An efficient indexing mechanism optimizes data retrieval speed for rapid access and processing. The storage location and status of each data entry are recorded to ensure complete data recording and accessibility. The resulting dataset is entered using the following formula:

[0061]

[0062] Among them, I t The index sequence represents the time interval, where N represents the total number of data received before time t, and c i The storage location coefficient t represents the first data unit. i The timestamp of the i-th data point is represented by T, and the time decay constant is represented by T.

[0063] S102: Using the timestamps and event codes entered in the dataset, perform time series analysis on the data, set thresholds to distinguish between regular and irregular fluctuations, and mark the irregular fluctuations to obtain the volatility-labeled dataset. The execution flow is as follows:

[0064] S102: Using timestamps and event codes from the dataset entry, time series analysis is performed to identify volatility patterns by calculating the moving average and variance of the data. Thresholds are set based on historical volatility data to help distinguish between regular and irregular volatility. Data on irregular volatility is labeled and recorded to provide a basis for subsequent fault detection procedures, resulting in a volatility-labeled dataset, using the formula:

[0065]

[0066] Among them, V k This represents the volatility measurement of the k-th data window, where W represents the window width, and x... j This represents the value of the j-th data point within the window. This represents the average value of all data points within the window.

[0067] S103: The execution flow is as follows: using the labels in the volatility label dataset, filtering data points that match historical fault patterns from the data, identifying fault signals by setting matching parameters, comparing historical data patterns with real-time monitoring data, identifying fault points, and obtaining the initial fault screening results.

[0068] S103: Utilizing the labels in the volatility-labeled dataset, in-depth data analysis is performed. By comparing the data with historical fault patterns using set matching parameters, fault data points are identified. The matching process calculates the similarity between real-time data and historical patterns, ensuring that only highly matching data points are labeled as fault signals, thus obtaining the initial fault screening results. The formula used is:

[0069]

[0070] Among them, F n This represents the score for fault detection, where M represents the number of parameters used for matching, and p m s represents the importance weight of the m-th parameter. m h represents the value of the m-th parameter in the real-time data. m σ represents the value of the m-th parameter in the historical failure modes. m This represents the standard deviation of the m-th parameter in the historical data.

[0071] Specifically, such as Figure 3 As shown, the specific steps for using the initial fault screening results to analyze the data, extract key data points, compare data changes over different time periods, identify abnormal patterns, automatically classify abnormal situations, and obtain the abnormal pattern classification results are as follows:

[0072] S201: Import data from the initial fault screening results, initialize the data reading process, set the filtering conditions including data generation time and data type, filter and analyze the data stream, extract data points related to the fault through numerical comparison, and generate a set of key data points. The execution process is as follows:

[0073] S201: After importing data from the initial fault screening results, initialize the data reading process, setting filtering conditions including data generation time and data type. Each data point is sorted in ascending order of generation time. Then, perform stratified filtering based on data type to ensure the accuracy of the filtering process. For data that meets the filtering conditions, perform numerical comparison, comparing the numerical differences between each data point and its neighboring points. Through difference threshold analysis, filter data points directly related to the fault. The filtered data points will be integrated into a key data point set to provide basic data for subsequent analysis, using the formula:

[0074]

[0075] Among them, K d Denotes the set of key data points, d i and d i-1 θ represents consecutive data points, n represents the total number of data points, and θ represents the set threshold for numerical difference.

[0076] S202: Based on the key data point set, a time window is set to compare data points, sort and mark data points, identify peak changes and abnormal data points, and use time stamps to record the changing trend of each data point to generate a record of the changing patterns. The execution flow is as follows:

[0077] S202: Based on a set of key data points, a time window is set up for comparison between data points. Data points within each time window are sorted according to their numerical values, and changes within each window are marked, with particular attention paid to peak values ​​and abnormal changes. A time stamp is assigned to each data point, and its trend is recorded, generating a detailed record of patterns in change. This record reflects the specific patterns of data point changes over time, aiding in subsequent analysis and decision-making. The formula used is:

[0078]

[0079] Among them, P t The value represents the ranking of the data points at time t, w represents the size of the time window, and x represents the ranking of the data points at time t. j t represents the value of the j-th data point. j This represents the time stamp of the j-th data point.

