A smart grid monitoring system

Abstract

The present invention discloses an intelligent grid monitoring system, which relates to the technical field of grid monitoring. The system comprises a data acquisition module, a benchmark cohesion acquisition module, a benchmark separation acquisition module, a comparative profile acquisition module, a real-time data monitoring module, a profile determination interval acquisition module and an abnormal node determination module. The system solves the technical problems that traditional grid monitoring systems can usually only monitor some basic electrical parameters, are difficult to fully reflect the operating status of the grid, lack refined monitoring of each node in the grid, and cannot timely detect abnormalities of local nodes. By comparing and analyzing the real-time profile with the profile determination interval, the abnormal nodes are accurately determined and output; refined monitoring of each node in the grid is achieved, which is conducive to timely detection of abnormal conditions of local nodes, so that operation and maintenance personnel can timely detect and handle abnormal nodes in the grid, and realize comprehensive, real-time and accurate monitoring of the status of each node in the grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid monitoring, and in particular relates to a smart power grid monitoring system. Background Art

[0002] With the continuous growth of energy demand and the increasing demands for power system reliability and efficiency, smart grids have become a major direction for future power grid development. Smart grids require real-time monitoring, precise control, and optimized management of grid operating conditions to improve their safety, stability, and economic efficiency. During grid operation, grid monitoring primarily oversees and controls the grid, enabling better identification of grid faults and operating conditions, ensuring timely resolution and resolution of issues. With the rapid development of information technology, power grid operations are becoming increasingly intelligent. As a key component of the power system, smart grids are directly linked to its stability and safety. They not only ensure high-precision monitoring data but also prevent energy loss during transmission.

[0003] Grid monitoring primarily utilizes advanced sensing and control technologies. Built on information and communication networks, it effectively monitors grid operations, making them more environmentally friendly, reliable, and economical. In short, grid monitoring can better meet people's daily electricity needs and ensure more efficient use of electrical energy resources.

[0004] Patent publication number CN113241848A discloses a comprehensive monitoring system for a distribution network. The system collects distribution network power consumption data through a power consumption information collection module and sends the data to an intelligent gateway. The power distribution equipment status monitoring module collects distribution equipment operating status data and sends the data to an intelligent gateway through a communication module. The intelligent gateway sends the distribution network power consumption data and the distribution equipment operating status data to an edge computing module for management. Edge computing is performed on the distribution network power consumption data and the distribution equipment operating status data to obtain power consumption data monitoring results. Through functions such as power consumption information collection and equipment operating status monitoring, the system comprehensively processes data information in the distribution network. Based on edge technology, the system monitors the distribution network operating status based on the collected power consumption information and the operating status of the distribution equipment in the distribution network environment, obtaining power consumption data monitoring results. This system can effectively and accurately monitor the data of the distribution system.

[0005] However, traditional power grid monitoring systems may have the problem of incomplete data collection, resulting in blind spots in the monitoring of power grid status. Traditional power grid monitoring systems can usually only monitor some basic electrical parameters, such as voltage and current, which makes it difficult to fully reflect the operating status of the power grid. Some nodes may have slight abnormalities in the early stage, but these abnormalities may not be enough to trigger the traditional fault alarm mechanism. Traditional power grid monitoring systems lack refined monitoring of each node in the power grid and cannot detect abnormal conditions of local nodes in time, which affects the normal operation of the power grid. Based on this, a smart grid monitoring system is proposed. Summary of the Invention

[0006] The purpose of the present invention is to provide an information interaction method for power field operations, which solves the technical problems that traditional power grid monitoring systems can usually only monitor some basic electrical parameters, are difficult to fully reflect the operating status of the power grid, lack refined monitoring of each node in the power grid, and cannot timely detect abnormal conditions of local nodes.

