Energy consumption supervision method and system based on thermal power plant equipment

Abstract

The invention discloses an energy consumption supervision method and system based on thermal power plant equipment, belongs to the technical field of equipment energy consumption supervision, and solves the problem that energy consumption conditions of other equipment associated with abnormal equipment are difficult to predict in the prior art. The method comprises the steps of performing digital layout on equipment, forming a complete equipment node network, obtaining energy consumption historical data information of each equipment node in the equipment node network, analyzing energy consumption relevance between the equipment in the equipment node network, performing comprehensive energy consumption real-time supervision on the equipment node network, analyzing equipment energy consumption abnormity reasons, and performing real-time monitoring on energy consumption of the equipment. According to the relevance, the influence value of the energy consumption abnormity of any equipment on other equipment is solved, the energy consumption abnormity condition of other equipment is predicted, and maintenance preparation is made in advance; according to the invention, visual supervision of equipment energy consumption data can be realized, energy consumption abnormal conditions of other equipment can be predicted, maintenance preparation can be made in advance, the working efficiency of energy consumption supervision can be improved, and resource waste can be reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of equipment energy consumption supervision and management, and relates to an energy consumption supervision method and system based on thermal power plant equipment. Background Art

[0002] Thermal power plants generate electricity primarily by burning fuels such as coal, oil or natural gas. The basic production process involves burning the fuel in a boiler, heating water to produce steam, which converts the chemical energy of the fuel into thermal energy. The pressure of this steam drives the turbine to rotate, converting the thermal energy into mechanical energy. The turbine then rotates the generator, which converts the mechanical energy into electrical energy.

[0003] During the operation of a thermal power plant, the equipment in the thermal power plant will consume energy. To ensure that the equipment can operate efficiently and stably, operators need to regularly overhaul abnormal equipment. This not only requires continuous monitoring of the energy consumption of the equipment so that timely action can be taken when energy consumption is abnormal, but also there is still energy waste even when the abnormal equipment is overhauled. Therefore, thermal power plants need a method for monitoring equipment energy consumption that not only overhauls the abnormal energy consumption of a single device, but also takes into account other equipment associated with the abnormal device, so as to more comprehensively optimize energy efficiency and solve existing technical problems.

[0004] For example, the invention patent with application publication number CN115296405A obtains the IP address of the power equipment node, builds a power topology network between the power equipment node and the control terminal, monitors the energy consumption parameters of the power equipment node to be monitored through a preset data collector, determines the energy consumption data, and performs multi-dimensional analysis and processing to determine the energy consumption results. Based on the energy consumption results, the power equipment node is optimized and upgraded. This method achieves the effect of monitoring the energy consumption parameters of the power equipment node and analyzing the energy consumption of the equipment by building a power topology network, but because it does not take into account the correlation between power equipment, it is difficult to predict the energy consumption of other equipment that is associated with the abnormal equipment. Summary of the invention

[0005] The technical solution of the present invention is used to solve the problem that the prior art can only monitor the abnormal energy consumption of thermal power plant equipment, but is difficult to predict the energy consumption of other equipment associated with the abnormal equipment, resulting in energy waste.

[0006] The present invention solves the above technical problems through the following technical solutions:

[0007] A method for monitoring energy consumption based on thermal power plant equipment, the method comprising:

[0008] S100, digitally layout the equipment to form a complete equipment node network;

[0009] S200, obtaining energy consumption historical data information of each device node in the device node network;

[0010] S300, analyzing the correlation of energy consumption between devices in the device node network according to the historical data information of device energy consumption of each node;

[0011] S400, conduct real-time monitoring of comprehensive energy consumption of device node network and analyze the causes of abnormal energy consumption of devices;

[0012] S500. When any device generates abnormal energy consumption, call other devices that are associated with the abnormal device, calculate the impact of the abnormal energy consumption of any device on other devices based on the association, predict the abnormal energy consumption of other devices, and make maintenance preparations in advance.

