A smart city monitoring method based on cloud computing

Abstract

The present invention discloses a smart city monitoring method based on cloud computing, which relates to the technical field of smart cities and solves the technical problem that the corresponding abnormal nodes or abnormal units cannot be found quickly and effectively, and the corresponding location power consumption anomalies can only be determined by big data, but the corresponding abnormal points cannot be found quickly and accurately. The acquired point consumption parameters are segmented and processed according to limited time periods to obtain the number of times the segment value parameters exceed the standard and the duration of the exceeding standard. According to the number of exceeding standards and the proportion of time periods, it is checked whether there is an abnormality and a consumption anomaly signal is generated. The consumption anomaly signal and the corresponding point consumption parameters are extracted, and the monitoring nodes are checked for abnormality. Not only can the corresponding power consumption anomaly point be found quickly and the external operating personnel can be informed quickly to check and process the consumption anomaly point, but also the accuracy of the power consumption anomaly point is improved, and power distribution can be carried out in advance to avoid subsequent power supply shortages and power outages.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart cities, and in particular is a smart city monitoring method based on cloud computing. Background Art

[0002] Smart city originated from the field of media. It refers to the application of intelligent computing technologies such as the Internet of Things, cloud computing, big data, and spatial geographic information integration in the fields of urban planning, design, construction, management, and operation. The key infrastructure components and services of cities such as urban management, education, medical care, real estate, transportation, public utilities, and public safety are made more interconnected, efficient, and intelligent, thereby providing citizens with better living and working services, creating a more favorable business development environment for enterprises, and enabling more efficient operation and management mechanisms for the government.

[0003] The invention with patent publication number CN103927694B provides a real-time analysis and decision-making system for urban power grid regional load, including a power grid dispatching system; the power grid dispatching system includes a power grid peripheral information collection device, a serial communication device, and a data collection and communication device interconnected by a bus, a backbone switch, a real-time database server, and a data collection and monitoring control server; and also includes a real-time database component application server and a relational database server that are interconnected with the power grid dispatching system through a bus. The working method of the system mainly includes the steps of collecting real-time data, establishing a corresponding relationship between the calculation point and the load data of each line in the region, calculating the total load of the region with a deviation rate and displaying and alarming, and outputting auxiliary decision results upon request. The invention has a simple structure, low cost, and is easy to implement. It can improve the accuracy and efficiency of power grid operation analysis, has an auxiliary decision-making function, and realizes the automation and intelligence of power grid operation at a higher level, ensuring the safe and stable operation of the power grid.

[0004] In the process of smart city power monitoring, different corresponding measures are generally taken according to the excessive power data and load, and then electric energy is delivered to the corresponding points in advance to ensure that the corresponding points have sufficient energy. However, when conducting abnormality investigation, it is not possible to quickly and effectively find the corresponding abnormal nodes or abnormal units. Only the corresponding location power consumption abnormality can be determined through big data, but the corresponding abnormal points cannot be found quickly and accurately. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a smart city monitoring method based on cloud computing, which is used to solve the technical problem that the corresponding abnormal nodes or abnormal units cannot be found quickly and effectively, and the corresponding location power consumption anomalies can only be determined through big data, but the corresponding abnormal points cannot be found quickly and accurately.

[0006] To achieve the above object, according to an embodiment of the first aspect of the present invention, a cloud computing-based smart city monitoring method is proposed, comprising the following steps:

[0007] S1. Obtain the power consumption parameters of the smart city in advance, and divide the acquired power consumption parameters into city-level consumption parameters, district-level consumption parameters, and point-level consumption parameters according to different monitoring nodes;

[0008] S2. Combine the city-level consumption parameters and the district-level consumption parameters to obtain the consumption ratio parameters of the corresponding locations, and then deliver energy to the corresponding locations in advance based on the obtained consumption ratio parameters to ensure sufficient energy;

[0009] S3, dividing the obtained point consumption parameters according to the limited time period, and formulating the divided point consumption parameters as segment value parameters, and then comparing the segment value parameters with the preset threshold value, obtaining the number of times the segment value parameters exceed the standard and the duration of the exceeding standard, and obtaining the corresponding time period proportion, according to the number of times the standard exceeds the standard and the time period proportion, checking whether the corresponding point consumption parameters are abnormal and generating a consumption abnormality signal;

[0010] S4. Extract the generated abnormal consumption signal and the corresponding point consumption parameter, and according to the monitoring node inside the point consumption parameter, check the abnormality of the geographical node monitored by the monitoring node here, and display the check result in the external display terminal.

