Unified management method and system for province, region and station scheduling

Abstract

The invention discloses a province, region and station scheduling unified management method and system, and relates to the technical field of integrated operation management, and the method comprises the steps: receiving province, region and station monitoring equipment data, including timestamps, event codes and associated parameters, analyzing the data, recognizing data fluctuation, and obtaining a fault preliminary screening result by comparing historical modes and screening potential fault signals. According to the province, region and station scheduling unified management method, early recognition of fault signals and accurate classification of abnormal modes are achieved by receiving and analyzing the data of the monitoring equipment, the efficiency and accuracy of data analysis are improved through comparison of real-time data and historical data, and by recognizing data changes of differentiated time periods, the accuracy of data analysis is improved. Fault detection is accelerated, the processing capability of complex abnormal conditions is enhanced, alarm response time and processing priorities are set for abnormal types, emergency response efficiency is improved, a network flow management strategy and a data transmission path are optimized, processing delay is reduced, network performance and stability are improved, and operation efficiency and user experience are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated transportation management, and in particular to a provincial and municipal station dispatching unified management method and system. Background Art

[0002] The field of integrated transportation management technology involves unified management and scheduling of various transportation modes and management methods through an integrated technology platform to improve overall transportation efficiency and service quality. This technology is mainly used in large-scale transportation networks, including railways, roads, air and water transportation, with the aim of achieving optimal resource allocation and automated operations through a high degree of information integration. The core of this field lies in the application of information technology, including real-time data collection, 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 improve user experience by providing a unified operating interface and decision-making tools.

[0003] Among them, the unified management method of provincial and municipal station dispatching is a strategy for optimizing the management of transportation hubs or stations. Its main purpose is to reasonably arrange station resources through efficient dispatching methods, reduce the space required for stations, and improve station operation efficiency. This method is applied to railways, subways or large bus stations. Through integrated information systems and intelligent dispatching algorithms, vehicles entering and leaving the station are precisely controlled and dispatched. Its purpose is to reduce physical space requirements, speed up vehicle turnover, reduce congestion, and improve passenger experience and station operation efficiency. The key to the theme is to achieve rapid, orderly and safe flow of transportation tools, so that each node in the transportation network can operate in the best state, thereby improving the efficiency and sustainability of transportation as a whole. Summary of the invention

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

[0005] Therefore, the technical problem solved by the present invention is that although the existing provincial and ground station dispatching unified management method has advantages in information integration and resource allocation, it still has limitations in quickly identifying and handling emergencies. Especially in large-scale transportation networks, traditional data processing methods rely on preset rules and models, and lack sufficient flexibility to cope with complex and changeable actual situations. For example, when faced with unconventional data fluctuations or sudden network congestion, the existing technology cannot quickly locate the problem or perform effective scheduling, resulting in extended response time and reduced processing efficiency. The lack of targeted alarm configuration and priority processing mechanism leads to omissions or erroneous responses to key events, causing a decline in service quality in emergency situations, increasing operating costs, and even affecting the safety and satisfaction of passengers.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a unified management method for provincial and local station dispatching, comprising:

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

[0008] Utilizing the preliminary fault screening results, analyzing data, extracting key data points, comparing data changes in differentiated time periods, identifying abnormal patterns, automatically classifying abnormal situations, and obtaining abnormal pattern classification results;

[0009] According to the abnormal pattern classification results, set the alarm configuration, configure the alarm response time and processing priority, record the alarm triggering and processing process, and establish the provincial ground station management alarm execution record;

[0010] Utilize the provincial ground station to manage alarm execution records, analyze the timeliness and effect of alarm response, adjust network traffic management strategy, optimize data transmission path, and generate optimized dispatch commands;

[0011] Based on the optimized scheduling commands, a series of network command tests are performed, the execution time and success rate of each command are recorded, the performance logs are analyzed, inefficient instructions are screened and optimized, and a result summary of the simulated operation is constructed;

[0012] Based on the summary of the results of the simulation operation, the performance analysis of the scheduling commands is carried out, the execution rate of each command is measured, and the evaluation is carried out against the predetermined performance target to obtain the scheduling unified management efficiency evaluation result.

[0013] As a preferred scheme of the provincial and ground station dispatching unified management method described in the present invention, wherein: the initial fault screening results include fault level and related parameters, the abnormal pattern classification results include abnormal category, abnormal frequency, and related impact, the provincial and ground station management alarm execution record includes alarm type, response time, and processing status, the optimized dispatching command includes command optimization target and expected adjustment effect, the result summary of the simulation operation includes test coverage, tuning success rate, and key performance indicators, and the dispatching unified management efficiency evaluation results include efficiency rating and response efficiency.

