CN111856157A - Lightning position measuring method, lightning position measuring device, computer equipment and storage medium - Google Patents
Lightning position measuring method, lightning position measuring device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a lightning position measuring method, a lightning position measuring device, a computer device and a storage medium. The method comprises the following steps: the method comprises the steps of obtaining the distance between each sensor in a sensor array and lightning, obtaining measurement data, obtaining grid units after target detection spaces corresponding to the sensors in the sensor array are divided, obtaining target parameters of the sensors in each grid unit according to the marks of the sensors in the sensor array, calculating the distribution probability of the lightning position in each grid unit according to the measurement data and the target parameters of the sensors, and determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit. In the method, the server determines the lightning position coordinate more accurately based on the distribution probability of the lightning position in each grid unit, and the problem of inaccurate measurement result caused by low measurement precision of the sensor is avoided.
Description
Technical Field
The present application relates to the field of positioning technologies, and in particular, to a lightning position measurement method and apparatus, a computer device, and a storage medium.
Background
Lightning is a dangerous weather phenomenon, and can cause great damage to forests, power stations, buildings, equipment and the like once struck by lightning. Lightning also causes interference with the radio and electronic equipment of the aircraft, which can have a deleterious effect on aviation soldiers in combat, training, flight safety and military operations. There is a constant effort to study lightning detection and location techniques.
However, since lightning has the characteristics of difficulty in accurate capture, strong uncertainty and the like, observation of the lightning direction is difficult to achieve. In the conventional lightning positioning method in the prior art, a server receives distance data sent by a sensor, and calculates a specific lightning position according to the distance data by a triangulation method, so as to complete the measurement of the lightning position. Wherein, after the sensor senses the electromagnetic field radiation data of lightning, the distance data is obtained through data conversion.
However, the prior art relies on distance data measured by a sensor, and the lightning position calculated by the lightning location method is also inaccurate due to the low measurement accuracy of the sensor.
Disclosure of Invention
In view of the above, it is necessary to provide a lightning location measurement method, an apparatus, a computer device and a storage medium for addressing the above technical problems.
In a first aspect, there is provided a lightning location measurement method, the method comprising:
acquiring the distance between each sensor in the sensor array and lightning to obtain measurement data;
acquiring grid units obtained after dividing target detection spaces corresponding to sensors in a sensor array;
acquiring target parameters of each sensor in each grid unit according to the identifier of each sensor in the sensor array, and calculating the distribution probability of the lightning position in each grid unit according to the measured data and the target parameters of each sensor;
and determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit.
In one embodiment, the grid cell is obtained by a pre-constructed fingerprint database; the construction mode of the fingerprint database comprises the following steps:
calculating lightning historical position coordinates corresponding to the historical measurement data according to the historical measurement data, and determining a grid unit where the historical measurement data are located according to the lightning historical position coordinates;
and counting and analyzing historical measurement data in each grid unit to obtain the corresponding relation among the grid unit, the identifier of the sensor and the parameter of the sensor.
In one embodiment, the calculating lightning historical position coordinates corresponding to each historical measurement data according to the historical measurement data, and determining the grid unit where each historical measurement data is located according to each lightning historical position coordinate includes:
calculating the lightning historical position coordinates of the historical measurement data by using a triangulation method according to the historical measurement data;
and determining the grid unit where the historical measurement data are located according to the coordinates of the historical positions of the lightning and the coordinate range of the grid unit.
In one embodiment, the counting and analyzing the historical measurement data in each grid cell to obtain a corresponding relationship among the grid cell, the identifier of the sensor, and the parameter of the sensor includes:
carrying out probability distribution analysis on historical measurement data in each grid unit to determine initial probability distribution parameters of sensors in each grid unit;
substituting the initial probability distribution parameters of the sensors in each network unit into a preset optimization objective function to carry out maximum value solution to obtain target parameters corresponding to the sensors;
and establishing a corresponding relation among the grid cells, the identifiers of the sensors and the parameters of the sensors according to the identifiers of the sensors in the grid cells and the target parameters of the sensors in the grid cells.