[0011] Preferably, in step S1, the specific manner of dividing the power consumption parameters is:

[0012] S11, according to the different monitoring nodes and geographical locations, the power consumption parameters of different monitoring nodes are obtained in advance, and the obtained parameters are the power consumption parameters obtained by the monitoring nodes within 24 hours;

[0013] S12, according to the location of the monitoring node, obtaining the district-level consumption parameters belonging to the same location;

[0014] S13, obtaining the city-level consumption parameters belonging to the same city level for the locations belonging to the same city level.

[0015] Preferably, in step S2, the city-level consumption parameters and the district-level consumption parameters are combined in the following manner:

[0016] S21. Mark the zone-level consumption parameters belonging to different zones as XH i , where i represents different locations, and the corresponding city-level consumption parameters are marked as SJ;

[0017] S22, use Get the consumption ratio parameter ZBi ;

[0018] S23, then according to the consumption ratio parameter ZB i The energy parameters that need to be transmitted are processed proportionally, and the energy parameters with the same consumption proportion parameter ZB are transmitted. i The energy is transmitted to the corresponding location to ensure sufficient energy supply in the location.

[0019] Preferably, in step S3, the specific method of dividing the acquired point consumption parameters according to the limited time periods is:

[0020] S31, dividing the acquired point consumption parameters into limited time periods, wherein the limited time period is controlled to be 1 minute, and arranging the segment value parameters after the division in sequence;

[0021] S32, mark the segment value parameter as DZ k-o , where k represents different segment value parameters, where o represents different point consumption parameters, and o also represents different monitoring nodes, the segment value parameters are compared with the preset threshold value X1, where the threshold value X1 is provided by the threshold unit, and the specific comparison method is:

[0022] When DZ k-o When ≤X1, the parameter of this segment value will not be processed;

[0023] When DZ k-o >X1, mark the corresponding segment value parameter as the warning parameter, then obtain the exceeding duration and number of exceeding times of all warning parameters within 24 hours, and mark the exceeding duration as CB o , mark the number of times exceeding the limit as CS o ;

[0024] S33, the duration of the excess time CB o To process the time period proportion, use Get the time period proportion SD o ;

[0025] S34, using YC o =0.465×SD o +0.535×CS o Get the abnormal comparison parameter YC o ;

[0026] S35, compare the abnormality to the parameter YC o Compare with the threshold X2, when YC o >X2, a consumption abnormality signal is generated. o When ≤X2, no signal is generated.

[0027] Preferably, in step S4, the specific method of performing abnormality checking is:

[0028] S41. Acquire the point node consumption data of the power transmission line in advance, and mark the different point node consumption data as DJX q , where q represents different point nodes, and the point node consumption data is also the total consumption data within 24 hours;

[0029] S42, the point node consumption data DJX q Perform mean processing to obtain the corresponding point node mean JZ, using DJX q -JZ=YCD q , get the check value YCD q , check the value YCD q Compare with the preset parameter Y1, when YCD q >Y1, the corresponding point node is marked as an important inspection node, otherwise, it is not marked;

[0030] S43, according to the determined important investigation node, the electric meter data existing in the important investigation node is obtained, and the electric meter data is marked as DBSJ q-t , where t represents different meter data and q represents different point nodes;

[0031] S44, the meter data DBSJ q-t Compare with the preset parameter Y2, when DBSJ q-t >Y2, the meter is marked as an abnormal meter, otherwise, no mark is made;

[0032] S45, transmitting the abnormal meter mark t and the corresponding meter data to an external display terminal for viewing by an external operator.

