A multi-site power environment monitoring method and system

Abstract

The application relates to the technical field of power environment monitoring, in particular to a multi-station power environment monitoring method and system, which specifically comprises the following steps: collecting power environment monitoring data of each machine room of each railway station; determining corresponding trend conditions by analyzing real-time change characteristics of the power environment monitoring data in machine rooms of different stations; predicting monitoring values at future moments in combination with the development trend of the monitoring data; determining the comprehensive alarm triggering degree of each station based on the difference between the predicted values and preset alarm threshold values of various monitoring data, and then determining alarm attributes of each station; and regulating and controlling maintenance resources through corresponding alarm mechanisms for early warning information of different stations, so that multi-station power environment monitoring is realized, the problem that the power environment monitoring effect is poor due to unreasonable alarm mechanisms is avoided, and the overall flexibility and management efficiency of station management are enhanced.

Description

Technical Field

[0001] This application relates to the field of power environment monitoring technology, specifically to a multi-site power environment monitoring method and system. Background Technology

[0002] Multi-site power environment monitoring aims to simultaneously monitor power environment parameters at multiple sites, such as temperature, humidity, power status, and safety status. Its core objective is to enable managers to grasp the real-time operating status of each site through remote monitoring and intelligent analysis, allowing for early detection and handling of potential problems. When abnormal data is detected, the alarm module triggers the corresponding alarm mechanism, notifying managers for maintenance, thereby ensuring the stable operation of the sites. In the current railway transportation system, the configuration and purpose of computer rooms may vary at different sites. A single site may have multiple computer rooms, and current early warning strategies for different sites are divided into individual alarms and centralized alarms.

[0003] Individual alarms mean that alarm information from each site is only sent to the relevant management personnel at that site. Centralized alarms, on the other hand, centralize alarm information from all sites into a central monitoring system for processing and management. This central system then performs unified analysis, recording, control, and takes corresponding measures. Currently, monitoring and early warning of power environment data from different sites is generally based on an individual alarm strategy. Each site independently manages its own alarm information, which is processed and sent directly to local management personnel for maintenance. However, because individual alarms are not interconnected and cannot share information and data, effective correlation analysis of alarm events from different monitoring points is impossible, reducing the overall effectiveness of multi-site power environment monitoring. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a multi-site power environment monitoring method and system, the specific technical solution of which is as follows:

[0005] In a first aspect, embodiments of this application provide a multi-site power environment monitoring method, which includes the following steps:

[0006] Collect power and environmental monitoring data for each site and each computer room;

[0007] The data trend degree of each monitoring data in each computer room is determined based on the changing trend of each monitoring data in each computer room; the degree of development trend of each monitoring data in each computer room at each time is determined based on the data trend degree of each monitoring data in each computer room and the local data changes.

[0008] Based on the magnitude of each monitoring data in each computer room at each time point and the corresponding trend, the predicted value of each monitoring data in each computer room at the next time point is determined; based on the difference between the predicted value and the preset alarm threshold of each monitoring data, the alarm triggering degree of each computer room at each time point is determined, and the comprehensive alarm triggering degree of each site is calculated.

[0009] Critical sites and ordinary sites are distinguished based on the overall alarm triggering level of each site; alarm mechanisms are used to monitor and control critical sites and ordinary sites respectively.

[0010] In one embodiment, the process of obtaining the data trend degree of each type of monitoring data in each computer room is as follows:

[0011] For any type of monitoring data in each computer room, calculate the difference between each time point and the previous time point of the same type of monitoring data, and record it as the first difference; take the mean of all the first differences of the same type of monitoring data as the data trend degree of the same type of monitoring data.

[0012] In one embodiment, the expression for the degree of development trend of each type of monitoring data in each computer room at each time point is:

[0013] In the formula, G k Let A represent the degree of trend of any monitoring data in any computer room at time k. k Let k be the actual value of any of the monitoring data at time k. Y is the mean of all historical data before time k for any one type of monitoring data, and Y is the data trend degree of any one type of monitoring data.