[0007] A smart grid monitoring system, comprising:

[0008] The data acquisition module collects the voltage corresponding to each node in the power grid at a preset number of times n;

[0009] The benchmark cohesion acquisition module analyzes the voltage data of each node in the power grid within a preset number of times n to obtain the benchmark cohesion corresponding to each node;

[0010] The reference separation acquisition module analyzes the voltage data of each node in the power grid within a preset number of times n to obtain the reference separation corresponding to each node;

[0011] The contrast contour acquisition module comprehensively analyzes the benchmark cohesion and benchmark separation of each node to obtain the contrast contour corresponding to each node;

[0012] Real-time data monitoring module obtains the real-time voltage value of each node for analysis and obtains the real-time contour corresponding to each node;

[0013] The contour determination interval acquisition module analyzes the comparative contour degrees corresponding to each node and generates the contour determination interval corresponding to each node;

[0014] The abnormal node determination module compares and analyzes the real-time contour degree corresponding to each node with its corresponding contour determination interval to determine the generation of abnormal nodes.

[0015] As a further solution of the present invention: the specific method for obtaining the benchmark cohesion corresponding to each node is:

[0016] The voltage values corresponding to each node in the preset number n are marked as vin, where i refers to different nodes in the power grid, i is a positive integer, i>1; through the formula, Calculate and obtain the benchmark cohesion Li corresponding to each node in the power grid;

[0017] Where vij refers to the voltage value of the i-th node measured at the j-th time, i ≥ j ≥ 1, and vpi is the average voltage of all measured values corresponding to each node in the preset number of times n.

[0018] As a further solution of the present invention, the specific method of obtaining the reference separation degree corresponding to each node is:

[0019] S1: Randomly select a node from all nodes without replacement as the analysis node;

[0020] S2: Randomly select one time from the preset number n without replacement as the target time, obtain the voltage value corresponding to the analysis node in the target time, and the average of the absolute values of the differences between the voltage value and other nodes, and use it as the node deviation value C1 corresponding to the analysis node in the target time;

[0021] S3: Repeat the above steps S1-S2 to obtain the node deviation values Cn corresponding to the analysis nodes in the preset number of times n, obtain the discrete value U1 of the node deviation value Cn, analyze the discrete value U1 of the node deviation value Cn, and obtain the reference separation corresponding to the analysis node;

[0022] S4: Repeat the above steps S1-S3 to obtain the reference separation degree Di corresponding to each node in the power grid.

[0023] As a further solution of the present invention, the discrete values of the node deviation values are analyzed to obtain the reference separation corresponding to the analysis node in the following specific manner:

[0024] When the discrete value U1 is less than or equal to the preset threshold Y1, the mean of the node deviation values Cn is used as the benchmark separation corresponding to the analysis node. When the discrete value U1 is greater than the preset threshold Y1, the mean of the maximum and minimum values of the node deviation values Cn is used as the benchmark separation corresponding to the analysis node.

[0025] As a further solution of the present invention, the specific method of obtaining the contrast contour corresponding to each node is:

[0026] The contrast profile Ki corresponding to each node is calculated using the formula: Ki = |Li-Di| / |Lmax-Dmax|, where Lmax and Dmax are the maximum values of the reference cohesion Li and the reference separation Di, respectively.

[0027] As a further solution of the present invention, the specific method of generating the contour determination interval corresponding to each node is as follows:

[0028] Obtain the value Kb of the comparison profile Ki corresponding to each node in the power grid that meets the preset condition E1, where b is the number of Ki that meets the preset condition E1, i≥b≥1, and compare b with the preset value Y2 to obtain the reference profile H;

[0029] The absolute value of the difference between the contrast contour Ki corresponding to each node in the power grid and the reference contour H is obtained, and it is used as the domain value Zi corresponding to each node in the power grid. The difference between the contrast contour Ki of each node and its corresponding domain value Zi is used as the lower limit value of the contour judgment interval of each node. The sum of the contrast contour Ki of each node and its corresponding domain value Zi is used as the upper limit value of the contour judgment interval of each node, and then the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node is obtained.