[0013] Furthermore, the method for digitally laying out the equipment to form a complete equipment node network described in step S100 is: obtaining equipment location information, locating the equipment location information on the established two-dimensional plane model, forming a number of equipment nodes, connecting two adjacent equipment nodes, and obtaining a equipment node network.

[0014] Furthermore, the method for analyzing the correlation of energy consumption between devices in the device node network according to the historical data information of device energy consumption of each node in step S300 is:

[0015] S301, establishing a plane rectangular coordinate system with the horizontal axis representing historical time and the vertical axis representing equipment energy consumption;

[0016] S302, locating the historical data of energy consumption of each node device on the plane rectangular coordinate system;

[0017] S303, fitting the historical data of energy consumption of the located equipment to obtain a fitting function of each node equipment;

[0018] S304, calculating the area of ​​the interval formed between two adjacent functions;

[0019] S305, setting a correlation threshold, if the area of ​​the interval formed between any two adjacent functions is less than or equal to the correlation threshold, it is determined that there is correlation between the two adjacent functions; if the area of ​​the interval formed between any two adjacent functions is greater than the correlation threshold, it is determined that there is no correlation between the two adjacent functions;

[0020] S306, calculating the average distance between two adjacent functions that have correlation;

[0021] S307, calculating the correlation degree between two adjacent functions that have correlation.

[0022] Furthermore, the calculation formula for the area of ​​the interval formed between two adjacent functions in step S304 is as follows:

[0023]

[0024] Where, X i is the area of ​​the interval formed by two adjacent functions, and a and b are the values ​​at both ends of the two adjacent functions; A i is the set of historical energy consumption data of the i-th device, n is the number of historical data of the i-th device; G(A i ) is the fitting function of the ith node device, G(A i+1 ) is the fitting function of the i+1th node device.

[0025] Furthermore, the calculation formula of the average distance between two adjacent functions with correlation in step S306 is as follows:

[0026]

[0027] In the formula, Yi is the adjacent function G(A i ) and G(A i+1 ) is the average distance between .

[0028] Furthermore, the calculation formula of the correlation degree between two adjacent functions having correlation in step S307 is as follows:

[0029]

[0030] In the formula, is the association degree between the i-th device and the i+1 device in the i-th device node network, α0 is the association weight coefficient, α1 is the average distance weight coefficient, and α2 is the error factor.

[0031] Furthermore, the method of performing real-time monitoring of comprehensive energy consumption of the device node network in step S400 and analyzing the cause of abnormal device energy consumption according to the monitoring results is as follows:

[0032] S401, monitoring the energy consumption of each device in the device node network to obtain a total device energy consumption data set;

[0033] S402, retrieving a local equipment energy consumption data set associated with an energy consumption abnormality cause analysis device;

[0034] S403, calculating the average difference between the abnormal value of the energy consumption of the equipment and the normal value of the energy consumption of the equipment;

[0035] S404. Set an abnormal threshold. If the average difference between the abnormal energy consumption value of any device and the normal energy consumption value of the device is greater than or equal to the abnormal threshold, it is determined that the abnormal energy consumption of other devices causes the abnormal energy consumption of the device; if the average difference between the abnormal energy consumption value of any device and the normal energy consumption value of the device is less than the abnormal threshold, it is determined that the abnormal energy consumption is caused by internal reasons of the device.

[0036] Furthermore, the calculation formula of the average difference between the abnormal value of the equipment energy consumption and the normal value of the equipment energy consumption in step S403 is as follows:

[0037]

[0038] Where M is the average difference between abnormal and normal energy consumption values ​​of equipment, b j is the energy consumption data of the jth device, b' j is the energy consumption data of the device associated with the j-th device, and k is the total number of devices associated with the j-th device.