In one embodiment, the dividing the target detection space corresponding to each sensor in the sensor array to obtain the grid unit includes:
determining an effective detection area of each sensor by taking the center of each sensor as a circle center and the effective detection radius of each sensor according to the arrangement mode of the sensor array;
determining a union region of the effective detection regions of all the sensors in the detection array according to the effective detection region of each sensor, and determining the minimum circumscribed rectangle of the union region as a target detection space;
and carrying out grid division on the target detection space according to a preset division step length to obtain a plurality of grid units.
In one embodiment, the calculating the distribution probability of the lightning positions in each grid cell according to the measurement data and the target parameters of each sensor includes:
calculating the distribution probability of each sensor in each grid unit by using a normal distribution probability calculation method according to the measured data and the target parameters of each sensor;
and performing multiplication calculation on the distribution probability of all the sensors in each grid unit to obtain the distribution probability of the lightning position in each grid unit.
In one embodiment, the determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit includes:
arranging the distribution probability of lightning positions in each grid unit in a descending order, and acquiring a preset number of grid units which are ranked in front as target grid units;
determining the center coordinates of each target grid cell according to the coordinates of each target grid cell;
and carrying out weighted average on the central coordinates of each target grid unit and the distribution probability of each target grid unit to determine the lightning position.
In a second aspect, there is provided a lightning location measurement apparatus, the apparatus comprising:
the acquisition module is used for acquiring the distance between each sensor in the sensor array and lightning to obtain measurement data;
the dividing module is used for acquiring grid units obtained after the target detection space corresponding to each sensor in the sensor array is divided;
the first calculation module is used for acquiring target parameters of each sensor in each grid unit according to the identification of each sensor in the sensor array, and calculating the distribution probability of lightning positions in each grid unit according to the measurement data and the target parameters of each sensor;
and the second calculation module is used for determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit.
In a third aspect, a computer device is provided, comprising a memory storing a computer program and a processor implementing the lightning position measurement method according to any of the above first aspects when the processor executes the computer program.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the lightning location measurement method according to any one of the above first aspects.
According to the lightning position measuring method, the lightning position measuring device, the computer equipment and the storage medium, the server obtains the distance between each sensor in the sensor array and lightning to obtain measuring data, obtains grid units after dividing the target detection space corresponding to each sensor in the sensor array, obtains the target parameters of each sensor in each grid unit according to the identification of each sensor in the sensor array, and calculates the distribution probability of the lightning position in each grid unit according to the measuring data and the target parameters of each sensor, so that the lightning position is determined according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit. In the method, the server determines the lightning position in a distribution probability calculating mode according to the pre-calculated target parameters of each sensor in the grid unit and the acquired current measurement data, the lightning position is not directly calculated through a mathematical calculation method according to the measurement data, the lightning position is independent of the measurement data acquired by each sensor, the problem that the measurement result of the lightning position is inaccurate due to low measurement precision of the sensor is solved, and the problem that the acquired measurement data cannot be solved due to different measurement reference systems of the sensors is solved.