[0030] As a further solution of the present invention: the specific method of obtaining the reference profile is:

[0031] Compare and analyze b with the preset value Y2. When b≥Y2, the mean Kp of Ki is used as the benchmark profile H. When b<Y2, the mean of the maximum and minimum values in Ki is used as the benchmark profile H, and Y2 is the preset value.

[0032] As a further solution of the present invention: the preset condition E1 is |Ki-Kp|>Y3, Kp is the average value of Ki, and Y3 is a preset value.

[0033] As a further solution of the present invention, the specific method of obtaining the real-time contour corresponding to each node is as follows:

[0034] Get the real-time voltage value Ri corresponding to each node in the power grid; through the formula, The real-time cohesion EAi corresponding to each node in the power grid is calculated; wherein Re is any value in Ri, and Rp is the mean of Ri. The mean of the absolute value of the difference between the voltage value of each node and the voltage value of other nodes is obtained, and it is used as the real-time separation EBi corresponding to each node in the power grid. The real-time cohesion EAi and the real-time separation EBi corresponding to each node in the power grid are respectively multiplied by the preset fixed coefficients β1 and β2 as the real-time contour Gi corresponding to each node. The preset fixed coefficients β1 and β2 are formulated by relevant personnel according to actual needs, satisfying 1=β1+β2 and β1>β2.

[0035] As a further solution of the present invention: the specific method of determining the generation of abnormal nodes is:

[0036] The real-time contour degree Gi corresponding to each node in the power grid is compared with the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node. When the real-time contour degree Gi belongs to the corresponding contour judgment interval, no processing is performed. When the real-time contour degree Gi does not belong to the corresponding contour judgment interval, the corresponding node is marked as an abnormal node.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention compares and analyzes the real-time contour degree with the contour judgment interval to accurately judge and output abnormal nodes; it realizes refined monitoring of each node in the power grid, accurately judges and outputs abnormal nodes, which is conducive to timely discovering abnormal conditions of local nodes, taking measures for maintenance and repair in advance, and avoiding further deterioration of the problem, thereby realizing comprehensive, real-time and accurate monitoring of the status of each node in the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of the system framework structure of the present invention;

[0040] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0041] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] Example 1

[0043] See also Figure 1-Figure 2 ,This application provides a smart grid monitoring system, including;

[0044] The data acquisition module is used to acquire the voltage corresponding to each node in the power grid multiple times and record the voltage acquired each time. The acquisition number is n, where n is a preset number and n>1;

[0045] By collecting voltage data from various nodes in the power grid multiple times and monitoring it in real time, the comprehensiveness and timeliness of the data are ensured, which provides accurate basic data for subsequent analysis.

[0046] The reference cohesion acquisition module is used to analyze the voltage of each node in the power grid in a preset number of times n, and obtain the reference cohesion corresponding to each node in the power grid according to the analysis results. The specific method is as follows:

[0047] The voltage values corresponding to each node in the preset number n are marked as vin, where i refers to different nodes in the power grid, i is a positive integer, and i>1;

[0048] By formula, Calculate and obtain the benchmark cohesion Li corresponding to each node in the power grid;

[0049] Where vij refers to the voltage value of the i-th node measured at the jth time, i ≥ j ≥ 1, and vpi is the average voltage of all measurements corresponding to each node in the preset number of times n;

[0050] The benchmark cohesion acquisition module and the benchmark separation acquisition module can accurately calculate the benchmark cohesion and benchmark separation of each node. The benchmark cohesion acquisition module and the benchmark separation reflect the similarities and differences between nodes, providing an important reference for fault detection.