[0014] As a preferred solution of the provincial and ground station dispatching unified management method described in the present invention, the steps of receiving provincial and ground station monitoring equipment data including timestamp, event code, and associated parameters, analyzing data to identify data fluctuations, and screening potential fault signals by comparing historical patterns to obtain preliminary fault screening results are specifically as follows:

[0015] Receive data from provincial and ground station monitoring equipment, including timestamps, event codes and associated parameters, and store them in the data buffer in the order in which they are received, ensuring that each piece of data can be recorded and accessed, and obtaining data set entries;

[0016] Using the timestamps and event codes in the data set input, time series analysis is performed on the data, thresholds are set to distinguish between regular and irregular fluctuations, irregular fluctuations are marked, and a volatility marked data set is obtained;

[0017] By using the marks in the volatility marking data set, data points matching the historical fault patterns are screened from the data, fault signals are identified by setting matching parameters, historical data patterns are compared with real-time monitoring data, fault points are identified, and preliminary fault screening results are obtained.

[0018] As a preferred solution of the provincial and local station dispatching unified management method described in the present invention, the steps of using the preliminary fault screening results, analyzing data, extracting key data points, comparing data changes in differentiated time periods, identifying abnormal patterns, and automatically classifying abnormal situations to obtain abnormal pattern classification results are specifically as follows:

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

[0020] Based on the key data point set, a time window is set, data points are compared, data points are sorted and marked, peak changes and abnormal data points are identified, and the change trend of each data point is recorded with a time stamp to generate a change regularity record;

[0021] Utilizing the record of regularity of change, a support vector machine algorithm is adopted to classify and mark the data, and classification parameters are set including fluctuation range and duration. The classification parameters are applied to automatically classify the data, and the characteristics of multiple types of abnormal patterns are recorded and analyzed according to the classification results to generate abnormal pattern classification results.

[0022] As a preferred solution of the provincial station dispatching unified management method described in the present invention, the support vector machine algorithm is expressed as:

[0023]

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

[0025] As a preferred solution of the unified management method for provincial and local station dispatching described in the present invention, the steps of setting alarm configuration, configuring alarm response time and processing priority, recording alarm triggering and processing process, and establishing provincial and local station management alarm execution records are specifically as follows:

[0026] Using the abnormal pattern classification results, setting alarm parameters for each abnormal pattern, defining a response time for severe abnormalities, defining a response time for minor abnormalities, assigning a processing priority according to the abnormality type, and obtaining an alarm parameter configuration;

[0027] Based on the alarm parameter configuration, define and set the alarm triggering conditions, equip each abnormality type with an event code and determine the response level to obtain an alarm triggering mechanism;

[0028] The alarm triggering mechanism is used to record the triggering and processing process of each alarm, the time, type, processing measures and results of the alarm, and establish a provincial ground station management alarm execution record.

[0029] As a preferred solution of the provincial and local station dispatching unified management method described in the present invention, the steps of using the provincial and local station management alarm execution record, analyzing the timeliness and effect of the alarm response, adjusting the network traffic management strategy, optimizing the data transmission path, and generating the optimized dispatching command are specifically as follows:

[0030] Extract response time and processing result data from the provincial ground station management alarm execution records, classify and sort event types and response timestamps using data integration technology, compare response time differences of differentiated alarm types through time series analysis, and perform efficiency evaluation to obtain alarm efficiency data;

[0031] Based on the alarm efficiency data, network node traffic analysis is performed to identify congested nodes, and the data transmission frequency and routing priority of the nodes are adjusted to obtain a network adjustment plan by dynamically adjusting the relief of network congestion;

[0032] By using the network adjustment scheme, new scheduling commands are written and implemented, command parameters are adjusted and matched to new network traffic configurations through a software programming interface, command updates are executed, and optimized scheduling commands are generated.

[0033] Another object of the present invention is to provide a provincial and metropolitan station dispatching unified management system, which can solve the problem that in large-scale transportation networks, traditional data processing methods rely on preset rules and models and lack sufficient flexibility to deal with complex and changing problems by constructing a provincial and metropolitan station dispatching unified management system.

[0034] In order to solve the above technical problems, the present invention provides the following technical solutions: a unified management system for provincial and municipal station dispatching, including: 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 the data of the provincial and municipal station monitoring equipment, performs a data integrity check, identifies abnormal fluctuations, and screens fault signals according to the characteristics of the fluctuations to obtain a fault data set; the data analysis module analyzes the change trend of each data point based on the fault data set, compares the data in the differentiated time periods through time series analysis, determines the abnormal mode, classifies the abnormal mode, and generates the abnormal situation classification result; the alarm configuration module The module uses the abnormal situation classification results to set alarm parameters for various abnormalities managed by provincial and ground stations, including response time and processing priority, record alarm activities in real time, and establish alarm activity records; the command optimization module analyzes the timeliness and effect of the response from the alarm activity records, adjusts the data flow and network settings of the provincial and ground stations to match real-time needs, optimizes the scheduling instructions, verifies the adjusted effects through field tests, 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 instructions, evaluates the operational efficiency and stability of provincial and ground station scheduling, and obtains the scheduling unified management efficiency evaluation results.