[0039] The present invention also provides an energy consumption monitoring system based on thermal power plant equipment, comprising: a digital layout module, a historical data acquisition module, a correlation analysis module, an abnormal energy consumption analysis module, and an abnormal energy consumption prediction module;

[0040] The digital layout module is used to digitally layout the equipment to form a complete equipment node network;

[0041] The historical data acquisition module is used to acquire energy consumption historical data information of each device node in the device node network;

[0042] The correlation analysis module is used to analyze the correlation of energy consumption between devices in the device node network according to the historical data information of device energy consumption of each node;

[0043] The abnormal energy consumption analysis module is used to monitor the comprehensive energy consumption of the device node network in real time and analyze the causes of abnormal energy consumption of the device;

[0044] The energy consumption abnormality prediction module is used to call other devices associated with the abnormal device when any device generates abnormal energy consumption, solve the impact value of the energy consumption abnormality of any device on other devices based on the correlation, predict the energy consumption abnormality of other devices, and make maintenance preparations in advance.

[0045] Furthermore, the digital layout module specifically includes: obtaining device location information, positioning the device location information on the established two-dimensional plane model, forming a plurality of device nodes, connecting two adjacent device nodes, and obtaining a device node network.

[0046] The advantages of the present invention are as follows: the present invention realizes visual supervision of equipment energy consumption data by constructing an equipment node network; constructs a fitting function on a plane rectangular coordinate system according to the historical energy consumption data of two adjacent equipment nodes, calculates the correlation between the two adjacent equipment nodes in combination with the area and average distance formed between the two adjacent fitting functions, digitally analyzes the correlation between the energy consumption of the two adjacent equipment nodes, analyzes the cause of the equipment abnormality when equipment energy consumption abnormality occurs, solves the impact value of the energy consumption abnormality of any equipment on other equipment according to the correlation, predicts the energy consumption abnormality of other equipment, makes maintenance preparations in advance, improves the work efficiency of energy consumption supervision, and reduces resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of an energy consumption monitoring method based on thermal power plant equipment according to an embodiment of the present invention;

[0048] Figure 2 is a schematic diagram of a device node network of an energy consumption monitoring method based on thermal power plant equipment according to an embodiment of the present invention;

[0049] Figure 3 It is a structural diagram of an energy consumption monitoring system based on thermal power plant equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not 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.

[0051] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0052] Embodiment 1

[0053] like Figure 1 As shown, specifically, a method for monitoring energy consumption based on thermal power plant equipment is disclosed, comprising:

[0054] S100, digitally layout the equipment to form a complete equipment node network;

[0055] Specifically, by acquiring device location information, the device location information is positioned on the established two-dimensional model to form a number of device nodes, and two adjacent device nodes are connected to obtain a device node network.

[0056] like Figure 2As shown, in this embodiment, a device node network is established and the device nodes are labeled to achieve visual management of each device node. The connection relationship between each device node can be intuitively displayed on the device node network.

[0057] S200, obtaining energy consumption historical data information of each device node in the device node network;

[0058] Specifically, obtain the historical data of energy consumption of each device node in the device node network Among them, A i It represents the set of historical energy consumption data of the ith device, and n represents that the ith device has n historical data. By obtaining the historical energy consumption data information in the device node network, the energy consumption correlation of each device node is calculated to facilitate the analysis of the energy consumption impact relationship between the device nodes and identify the device nodes that need to be repaired.

[0059] S300: Analyze the correlation of energy consumption between devices in the device node network according to the historical data information of device energy consumption of each node.

[0060] Specifically, step S300 includes:

[0061] S301, establishing a plane rectangular coordinate system with the horizontal axis representing historical time and the vertical axis representing equipment energy consumption.

[0062] S302: Position the historical data of energy consumption of each node device on the plane rectangular coordinate system.

[0063] S303, fitting the historical data of energy consumption of the located equipment to obtain a fitting function of each node equipment;

[0064] Specifically, the energy consumption historical data information of each device node on the node network is used as multiple groups of data points, and the energy consumption historical data of each device node is represented by a group of data points and fitted into a group of functions by function fitting to obtain the fitting function G(A i ), fitting function G(A i ) is the fitting function of the ith node device; different from the manual judgment method, the energy consumption data is transformed to make the energy consumption device data visualized.

