CN115438913A - Power grid pollution degree evaluation method, system and equipment based on environmental big data analysis - Google Patents
Power grid pollution degree evaluation method, system and equipment based on environmental big data analysis Download PDFInfo
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Abstract
The application provides a power grid pollution degree evaluation method, system and device based on environmental big data analysis. The method comprises the steps of carrying out grid division on a power grid area to be monitored, correcting the AQI values of all nodes in a grid through an environmental data prediction model and a waste gas point source pollution diffusion model, establishing a dynamic relation between the AQI values and the pollutant accumulation rate, inputting the AQI correction values of any place into a pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the pollutant accumulation rate of any place, predicting the equivalent salt deposit density through the pollutant accumulation rate, and evaluating the current and future pollution degree of the power grid. The method and the device solve the problems that the existing sewage area is unreasonably divided, and the reaction of the sewage accumulation condition is delayed.
Description
Technical Field
The application relates to the technical field of pollution flashover prevention of a power grid, in particular to a power grid pollution degree assessment method, system and device based on environmental big data analysis.
Background
With the enlargement of the scale of a power grid, the pollution accumulation of partial towers in a heavy pollution area is serious, and the pollution flashover risk of a line is increased sharply. Due to geographical reasons, rainfall days are more in southern areas, the relative humidity of the environment is high, and the problem of industrial emission in local areas is serious, so that the surface pollution deposition of the insulator of the power transmission line is more serious, the electrical insulation problem and pollution flashover discharge accidents can be caused, and the safe and stable operation of a power grid is seriously threatened.
At present, the polluted area graph is divided mainly by measuring the pollution degree of a tower insulator in China, however, the method has limitations, firstly, the atmospheric condition changes are complex, the relation between the actual atmospheric pollution condition and the pollution of the line cannot be considered when an anti-pollution flashover treatment strategy is established, and the establishment of the anti-pollution flashover strategy of the line has certain blindness; secondly, the existing pollution area graph is made based on historical insulator pollution measurement data and operation experience, cannot reflect the current pollution accumulation condition, and has larger hysteresis.
Therefore, the application provides a power grid pollution degree evaluation method, system and device based on environmental big data analysis.
Disclosure of Invention
The embodiment of the application aims to provide a power grid pollution degree evaluation method, system and device based on environmental big data analysis, so as to solve the problems that at present, pollution areas are unreasonably divided, and reaction of pollution accumulation conditions is delayed. The specific technical scheme is as follows: in a first aspect, a power grid pollution degree assessment method based on environmental big data analysis is provided, and the method includes:
taking projection of a pollution point source on the ground as a coordinate origin, and taking a main wind direction as an X-axis forward direction to establish a wind axis coordinate system;
carrying out grid division on a power grid area to be monitored based on the wind axis coordinate system, and obtaining coordinates of each grid node;
inputting the coordinates of each grid node into a pre-constructed environment data prediction model to obtain simulated environment data of each grid node;
calculating a first AQI (Air Quality Index) value based on the simulated environment data;
inputting the grid node coordinates into an exhaust gas point source pollution diffusion model to obtain a second AQI value;
correcting according to the first AQI value and the second AQI value to obtain an AQI correction value of each grid node;
carrying out interpolation calculation on the AQI correction values of all grid nodes by using an interpolation method to obtain the AQI correction values of any place in a power grid area to be monitored;
and inputting the AQI corrected value of the arbitrary place into a pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the pollution rate of the arbitrary place.
Optionally, the construction process of the environmental data prediction model includes the following steps:
acquiring air quality data, meteorological data and longitude and latitude coordinate data of monitoring points of a power grid area to be monitored;
fusing the air quality data and the meteorological data to obtain an environment characteristic value data set;
and training an environment data prediction model through the environment characteristic value data set and the longitude and latitude coordinate data of the monitoring points, so that the environment data prediction model outputs the environment data of any place.