[0014] In one embodiment, the expression for the predicted value of each monitoring data point in each computer room at the next moment is:

[0015] A′ k+1 =A k ×(1+G′ k In the formula, A′ k+1 Let A be the predicted value of any of the monitoring data at time k+1. k Let G′ be the actual value of any of the monitoring data at time k. k Let be the normalized value of the development trend of any monitoring data at time k.

[0016] In one embodiment, the expression for the alarm triggering level of each computer room at each time is:

[0017] In the formula, Let N be the alarm trigger level of the a-th computer room at time k; N is the number of monitoring data types for each computer room. Let μ be the predicted value of the b-th monitoring data in the a-th computer room at time k+1; a,b The preset alarm threshold for the b type of monitoring data in the a-th computer room.

[0018] In one embodiment, the expression for the overall alarm triggering level of each site is:

[0019] The overall alarm triggering level of each site at each time is determined by the fusion value of the alarm triggering levels of all computer rooms within each site at each time.

[0020] In one embodiment, the calculation expression for the comprehensive alarm triggering level is:

[0021] In the formula, Let S be the overall alarm triggering level of any site at time k, and let S be the number of data centers contained within any given site. Let exp() be the alarm triggering level of the i-th computer room in any of the sites at time k, and let e be an exponential function with the natural number e as the base.

[0022] In one embodiment, the distinction between critical sites and ordinary sites based on the comprehensive alarm triggering level of each site is specifically as follows: sites whose normalized value of the comprehensive alarm triggering level is greater than or equal to a preset first threshold are designated as critical sites, and sites whose value is less than the preset first threshold are designated as ordinary sites.

[0023] In one embodiment, the monitoring and control of critical sites and ordinary sites using alarm mechanisms specifically involves: monitoring and controlling critical sites using separate alarms, and monitoring and controlling ordinary sites using centralized alarms.

[0024] Secondly, embodiments of this application also provide a multi-site power environment monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0025] The embodiments of this application have at least the following beneficial effects:

[0026] This application optimizes the high management and maintenance costs caused by the inability to effectively correlate alarm events at different stations when issuing early warnings based on individual alarm strategies. It analyzes the monitoring data trends of different stations for real-time data prediction, and determines the comprehensive alarm triggering level of each station based on the difference between the prediction results and the preset alarm thresholds for various monitoring data. Then, based on the differences in the comprehensive alarm triggering level, it integrates individual alarm and centralized alarm methods to set different alarm strategies for different stations, determining corresponding alarm mechanisms for early warning at different stations. This ensures rapid response and autonomy at key stations while improving the synchronization of unified station management and comprehensive analysis, enhancing the overall flexibility and efficiency of railway station management. Furthermore, it uses corresponding alarm mechanisms to regulate maintenance resources based on early warning information from different stations, achieving multi-site power environment monitoring and avoiding the problem of poor power environment monitoring results due to unreasonable alarm mechanisms. Attached Figure Description

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating the steps of a multi-site power environment monitoring method provided in one embodiment of this application;

[0029] Figure 2 This diagram illustrates the process of acquiring the data trend of each type of monitoring data in each computer room. Detailed Implementation

[0030] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a multi-site dynamic environment monitoring method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0032] The following description, in conjunction with the accompanying drawings, details the specific scheme of a multi-site power environment monitoring method and system provided in this application.

[0033] Please see Figure 1 The diagram illustrates a flowchart of a multi-site power environment monitoring method according to an embodiment of this application. The method includes the following steps:

[0034] Step S1: Collect power and environmental monitoring data for each computer room at each site.

[0035] Sensors in each computer room at different sites monitor and collect various power and environmental parameters in real time, including temperature, humidity, power status, and access control status. Alarm strategies for different sites are determined based on the trend characteristics of this power and environmental monitoring data to improve site management efficiency.

[0036] Preferably, in one embodiment of this application, the data acquisition time interval for power and environmental monitoring data collection in each site's computer room is set to 1 second. In other embodiments of this application, implementers can set the data acquisition time interval according to their actual circumstances.