[0035] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the provincial ground station dispatching unified management method are implemented.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned provincial ground station dispatching unified management method.

[0037] Beneficial effects of the present invention: The unified management method for provincial and local station dispatching provided by the present invention receives and analyzes data transmitted from monitoring equipment to achieve early identification of fault signals and accurate classification of abnormal patterns. It uses real-time data analysis and historical data comparison to significantly improve the efficiency and accuracy of data analysis. By comparing data changes in differentiated time periods, it can identify and classify data abnormal patterns, which not only accelerates the fault detection process, but also enhances the ability to handle complex abnormal situations. By automatically classifying abnormal situations, corresponding alarm response times and processing priorities can be set for different abnormal types. Such a strategy improves the efficiency and effectiveness of emergency response. The optimized network traffic management strategy and data transmission path can effectively reduce data processing delays, improve the performance and stability of the overall network, and provide an efficient, flexible and safe management method for provincial and local station dispatching, thereby improving overall operational efficiency and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 An overall flow chart of a unified management method for provincial and municipal station dispatching provided by an embodiment of the present invention.

[0040] Figure 2 A flow chart of receiving provincial and municipal station monitoring equipment data in a provincial and municipal station dispatching unified management method provided by an embodiment of the present invention.

[0041] Figure 3 A flow chart of obtaining abnormal pattern classification results of a provincial and municipal station dispatching unified management method provided in one embodiment of the present invention.

[0042] Figure 4 A flowchart of establishing a provincial and municipal station management alarm execution record for a provincial and municipal station dispatch unified management method provided in one embodiment of the present invention.

[0043] Figure 5 A flow chart of optimized dispatching command generation for a provincial and local station dispatching unified management method provided in one embodiment of the present invention.

[0044] Figure 6 A flow chart summarizing the results of simulation operations for constructing a unified management method for provincial and municipal station dispatching provided by an embodiment of the present invention.

[0045] Figure 7 A flowchart of evaluating a method for unified management of provincial and municipal station dispatching against predetermined performance targets provided in one embodiment of the present invention.

[0046] Figure 8 The overall structure diagram of the provincial and municipal station dispatching unified management system provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Example 1

[0050] Reference Figure 1-Figure 7 , is an embodiment of the present invention, providing a unified management method for provincial station dispatching, including:

[0051] S1: Receive data from provincial and ground station monitoring equipment, including timestamps, event codes, and associated parameters, analyze the data, identify indicators of conventional and unconventional data fluctuations, screen potential fault signals by comparing historical data patterns, and obtain preliminary fault screening results;

[0052] S2: Use the initial fault screening results to analyze the data, extract key data points, compare data changes in different time periods, identify data abnormal patterns, automatically classify abnormal situations based on the characteristics of data abnormal patterns, and obtain abnormal pattern classification results;

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

[0054] S4: Use provincial ground stations to manage alarm execution records, analyze the timeliness and effectiveness of alarm responses, adjust network traffic management strategies based on 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, tune the commands, implement feedback corrections, evaluate the effectiveness of the corrected command configuration, and build a summary of the simulation operation results;

[0056] S6: Based on the summary of the results of the simulation operations, the performance of the scheduling commands is analyzed one by one, the stability and execution rate of each command are measured, the performance indicators are recorded, and the evaluation is performed against the predetermined performance goals to obtain the scheduling unified management efficiency evaluation results.

[0057] The initial fault screening results include fault level and related parameters. The abnormal pattern classification results include abnormal category, abnormal frequency and related impact. The provincial ground station management alarm execution records include alarm type, response time and processing status. The optimized dispatching command includes command optimization target and expected adjustment effect. The result summary of simulation operation includes test coverage, tuning success rate and key performance indicators. The dispatching unified management efficiency evaluation results include efficiency rating and response efficiency.