[0080] S203: Utilizing the record of change patterns, the support vector machine algorithm is used to classify and label the data. Classification parameters are set, including fluctuation range and duration. The data is automatically classified using the classification parameters. Based on the classification results, the characteristics of multiple abnormal patterns are recorded and analyzed to generate abnormal pattern classification results. The execution flow is as follows:

[0081] S203: Utilizing the recorded patterns of change, the data classification process begins. Classification parameters are defined, including the range and duration of data fluctuations. These parameters are used in the Support Vector Machine (SVM) algorithm for detailed data classification. The algorithm automatically classifies data into different anomaly patterns by analyzing the amplitude and duration of data point fluctuations. Based on the classification results, the characteristics of each anomaly pattern are recorded and analyzed, generating anomaly pattern classification results. This provides a deeper understanding of potential fault causes and a basis for developing countermeasures.

[0082] The formula for the Support Vector Machine algorithm is as follows:

[0083]

[0084] Where f(x) represents the decision function, w is the normal vector of the hyperplane, x is the input feature vector, and α i Lagrange multipliers for each support vector, k(x,x) i ) is the kernel function, γ is the weighting coefficient of the kernel function, and b is the bias term.

[0085] The execution process is as follows:

[0086] x retains its role as the feature vector representing the input, containing the features of the data points to be classified. w continues as the weight vector learned from the training data, determining the classification boundary. b acts as a bias term, adjusting the position of the entire classification hyperplane. The newly added α... i These are Lagrange multipliers used to determine the influence strength of each support vector, k(x,x). i ) is the kernel function, used to map data to a high-dimensional space in the case of non-linear separability, thereby improving classification accuracy. γ is the weight coefficient of the kernel function, which determines the influence of the kernel function on the overall classification function. The specific value of γ needs to be determined during training through methods such as cross-validation and grid search.

[0087] Specifically, such as Figure 4 As shown, based on the anomaly pattern classification results, the specific steps for setting alarm configurations, configuring alarm response times and processing priorities, recording alarm triggering and processing processes, and establishing provincial and regional station management alarm execution records are as follows:

[0088] S301: Using the anomaly pattern classification results, set alarm parameters for each anomaly pattern, define response time for severe anomalies, define response time for minor anomalies, and assign processing priorities according to anomaly type. The execution flow of alarm parameter configuration is as follows:

[0089] S301: Based on the anomaly pattern classification results, specific alarm parameters are first set for each anomaly pattern, including defining a response time and processing priority for each anomaly. Severe anomalies (such as equipment downtime) are assigned shorter response times, while minor anomalies (such as performance degradation) are assigned relatively longer response times. Processing priorities are assigned based on the potential impact and urgency of the anomaly; the strategy is set by analyzing historical data and potential impacts. The priority and response time settings are based on minimizing operational interruptions and maximizing resource efficiency. After configuration, an alarm parameter configuration table is generated. This table will be used for subsequent alarm triggering and processing, using the formula:

[0090]

[0091] Among them, A p This represents the score for alarm parameter configuration, where n represents the number of exception types, and p... i λ represents the processing priority of the i-th type of exception. i Let t represent the response time coefficient for the i-th anomaly. i Indicates the response time.

[0092] S302: Based on the alarm parameter configuration, define and set the alarm triggering conditions, equip each exception type with an event code and determine the response level, and obtain the following execution flow of the alarm triggering mechanism;

[0093] S302: Based on alarm parameter configuration, define and set alarm trigger conditions for each anomaly type. Assign a unique event code to each anomaly and determine the corresponding response level. Trigger conditions include the severity, frequency, and performance impact of the anomaly. By analyzing anomaly data and its impact, set a threshold for each anomaly; once the actual monitored data exceeds the threshold, the corresponding alarm is automatically triggered. This process ensures the timeliness and accuracy of alarms, avoids human delays, and improves automation. The formula used is:

[0094]

[0095] Among them, T c This indicates the alarm trigger condition, m represents the number of monitored data points, and δ j x represents the importance coefficient of data point j. j θ represents the real-time value of data point j. j This represents the threshold value for data point j.