[0037] Step S2: Determine the data trend degree of each monitoring data in each computer room based on the changing trend of each monitoring data in each computer room; determine the development trend degree of each monitoring data in each computer room at each time based on the data trend degree of each monitoring data in each computer room and the local data changes.

[0038] Because different alarm mechanisms have varying responsiveness to maintenance at different stations, and in actual railway transportation, different stations have different maintenance needs, some large, critical stations may require faster maintenance speeds and more resources, while smaller, ordinary stations have lower priority maintenance needs compared to critical stations. Therefore, by identifying stations with different attributes based on real-time data, the corresponding alarm mechanism is then determined based on these station attributes.

[0039] Traditional threshold-based early warning triggering mechanisms suffer from latency in actual monitoring, preventing maintenance personnel from reacting immediately to anomalies and leading to adverse consequences. Therefore, this embodiment uses real-time monitoring data trends for data prediction, and determines the early warning trigger time based on the prediction results.

[0040] (1) Calculate the data trend degree of various power environment monitoring data for each computer room based on the changing trends of various power environment monitoring data for each computer room. The expression is as follows:

[0041] In the formula, Y a,b Let M represent the data trend degree of the b-th type of monitoring data in the a-th computer room, and M be the number of various monitoring data collected at the current moment. These are the values ​​of the b-th type of monitoring data collected at time k and time k-1, respectively, for the a-th computer room. This is the first difference.

[0042] The overall degree of change in data is measured by the difference between two adjacent time points. A trend greater than zero indicates that the data shows an overall upward trend, while a trend less than zero indicates that the data shows an overall downward trend.

[0043] (2) Based on the data trend and local data changes of various power and environmental monitoring data in each computer room, the development trend of various power and environmental monitoring data in each computer room at each moment is calculated. The expression is:

[0044] In the formula, G k Let A represent the degree of trend of any monitoring data in any computer room at time k. k Let k be the actual value of any of the monitoring data at time k. Y is the mean of all historical data before time k for any one type of monitoring data, and Y is the data trend degree of any one type of monitoring data.

[0045] The larger the value of G, the greater the degree of local variation in the data, and the more obvious the trend change. k The larger the value, the greater the growth trend of any monitoring data at that moment; G k The smaller the value, the greater the downward trend of any monitoring data at that moment.

[0046] (3) Normalize the development trend of all monitoring data in all computer rooms at all times, with a normalization range of [-1, 1]. For the normalization of the development trend, this application uses the maximum value normalization algorithm. There are many existing normalization algorithms, and implementers may also use other normalization algorithms to normalize the development trend. This application does not impose any specific restrictions.

[0047] Step S3: Determine the predicted value of each monitoring data in each computer room at the next moment based on the magnitude of each monitoring data at each moment and the corresponding trend degree; determine the alarm triggering degree of each computer room at each moment based on the difference between the predicted value and the preset alarm threshold of each monitoring data, and calculate the comprehensive alarm triggering degree of each site.

[0048] Since traditional threshold-based abnormal data early warning mechanisms have a lag, predictions can be made based on the trend values ​​of real-time data, and the degree of early warning triggering for the corresponding data center can be determined based on the prediction results.

[0049] (1) Based on the magnitude of various monitoring data in each computer room at each moment and the corresponding trend, calculate the predicted value of various monitoring data in each computer room at the next moment. The expression is as follows:

[0050] A′ k+1 =A k ×(1+G′ k In the formula, A′ k+1 Let A be the predicted value of any of the monitoring data at time k+1. k Let G′ be the actual value of any of the monitoring data at time k. k Let be the normalized value of the development trend of any monitoring data at time k.

[0051] Due to G′ k The sign and relative magnitude of the value can represent the growth or decline of the data trend at that moment and its relative degree. Therefore, the magnitude of the predicted value is correlated with the real-time development trend. G′ k The larger the value of G′, the larger the predicted value. k The smaller the value, the smaller the predicted value. Predicting real-time data can provide early warnings for abnormal data, improving the timeliness of maintenance.