[0058] Specifically, if Figure 2 As shown, the steps of receiving provincial ground station monitoring equipment data, including timestamp, event code, and associated parameters, analyzing the data to identify data fluctuations, and screening potential fault signals by comparing historical patterns to obtain preliminary fault screening results are as follows:

[0059] S101: receiving provincial and ground station monitoring equipment data, including timestamps, event codes and associated parameters, and storing them in the data buffer in the order of receiving, ensuring that each piece of data can be recorded and accessed, and obtaining the execution flow of data set entry as follows;

[0060] S101: Receive data from provincial and ground station monitoring equipment, including timestamps, event codes and associated parameters, perform data format verification and time synchronization to ensure the integrity of each data packet, sort the data packets according to the timestamp and store them in the data buffer, optimize the data retrieval speed through an efficient indexing mechanism for fast access and processing, record the storage location and status of each data, ensure the complete record and accessibility of the data, and obtain the data set entry, using the formula:

[0061]

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

[0063] S102: Use the timestamp and event code in the data set input to perform time series analysis on the data, set thresholds to distinguish between regular and irregular fluctuations, mark irregular fluctuations, and obtain the volatility marked data set. The execution process is as follows;

[0064] S102: Use the timestamps and event codes in the data set input to perform time series analysis, and identify the fluctuation pattern by calculating the moving average and variance of the data. Set a threshold based on the historical volatility data, which helps distinguish between regular fluctuations and irregular fluctuations. The irregular fluctuation data is marked and recorded to provide a basis for the subsequent fault detection process, and a volatility marked data set is obtained, using the formula:

[0065]

[0066] Among them, V k represents the volatility measurement value of the kth data window, W represents the window width, x j represents the value of the jth data point in the window, Represents the average of all data points within the window.

[0067] S103: Using the markers in the volatility marker data set, filter out data points that match the historical fault pattern from the data, identify the fault signal by setting matching parameters, compare the historical data pattern with the real-time monitoring data, identify the fault point, and obtain the initial fault screening result. The execution process is as follows;

[0068] S103: Using the markers in the volatility marker data set, the data is deeply analyzed, and the fault data points are identified by comparing the set matching parameters with the historical fault patterns. The matching process is performed by calculating the similarity between the real-time data and the historical patterns to ensure that only highly matched data points are marked as fault signals, thereby obtaining the initial fault screening results, using the formula:

[0069]

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

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

[0072] S201: Import data from the initial fault screening results, initialize the data reading process, set the screening conditions including data generation time and data type, screen and analyze the data stream, extract the data points associated with the fault through numerical comparison, and generate the key data point set. The execution process is as follows;

[0073] S201: After importing data from the initial fault screening results, initialize the data reading process, set the screening conditions including data generation time and data type, arrange each data point in ascending order of generation time, and then perform hierarchical screening according to data type to ensure the accuracy of the screening process. For data that meets the screening conditions, perform numerical comparison to compare the numerical difference between each data point and its adjacent points. Through difference threshold analysis, screen the data points directly related to the fault. The screened data points will be integrated into the key data point set to provide basic data for subsequent analysis, using the formula:

[0074]

[0075] Among them, K d represents the key data point set, d i and d i-1 They represent continuous data points, n represents the total number of data points, and θ represents the set numerical difference threshold.

[0076] S202: Based on the key data point set, a time window is set, data points are compared, data points are sorted and marked, peak changes and abnormal data points are identified, and the change trend of each data point is recorded using a time tag to generate a change regularity record. The execution process is as follows;

[0077] S202: Based on the key data point set, a time window is set to compare the data points. The data points in each time window are sorted according to their numerical values, and the data changes in each window are marked. Special attention is paid to the data points with peak values ​​and abnormal changes. A time tag is assigned to each data point, and its change trend is recorded. A detailed record of the regularity of changes is generated. The record reflects the specific law of the change of the data point over time, which helps subsequent analysis and decision-making. The formula is used:

[0078]

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

[0080] S203: Using the change regularity record, adopting the support vector machine algorithm, classifying and marking the data, setting the classification parameters including the fluctuation range and duration, applying the classification parameters to automatically classify the data, recording and analyzing the characteristics of multiple types of abnormal patterns according to the classification results, and generating the abnormal pattern classification results. The execution process is as follows;

[0081] S203: Using the change regularity record, the data classification process is started, and the classification parameters are defined, including the data fluctuation range and duration. The parameters will be used in the support vector machine algorithm to perform detailed classification processing on the data. The algorithm automatically classifies the data into different abnormal modes by analyzing the fluctuation range and duration of the data points. According to the classification results, the characteristics of each abnormal mode are recorded and analyzed to generate the abnormal mode classification results, which provides an in-depth understanding of the potential causes of failures and the basis for the formulation of countermeasures.