[0033] Compared with the prior art, the invention has the following beneficial effects: the acquired power consumption parameters are divided in advance, and the power consumption parameters are divided into city-level consumption parameters, district-level consumption parameters and point-level consumption parameters according to different monitoring nodes, and the city-level consumption parameters and district-level consumption parameters are combined and processed to obtain the consumption proportion parameters of the corresponding location, and then according to the obtained consumption proportion parameters, energy is delivered to the corresponding location in advance to ensure sufficient energy;

[0034] The obtained point consumption parameters are divided according to limited time periods, and the divided point consumption parameters are formulated as segment value parameters. The segment value parameters are then compared with preset thresholds to obtain the number of times the segment value parameters exceed the standard and the duration of the exceedance, and the corresponding time period ratio is obtained. According to the number of times the standard exceeds the standard and the time period ratio, check whether the corresponding point consumption parameters are abnormal and generate a consumption abnormality signal. The generated consumption abnormality signal and the corresponding point consumption parameters are extracted. According to the monitoring nodes inside the point consumption parameters, the geographical nodes monitored by the monitoring nodes here are checked for abnormality, and the checking results are displayed in an external display terminal. Not only can the corresponding power consumption abnormal points be found quickly and external operators can be informed quickly to check and deal with the consumption abnormal points, but also the accuracy of the power consumption abnormal points can be improved, and power can be distributed in advance to avoid subsequent power supply shortages and power outages. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention;

[0036] Figure 2 It is a schematic diagram of the framework principle of the present invention. DETAILED DESCRIPTION

[0037] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 , the present application provides a smart city monitoring method based on cloud computing, comprising the following steps:

[0039] S1. The power consumption parameters of the smart city are obtained in advance, and the obtained power consumption parameters are divided into city-level consumption parameters, district-level consumption parameters and point-level consumption parameters according to different monitoring nodes. The specific division method is as follows:

[0040] S11, according to the different monitoring nodes and geographical locations, pre-acquire the power consumption parameters of different monitoring nodes (the acquired parameters are the power consumption parameters acquired by the monitoring nodes within 24 hours);

[0041] S12, according to the location of the monitoring node, obtaining the district-level consumption parameters belonging to the same location;

[0042] S13, then obtain the city-level consumption parameters belonging to the same city level for the locations belonging to the same city level (specifically, the power consumption parameters of different monitoring nodes can be understood as the power consumption parameters of different communities, the district-level consumption parameters of the locations can be understood as the power consumption parameters of the corresponding communities, and the city level can be understood as a certain smart city);

[0043] S2. Combine the city-level consumption parameters and the district-level consumption parameters to obtain the consumption ratio parameters of the corresponding locations. Then, according to the obtained consumption ratio parameters, deliver energy to the corresponding locations in advance to ensure sufficient energy. The specific combination processing method is as follows:

[0044] S21. Mark the zone-level consumption parameters belonging to different zones as XH i , where i represents different locations, and the corresponding city-level consumption parameters are marked as SJ;

[0045] S22, use Get the consumption ratio parameter ZB i ;

[0046] S23, then according to the consumption ratio parameter ZB i The energy parameters that need to be transmitted are processed proportionally, and the energy parameters with the same consumption proportion parameter ZB are transmitted. i The energy is transmitted to the corresponding location to ensure sufficient energy supply in the location;

[0047] S3. Segment the acquired point consumption parameters according to the limited time periods, formulate the segmented point consumption parameters as segment value parameters, and then compare the segment value parameters with the preset threshold value to obtain the number of times the segment value parameters exceed the standard and the duration of the exceeding standard, and obtain the corresponding time period proportion. According to the number of times the standard exceeds the standard and the time period proportion, check whether the corresponding point consumption parameters are abnormal and generate a consumption abnormality signal, wherein the specific processing method is as follows:

[0048] S31, dividing the acquired point consumption parameters according to a limited time period, wherein the limited time period is controlled to be 1 minute, and arranging the segment value parameters after the division in sequence;

[0049] S32, mark the segment value parameter as DZ k-o , where k represents different segment value parameters, where o represents different point consumption parameters, and o can also represent different monitoring nodes. Since the corresponding monitoring nodes correspond to a set of point consumption parameters, the segment value parameters are compared with the preset threshold value X1, where the threshold value X1 is provided by the threshold unit. The specific comparison method is:

[0050] When DZ k-o When ≤X1, the parameter of this segment value will not be processed;

[0051] When DZk-o >X1, mark the corresponding segment value parameter as the warning parameter, then obtain the exceeding duration and number of exceeding times of all warning parameters within 24 hours, and mark the exceeding duration as CB o , mark the number of times exceeding the limit as CS o ;

[0052] S33, the duration of the excess time CB o To process the time period proportion, use Get the time period proportion SD o , specifically, the duration of the overrun CB o The time unit is minutes, and the duration of the entire warning parameter exceeding the standard belongs to the time parameter within a day, so the time of a day is converted into minutes, and then processed to obtain the time period proportion SD o ;