[0052] (2) Generally, different monitoring data are compared in real time with the corresponding alarm thresholds set in the control center of the railway station to determine whether an alarm should be triggered. This application determines the alarm triggering level by comparing the corresponding predicted value with the threshold at the current moment. Since any monitoring data in the computer room exceeding the threshold will trigger the corresponding alarm, it is necessary to consider the predicted value of each data. Based on the above analysis, the alarm triggering level of each computer room at each moment is calculated, and the expression is:

[0053] In the formula, The alarm trigger level of the a-th computer room at time k; N is the number of monitoring data types for each computer room, and the value of N in this embodiment is 4; Let μ be the predicted value of the b-th monitoring data in the a-th computer room at time k+1; a,b The alarm thresholds for the b-th type of monitoring data in the a-th computer room are preset. The alarm thresholds for various monitoring data can be obtained from the control center of the railway station.

[0054] therefore This represents the difference between the predicted value of the monitoring data and the alarm threshold. The closer the predicted value is to the alarm threshold, the smaller its value, and the higher the degree of alarm triggering. The greater the difference between the predicted value of the monitoring data and the corresponding alarm threshold, the lower the degree of alarm triggering.

[0055] (3) Determine the comprehensive alarm triggering level of each site at each time based on the fusion value of the alarm triggering level of all computer rooms in each site at each time.

[0056] It should be noted that the fusion described in this application refers to combining multiple variables. The specific fusion method can be determined according to the actual situation during application, and this application does not impose any special restrictions.

[0057] Preferably, in this embodiment of the application, the expression for the comprehensive alarm triggering level of each site is:

[0058] In the formula, Let S be the overall alarm triggering level of any site at time k, and let S be the number of data centers contained within any given site. Let exp() be the alarm triggering level of the i-th computer room in any of the sites at time k, and let e be an exponential function with the natural number e as the base.

[0059] The above method can be used to calculate the comprehensive alarm triggering level for all stations at the corresponding time, and then the alarm attributes of the corresponding stations can be determined based on the comprehensive alarm triggering level.

[0060] The higher the overall alarm trigger level value, the faster the corresponding site needs abnormal maintenance to maintain normal operation. Therefore, the higher the probability that it is a critical site, and vice versa.

[0061] Step S4: Based on the comprehensive alarm triggering level of each site, distinguish between critical sites and ordinary sites; use alarm mechanisms to monitor and control critical sites and ordinary sites respectively.

[0062] Sites with a normalized value of the overall alarm triggering level greater than or equal to a first threshold are designated as critical sites, while sites with a normalized value of the overall alarm triggering level less than the first threshold are designated as ordinary sites. Preferably, in one embodiment of this application, the first threshold is set to 0.85. As in other embodiments of this application, implementers can set the first threshold according to actual conditions.

[0063] Alarm thresholds for each type of monitoring data are obtained through the control centers of railway stations. The alarm mechanism for critical stations is set to individual alarms. When the predicted value of each type of monitoring data in each equipment room of a critical station exceeds the corresponding alarm threshold in the next moment, an early warning information is sent to the critical station, enabling rapid processing of early warning information through local maintenance resources and improving the ability to respond to anomalies. The alarm mechanism for ordinary stations is set to centralized alarms. When the predicted value of each type of monitoring data in each equipment room of an ordinary station exceeds the corresponding alarm threshold in the next moment, the early warning information is comprehensively analyzed before being fed back to the ordinary station for processing, thereby coordinating maintenance resources and reducing maintenance costs. Furthermore, by adjusting the alarm mechanisms of stations, the scheduling of maintenance resources is controlled, enabling monitoring of the power environment at multiple stations.

[0064] A schematic diagram illustrating the process of acquiring the data trend degree for each type of monitoring data in each computer room is shown below. Figure 2 As shown.

[0065] Based on the same inventive concept as the above method, this application embodiment also provides a multi-site power environment monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described multi-site power environment monitoring methods.