Drawings
FIG. 1 is a diagram of an environment in which the lightning location measurement method is applied in one embodiment;
FIG. 2 is a schematic flow diagram of a lightning location measurement method in one embodiment;
FIG. 3 is a schematic flow diagram of a lightning location measurement method in one embodiment;
FIG. 3a is a schematic diagram of a grid cell of an effective detection volume of a sensor array in one embodiment;
FIG. 4 is a schematic flow diagram of a lightning location measurement method in one embodiment;
FIG. 5 is a schematic flow chart of a lightning location measurement method in one embodiment;
FIG. 6 is a schematic flow chart of a lightning location measurement method in one embodiment;
FIG. 7 is a schematic flow chart of a lightning location measurement method in one embodiment;
FIG. 8 is a schematic flow chart of a lightning location measurement method in one embodiment;
FIG. 9 is a schematic flow chart of a lightning location measurement method in one embodiment;
FIG. 10 is a block diagram of the structure of a lightning location measurement device in one embodiment;
FIG. 11 is a block diagram of the structure of a lightning location measurement device in one embodiment;
FIG. 12 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The lightning position measuring method provided by the application can be applied to the application environment shown in figure 1. Wherein the server 101 communicates with the sensor array 102 via a communication interface. The server 101 can be realized by an independent server or a server cluster formed by a plurality of servers, and the server 101 comprises a fingerprint database module, a data fusion module, a data processing module, a communication interface and the like; the sensor array 102 includes a plurality of lightning sensors that sense electromagnetic radiation generated by the occurrence of a lightning and convert the electromagnetic radiation signals into distance data.
The following describes in detail the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems by embodiments and with reference to the drawings. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. It should be noted that the embodiment of fig. 2 to 9 of the present application provides a lightning position measuring method, the execution subject of which is a server, and the lightning position measuring apparatus may also be a lightning position measuring apparatus, and the lightning position measuring apparatus may be a part or all of the server by software, hardware, or a combination of software and hardware. In the following method embodiments, the following method embodiments are all described by taking the example where the execution subject is a server.
In one embodiment, as shown in fig. 2, there is provided a lightning position measuring method, which relates to a process that a server collects lightning measurement data through a sensor array and a preset fingerprint database to determine a distribution probability of a lightning position in each grid unit, and further calculates a position coordinate of the lightning according to the distribution probability of the lightning position in each grid unit and a coordinate of each grid unit, and includes the following steps:
s201, obtaining the distance between each sensor in the sensor array and lightning to obtain measurement data.
The sensor array comprises a plurality of sensors, and the arrangement mode of the sensor array and the number of the sensors in the sensor array are determined according to the actual environment condition and the measurement requirement of the lightning position. Optionally, the sensor is a lightning sensor, and during the process of collecting data by the lightning sensor, the lightning sensor may convert a magnetic field radiation signal sensed when lightning occurs into distance data, where the distance data is the measurement data.
In the present embodiment, in the event of lightning, the server acquires measurement data collected by all sensors in the sensor array. Since lightning is a transient weather phenomenon, when lightning occurs, generally, all sensors in a sensor array acquire the lightning at the same momentFor example, assuming that the sensor array includes N sensors, and the distance data acquired by each sensor is D, when a lightning occurs, the measurement data D acquired by the sensor array acquired by the server may be represented as D ═ D1，d2，…，dn]Optionally, a complete set of measurement data may also be referred to as a piece of fingerprint data, i.e. a piece of fingerprint data includes distance data d collected by N sensorsnIt should be noted that, in the event of lightning, not all sensors can collect data, so in a piece of fingerprint data, for a sensor that is not collected, 0 is filled in the corresponding position.
S202, obtaining grid units obtained after dividing target detection spaces corresponding to the sensors in the sensor array.
The target detection space refers to a detection space determined after processing effective detection ranges of all the sensors, and generally, the target detection space is rectangular.
In this embodiment, the server divides the target detection space, optionally, the server may divide the target detection space according to the division step length, specifically, the server determines a horizontal division step length and a vertical division step length according to the target detection space, and divides the target detection space according to the horizontal division step length and the vertical division step length, respectively, to obtain a plurality of equally divided grid units; optionally, the server may further divide the target detection space according to a designated division pattern to obtain a plurality of grid cells. After determining the grid cells, the server may determine the target parameters of each sensor according to the grid cells, which is not limited in this embodiment.
S202, acquiring target parameters of each sensor in each grid unit according to the identification of each sensor in the sensor array, and calculating the distribution probability of the lightning position in each grid unit according to the measurement data and the target parameters of each sensor.