Optionally, the fusing the air quality data and the meteorological data to obtain the environment characteristic value data set includes: respectively extracting air quality data characteristics in the air quality data and meteorological data condition characteristics in the meteorological data; the air quality data characteristics include PM2.5, PM10, SO 2 、CO、NO 2 And O 3 Concentration of solid contaminants, said weather data stripThe characteristics of the device comprise wind direction, wind speed, rainfall and humiture;
adding the meteorological data condition characteristics into air quality data characteristics to obtain an air quality-meteorological condition characteristic data set; and carrying out data cleaning pretreatment on the air quality-meteorological condition characteristic data set to obtain an environment characteristic value data set.
Optionally, the training of the environmental data prediction model through the environmental characteristic value data set and the longitude and latitude coordinate data of the monitoring point includes:
converting the longitude and latitude coordinates of the monitoring points and the longitude and latitude coordinates of any target location to obtain plane rectangular coordinates of the monitoring points and plane rectangular coordinates of any target location;
and based on the plane rectangular coordinates of the monitoring points and the environment characteristic value data sets of the monitoring points, performing interpolation operation by using an inverse distance weighting interpolation method to obtain environment simulation data of the plane rectangular coordinates of any target site.
Optionally, the inputting the grid node coordinates into the exhaust gas point source pollution diffusion model to obtain a second AQI value includes:
inputting the grid node coordinates into the following formula to obtain the concentration of any pollutant
(ii) a Adding the concentrations of the pollutants to obtain a second AQI value; wherein, the first and the second end of the pipe are connected with each other,
concentration of contaminants at any point in C-space, mg/m 3 ；
μ -mean wind speed, m/s;
δ y — lateral diffusion parameter, m;
δ z-vertical diffusion parameter, m;
x, y, z-space coordinates;
q-the discharge amount of each atmospheric pollutant in unit time, mg/s;
h-the effective height of the pollution point source, m;
x is the distance from the discharge point of the pollution source to any point in the downwind direction, m;
y is the distance m from the central axis of the flue gas to any point in the right-angle horizontal direction;
z-height from the surface to any point, m.
Optionally, the inputting the AQI correction value of the arbitrary location into a pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the fouling rate of the arbitrary location includes:
inputting the AQI corrected value of any place into the following formula to obtain the fouling rate of any place:
τ k ＝m·AQI n
wherein A is saturated equivalent salt deposit density, and m and n are constants.
Optionally, the saturated equivalent salt deposit density a, the constants m and n are obtained by fitting through a simulated annealing algorithm.
In a second aspect, the present application provides a power grid pollution degree evaluation system based on environmental big data analysis, the system includes:
the system comprises a coordinate establishing unit, a data processing unit and a data processing unit, wherein the coordinate establishing unit is used for establishing a wind axis coordinate system by taking the projection of a pollution point source on the ground as a coordinate origin and taking the main wind direction as the X axis forward direction;
the grid division unit is used for carrying out grid division on the power grid area to be monitored based on the wind axis coordinate system and obtaining the coordinates of each grid node;
the prediction unit is used for inputting the coordinates of each grid node into a pre-constructed environment data prediction model to obtain simulated environment data of each grid node;
the calculating unit is used for calculating a first AQI value based on the simulated environment data;
the model calculation unit is used for inputting the grid node coordinates into an exhaust gas point source pollution diffusion model to obtain a second AQI value; the correcting unit is used for correcting according to the first AQI value and the second AQI value to obtain an AQI correction value of each grid node;
the interpolation unit is used for carrying out interpolation calculation on the AQI correction value of each grid node by using an interpolation method to obtain the AQI correction value of any place in the power grid area to be monitored;
and the fouling rate calculation unit is used for inputting the AQI correction value of any place into a pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the fouling rate of any place.
In a third aspect, the present application provides an electronic device, including a processor, a communication interface, a memory and a communication bus, where the processor, the communication interface, and the memory complete mutual communication through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of the first aspect when executing a program stored in the memory.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method steps of any of the first aspects.
In a fifth aspect, a computer program product containing instructions is provided, which when run on a computer causes the computer to execute any one of the above described methods for grid pollution assessment based on environmental big data analysis.