[0065] S304, calculating the area of ​​the interval formed between two adjacent functions;

[0066] Specifically, the calculation formula for the area of ​​the interval formed between two adjacent functions in step S304 is as follows:

[0067]

[0068] Where, X iis the area of ​​the interval formed by two adjacent functions, and a and b are the values ​​at both ends of the two adjacent functions; A i is the set of historical energy consumption data of the i-th device, n is the number of historical data of the i-th device; G(A i ) is the fitting function of the ith node device, G(A i+1 ) is the fitting function of the i+1th node device.

[0069] S305, setting a correlation threshold, if the area of ​​the interval formed between any two adjacent functions is less than or equal to the correlation threshold, it is determined that there is correlation between the two adjacent functions; if the area of ​​the interval formed between any two adjacent functions is greater than the correlation threshold, it is determined that there is no correlation between the two adjacent functions;

[0070] Set the correlation threshold x. In this embodiment, the relationship between two adjacent functions is expressed by solving the integral of the area formed by the two fitting functions to the present. When Xi≤x, it is determined that the two adjacent functions G(A i ) and G(A i+1 ) is related to each other, X i The smaller it is, the higher the correlation between the two adjacent functions. When the functions between the two almost completely overlap, it can be judged that the correlation between the fitting functions of the two energy-consuming devices is extremely high; when X i >x, determine two adjacent functions G(A i ) and G(A i+1 ) there is no correlation between them.

[0071] S306, calculating the average distance between two adjacent functions that have correlation;

[0072] Specifically, the calculation formula of the average distance between two adjacent functions with correlation in step S306 is as follows:

[0073]

[0074] In the formula, Yi is the adjacent function G(A i ) and G(A i+1 ) is the average distance between .

[0075] S307, calculating the correlation degree between two adjacent functions that have correlation;

[0076] Specifically, the calculation formula for the correlation between two adjacent functions that are correlated is as follows:

[0077]

[0078] In the formula, is the association degree between the i-th device and the i+1 device in the i-th device node network, α0 is the association weight coefficient, α1 is the average distance weight coefficient, and α2 is the error factor.

[0079] Calculating the average distance between two adjacent functions with correlation is used to determine whether the fitting data of the energy consumption data of two adjacent device nodes are close to each other. When the fitting data of two adjacent device nodes are close to each other, the influence of the energy consumption device data of the two adjacent device nodes can be solved by combining the average distance of the fitting function and the interval area.

[0080] S400, conduct real-time monitoring of comprehensive energy consumption of device node network and analyze causes of abnormal energy consumption of devices.

[0081] Specifically, step S400 includes:

[0082] S401, monitoring the energy consumption of each device in the device node network to obtain a total device energy consumption data set;

[0083] Specifically, the total equipment energy consumption data set B = {b1, b2, b3, ..., b m}, m means there are m devices in the device node network, b m Represents the energy consumption data of the mth device.

[0084] S402, retrieving a local equipment energy consumption data set associated with an energy consumption abnormality cause analysis device;

[0085] Specifically, the local device energy consumption data set B' = {b'1, b'2, b'3, ..., b' k}, k≤m.

[0086] S403, calculating the average difference between the abnormal value of the energy consumption of the equipment and the normal value of the energy consumption of the equipment;

[0087] Specifically, the calculation formula of the average difference between the abnormal value of the equipment energy consumption and the normal value of the equipment energy consumption in step S403 is as follows:

[0088]

[0089] Where M is the average difference between abnormal and normal energy consumption values ​​of equipment, b j is the energy consumption data of the jth device, b' j is the energy consumption data of the device associated with the j-th device, and k is the total number of devices associated with the j-th device.

[0090] S404. Set an abnormal threshold. If the average difference between the abnormal energy consumption value of any device and the normal energy consumption value of the device is greater than or equal to the abnormal threshold, it is determined that the abnormal energy consumption of other devices causes the abnormal energy consumption of the device; if the average difference between the abnormal energy consumption value of any device and the normal energy consumption value of the device is less than the abnormal threshold, it is determined that the abnormal energy consumption is caused by internal reasons of the device.