[0066] In summary, this application provides a multi-site power environment monitoring method. It performs real-time data prediction by analyzing the monitoring data trends of different sites. Based on the difference between the prediction results and preset alarm thresholds for various monitoring data, it determines the comprehensive alarm triggering level of each site. Then, based on the difference in the comprehensive alarm triggering level, it combines individual alarm and centralized alarm methods to set different alarm strategies for different sites. It determines corresponding alarm mechanisms for different sites to provide early warnings, optimizing the high management and maintenance costs caused by the inability to effectively correlate alarm events at different sites when using individual alarm strategies for early warning. This method ensures rapid response and autonomy for critical sites while improving the synchronization of unified site management and comprehensive analysis, enhancing the overall flexibility and efficiency of site management. It also regulates maintenance resources through corresponding alarm mechanisms for early warning information from different sites, achieving multi-site power environment monitoring and avoiding poor monitoring results due to unreasonable alarm mechanisms.

[0067] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0068] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0069] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A multi-site power environment monitoring method, characterized by, The method comprises the following steps: Collecting power environment monitoring data of each machine room of each site; Determining a data trend degree of each kind of monitoring data of each machine room based on the change trend of each kind of monitoring data of each machine room; determining a development trend degree of each kind of monitoring data of each machine room at each time based on the data trend degree of each kind of monitoring data of each machine room and the local data change condition; a monitoring data of each machine room at each time point and a corresponding development trend degree to determine a predicted value of each monitoring data of each machine room at a next time point; based on a difference between the predicted value and a preset alarm threshold of each monitoring data, a degree of alarm triggering of each machine room at each time point is determined, and a comprehensive degree of alarm triggering of each site is calculated; an expression of the degree of alarm triggering of each machine room at each time point is: , wherein, is the degree of alarm triggering of the a th machine room at the k th time point; N is the number of types of monitoring data of each machine room; is the predicted value of the b th monitoring data of the a th machine room at the k+1 th time point; is a preset alarm threshold of the b th monitoring data of the a th machine room; the comprehensive degree of alarm triggering of each site is determined based on a fusion value of the degrees of alarm triggering of all machine rooms in each site at each time point. Distinguishing key sites and ordinary sites based on the comprehensive alarm triggering degree of each site; respectively adopting alarm mechanisms to monitor and control the key sites and the ordinary sites, adopting separate alarm to monitor and control the key sites, and adopting centralized alarm to monitor and control the ordinary sites; The acquisition process of the data trend degree of each kind of monitoring data of each machine room is as follows: For each kind of monitoring data of each machine room, the difference value of the monitoring data at each time and the previous time is calculated, which is recorded as a first difference value; the mean value of all the first difference values of the monitoring data is taken as the data trend degree of the monitoring data; The expression of the development trend degree of each kind of monitoring data of each machine room at each time is as follows: , wherein, is a development trend degree of any monitoring data of an arbitrary machine room at the kth moment, is an actual value of the any monitoring data at the kth moment, is a mean value of all historical data of the any monitoring data before the kth moment, and Y is a data trend degree of the any monitoring data.

2. A multi-site power environment monitoring method as claimed in claim 1, wherein, The expression of the predicted value of each kind of monitoring data of each machine room at the next time is as follows: wherein, is a predicted value of the any kind of monitoring data at the k+1th moment, is an actual value of the any kind of monitoring data at the kth moment, is a normalized value of the degree of development trend of the any kind of monitoring data at the kth moment.

3. A multi-site power environment monitoring method as recited in claim 1, wherein, The calculation expression of the comprehensive alarm triggering degree is as follows: wherein, is the integrated alarm triggering degree of any station at the kth moment, is the number of machine rooms contained in the any station, is the alarm triggering degree of the ith machine room in the any station at the kth moment, is the exponential function with natural number e as the base.

4. A multi-site power environment monitoring method as recited in claim 1, wherein, The key sites and the ordinary sites are distinguished based on the comprehensive alarm triggering degree of each site, specifically: the sites with the normalized value of the comprehensive alarm triggering degree greater than or equal to a preset first threshold value are taken as the key sites, and the sites with the normalized value less than the preset first threshold value are taken as the ordinary sites.

5. A multi-site power environment monitoring system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein, The processor implements the steps of the method of any one of claims 1-4 when executing the computer program.

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