The embodiment of the application has the following beneficial effects:
the embodiment of the application provides a power grid pollution degree evaluation method, system and equipment based on environmental big data analysis, and the method dynamically corrects an AQI value of a power grid area by coupling air quality, meteorological data, pollution points and a pollution diffusion principle, and provides a more visual basis for anti-pollution flashover work of power equipment; in addition, the method and the device disclose the equivalent salt deposit density dynamic accumulation rule of the insulator by establishing the relation between the pollution accumulation rate and the AQI value. And the region of the power grid to be monitored is divided into equal-area networks, the future equivalent salt deposit density in each network is predicted and counted to be used as an index for evaluating the pollution accumulation degree of the insulator, and then pollution flashover prevention work such as pollution area division, insulator cleaning plan making and the like can be guided. The method and the device solve the problems that the existing sewage area is unreasonably divided, and the reaction of the sewage accumulation condition is delayed.
Of course, it is not necessary for any product or method of the present application to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a power grid pollution degree evaluation method based on environmental big data analysis according to an embodiment of the present application;
fig. 2 is a schematic diagram of dividing a tower grid of a power grid of more than 110kV provided in the embodiment of the present application;
FIG. 3 is a graphical representation of the air quality values AQI of the grid of FIG. 2 as provided by an embodiment of the present application;
fig. 4 is a comparison graph of the prediction result and the measured data of the salt deposit density monitoring point provided in the embodiment of the present application;
fig. 5 is a schematic structural diagram of a power grid pollution degree evaluation system based on environmental big data analysis according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The embodiment of the present application provides a power grid pollution degree evaluation method based on environmental big data analysis, and the power grid pollution degree evaluation method based on environmental big data analysis provided by the embodiment of the present application is described in detail below with reference to specific implementation manners, and as shown in fig. 1, the specific steps are as follows:
step S101: a wind axis coordinate system is established by taking the projection of a pollution point source on the ground as a coordinate origin and taking the main wind direction as the X axis forward direction.
In this step, the origin and the X-axis are determined, and the Y-axis and the Z-axis are defined using the right-hand rule. By establishing the wind axis coordinate system, the influence of data such as a pollution source and a wind direction on the tower pollution is taken into consideration, so that the evaluation result is more accurate.
Step S102: and carrying out grid division on the power grid area to be monitored based on the wind axis coordinate system, and obtaining the coordinates of each grid node.
In one example, the size of the grid to be divided may be determined according to the distribution density of the pollution sources in the area of the power grid to be monitored, for example, the area near the pollution source may be divided into denser grids, and the area farther away may be divided into less dense grids.
In addition, in the embodiment of the present application, the coordinates of the mesh nodes include coordinates of four vertices of the mesh, and the coordinates of the four vertices of each mesh can be obtained according to the known mesh scale.
Step S103: and inputting the coordinates of each grid node into a pre-constructed environmental data prediction model to obtain simulated environmental data of each grid node.
In this step, the simulated environment data of the grid node refers to the average of the environment data at the four vertices of the grid.
Optionally, the construction process of the environmental data prediction model includes the following steps:
the method comprises the steps of obtaining air quality data, meteorological data and longitude and latitude coordinate data of monitoring points of a power grid area to be monitored.
The air quality data is formed by collecting and summarizing data collected by each air quality monitoring station over the years, the data is two-dimensional data, and the air quality value of each day is taken as the average value of each hour. The meteorological data are formed by collecting and summarizing data collected by each meteorological monitoring point over the years and are two-dimensional data.
The longitude and latitude coordinate data of the monitoring points refer to the coordinate data of the air quality monitoring station and the meteorological monitoring points.
And fusing the air quality data and the meteorological data to obtain an environment characteristic value data set.
Optionally, the fusing the air quality data and the meteorological data to obtain the environment characteristic value data set includes: respectively extracting air quality data characteristics in the air quality data and meteorological data condition characteristics in the meteorological data; the air quality data characteristics comprise PM2.5, PM10, SO2, CO, NO2 and O3 entity pollutant concentrations, and the meteorological data condition characteristics comprise wind direction, wind speed, rainfall and temperature and humidity;
adding the meteorological data condition characteristics into air quality data characteristics to obtain an air quality-meteorological condition characteristic data set; and carrying out data cleaning pretreatment on the air quality-meteorological condition characteristic data set to obtain an environment characteristic value data set.