[0053] S34, using YC o =0.465×SD o +0.535×CS o Get the abnormal comparison parameter YC o , compare the abnormality to the parameter YC o Transmitted to the threshold unit;

[0054] S35, the threshold unit is provided with thresholds X1 and X2 respectively, and the abnormal comparison parameter YC o Compare with the threshold X2, when YC o >X2, a consumption abnormality signal is generated. o When ≤X2, no signal is generated;

[0055] S4. Extract the generated consumption abnormality signal and the corresponding point consumption parameter, and perform abnormality check on the geographical node monitored by the monitoring node according to the monitoring node inside the point consumption parameter, and display the check result in an external display terminal, wherein the specific method of performing abnormality check is as follows:

[0056] S41. Acquire the point node consumption data of the power transmission line in advance (specifically, the point node can be understood as a certain transmission node, and the corresponding transmission node is provided with a corresponding data sensor to acquire the point node consumption data), and mark the different point node consumption data as DJX q , where q represents different point nodes, and the point node consumption data is also the total consumption data within 24 hours;

[0057] S42, the point node consumption data DJX q Perform mean processing to obtain the corresponding point node mean JZ, using DJX q -JZ=YCDq , get the check value YCD q , check the value YCD q Compare with the preset parameter Y1, when YCD q >Y1, the corresponding point node is marked as an important inspection node, otherwise, it is not marked;

[0058] S43, according to the determined important investigation node, the electric meter data existing in the important investigation node is obtained, and the electric meter data is marked as DBSJ q-t , where t represents different meter data and q represents different point nodes;

[0059] S44, the meter data DBSJ q-t Compare with the preset parameter Y2, when DBSJ q-t >Y2, the meter is marked as an abnormal meter, otherwise, no mark is made;

[0060] S45, transmitting the abnormal meter mark t and the corresponding meter data to an external display terminal for viewing by an external operator.

[0061] See also Figure 2 , a cloud computing-based smart city monitoring system, including a data collection terminal, a monitoring and control center, and a display terminal;

[0062] The output end of the data acquisition terminal is electrically connected to the input end of the monitoring and control center, and the output end of the monitoring and control center is electrically connected to the input end of the display terminal;

[0063] The monitoring and control center includes a power data monitoring unit, a data proportion processing unit, a transmission control unit, an abnormal data troubleshooting unit and a threshold unit;

[0064] The output end of the power data monitoring unit is electrically connected to the data proportion processing unit and the input end of the abnormal data investigation unit respectively, the data proportion processing unit is bidirectionally connected to the transmission control unit, and the abnormal data investigation unit is bidirectionally connected to the threshold unit.

[0065] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0066] The working principle of the present invention is as follows: the power consumption parameters of the smart city are acquired in advance, and the acquired power consumption parameters are divided. According to different monitoring nodes, the power consumption parameters are divided into city-level consumption parameters, district-level consumption parameters and point-level consumption parameters. The city-level consumption parameters and district-level consumption parameters are merged and processed to obtain the consumption ratio parameters of the corresponding location. Then, according to the obtained consumption ratio parameters, energy is delivered to the corresponding location in advance to ensure sufficient energy. The acquired point-level consumption parameters are divided according to limited time periods, and the divided point-level consumption parameters are formulated as segment value parameters. Then, the segment value parameters are compared with preset thresholds to obtain the number of times the segment value parameters exceed the standard and the value of the exceeding standard. The duration of the power consumption is calculated, and the corresponding time period ratio is obtained. According to the number of times of exceeding the standard and the time period ratio, the corresponding point consumption parameters are checked for abnormalities and consumption abnormality signals are generated. The generated consumption abnormality signals and the corresponding point consumption parameters are extracted. According to the monitoring nodes inside the point consumption parameters, the geographical nodes monitored by the monitoring nodes here are checked for abnormalities, and the checking results are displayed on the external display terminal. Not only can the corresponding power consumption abnormal points be found quickly and external operators can be informed quickly to check and deal with the consumption abnormal points, but also the accuracy of power consumption abnormal points can be improved, and power can be distributed in advance to avoid insufficient power supply in the future and cause power outages.