[0082] The formula for the support vector machine algorithm is as follows:

[0083]

[0084] Among them, f(x) represents the decision function, w is the normal vector of the hyperplane, x is the input feature vector, α i is the Lagrange multiplier for each support vector, k(x,x i ) is the kernel function, γ is the weight 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 input feature vector, which contains the features of the data points to be classified. w continues to be the weight vector learned from the training data, determining the classification boundary. b acts as a bias term to adjust the position of the entire classification hyperplane. The newly added α i is the Lagrange multiplier for the influence strength of each support vector, k(x,x i ) is the kernel function, which is used to map data to a high-dimensional space in the case of nonlinear separability to improve classification accuracy. γ is the weight coefficient of the kernel function, which determines the influence of the kernel function in the overall classification function. The specific value of γ needs to be determined during the training process through methods such as cross-validation and grid search.

[0087] Specifically, if Figure 4 As shown in the figure, according to the abnormal pattern classification results, set the alarm configuration, configure the alarm response time and processing priority, record the alarm triggering and processing process, and establish the provincial station management alarm execution record. The specific steps are:

[0088] S301: Using the abnormal pattern classification results, set alarm parameters for each abnormal pattern, define response time for severe abnormalities, define response time for minor abnormalities, assign processing priority according to abnormality types, and obtain the execution flow of alarm parameter configuration as follows;

[0089] S301: Using the abnormal pattern classification results, first set specific alarm parameters for each abnormal pattern, including defining a response time and processing priority for each abnormality. Severe abnormalities (such as equipment downtime) are set with shorter response times, while minor abnormalities (such as performance degradation) are set with relatively longer response times. According to the potential impact and urgency of the abnormality, the processing priority is assigned, and the strategy is set by analyzing historical data and potential impacts. The priority and response time are set based on reducing operational interruptions and maximizing resource efficiency. After completing the configuration, an alarm parameter configuration table is generated, which will be used for subsequent alarm triggering and processing, using the formula:

[0090]

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

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

[0093] S302: Based on the alarm parameter configuration, define and set the alarm trigger conditions for each abnormality type. Each abnormality is equipped with a unique event code and the corresponding response level is determined. The trigger conditions include the severity and frequency of the abnormality and its impact on performance. By analyzing the abnormal data and impact, a threshold is set for each abnormality. Once the actual monitoring data exceeds the threshold, the corresponding alarm is automatically triggered. The process ensures the timeliness and accuracy of the alarm, avoids human delays, and improves the level of automation. The formula is used:

[0094]

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

[0096] S303: Using the alarm trigger mechanism, record the triggering and processing process of each alarm, record the time, type, processing measures and processing results of the alarm, and establish the execution process of the provincial station management alarm execution record as follows;

[0097] S303: Use the alarm trigger mechanism to record the triggering and processing of each alarm, including the time, type, processing measures taken and the results after processing. After each alarm is triggered, all related information is automatically recorded and the processing effect is analyzed. The record is not only used for daily management, but also provides data support for optimizing alarm parameters and improving future response strategies. The established alarm execution record is a direct reflection of the management efficiency of the provincial ground station, helping the management to monitor and evaluate the effectiveness of the alarm, using the formula:

[0098]

[0099] Among them, R e represents the effectiveness score of the alarm execution record, q represents the number of entries in the record, and w k represents the weight of the kth record, y k Indicates the actual effect of the treatment measures, v k Indicates the expected effect.

[0100] Specifically, if Figure 5 As shown in the figure, the steps of using provincial ground stations to manage alarm execution records, analyze the timeliness and effect of alarm responses, adjust network traffic management strategies, optimize data transmission paths, and generate optimized scheduling commands are as follows:

[0101] S401: Extract response time and processing result data from the provincial and local station management alarm execution records, classify and sort event types and response timestamps using data integration technology, compare response time differences of differentiated alarm types through time series analysis, and perform efficiency evaluation to obtain alarm efficiency data. The execution process is as follows;

[0102] S401: Extract response time and processing result data from the provincial and local station management alarm execution records. The process first involves data collation and classification. Data integration technology is used to classify event types and sort them according to response timestamps to ensure the timing correctness of the data. Time series analysis technology is used to conduct in-depth analysis of the classified data and compare the response time differences of different alarm types. The average response time of each type of alarm is calculated through statistical analysis methods and compared with the preset standard response time to conduct efficiency evaluation. The evaluation helps identify inefficient alarm processing processes and provides data support for subsequent optimization. Finally, the alarm efficiency data is obtained using the formula:

[0103]

[0104] Among them, E t represents the efficiency score of alarm type t, N represents the number of events of this type of alarm, T irepresents the response time of the ith event, μ represents the average response time of the alarm type, and σ represents the standard deviation of the response time.