In one example, the process of data cleansing pre-processing is, for example, to replace missing observations on any day with the mean of the nearest neighbor 3 observations.
And training an environment data prediction model through the environment characteristic value data set and the longitude and latitude coordinate data of the monitoring points, so that the environment data prediction model outputs the environment data of any place.
Optionally, the training of the environmental data prediction model through the environmental characteristic value data set and the longitude and latitude coordinate data of the monitoring point includes:
converting the longitude and latitude coordinates of the monitoring points and the longitude and latitude coordinates of any target location to obtain plane rectangular coordinates of the monitoring points and plane rectangular coordinates of any target location;
and based on the plane rectangular coordinates of the monitoring points and the environment characteristic value data sets of the monitoring points, performing interpolation operation by using an inverse distance weighted interpolation method to obtain environment simulation data of the plane rectangular coordinates of any target location.
In one example, the interpolation using inverse distance weighted interpolation may be:
and taking any target site as a point to be interpolated, calculating the distance d from each monitoring point to the point to be interpolated, calculating the distance weighting coefficient of each monitoring point according to the distance d, and calculating the environment characteristic value of the point to be interpolated according to the environment characteristic value and the distance weighting coefficient of each monitoring point.
Step S104: a first AQI value is calculated based on the simulated environmental data.
The simulated environmental data includes the concentration of each data, and the concentrations of each data are added to obtain a first AQI value.
Step S105: and inputting the grid node coordinates into an exhaust gas point source pollution diffusion model to obtain a second AQI value.
Optionally, the inputting the grid node coordinates into the exhaust gas point source pollution diffusion model to obtain a second AQI value comprises:
inputting the grid node coordinates into the following formula to obtain the concentration of any pollutant
Adding the concentrations of the pollutants to obtain a second AQI value; wherein, the first and the second end of the pipe are connected with each other,
concentration of contaminants at any point in C-space, mg/m 3 ；
μ -mean wind speed, m/s;
δ y — lateral diffusion parameter, m;
δ z-vertical diffusion parameter, m;
x, y, z-space coordinates;
q-the discharge amount of each atmospheric pollutant in unit time, mg/s;
h-the effective height of the pollution point source, m;
x is the distance m from the discharge point of the pollution source to any point in the upwind direction;
y is the distance m from the central axis of the flue gas to any point in the right-angle horizontal direction;
z-height from the surface to any point, m.
Step S106: and correcting according to the first AQI value and the second AQI value to obtain an AQI correction value of each grid node.
Step S107: and carrying out interpolation calculation on the AQI correction values of all grid nodes by using an interpolation method to obtain the AQI correction values of any place in the power grid area to be monitored.
In this step, an inverse distance weighted interpolation method may also be used for interpolation calculation, and the method may refer to the above interpolation process, which is not described herein again.
Step S108: and inputting the AQI corrected value of the arbitrary place into a pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the pollution rate of the arbitrary place.
In the embodiment of the application, the pollution degree of the next day can be predicted through the pollution accumulation rate and the pollution degree value of the current day, and the pollution degree is expressed through equivalent salt deposit density and is used for evaluating the pollution degree of the insulator on the tower.
And obtaining a pollution degree evaluation parameter of the insulator according to the steps, and formulating an anti-pollution flashover measure based on the pollution degree of the insulator based on the evaluation parameter and the operating insulator ledger.
Optionally, the inputting the AQI correction value of the arbitrary location into a pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the fouling rate of the arbitrary location includes:
inputting the AQI corrected value of any place into the following formula to obtain the fouling rate of any place:
τ k ＝m·AQI n formula (2);
wherein A is saturated equivalent salt deposit density, and m and n are constants. And the saturated equivalent salt deposit density A, the constants m and n are obtained by adopting a simulated annealing algorithm in a fitting mode.