The identifier of the sensor may be a unique number of the sensor, or may also be a factory serial number of the sensor. The server determines target parameters corresponding to each sensor in each grid unit according to the identifier of the sensor, wherein the target parameters refer to probability distribution parameters of the distance data acquired by the sensor in each grid unit, and according to expert experience analysis, generally, the probability distribution of the distance data acquired by the sensor in each grid unit is subject to normal distribution, so the target parameters comprise distribution parameters mu and sigma.
In this embodiment, the server determines the target parameter of each sensor from each grid cell according to the identifier of each sensor in the sensor array, for example, the sensor array includes N sensors, the target detection space includes M grid cells, and the server obtains a group of [ mu ] when determining the target parameter corresponding to all the sensor identifiers in each grid cell1σ1，μ2σ2，...，μnσn]And obtaining M groups [ mu ] when determining the target parameters corresponding to all the sensor identifications of all the grid units1σ1，μ2σ2，...，μnσn]，
Optionally, after determining the target parameters of the sensors in each grid unit, the server determines the distribution characteristics of the sensors, and the server may calculate the distribution probability of the sensors in each grid unit according to the calculation manner of the corresponding distribution function, and further calculate the distribution probability of all the sensors in each grid unit of the server to obtain the distribution probability of the lightning position in each grid unit. Optionally, the server may calculate the distribution probability of the lightning position in each grid unit by performing a multiplication calculation on the distribution probability of each sensor in each grid unit; the server may further calculate an average value of the distribution probabilities of the sensors in each grid unit to obtain the distribution probability of the lightning position in each grid unit, which is not limited in this embodiment.
And S204, determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit.
Wherein, the distribution probability of the lightning position in each grid unit represents the probability that the lightning position is in each grid unit when the lightning occurs, and the larger the distribution probability of the grid unit is, the closer the position of the current grid unit is to the lightning position is.
In this embodiment, the server may rank the distribution probabilities of the lightning positions in each grid cell, for example, a preset number of target grid cells ranked at the top may be determined according to the arrangement of the distribution probabilities from large to small, for example, the first 10 grid cells are determined as the target grid cells; it is also possible to determine the grid cells with probability values greater than a certain threshold value directly according to the distribution probability of each grid cell, for example, determine the target grid cells with distribution probability values greater than 75%. After determining the target grid cell, the server may calculate the location coordinates of the lightning based on the distribution probability of the target grid cell and the coordinates of the target grid cell. Optionally, the calculation manner may be calculated by weighted average, or by selecting a median of the target grid unit as a position coordinate of the lightning, which is not limited in this embodiment.
In the lightning position measuring method, the server obtains measuring data by obtaining the distance between each sensor in the sensor array and lightning, obtains grid units obtained after dividing a target detection space corresponding to each sensor in the sensor array, obtains target parameters of each sensor in each grid unit according to the identification of each sensor in the sensor array, and calculates the distribution probability of the lightning position in each grid unit according to the measuring data and the target parameters of each sensor, thereby determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit. In the method, the server determines the lightning position in a distribution probability calculating mode according to the pre-calculated target parameters of each sensor in the grid unit and the acquired current measurement data, the lightning position is not directly calculated through a mathematical calculation method according to the measurement data, the lightning position is independent of the measurement data acquired by each sensor, the problem that the measurement result of the lightning position is inaccurate due to low measurement precision of the sensor is solved, and the problem that the acquired measurement data cannot be solved due to different measurement reference systems of the sensors is solved.
Before the server performs data analysis on the measurement data by using the fingerprint database, the fingerprint database needs to be constructed, and in one embodiment, as shown in fig. 3, the grid unit is specifically obtained by using the fingerprint database constructed in advance; the construction mode of the fingerprint database comprises the following steps:
s301, calculating lightning historical position coordinates corresponding to the historical measurement data according to the historical measurement data, and determining grid units where the historical measurement data are located according to the lightning historical position coordinates.