[0096] S303: Use an alarm triggering mechanism to record the triggering and handling process of each alarm, record the alarm time, type, handling measures and results after handling, and establish the following execution process for provincial and municipal station management alarm execution records;

[0097] S303: Utilize an alarm triggering mechanism to record the triggering and handling process of each alarm, including the alarm time, type, handling measures taken, and post-processing results. After each alarm trigger, all related information is automatically recorded, and the handling effect is analyzed. These records are not only used for daily management but also provide data support for optimizing alarm parameters and improving future response strategies. The established alarm execution log directly reflects the management efficiency of provincial and regional stations, helping management monitor and evaluate the effectiveness of alarms, using the formula:

[0098]

[0099] Among them, R e The score represents the validity of the alarm execution record, where q represents the number of records and w represents the number of entries. k y represents the weight of the k-th record. k Indicates the actual effect of the treatment measures, v k Indicates the desired effect.

[0100] Specifically, such as Figure 5 As shown, the specific steps for analyzing the timeliness and effectiveness of alarm responses, adjusting network traffic management strategies, optimizing data transmission paths, and generating optimized scheduling commands using provincial and municipal station management alarm execution records are as follows:

[0101] S401: Extract response time and processing result data from the alarm execution records of the provincial and municipal stations, use data integration technology to classify and sort the event types and response timestamps, compare the response time differences of different alarm types through time series analysis, and conduct efficiency evaluation to obtain the alarm efficiency data. The execution process is as follows:

[0102] S401: Extract response time and processing result data from the alarm execution records of provincial and municipal stations. The process first involves data organization and classification. Data integration techniques are used to categorize event types and sort them according to response timestamps to ensure the accuracy of the data's time sequence. Time series analysis techniques are then used to conduct in-depth analysis of the categorized data, comparing the response time differences between different alarm types. Statistical analysis methods are used to calculate the average response time for each type of alarm and compare it with preset standard response times to conduct an efficiency assessment. This assessment helps identify inefficient alarm processing procedures and provides data support for subsequent optimization. The final alarm efficiency data is obtained using the formula:

[0103]

[0104] Among them, E t T represents the efficiency score for alarm type t, where N represents the number of events for that type of alarm, and T represents the number of events for that type of alarm. iLet μ represent the response time of the i-th event, μ represent the average response time of this alarm type, and σ represent the standard deviation of the response time.

[0105] S402: Based on alarm efficiency data, perform network node traffic analysis, identify congested nodes, adjust the data transmission frequency and routing priority of nodes, and alleviate network congestion through dynamic adjustments. The execution flow of the network adjustment plan is as follows.

[0106] S402: Based on alarm efficiency data, network node traffic analysis is performed. First, congested nodes in the network are identified, which involves monitoring the data transmission frequency and receiving capacity of each node. Based on the analysis results, the data transmission frequency and routing priority of the nodes are adjusted, employing a dynamic adjustment strategy to adapt to different network load conditions. This adjustment aims to balance network load, reduce data transmission latency, and alleviate network congestion by optimizing routing strategies. Finally, a network adjustment plan is generated, which details the adjustment parameters and expected effects for each node, using the following formula:

[0107]

[0108] Among them, C n The complexity score represents the network adjustment, where J represents the number of nodes analyzed, and α... j f represents the adjustment weight of the j-th node. j F represents the actual traffic of the node. j This indicates the maximum design throughput of the node.