In the embodiment of the present application, equation (2) is derived by the following steps:
firstly, an empirical formula for obtaining an equivalent salt deposit density accumulation rule based on the existing research is as follows:
ESDD = a × (1-K × exp (-t/τ)) formula (3);
wherein ESDD is equivalent salt deposit density, and A is saturated equivalent salt deposit density mg/cm 2 The structure type and the operation environment of the insulator are related; k is the current moment, and t is the dirt accumulation time; τ is a constant characterizing the fouling rate.
Equation (3) indicates that the equivalent salt deposit density accumulation law should be a piecewise function in days. In order to realize the dynamic of the model parameters, let us note that the ESDD accumulation amount in equation (3) is a function S (t) of time, and assuming that the accumulated salt density is 0 at zero time, the empirical equation (3) is changed to the following equation:
s (t) = A × (1-exp (-t/τ)) formula (4)
Firstly, neglecting the influence of the pollution degree change on the pollution rate, assuming that the pollution rate is a function of the pollution time, and converting the formula (4) into an integral equation to obtain:
and S (0) =0
When meteorological elements such as precipitation and the atmospheric pollution degree change, the dirt deposition rate tau is no longer a constant irrelevant to time, and the ESDD time sequence of the insulator is no longer in a negative value change trend along with the time, but has a more complex relation with the meteorological elements and the atmospheric pollution degree. Therefore, considering that the increase rate of ESDD at any time is expressed as a function of ESDD at that time, ESDD at k +1, which is obtained by forward differentiating equation (4), can be expressed as
Substituting equation (4) into equation (6) yields:
as can be seen from equation (7), the increment of ESDD in adjacent time segments is made a function of parameters a and τ by integral discretization of the continuous model. That is, if the values of the parameters a and τ on the current day are known, the ESDD on the next day can be predicted according to equation (7).
In consideration of the applicability and robustness of the model, the air quality value AQI is used as the index quantity of the atmospheric pollution degree, because the monitoring value of the AQI is not only easy to obtain, but also the AQI can represent the characteristics of the primary pollutants, and the robustness of the model is enhanced.
I.e., formula (7) is rewritten as
Comprehensively considering the influence of differences of chemical compositions of pollutants in various regions on differences of contribution factors of AQI to the pollutant deposition rate, the following value function relation is provided:
τ k ＝m·AQI n formula (2)
Wherein: AQI is the local k day air quality value. The function satisfies two basic conditions of monotonicity and zero crossing.
The method of the examples of the present application can be verified by the following experimental procedure.
The method comprises the following steps: carrying out grid division on the space where the line tower of the power grid of 110kV or above is located; and finding the maximum value and the minimum value of the longitude and latitude of all towers governed by the team to form a space (A) taking extreme value data as a boundary, taking 10 kilometers by 10 kilometers as a minimum grid unit, and dividing the space into a plurality of cells. As shown in fig. 2, 195 grids (15 × 13, 15 in the horizontal direction, and 13 in the vertical direction) were obtained, wherein 111 grids were obtained with the towers. The numbers in the grid in fig. 2 are the numbers of the towers.
Step two: the air quality value AQI of each grid node is calculated by the method described above. And correcting and calculating the air quality value AQI according to the environmental data prediction model and the waste gas point source pollution diffusion model. As shown in fig. 3. The air quality value is expressed by different color depth.
Step three: and respectively estimating and predicting equivalent salt deposit density data of the salt deposit density monitoring points by adopting an equivalent salt deposit density dynamic accumulation model.
Step five: the predicted results are compared to the measured data as shown in fig. 4. It can be seen that the prediction result is closer to the actually measured data, so that the model of the method has better robustness and accuracy.