[0067] The above embodiments are only used to illustrate the technical method 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 method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A smart city monitoring method based on cloud computing, characterized in that: The following steps are involved: S1. Obtain the power consumption parameters of the smart city in advance, and divide the acquired power consumption parameters into city-level consumption parameters, district-level consumption parameters, and point-level consumption parameters according to different monitoring nodes; S2. Combine the city-level consumption parameters and the district-level consumption parameters to obtain the consumption ratio parameters of the corresponding locations, and then deliver energy to the corresponding locations in advance based on the obtained consumption ratio parameters to ensure sufficient energy; S3. Divide the acquired point consumption parameters according to the limited time periods, formulate the divided point consumption parameters as segment value parameters, and then compare the segment value parameters with the preset threshold value to obtain the number of times and duration of the segment value parameters exceeding the standard, and obtain the corresponding time period proportion. According to the number of times and the time period proportion, check whether the corresponding point consumption parameters are abnormal and generate a consumption abnormality signal. The specific method is as follows: S31, dividing the acquired point consumption parameters into limited time periods, wherein the limited time period is controlled to be 1 minute, and arranging the segment value parameters after the division in sequence; S32, mark the segment value parameter as DZ k-o , where k represents different segment value parameters, where o represents different point consumption parameters, and o also represents different monitoring nodes, the segment value parameters are compared with the preset threshold value X1, where the threshold value X1 is provided by the threshold value unit, and the specific comparison method is: When DZ k-o When ≤X1, the parameter of this segment value will not be processed; When DZ k-o >X1, mark the corresponding segment value parameter as the warning parameter, then obtain the exceeding duration and number of exceeding times of all warning parameters within 24 hours, and mark the exceeding duration as CB o , mark the number of times exceeding the limit as CS o ; S33, the excess time CB o To process the time period proportion, use Get the time period proportion SD o ; S34, use Get the abnormal comparison parameter YC o ; S35, compare the abnormality to the parameter YC o Compare with the threshold X2, when YC o >X2, a consumption abnormality signal is generated. o When ≤X2, no signal is generated; S4. Extract the generated consumption abnormality signal and the corresponding point consumption parameter, and check the abnormality of the geographical node monitored by the monitoring node according to the monitoring node inside the point consumption parameter, and display the check result in the external display terminal, specifically in the following manner: S41. Acquire the point node consumption data of the power transmission line in advance, and mark the different point node consumption data as DJX q , where q represents different point nodes, and the point node consumption data is the total consumption data within 24 hours; S42, the point node consumption data DJX q Perform mean processing to obtain the corresponding point node mean JZ, using DJX q -JZ=YCD q , get the check value YCD q , check the value YCD q Compare with the preset parameter Y1, when YCD q >Y1, the corresponding point node is marked as an important inspection node, otherwise, it is not marked; S43, according to the determined important investigation node, the electric meter data existing in the important investigation node is obtained, and the electric meter data is marked as DBSJ q-t , where t represents different meter data and q represents different point nodes; S44, the meter data DBSJ q-t Compare with the preset parameter Y2, when DBSJ q-t >Y2, the meter is marked as an abnormal meter, otherwise, no mark is made; S45, transmitting the abnormal meter mark t and the corresponding meter data to an external display terminal for viewing by an external operator.

2. The cloud computing-based smart city monitoring method according to claim 1, characterized in that: In step S1, the specific method of dividing the power consumption parameters is: S11, according to the different monitoring nodes and geographical locations, the power consumption parameters of different monitoring nodes are obtained in advance, and the obtained parameters are the power consumption parameters obtained by the monitoring nodes within 24 hours; S12, according to the location of the monitoring node, obtaining the district-level consumption parameters belonging to the same location; S13. According to a plurality of areas belonging to the same city level, confirm the city-level consumption parameters belonging to the same city level.

3. The cloud computing-based smart city monitoring method according to claim 2 is characterized in that: In step S2, the city-level consumption parameters and the district-level consumption parameters are combined in the following manner: S21. Mark the zone-level consumption parameters belonging to different zones as XH i , where i represents different locations, and the corresponding city-level consumption parameters are marked as SJ; S22, use Get the consumption ratio parameter ZB i ; S23, then according to the consumption ratio parameter ZB i The energy parameters that need to be transmitted are processed proportionally, and the energy parameters with the same consumption proportion parameter ZB are transmitted. i The energy is transmitted to the corresponding location to ensure sufficient energy supply in the location.

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