[0109] S403: Using the network adjustment scheme, write and implement new scheduling commands, adjust command parameters and match the new network traffic configuration through the software programming interface, execute command updates, and generate optimized scheduling commands. The execution flow is as follows:

[0110] S403: Utilize network adjustment schemes to write and implement new scheduling commands, including adjusting command parameters via software programming interfaces to match new network traffic configurations. This adjustment includes updating routing tables, bandwidth allocation, and priority settings. Command parameter adjustments are based on the latest network analysis results to ensure data transmission efficiency and stability. After executing the command update, optimized scheduling commands are generated. These commands aim to improve overall network performance and responsiveness, using the following formula:

[0111]

[0112] Among them, R c This represents the responsiveness of the scheduling command adjustment, where K represents the number of commands to be adjusted, and β represents the responsiveness of the scheduling command adjustment. k p represents the importance coefficient of the k-th command. k This indicates the command parameters before adjustment, q kThis indicates the adjusted command parameters.

[0113] Specifically, such as Figure 6 As shown, based on the optimized scheduling commands, a series of network command tests were executed, the execution time and success rate of each command were recorded, performance logs were analyzed, inefficient commands were filtered and optimized, and the results of the simulated operation were summarized in the following steps:

[0114] S501: Based on the optimized scheduling command, initialize the network connection, send the specified test command, receive the corresponding output, and record the execution time and success rate of each command through the command line interface. The feedback of each command is automatically recorded and classified. The execution flow of the command test record is as follows.

[0115] S501: Based on the optimized scheduling commands, it first initializes the network connection, ensuring all devices and interfaces are correctly configured and ready. It then sends the specified test commands, executes them through the command-line interface, and receives the corresponding output. The execution time and success rate of each command are automatically recorded in the database for subsequent analysis. The automatic recording process includes timestamps and status classification of command feedback to ensure data accuracy and completeness. In this way, detailed command test records are generated. These records will be used to evaluate the effectiveness and stability of the scheduling commands, using the following formula:

[0116]

[0117] Among them, T i Let t represent the average execution time of the i-th command, N represent the total number of test commands, and t represent the average execution time of the i-th command. n This indicates the time of the nth execution.

[0118] S502: Utilize command test records to analyze command execution time and success rate, identify commands with low execution efficiency, check the parameter configuration and response time of each command, compare the command presets with real-time output, filter the list of commands that need optimization, and obtain the execution flow of the list of inefficient commands as follows;

[0119] S502: Utilize command test logs to analyze the execution time and success rate of each instruction. Through detailed statistical analysis of the data, identify instructions with low execution efficiency. This includes checking the parameter configuration and response time of each instruction, comparing the preset values ​​of the instructions with the real-time output to determine differences and inconsistencies. Based on the analysis results, a list of instructions requiring optimization is generated, creating a list of inefficient instructions. This list will guide the next steps in parameter adjustment and command optimization, using the following formula:

[0120]

[0121] Among them, E f This represents the instruction failure rate percentage, where M represents the number of instructions analyzed, and s... m Let S represent the number of times the m-th instruction was successfully executed, and let S represent the total number of tests performed on the m-th instruction.

[0122] S503: Based on the list of inefficient instructions, the parameters of the selected inefficient instructions are adjusted and the commands are optimized. By modifying the structure and parameters of the instructions, the performance of each instruction is retested to determine whether the adjusted commands achieve the expected effect. The test is repeated until the performance meets the requirements. The execution flow of the simulation operation results summary is as follows.

[0123] S503: Based on the list of inefficient instructions, select inefficient instructions undergo parameter adjustment and command optimization. By modifying the instruction structure and parameters, the performance of each instruction is retested to ensure that each modification improves the execution efficiency and success rate. Adjustments are made through repeated testing and analysis until the performance of each instruction meets the predetermined requirements. Through this iterative optimization process, the results of simulated operations are summarized to ensure that the final network command set achieves optimal performance in actual operation, using the formula:

[0124]

[0125] Among them, P r p' represents the percentage performance improvement, K represents the number of instructions optimized, and p' represents the percentage of performance improvement. k p represents the performance of the k-th instruction after adjustment. k This indicates the performance of the k-th instruction before the adjustment.