In a second aspect, the present application provides a power grid pollution degree evaluation system based on environmental big data analysis, as shown in fig. 5, the system includes:
a coordinate establishing unit 501, configured to establish a wind axis coordinate system by taking a projection of a pollution point source on the ground as an origin of coordinates and taking a main wind direction as an X axis forward direction;
a grid division unit 502, configured to perform grid division on a to-be-monitored power grid region based on the wind axis coordinate system, and obtain coordinates of each grid node;
the prediction unit 503 is configured to input the coordinates of each grid node into a pre-constructed environment data prediction model to obtain simulated environment data of each grid node;
a calculating unit 504, configured to calculate a first AQI value based on the simulated environment data;
the model calculation unit 505 is configured to input the grid node coordinates into an exhaust gas point source pollution diffusion model to obtain a second AQI value;
a correcting unit 506, configured to correct the first AQI value and the second AQI value to obtain an AQI correction value for each grid node;
the interpolation unit 507 is used for carrying out interpolation calculation on the AQI correction values of the grid nodes by using an interpolation method to obtain the AQI correction values of any place in the power grid area to be monitored;
and the fouling rate calculating unit 508 is used for inputting the AQI corrected value of the arbitrary site into a pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the fouling rate of the arbitrary site.
Based on the same technical concept, an embodiment of the present invention further provides an electronic device, as shown in fig. 6, including a processor 601, a communication interface 602, a memory 603 and a communication bus 604, where the processor 601, the communication interface 602, and the memory 603 complete mutual communication through the communication bus 604, and the memory 603 is used for storing a computer program;
the processor 601 is configured to implement the steps of the power grid pollution degree evaluation method based on the environmental big data analysis when executing the program stored in the memory 603.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
In another embodiment of the present invention, a computer-readable storage medium is further provided, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the steps of any one of the above-mentioned grid pollution degree assessment methods based on environmental big data analysis.
In another embodiment of the present invention, a computer program product containing instructions is further provided, which when executed on a computer, causes the computer to execute any one of the above-mentioned methods for evaluating pollution degree of a power grid based on environmental big data analysis.
In the above embodiments, all or part of the implementation may be realized by software, hardware, firmware, or any combination thereof. When implemented in software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The power grid pollution degree assessment method based on environment big data analysis is characterized by comprising the following steps:
taking projection of a pollution point source on the ground as a coordinate origin, and taking a main wind direction as an X-axis forward direction to establish a wind axis coordinate system;
carrying out grid division on a power grid area to be monitored based on the wind axis coordinate system, and obtaining coordinates of each grid node;
inputting the coordinates of each grid node into a pre-constructed environment data prediction model to obtain simulated environment data of each grid node;
calculating a first AQI value based on the simulated environment data;
inputting the grid node coordinates into an exhaust gas point source pollution diffusion model to obtain a second AQI value;
correcting according to the first AQI value and the second AQI value to obtain an AQI correction value of each grid node;
carrying out interpolation calculation on the AQI correction values of all grid nodes by using an interpolation method to obtain the AQI correction values of any place in a power grid area to be monitored;
and inputting the AQI corrected value of any place into a pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the pollution rate of any place.
2. The power grid pollution degree evaluation method based on environmental big data analysis according to claim 1, wherein the construction process of the environmental data prediction model comprises the following steps:
acquiring air quality data, meteorological data and longitude and latitude coordinate data of monitoring points of a power grid area to be monitored;
fusing the air quality data and the meteorological data to obtain an environment characteristic value data set;
and training an environmental data prediction model through the environmental characteristic value data set and the longitude and latitude coordinate data of the monitoring points, so that the environmental data of any place is output by the environmental data prediction model.
3. The grid pollution degree evaluation method based on environmental big data analysis according to claim 2, wherein the fusing the air quality data and the meteorological data to obtain an environmental characteristic value data set comprises:
respectively extracting air quality data characteristics in the air quality data and meteorological data condition characteristics in the meteorological data; the air quality data characteristics include PM2.5, PM10, SO 2 、CO、NO 2 And O 3 The concentration of the solid pollutants, wherein the meteorological data condition characteristics comprise wind direction, wind speed, rainfall and temperature and humidity;
adding the meteorological data condition characteristics into air quality data characteristics to obtain an air quality-meteorological condition characteristic data set;
and carrying out data cleaning pretreatment on the air quality-meteorological condition characteristic data set to obtain an environment characteristic value data set.