[0051] The reference separation degree acquisition module is used to analyze the voltage of each node in the power grid in a preset number of times n, and obtain the reference separation degree corresponding to each node in the power grid according to the analysis results. The specific method is:

[0052] Obtain the average of the absolute values of the differences between the voltage values of each node and other nodes each time in a preset number of times n, and mark them as node deviation values, and analyze the node deviation values corresponding to each node each time in the preset number of times n, thereby obtaining the reference separation corresponding to each node;

[0053] S1: Randomly select a node from all nodes without replacement as the analysis node;

[0054] S2: Randomly select one target time from the preset number n without replacement;

[0055] Obtain the voltage value corresponding to the analysis node in the target time and the average of the absolute values of the differences between the voltage value and other nodes, and use it as the node deviation value C1 corresponding to the analysis node in the target time;

[0056] S3: Repeat the above steps S1-S2 to obtain the node deviation values Cn corresponding to the analysis nodes in the preset number of times n;

[0057] Obtain the discrete value U1 of the node deviation value Cn, analyze and compare the discrete value U1 with the preset threshold value Y1. When the discrete value U1 is less than or equal to the preset threshold value Y1, the mean of the node deviation value Cn is used as the benchmark separation degree D1 corresponding to the analysis node. When the discrete value U1 is greater than the preset threshold value Y1, the mean of the maximum and minimum values of the node deviation value Cn is used as the benchmark separation degree D1 corresponding to the analysis node, that is, D1 = (Cmin + Cmax) / 2, where Cmin is the minimum value in Cn and Cmax is the maximum value in Cn. The specific value of the preset threshold value Y1 is formulated by relevant personnel according to actual needs;

[0058] S4: Repeat the above steps S1-S3 to obtain the reference separation degree Di corresponding to each node in the power grid;

[0059] The benchmark cohesion and benchmark separation of nodes are comprehensively analyzed to generate the comparative contour. This indicator further quantifies the characteristics of nodes in the power grid and helps to identify abnormal nodes more intuitively.

[0060] The contrast profile acquisition module is used to perform a comprehensive analysis of the benchmark cohesion and benchmark separation corresponding to each node in the power grid, and obtain the contrast profile corresponding to each node in the power grid according to the analysis results. The specific method is as follows:

[0061] Obtain the maximum values Lmax and Dmax of the benchmark cohesion Li and benchmark separation Di corresponding to each node respectively;

[0062] By using the formula, Ki=|Li-Di| / |Lmax-Dmax|, the contrast contour Ki corresponding to each node is calculated;

[0063] The contour determination interval acquisition module obtains the domain value corresponding to each node in the power grid according to the comparative contour degree analysis corresponding to each node in the power grid, and generates the contour determination interval corresponding to each node according to the comparative contour degree and domain value analysis corresponding to each node. The specific method is as follows:

[0064] Obtain the values of the contrast profile Ki corresponding to each node in the power grid that meet the preset condition E1 and mark them as Kb, where b is the number of Ki that meet the preset condition E1, i≥b≥1;

[0065] Compare b with the preset value Y2. When b≥Y2, the mean value Kp of Ki is used as the reference profile H. When b<Y2, the mean value of the maximum and minimum values of Ki is used as the reference profile H, that is, H1=(Kmin+Kmax) / 2, where Kmin is the minimum value of Ki and Kmax is the maximum value of Ki.

[0066] The preset condition E1 is: |Ki-Kp|>Y3, Y2 and Y3 are both preset values. The specific values of Y2 and Y3 can be formulated by relevant personnel based on actual needs and application scenarios;

[0067] Obtain the absolute value of the difference between the comparison profile Ki corresponding to each node in the power grid and the reference profile H, and use it as the domain value Zi corresponding to each node in the power grid;

[0068] The difference between the contrast contour degree Ki of each node and its corresponding domain value Zi is used as the lower limit of the contour judgment interval of each node, and the sum of the contrast contour degree Ki of each node and its corresponding domain value Zi is used as the upper limit of the contour judgment interval of each node, thereby obtaining the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node;

[0069] Perform personalized assessments based on the characteristics of different nodes. Each node's profile varies due to factors such as its location in the grid and load conditions. By determining a specific profile determination interval for each node, the assessment of node status becomes more objective and accurate. Obtaining the profile determination interval corresponding to each node is of great significance to smart grid monitoring systems. It can improve monitoring accuracy, enable refined management, and ensure the reliability and stability of the grid. It can better adapt to the actual conditions of different nodes and achieve refined monitoring.