[0126] Specifically, such as Figure 7 As shown, the specific steps for summarizing the results of simulation operations, performing performance analysis on scheduling commands, measuring the execution rate of each command, evaluating against predetermined performance targets, and obtaining the unified scheduling management effectiveness assessment results are as follows:

[0127] S601: Collect the response time and completion time data of each scheduling command from the summary of simulation operation results, use time tracking technology to record the time from command activation to completion, analyze the time data to determine the execution rate and stability of the command, and obtain the execution flow of the command execution dataset as follows;

[0128] S601: From the results of the simulation operation, firstly, the response time and completion time data of each scheduling command are collected. Using advanced time tracking technology, the entire cycle of each command from activation to completion is accurately recorded. This process includes the application of timestamps to ensure the accuracy of the records at each time point. The time data is then analyzed in depth to calculate the execution rate of the commands and evaluate their stability. Through data analysis, the efficiency and reliability of each command are determined, ultimately resulting in a detailed command execution dataset, using the formula:

[0129]

[0130] Among them, V c This represents the average execution rate, where N represents the total number of commands. and Let represent the completion time and start time of the i-th command, respectively.

[0131] S602: Based on the command execution dataset, record the execution stability and rate indicators of each command one by one, perform statistical analysis on the indicators, compare the analysis results with the preset performance standards, determine the performance level of each command through comparison, and generate a performance comparison analysis table. The execution process is as follows:

[0132] S602: Based on the command execution dataset, record the execution stability and rate metrics for each command, perform detailed statistical analysis on the metrics, including calculating the mean, variance, and correlation statistics, to evaluate the performance consistency and response efficiency of each command. Compare the analysis results with preset performance standards to determine whether the performance of each command meets the expected level, and generate a detailed performance comparison analysis table. This table clearly indicates which commands perform better or worse than the standard, using the following formula:

[0133]

[0134] Among them, S i μ represents the performance score of the i-th command. i and σ i Let μ represent the average response time and standard deviation of command i, respectively. std This indicates the average response time of the preset performance standard.

[0135] S603: Using a performance comparison analysis table, perform an overall performance evaluation of scheduling commands, compare the performance data of each command with the target standard of management efficiency, identify commands with performance below the standard, and construct the execution process of the unified management efficiency evaluation results of scheduling as follows;

[0136] S603: Utilize a performance comparison analysis table to conduct an overall performance evaluation of scheduling commands. Carefully verify the performance data of each command against the management efficiency target standards, identifying commands whose performance falls below the standard. This step aims to ensure that all commands meet or exceed the management efficiency targets through evaluation. Commands with insufficient performance are marked, and a unified management efficiency evaluation result containing all scheduling commands is constructed to provide a basis for further performance optimization and command adjustments. The formula used is:

[0137]

[0138] Among them, E total This represents the overall performance score, where K represents the number of commands, and w k S represents the weight of the k-th command. k This represents the performance score of the k-th command.

[0139] Example 2

[0140] Reference Figure 8 As an embodiment of the present invention, a unified management system for provincial and regional station dispatching is provided, comprising:

[0141] The system includes a data receiving module 100, a data analysis module 200, an alarm configuration module 300, a command optimization module 400, and a performance evaluation module 500.

[0142] The data receiving module 100 receives data from the provincial and municipal monitoring equipment, performs data integrity checks, identifies abnormal fluctuations, filters fault signals based on the characteristics of the fluctuations, and obtains a fault dataset.

[0143] Based on the fault dataset, the data analysis module 200 analyzes the changing trend of each data point, compares data within different time periods through time series analysis, determines the abnormal pattern, classifies the abnormal pattern, and generates an abnormal situation classification result.