[0091] An abnormal threshold c is set. For the average difference M between the abnormal energy consumption value and the normal energy consumption value of any device, when M≥c, the energy consumption value of the associated device is significantly different from the normal energy consumption value, which will have an impact on the associated device. Therefore, the judgment result is that the abnormal energy consumption of the device is caused by the abnormal energy consumption of other devices; when M<c, the judgment result is that the abnormal energy consumption is caused by internal reasons of the device.

[0092] S500. When any device generates abnormal energy consumption, call other devices that are associated with the abnormal device, calculate the impact of the abnormal energy consumption of any device on other devices based on the association, predict the abnormal energy consumption of other devices, and make maintenance preparations in advance.

[0093] Embodiment 2

[0094] This embodiment discloses an energy consumption monitoring system based on thermal power plant equipment, including: a digital layout module, a historical data acquisition module, a correlation analysis module, an abnormal energy consumption analysis module, and an abnormal energy consumption prediction module;

[0095] The digital layout module is used to digitally layout the equipment to form a complete equipment node network;

[0096] The historical data acquisition module is used to acquire energy consumption historical data information of each device node in the device node network;

[0097] The correlation analysis module is used to analyze the correlation of energy consumption between devices in the device node network according to the historical data information of device energy consumption of each node;

[0098] The abnormal energy consumption analysis module is used to monitor the comprehensive energy consumption of the device node network in real time and analyze the causes of abnormal energy consumption of the device;

[0099] The energy consumption abnormality prediction module is used to call other devices associated with the abnormal device when any device generates abnormal energy consumption, solve the impact value of the energy consumption abnormality of any device on other devices based on the correlation, predict the energy consumption abnormality of other devices, and make maintenance preparations in advance.

[0100] Specifically, the specific process of the digital layout module is: obtaining device location information, positioning the device location information on the established two-dimensional plane model, forming a number of device nodes, connecting two adjacent device nodes, and obtaining a device node network.

[0101] Specifically, the correlation analysis module includes: a coordinate system establishment unit, a historical data positioning unit, a relationship function fitting unit, an interval area solving unit, a correlation judgment unit, an average distance solving unit, and a correlation calculation unit;

[0102] The coordinate system establishment unit is used to establish a plane rectangular coordinate system with the horizontal axis being the historical time and the vertical axis being the energy consumption of the equipment;

[0103] The historical data positioning unit is used to position the historical data of energy consumption of each node device on the plane rectangular coordinate system;

[0104] The relationship function fitting unit is used to fit the historical data of the energy consumption of the located equipment to obtain the fitting function of each node equipment;

[0105] The interval area solving unit is used to calculate the area of ​​the interval formed between two adjacent functions;

[0106] The correlation judgment unit is used to set a correlation threshold. If the area of ​​the interval formed between any two adjacent functions is less than or equal to the correlation threshold, it is determined that there is a correlation between the two adjacent functions; if the area of ​​the interval formed between any two adjacent functions is greater than the correlation threshold, it is determined that there is no correlation between the two adjacent functions.

[0107] The average distance solving unit is used to calculate the average distance between two adjacent functions that have correlation;

[0108] The correlation degree calculation unit is used to calculate the correlation degree between two adjacent functions that have correlation.

[0109] More specifically, the calculation formula for the area of ​​the interval formed between two adjacent functions in the interval area solving unit is as follows:

[0110]

[0111] Where, X i is the area of ​​the interval formed by two adjacent functions, and a and b are the values ​​at both ends of the two adjacent functions; A i is the set of historical energy consumption data of the i-th device, n is the number of historical data of the i-th device; G(A i ) is the fitting function of the ith node device, G(A i+1 ) is the fitting function of the i+1th node device.