[0070] Example 2

[0071] As the second embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the technical solution of this embodiment differs from that of the first embodiment only in that in this embodiment, the real-time data monitoring module and the abnormal node determination module;

[0072] The real-time data monitoring module is used to obtain the real-time voltage value corresponding to each node in the power grid and analyze it to obtain the real-time profile corresponding to each node. The specific method is as follows:

[0073] Obtain the real-time voltage value Ri corresponding to each node in the power grid;

[0074] By formula, Calculate and obtain the real-time cohesion EAi corresponding to each node in the power grid; where Re is any value in Ri, Rp is the mean value of Ri, i≥e≥1;

[0075] Obtain the average of the absolute values of the differences between the voltage values of each node and the voltage values of other nodes, and use it as the real-time separation degree EBi corresponding to each node in the power grid;

[0076] The real-time cohesion EAi and real-time separation EBi corresponding to each node in the power grid are respectively multiplied by the preset fixed coefficients β1 and β2 as the real-time profile Gi corresponding to each node, where the preset fixed coefficients β1 and β2 are formulated by relevant personnel according to actual needs, satisfying 1=β1+β2 and β1>β2;

[0077] The abnormal node determination module is used to compare and analyze the real-time contour degree corresponding to each node in the power grid with the contour determination interval corresponding to each node, and determine the abnormal node based on the analysis results. The specific method is as follows:

[0078] Compare the real-time contour Gi corresponding to each node in the power grid with the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node. When the real-time contour Gi falls within the corresponding contour judgment interval, no processing is performed. When the real-time contour Gi does not fall within the corresponding contour judgment interval, the corresponding node is marked as an abnormal node and output;

[0079] Based on comparative profile analysis, a profile determination interval is generated for each node. This interval serves as a criterion for determining whether a node is abnormal, improving fault detection accuracy. Real-time profiles are compared and analyzed with the profile determination interval to accurately identify and output abnormal nodes. By identifying abnormal nodes, potential problem nodes can be more accurately detected, allowing proactive maintenance and repair measures to prevent further deterioration. This allows operations and maintenance personnel to promptly identify and address grid faults, preventing them from escalating. Accurate fault detection and real-time early warning capabilities reduce the workload of operations and maintenance personnel, allowing them to promptly identify and address abnormal nodes in the grid, ensuring safe grid operation. This addresses the problem of traditional monitoring systems being unable to promptly detect abnormalities in local nodes, enabling comprehensive, real-time, and accurate monitoring of the status of every node in the grid.

[0080] Example 3

[0081] As the third embodiment of the present invention, when this application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments.