[0144] The alarm configuration module 300 uses the abnormal situation classification results to set alarm parameters for various abnormalities managed by the provincial and local stations, including response time and processing priority, records alarm activities in real time, and establishes alarm activity records;

[0145] The command optimization module 400 analyzes the timeliness and effectiveness of the response from the alarm activity record, adjusts the data flow and network settings of the provincial and local stations to match real-time needs, optimizes the scheduling instructions, verifies the effect of the adjustment through field testing, and creates scheduling optimization details;

[0146] The performance evaluation module 500 performs performance tests based on the scheduling optimization details, monitors the execution efficiency and success rate of multiple instructions, evaluates the operational efficiency and stability of provincial and regional station scheduling, and obtains the evaluation results of the unified scheduling management effectiveness.

[0147] Example 3

[0148] One embodiment of the present invention differs from the previous two embodiments in that:

[0149] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0150] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0151] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0152] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A unified management method for provincial and regional station dispatching, characterized in that, include: Receive data from provincial and municipal monitoring equipment, including timestamps, event codes, and associated parameters; analyze the data to identify data fluctuations; and filter potential fault signals by comparing with historical patterns to obtain preliminary fault screening results. Using the initial fault screening results, the data is analyzed, key data points are extracted, data changes are compared across different time periods, abnormal patterns are identified, abnormal situations are automatically classified, and abnormal pattern classification results are obtained. Based on the abnormal pattern classification results, set alarm configuration, configure alarm response time and processing priority, record alarm triggering and processing process, and establish provincial and municipal station management alarm execution records; By utilizing the provincial and municipal station management alarm execution records, the timeliness and effectiveness of alarm responses are analyzed, network traffic management strategies are adjusted, data transmission paths are optimized, and optimized scheduling commands are generated. Based on the optimized scheduling command, a series of network command tests were performed, the execution time and success rate of each command were recorded, the performance logs were analyzed, inefficient commands were filtered out and optimized, and a summary of the results of the simulated operation was constructed. Based on the results of the simulation operation, the performance of the scheduling commands is analyzed, the execution rate of each command is measured, and the performance is evaluated against the predetermined performance targets to obtain the evaluation results of the unified scheduling management effectiveness. The process of using the provincial and municipal station management alarm execution records to analyze the timeliness and effectiveness of alarm responses, adjust network traffic management strategies, optimize data transmission paths, and generate optimized scheduling commands includes: extracting response time and processing result data from the provincial and municipal station management alarm execution records; using data integration technology to classify and sort event types and response timestamps; comparing response time differences of different alarm types through time series analysis; and conducting efficiency evaluation to obtain alarm efficiency data. Based on the alarm efficiency data, network node traffic analysis is performed to identify congested nodes, adjust the data transmission frequency and routing priority of the nodes, and obtain a network adjustment scheme by dynamically adjusting to alleviate network congestion. Using the network adjustment scheme, new scheduling commands are written and implemented. The command parameters are adjusted and matched with the new network traffic configuration through the software programming interface. The command is then updated to generate optimized scheduling commands. The specific steps for analyzing data, extracting key data points, comparing data changes over different time periods, identifying abnormal patterns, automatically classifying abnormal situations, and obtaining abnormal pattern classification results using the aforementioned initial fault screening results are as follows: Import data from the initial fault screening results, initialize the data reading process, set the filtering conditions including data generation time and data type, filter and analyze the data stream, extract data points associated with the fault through numerical comparison, and generate a set of key data points; Based on the set of key data points, a time window is set to compare the data points, sort and mark the data points, identify peak changes and abnormal data points, use time stamps to record the changing trend of each data point, and generate a record of the changing patterns. Using the recorded patterns of change, the data is classified and labeled using the support vector machine algorithm. Classification parameters, including fluctuation range and duration, are set. The data is automatically classified using the classification parameters. Based on the classification results, the characteristics of multiple abnormal patterns are recorded and analyzed to generate abnormal pattern classification results. The support vector machine algorithm is expressed as follows: in, Represents the decision function. Let be the normal vector of the hyperplane. The input feature vector, For each support vector, the Lagrange multiplier, For kernel function, These are the weighting coefficients of the kernel function. For bias terms; Based on the abnormal pattern classification results, the specific steps for setting alarm configurations, configuring alarm response times and processing priorities, recording alarm triggering and processing processes, and establishing provincial and regional station management alarm execution records are as follows: Using the anomaly pattern classification results, alarm parameters are set for each anomaly pattern, response times are defined for severe anomalies and for minor anomalies, and processing priorities are assigned according to the anomaly type to obtain the alarm parameter configuration; Based on the alarm parameter configuration, the alarm triggering conditions are defined and set, an event code is assigned to each type of anomaly, and the response level is determined to obtain the alarm triggering mechanism; Using the aforementioned alarm triggering mechanism, the triggering and processing of each alarm are recorded, including the alarm time, type, processing measures, and post-processing results, thus establishing a provincial and municipal station management alarm execution record.