[0112] More specifically, the calculation formula of the average distance between two adjacent functions with correlation in the average distance solving unit is as follows:

[0113]

[0114] In the formula, Yi is the adjacent function G(A i ) and G(A i+1 ) is the average distance between .

[0115] More specifically, the calculation formula of the correlation between two adjacent functions having correlation in the correlation calculation unit is as follows:

[0116]

[0117] In the formula, is the association degree between the i-th device and the i+1 device in the i-th device node network, α0 is the association weight coefficient, α1 is the average distance weight coefficient, and α2 is the error factor.

[0118] Specifically, the correlation analysis module includes: an equipment energy consumption monitoring unit, a correlation energy consumption retrieval unit, an average difference solving unit, and an abnormal cause judgment unit;

[0119] The device energy consumption monitoring unit is used to monitor the energy consumption of each device in the device node network to obtain a total device energy consumption data set;

[0120] The associated energy consumption retrieval unit is used to retrieve a local device energy consumption data set associated with the energy consumption abnormality cause analysis device;

[0121] The average difference solving unit is used to calculate the average difference between the abnormal value of equipment energy consumption and the normal value of equipment energy consumption;

[0122] The abnormal cause judgment unit is used to set an abnormal threshold. If the average difference between the abnormal energy consumption value of any device and the normal energy consumption value of the device is greater than or equal to the abnormal threshold, the judgment result is that the abnormal energy consumption of other devices causes the abnormal energy consumption of the device; if the average difference between the abnormal energy consumption value of any device and the normal energy consumption value of the device is less than the abnormal threshold, the judgment result is that the energy consumption abnormality of the device is caused by internal reasons of the device.

[0123] More specifically, the calculation formula of the average difference between the abnormal value of the equipment energy consumption and the normal value of the equipment energy consumption in the average difference solving unit is as follows:

[0124]

[0125] Where M is the average difference between abnormal and normal energy consumption values ​​of equipment, b jis the energy consumption data of the jth device, b' j is the energy consumption data of the device associated with the j-th device, and k is the total number of devices associated with the j-th device.

[0126] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring energy consumption based on thermal power plant equipment, characterized in that: The method comprises: S100, digitally layout the equipment to form a complete equipment node network; S200, obtaining energy consumption historical data information of each device node in the device node network; S300, analyzing the correlation of energy consumption between devices in the device node network according to the historical data information of device energy consumption of each node; S400, conduct real-time monitoring of comprehensive energy consumption of device node network and analyze the causes of abnormal energy consumption of devices; S500. When any device generates abnormal energy consumption, call other devices that are associated with the abnormal device, calculate the impact of the abnormal energy consumption of any device on other devices based on the association, predict the abnormal energy consumption of other devices, and make maintenance preparations in advance.

2. The energy consumption monitoring method based on thermal power plant equipment according to claim 1 is characterized in that: The method for digitally laying out the equipment to form a complete equipment node network described in step S100 is: obtaining equipment location information, locating the equipment location information on the established two-dimensional plane model, forming a number of equipment nodes, connecting two adjacent equipment nodes, and obtaining a equipment node network.

3. The energy consumption monitoring method based on thermal power plant equipment according to claim 1 is characterized in that: The method for analyzing the correlation of energy consumption between devices in the device node network according to the historical data information of device energy consumption of each node in step S300 is: S301, establishing a plane rectangular coordinate system with the horizontal axis representing historical time and the vertical axis representing equipment energy consumption; S302, locating the historical data of energy consumption of each node device on the plane rectangular coordinate system; S303, fitting the historical data of energy consumption of the located equipment to obtain a fitting function of each node equipment; S304, calculating the area of ​​the interval formed between two adjacent functions; S305, setting a correlation threshold, if the area of ​​the interval formed between any two adjacent functions is less than or equal to the correlation threshold, it is determined that there is correlation between the two adjacent functions; if the area of ​​the interval formed between any two adjacent functions is greater than the correlation threshold, it is determined that there is no correlation between the two adjacent functions; S306, calculating the average distance between two adjacent functions that have correlation; S307, calculating the correlation degree between two adjacent functions that have correlation.