[0105] S402: Based on the alarm efficiency data, the network node traffic is analyzed, the congested nodes are identified, the data transmission frequency and routing priority of the nodes are adjusted, and the network congestion is alleviated by dynamic adjustment. The execution process of the network adjustment plan is as follows;

[0106] S402: Based on the 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. According to the analysis results, the data transmission frequency and routing priority of the node are adjusted, and a dynamic adjustment strategy is adopted to adapt to different network load conditions. This adjustment aims to balance the network load, reduce data transmission delays, and alleviate network congestion by optimizing routing strategies. Finally, a network adjustment plan is generated, which records in detail the parameters and expected effects of each node adjustment, using the formula:

[0107]

[0108] Among them, C n represents the complexity score of network adjustment, J represents the number of nodes analyzed, and α j represents the adjusted weight f of the jth node j represents the actual traffic of the node, F j Represents the designed maximum flow of the node.

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

[0110] S403: Using the network adjustment plan, write and implement new scheduling commands, including adjusting command parameters through the software programming interface to match the new network traffic configuration. This adjustment includes updates to routing tables, bandwidth allocation, and priority settings. The adjustment of command parameters is based on the latest network analysis results to ensure the efficiency and stability of data transmission. After executing the command update, an optimized scheduling command is generated. The command is designed to improve the overall performance and responsiveness of the network, using the formula:

[0111]

[0112] Among them, R c represents the adjustment responsiveness of the scheduling command, K represents the number of adjusted commands, β k Represents the importance coefficient p of the kth command k Indicates the command parameters before adjustment, q kIndicates the adjusted command parameters.

[0113] Specifically, if Figure 6 As shown in the figure, based on the optimized scheduling command, a series of network command tests are executed, the execution time and success rate of each command are recorded, the performance log is analyzed, the inefficient instructions are screened and tuned, and the steps for building a summary of the results of the simulation operation are as follows:

[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, and the execution process of the command test record is as follows;

[0115] S501: Based on the optimized scheduling command, first initialize the network connection, ensure that all devices and interfaces are correctly configured and ready, send the specified test command, execute the command through the command line interface, and receive 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 the timestamp and status classification of the command feedback to ensure the accuracy and completeness of the data. In this way, a detailed command test record is generated, which will be used to evaluate the effect and stability of the scheduling command, using the formula:

[0116]

[0117] Among them, T i represents the average execution time of the ith command, N represents the total number of test commands, and t n Indicates the time of the nth execution.

[0118] S502: Analyze the execution time and success rate of commands using command test records, identify instructions with low execution efficiency, check the parameter configuration and response time of each instruction, compare the instruction preset with the real-time output, screen the list of instructions to be optimized, and obtain the execution process of the list of inefficient instructions as follows;

[0119] S502: Analyze the execution time and success rate of each instruction using the command test records. Identify instructions with low execution efficiency by performing detailed statistical analysis on the data, including checking the parameter configuration and response time of each instruction, and comparing the preset value of the instruction with the real-time output to determine the differences and inconsistencies. Based on the analysis results, screen the list of instructions that need to be optimized and generate a list of inefficient instructions. This list will be used to guide the next step of parameter adjustment and command optimization work, using the formula:

[0120]

[0121] Among them, E f represents the failure rate percentage of the instruction, M represents the number of instructions analyzed, and s m It represents the number of successful executions of the m-th instruction, and S represents the total number of tests of the m-th instruction.

[0122] S503: According to the list of inefficient instructions, parameters of the selected inefficient instructions are adjusted and commands are optimized. By modifying the structure and parameters of the instructions, the performance of each instruction is retested to determine whether the adjusted command achieves the expected effect. The test is repeated until the performance meets the requirements. The execution process of constructing the result summary of the simulation operation is as follows;

[0123] S503: According to the inefficient instruction list, adjust the parameters and optimize the commands for the selected inefficient instructions. Retest the performance of each instruction by modifying the structure and parameters of the instructions to ensure that each modification can improve the execution efficiency and success rate of the instructions. The adjustment is carried out through repeated testing and analysis until the performance of each instruction meets the predetermined requirements. Through this iterative optimization process, the result summary of the simulated operation is constructed to ensure that the final network command set can achieve the optimal performance in actual operation. The formula is used:

[0124]

[0125] Among them, P r represents the performance improvement percentage, K represents the number of optimized instructions, and p k represents the performance of the kth instruction after adjustment, p k Represents the performance of the kth instruction before adjustment.