2. The unified management method for provincial and regional station scheduling as described in claim 1, characterized in that: The initial fault screening results include fault level and associated parameters; the anomaly pattern classification results include anomaly category, anomaly frequency, and associated impact; the provincial and regional station management alarm execution records include alarm type, response time, and processing status; the optimized scheduling commands include command optimization objectives and expected adjustment effects; the summary of simulation operation results includes test coverage, optimization success rate, and key performance indicators; and the unified scheduling management efficiency evaluation results include efficiency rating and response efficiency.

3. The unified management method for provincial and regional station dispatching as described in claim 2, characterized in that: The data received from the provincial and municipal monitoring equipment includes timestamps, event codes, and associated parameters. The specific steps for analyzing the data to identify data fluctuations, comparing historical patterns, filtering potential fault signals, and obtaining preliminary fault screening results are as follows: Receive data from provincial and municipal monitoring equipment, including timestamps, event codes, and associated parameters, and store them in the data buffer in the order they are received. Ensure that each piece of data can be recorded and accessed to obtain the dataset entry. Using the timestamps and event codes entered in the dataset, time series analysis is performed on the data. Thresholds are set to distinguish between regular and irregular fluctuations. Irregular fluctuations are marked to obtain a volatility-labeled dataset. Using the markers in the volatility marker dataset, data points that match historical fault patterns are filtered from the data. Fault signals are identified by setting matching parameters. By comparing historical data patterns with real-time monitoring data, fault points are identified, and initial fault screening results are obtained.

4. A system employing the unified management method for provincial and regional station scheduling as described in any one of claims 1 to 3, characterized in that, include: The system includes a data receiving module (100), a data analysis module (200), an alarm configuration module (300), a command optimization module (400), and a performance evaluation module (500). The data receiving module (100) receives data from the provincial and municipal monitoring equipment, performs data integrity checks, identifies abnormal fluctuations, filters fault signals based on the characteristics of the fluctuations, and obtains a fault dataset. The data analysis module (200) analyzes the changing trend of each data point based on the fault dataset, compares the data in different time periods through time series analysis, determines the abnormal pattern, classifies the abnormal pattern, and generates abnormal situation classification results. The alarm configuration module (300) uses the abnormal situation classification results to set alarm parameters for various abnormalities managed by the provincial and local stations, including response time and processing priority, records alarm activities in real time, and establishes alarm activity records; The command optimization module (400) analyzes the timeliness and effectiveness of the response from the alarm activity record, adjusts the data flow and network settings of the provincial and local stations and matches real-time requirements, optimizes the scheduling instructions, verifies the effect of the adjustment through field testing, and creates scheduling optimization details; The performance evaluation module (500) performs performance tests based on the scheduling optimization details, monitors the execution efficiency and success rate of multiple instructions, evaluates the operational efficiency and stability of provincial and regional station scheduling, and obtains the evaluation results of the unified scheduling management effectiveness.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the unified management method for provincial and regional station scheduling as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the unified management method for provincial and regional station scheduling as described in any one of claims 1 to 3.

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