4. The energy consumption monitoring method based on thermal power plant equipment according to claim 3 is characterized in that: The calculation formula for the area of ​​the interval formed between two adjacent functions in step S304 is as follows: In the formula, X i is the area of ​​the interval formed by two adjacent functions, and a and b are the values ​​at both ends of the two adjacent functions; A i is the set of historical energy consumption data of the i-th device, n is the number of historical data of the i-th device; G(A i ) is the fitting function of the ith node device, G(A i+1 ) is the fitting function of the i+1th node device.

5. The energy consumption monitoring method based on thermal power plant equipment according to claim 4 is characterized in that: The calculation formula of the average distance between two adjacent functions with correlation in step S306 is as follows: In the formula, Yi is the adjacent function G(A i ) and G(A i+1 ) is the average distance between .

6. The method for monitoring energy consumption based on thermal power plant equipment according to claim 5, characterized in that: The calculation formula of the correlation degree between two adjacent functions having correlation in step S307 is as follows: In the formula, is the association degree between the i-th device and the i+1 device in the i-th device node network, α0 is the association weight coefficient, α1 is the average distance weight coefficient, and α2 is the error factor.

7. The method for monitoring energy consumption based on thermal power plant equipment according to claim 1, characterized in that: The method of performing real-time monitoring of comprehensive energy consumption of the device node network in step S400 and analyzing the cause of abnormal device energy consumption according to the monitoring results is as follows: S401, monitoring the energy consumption of each device in the device node network to obtain a total device energy consumption data set; S402, retrieving a local equipment energy consumption data set associated with an energy consumption abnormality cause analysis device; S403, calculating the average difference between the abnormal value of the energy consumption of the equipment and the normal value of the energy consumption of the equipment; S404, setting an abnormal threshold, if the average difference between the abnormal energy consumption value of any device and the normal energy consumption value of the device is greater than or equal to the abnormal threshold, the result of the determination is that the abnormal energy consumption of other devices causes the abnormal energy consumption of the device; If the average difference between the abnormal energy consumption value of any device and the normal energy consumption value of the device is less than the abnormal threshold, the determination result is that the energy consumption abnormality is caused by internal reasons of the device.

8. The method for monitoring energy consumption based on thermal power plant equipment according to claim 7, characterized in that: The calculation formula of the average difference between the abnormal value of the equipment energy consumption and the normal value of the equipment energy consumption in step S403 is as follows: Where M is the average difference between abnormal and normal energy consumption values ​​of equipment, b j is the energy consumption data of the jth device, b' j is the energy consumption data of the device associated with the j-th device, and k is the total number of devices associated with the j-th device.

9. An energy consumption monitoring system based on thermal power plant equipment, characterized in that: include: Digital layout module, historical data acquisition module, correlation analysis module, abnormal energy consumption analysis module, and abnormal energy consumption prediction module; The digital layout module is used to digitally layout the equipment to form a complete equipment node network; The historical data acquisition module is used to acquire energy consumption historical data information of each device node in the device node network; The correlation analysis module is used to analyze the correlation of energy consumption between devices in the device node network according to the historical data information of device energy consumption of each node; The abnormal energy consumption analysis module is used to monitor the comprehensive energy consumption of the device node network in real time and analyze the causes of abnormal energy consumption of the device; The energy consumption abnormality prediction module is used to call other devices associated with the abnormal device when any device generates abnormal energy consumption, solve the impact value of the energy consumption abnormality of any device on other devices based on the correlation, predict the energy consumption abnormality of other devices, and make maintenance preparations in advance.

10. The method for monitoring energy consumption based on thermal power plant equipment according to claim 9, characterized in that: The digital layout module specifically includes: obtaining device location information, positioning the device location information on the established two-dimensional plane model, forming a plurality of device nodes, connecting two adjacent device nodes, and obtaining a device node network.

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