4. The grid pollution degree evaluation method based on environmental big data analysis according to claim 2, wherein the training of the environmental data prediction model through the environmental characteristic value data set and the monitoring point longitude and latitude coordinate data comprises:
converting the longitude and latitude coordinates of the monitoring points and the longitude and latitude coordinates of any target location to obtain plane rectangular coordinates of the monitoring points and plane rectangular coordinates of any target location;
and based on the plane rectangular coordinates of the monitoring points and the environment characteristic value data sets of the monitoring points, performing interpolation operation by using an inverse distance weighting interpolation method to obtain environment simulation data of the plane rectangular coordinates of any target site.
5. The power grid pollution degree evaluation method based on environmental big data analysis according to claim 1, wherein the inputting the grid node coordinates into an exhaust gas point source pollution diffusion model to obtain a second AQI value comprises:
inputting the grid node coordinates into the following formula to obtain the concentration of any pollutant; adding the concentrations of the pollutants to obtain a second AQI value; wherein the content of the first and second substances,
concentration of contaminants at any point in C-space, mg/m 3 ；
μ -mean wind speed, m/s;
δ y — lateral diffusion parameter, m;
δ z-vertical diffusion parameter, m;
x, y, z-space coordinates;
q-the discharge amount of each atmospheric pollutant in unit time, mg/s;
h-the effective height of the pollution point source, m;
x is the distance from the discharge point of the pollution source to any point in the downwind direction, m;
y is the distance m from the central axis of the flue gas to any point in the right-angle horizontal direction;
z-height from the surface to any point, m.
6. The power grid pollution degree evaluation method based on environmental big data analysis according to claim 1, wherein the step of inputting the AQI correction value of any place into the pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the pollution rate of any place comprises the following steps:
inputting the AQI corrected value of any place into the following formula to obtain the fouling rate of any place:
τ k ＝m·AQI n
wherein A is saturated equivalent salt deposit density, and m and n are constants.
7. The power grid pollution degree evaluation method based on environmental big data analysis according to claim 5, wherein the saturated equivalent salt deposit density A, the constants m and n are obtained by adopting a simulated annealing algorithm.
8. Power grid pollution degree evaluation system based on environment big data analysis, characterized in that, the system includes:
the method comprises the steps of establishing a coordinate unit, wherein the coordinate unit is used for establishing a wind axis coordinate system by taking the projection of a pollution point source on the ground as an origin of coordinates and taking the main wind direction as the X axis forward direction;
the grid division unit is used for carrying out grid division on the power grid area to be monitored based on the wind axis coordinate system and obtaining the coordinates of each grid node;
the prediction unit is used for inputting the coordinates of each grid node into a pre-constructed environment data prediction model to obtain simulated environment data of each grid node;
the calculating unit is used for calculating a first AQI value based on the simulated environment data;
the model calculation unit is used for inputting the grid node coordinates into an exhaust gas point source pollution diffusion model to obtain a second AQI value;
the correcting unit is used for correcting according to the first AQI value and the second AQI value to obtain an AQI correction value of each grid node;
the interpolation unit is used for carrying out interpolation calculation on the AQI correction values of all grid nodes by using an interpolation method to obtain the AQI correction values of any place in the power grid area to be monitored;
and the fouling rate calculation unit is used for inputting the AQI correction value of any place into a pre-constructed equivalent salt deposit density dynamic accumulation model to obtain the fouling rate of any place.
9. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 7 when executing a program stored in the memory.
10. Computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any of the claims 1-7.
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| CN117078682A (en) * | 2023-10-17 | 2023-11-17 | 山东省科霖检测有限公司 | Large-scale grid type air quality grade accurate assessment method |
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| CN116578825A (en) * | 2022-12-28 | 2023-08-11 | 上海勘测设计研究院有限公司 | Meteorological prediction error correction method, device, medium and electronic equipment |
| CN117078682A (en) * | 2023-10-17 | 2023-11-17 | 山东省科霖检测有限公司 | Large-scale grid type air quality grade accurate assessment method |
| CN117078682B (en) * | 2023-10-17 | 2024-01-19 | 山东省科霖检测有限公司 | Large-scale grid type air quality grade accurate assessment method |
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