[0126] Specifically, if Figure 7 As shown, based on the result summary of the simulation operation, the performance analysis of the scheduling command is performed, the execution rate of each command is measured, and the evaluation is performed against the predetermined performance target. The specific steps for obtaining the scheduling unified management efficiency evaluation result are as follows:

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

[0128] S601: From the summary of the results of the simulation operation, first collect the response time and completion time data of each scheduling command, use advanced time tracking technology to accurately record the entire cycle of each command from activation to execution completion, including the application of timestamps to ensure that the records of each time point are accurate, conduct in-depth analysis of time data, calculate the execution rate of commands, and evaluate its stability. Through data analysis, measure the efficiency and reliability of each command, and finally obtain a detailed command execution data set using the formula:

[0129]

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

[0131] S602: Based on the command execution data set, record the execution stability and rate index of each command one by one, perform statistical analysis on the index, compare the analysis result with the preset performance standard, determine the performance level of each command through comparison, and generate the execution process of the performance comparison analysis table as follows;

[0132] S602: Based on the command execution data set, record the execution stability and rate indicators of each command one by one, and perform detailed statistical analysis on the indicators, including calculating the mean, variance, and associated statistics, to evaluate the performance consistency and response efficiency of each command, compare the analysis results with the preset performance standards, and clarify whether the performance of each command reaches the expected level. Generate a detailed performance comparison analysis table, which clearly indicates which commands have better or worse performance than the standard, using the formula:

[0133]

[0134] Among them, S i represents the performance score of the ith command, μ i and σ i represent the average response time and standard deviation of command i, μ std Indicates the average response time for a preset performance standard.

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

[0136] S603: Use the performance comparison analysis table to conduct an overall performance evaluation of the scheduling commands, carefully check the performance data of each command against the target standard of management efficiency, and identify those commands whose performance is below the standard. This step aims to ensure that all commands meet or exceed the management efficiency target through evaluation. Mark the commands with insufficient performance, and build a unified management efficiency evaluation result that includes all scheduling commands to provide a basis for further performance optimization and command adjustment. The formula is:

[0137]

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

[0139] Example 2

[0140] Reference Figure 8 , is an embodiment of the present invention, providing a provincial station dispatching unified management system, including:

[0141] Data receiving module 100, data analysis module 200, alarm configuration module 300, command optimization module 400 and performance evaluation module 500;

[0142] The data receiving module 100 receives data from provincial ground station monitoring equipment, performs data integrity check, identifies abnormal fluctuations, and screens fault signals according to the characteristics of the fluctuations to obtain a fault data set;

[0143] The data analysis module 200 analyzes the change trend of each data point based on the fault data set, compares the data in the differentiated time periods through time series analysis, determines the abnormal pattern, classifies the abnormal pattern, and generates the 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 ground station, including response time and processing priority, record alarm activities in real time, and establish alarm activity records;

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

[0146] The performance evaluation module 500 performs performance testing according to the scheduling optimization details, monitors the execution efficiency and success rate of multiple instructions, evaluates the operational efficiency and stability of provincial ground station scheduling, and obtains the scheduling unified management efficiency evaluation results.

[0147] Example 3

[0148] An embodiment of the present invention is different from the first two embodiments in that:

[0149] If the functions are implemented in the form of 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

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

[0151] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0152] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A unified management method for provincial and local station dispatching, characterized in that: include: Receive data from provincial and ground station monitoring equipment, including timestamps, event codes, and associated parameters, analyze the data to identify data fluctuations, and screen potential fault signals by comparing historical patterns to obtain preliminary fault screening results; Utilizing the preliminary fault screening results, analyzing data, extracting key data points, comparing data changes in differentiated time periods, identifying abnormal patterns, automatically classifying abnormal situations, and obtaining abnormal pattern classification results; According to the abnormal pattern classification results, set the alarm configuration, configure the alarm response time and processing priority, record the alarm triggering and processing process, and establish the provincial ground station management alarm execution record; Utilize the provincial ground station to manage alarm execution records, analyze the timeliness and effect of alarm response, adjust network traffic management strategy, optimize data transmission path, and generate optimized dispatch commands; Based on the optimized scheduling commands, a series of network command tests are performed, the execution time and success rate of each command are recorded, the performance logs are analyzed, inefficient instructions are screened and optimized, and a result summary of the simulated operation is constructed; Based on the summary of the results of the simulation operation, the performance analysis of the scheduling commands is carried out, the execution rate of each command is measured, and the evaluation is carried out against the predetermined performance target to obtain the scheduling unified management efficiency evaluation result.

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

3. The provincial station dispatching unified management method as claimed in claim 2, characterized in that: The steps of receiving provincial and ground station monitoring equipment data including timestamp, event code, and associated parameters, analyzing the data to identify data fluctuations, and screening potential fault signals by comparing historical patterns to obtain preliminary fault screening results are specifically as follows: Receive data from provincial and ground station monitoring equipment, including timestamps, event codes and associated parameters, and store them in the data buffer in the order in which they are received, ensuring that each piece of data can be recorded and accessed, and obtaining data set entries; Using the timestamps and event codes in the data set input, time series analysis is performed on the data, thresholds are set to distinguish between regular and irregular fluctuations, irregular fluctuations are marked, and a volatility marked data set is obtained; By using the marks in the volatility marking data set, data points matching the historical fault patterns are screened from the data, fault signals are identified by setting matching parameters, historical data patterns are compared with real-time monitoring data, fault points are identified, and preliminary fault screening results are obtained.