[0082] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0083] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A smart grid monitoring system, characterized in that: include: The data acquisition module collects the voltage corresponding to each node in the power grid at a preset number of times n; The benchmark cohesion acquisition module analyzes the voltage data of each node in the power grid within a preset number of times n to obtain the benchmark cohesion corresponding to each node; The reference separation acquisition module analyzes the voltage data of each node in the power grid within a preset number of times n to obtain the reference separation corresponding to each node; The contrast contour acquisition module comprehensively analyzes the benchmark cohesion and benchmark separation of each node to obtain the contrast contour corresponding to each node; Real-time data monitoring module obtains the real-time voltage value of each node for analysis and obtains the real-time contour corresponding to each node; The contour determination interval acquisition module analyzes the comparative contour degrees corresponding to each node and generates the contour determination interval corresponding to each node; The abnormal node determination module compares and analyzes the real-time contour degree corresponding to each node with its corresponding contour determination interval to determine the generation of abnormal nodes; The specific method for obtaining the benchmark cohesion corresponding to each node is: The voltage values corresponding to each node in the preset number n are marked as vin, where i refers to different nodes in the power grid, i is a positive integer, i>1; through the formula, Calculate and obtain the benchmark cohesion Li corresponding to each node in the power grid; Where vij refers to the voltage value of the i-th node measured at the jth time, i ≥ j ≥ 1, and vpi is the average voltage of all measurements corresponding to each node in the preset number of times n; The specific method of obtaining the benchmark separation corresponding to each node is: S1: Randomly select a node from all nodes without replacement as the analysis node; S2: Randomly select one time from the preset number n without replacement as the target time, obtain the voltage value corresponding to the analysis node in the target time, and the average of the absolute values of the differences between the voltage value and other nodes, and use it as the node deviation value C1 corresponding to the analysis node in the target time; S3: Repeat the above steps S1-S2 to obtain the node deviation values Cn corresponding to the analysis nodes in the preset number of times n, obtain the discrete value U1 of the node deviation value Cn, analyze the discrete value U1 of the node deviation value Cn, and obtain the reference separation corresponding to the analysis node; S4: Repeat the above steps S1-S3 to obtain the reference separation degree Di corresponding to each node in the power grid; The specific method of analyzing the discrete values of the node deviation value and obtaining the benchmark separation corresponding to the analysis node is as follows: When the discrete value U1 is less than or equal to the preset threshold value Y1, the mean of the node deviation values Cn is used as the benchmark separation corresponding to the analysis node; when the discrete value U1 is greater than the preset threshold value Y1, the mean of the maximum and minimum values of the node deviation values Cn is used as the benchmark separation corresponding to the analysis node; The specific method of obtaining the contrast contour corresponding to each node is: The contrast profile Ki corresponding to each node is calculated using the formula Ki = |Li-Di| / |Lmax-Dmax|, where Lmax and Dmax are the maximum values of the reference cohesion Li and reference separation Di, respectively. The specific method of generating the contour determination interval corresponding to each node is: Obtain the value Kb of the comparison profile Ki corresponding to each node in the power grid that meets the preset condition E1, where b is the number of Ki that meets the preset condition E1, i≥b≥1, and compare b with the preset value Y2 to obtain the reference profile H; Obtain the absolute value of the difference between the contrast profile Ki corresponding to each node in the power grid and the reference profile H, and use it as the domain value Zi corresponding to each node in the power grid. The difference between the contrast profile Ki of each node and its corresponding domain value Zi is used as the lower limit value of the contour judgment interval of each node. The sum of the contrast profile Ki of each node and its corresponding domain value Zi is used as the upper limit value of the contour judgment interval of each node, thereby obtaining the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node. The specific method of obtaining the reference profile is: Compare and analyze b with the preset value Y2. When b≥Y2, the mean Kp of Ki is used as the benchmark profile H. When b<Y2, the mean of the maximum and minimum values in Ki is used as the benchmark profile H, and Y2 is the preset value.

2. A smart grid monitoring system according to claim 1, characterized in that: The preset condition E1 is |Ki-Kp|>Y3, Kp is the average value of Ki, and Y3 is a preset value.

3. The smart grid monitoring system according to claim 1, characterized in that: The specific method of obtaining the real-time contour corresponding to each node is: Get the real-time voltage value Ri corresponding to each node in the power grid; through the formula, The real-time cohesion EAi corresponding to each node in the power grid is calculated; wherein Re is any value in Ri, and Rp is the mean of Ri. The mean of the absolute value of the difference between the voltage value of each node and the voltage value of other nodes is obtained, and used as the real-time separation EBi corresponding to each node in the power grid. The sum of the products of the real-time cohesion EAi and the real-time separation EBi corresponding to each node in the power grid and the preset fixed coefficients β1 and β2 is used as the real-time contour Gi corresponding to each node. The preset fixed coefficients β1 and β2 are formulated by relevant personnel according to actual needs, satisfying 1=β1+β2 and β1>β2.

4. The smart grid monitoring system according to claim 1, characterized in that: The specific method for determining the generation of abnormal nodes is as follows: The real-time contour degree Gi corresponding to each node in the power grid is compared with the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node. When the real-time contour degree Gi belongs to the corresponding contour judgment interval, no processing is performed. When the real-time contour degree Gi does not belong to the corresponding contour judgment interval, the corresponding node is marked as an abnormal node.

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