4. The provincial station dispatching unified management method as claimed in claim 3, characterized in that: The steps of using the initial fault screening results to analyze data, extract key data points, compare data changes in differentiated time periods, identify abnormal patterns, automatically classify abnormal situations, and obtain abnormal pattern classification results are as follows: Import data from the preliminary fault screening results, initialize the data reading process, set screening conditions including data generation time and data type, screen and analyze the data stream, extract data points associated with the fault through numerical comparison, and generate a key data point set; Based on the key data point set, a time window is set, data points are compared, data points are sorted and marked, peak changes and abnormal data points are identified, and the change trend of each data point is recorded with a time stamp to generate a change regularity record; Utilizing the record of regularity of change, a support vector machine algorithm is adopted to classify and mark the data, and classification parameters are set including fluctuation range and duration. The classification parameters are applied to automatically classify the data, and the characteristics of multiple types of abnormal patterns are recorded and analyzed according to the classification results to generate abnormal pattern classification results.

5. The provincial station dispatching unified management method as claimed in claim 4 is characterized by: The support vector machine algorithm is expressed as: Among them, f ( x) represents the decision function, w is the normal vector of the hyperplane, x is the input feature vector, α i is the Lagrange multiplier for each support vector, k(x,x i ) is the kernel function, γ is the weight coefficient of the kernel function, and b is the bias term.

6. The provincial station dispatching unified management method as claimed in claim 5, characterized in that: According to the abnormal pattern classification results, the steps of setting alarm configuration, configuring alarm response time and processing priority, recording alarm triggering and processing process, and establishing provincial ground station management alarm execution records are as follows: Using the abnormal pattern classification results, setting alarm parameters for each abnormal pattern, defining a response time for severe abnormalities, defining a response time for minor abnormalities, assigning a processing priority according to the abnormality type, and obtaining an alarm parameter configuration; Based on the alarm parameter configuration, define and set the alarm triggering conditions, equip each abnormality type with an event code and determine the response level to obtain an alarm triggering mechanism; The alarm triggering mechanism is used to record the triggering and processing process of each alarm, the time, type, processing measures and results of the alarm, and establish a provincial ground station management alarm execution record.

7. The provincial station dispatching unified management method according to claim 6, characterized in that: The steps of using the provincial ground station to manage the alarm execution record, analyzing the timeliness and effect of the alarm response, adjusting the network traffic management strategy, optimizing the data transmission path, and generating the optimized dispatch command are as follows: Extract response time and processing result data from the provincial ground station management alarm execution records, classify and sort event types and response timestamps using data integration technology, compare response time differences of differentiated alarm types through time series analysis, and perform efficiency evaluation to obtain alarm efficiency data; Based on the alarm efficiency data, network node traffic analysis is performed to identify congested nodes, and the data transmission frequency and routing priority of the nodes are adjusted to obtain a network adjustment plan by dynamically adjusting the relief of network congestion; By using the network adjustment scheme, new scheduling commands are written and implemented, command parameters are adjusted and matched to new network traffic configurations through a software programming interface, command updates are executed, and optimized scheduling commands are generated.

8. A system using the provincial station dispatching unified management method as claimed in any one of claims 1 to 7, characterized in that: include: A data receiving module (100), a data analyzing 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 provincial ground station monitoring equipment, performs data integrity check, identifies abnormal fluctuations, and screens fault signals according to the characteristics of the fluctuations to obtain a fault data set; The data analysis module (200) analyzes the change trend of each data point based on the fault data set, compares the data in the differentiated time periods through time series analysis, determines the abnormal pattern, classifies the abnormal pattern, and generates an abnormal situation classification result; The alarm configuration module (300) uses the abnormal situation classification results to set alarm parameters for various abnormalities managed by the provincial ground station, including response time and processing priority, record alarm activities in real time, and establish alarm activity records; The command optimization module (400) analyzes the timeliness and effect of the response from the alarm activity record, adjusts the data flow and network settings of the provincial and local stations to match the real-time needs, optimizes the dispatch instructions, verifies the effect of the adjustment through field tests, and creates dispatch optimization details; The performance evaluation module (500) performs performance testing according to the scheduling optimization details, monitors the execution efficiency and success rate of multiple instructions, evaluates the operational efficiency and stability of provincial station scheduling, and obtains a scheduling unified management efficiency evaluation result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the provincial and local station dispatching unified management method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the provincial and local station dispatching unified management method described in any one of claims 1 to 7 are implemented.

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