The historical measurement data refers to distance data collected by a plurality of groups of sensor arrays.
In this embodiment, the server may calculate the position coordinates of the lightning corresponding to each set of measurement data according to each set of measurement data by using a commonly used triangulation method or other position calculation method, for example, according to the first set of historical measurement data D1＝[d1,d2,…,dn]Calculating lightning position coordinates D of the first group of historical measurement data by using a triangulation method1,(x1,y1,z1). After determining the lightning position coordinates corresponding to each set of measurement data, the server determines the coordinate range of each grid unit area according to the vertex coordinates of each grid unit, so as to map the lightning position coordinates corresponding to each measurement data into the corresponding grid unit, for example, an example of a grid unit is shown in fig. 3a, wherein S isiWhich indicates the ith sensor and M indicates the position of the lightning, but this embodiment is not limited thereto.
S302, counting and analyzing historical measurement data in each grid unit to obtain the corresponding relation among the grid units, the identifiers of the sensors and the parameters of the sensors.
In this embodiment, through the above steps, each grid unit is assigned with a plurality of groups of measurement data, and for each grid unit, the probability distribution of distance data acquired by each sensor in the plurality of groups of measurement data is analyzed to obtain N analysis results, optionally, the analysis results may be represented in the form of a histogram, that is, the histograms corresponding to N sensors in M grid units are obtained, and in total, MxN histograms are obtained; further, distribution fitting is carried out on each histogram through a naive Bayes method to obtain distribution parameters corresponding to N sensors in M grid units, and a corresponding relation among the grid units, the identifiers of the sensors and the parameters of the sensors is formed.
In the embodiment, the fingerprint database is constructed according to historical measurement data, detection spaces of sensors and parameters of the sensors, the historical measurement data is used as sample data, the distribution probability in each grid unit of a lightning position is calculated through multi-dimensional and large-batch sample data, the probability that a small amount of measurement data cannot be calculated is reduced, the lightning position is determined by the server based on the distribution probability in each grid unit of the lightning position determined through the fingerprint database, the lightning position is not directly determined through simple mathematical calculation according to the measurement data, the method is not dependent on the measurement data collected by the sensors, the method is not limited by the limitation of a measurement data calculation method, and the measurement result of the lightning position is more reliable.
In calculating the lightning position coordinates of the historical measurement data, the lightning position coordinates corresponding to each set of historical measurement data may be calculated by triangulation, and in one embodiment, as shown in fig. 4, the calculating the lightning historical position coordinates corresponding to each set of historical measurement data according to each set of historical measurement data, and determining the grid unit where each set of historical measurement data is located according to each lightning historical position coordinate includes:
s401, calculating lightning historical position coordinates of the historical measurement data by using a triangulation method according to the historical measurement data.
The triangulation method is to detect the position of a target at different positions by a plurality of sensors and then determine the position and distance of the target by using the principle of triangulation geometry.
In this embodiment, each set of historical measurement data includes distance data acquired by a plurality of sensors, and the server calculates the historical position coordinates of lightning corresponding to each set of historical measurement data by using a triangulation method and using a trigonometric theory according to the distance data acquired by all the sensors in each set of historical data.
S402, determining the grid unit where the historical measurement data are located according to the lightning historical position coordinates and the coordinate range of each grid unit.
The coordinate range of each grid cell refers to a coordinate range determined according to the coordinates of four vertices of the grid cell.
In this embodiment, the server may first determine, according to the vertex coordinates of each grid unit, a value range of an abscissa and a value range of an ordinate of each grid unit in a two-dimensional coordinate system, so as to compare each lightning historical position coordinate with the value range of the coordinates of each grid unit, and when the lightning historical position coordinate belongs to the value range of the coordinates of one grid unit, determine that the historical position is in the current grid unit, that is, determine the grid unit corresponding to the historical measurement data corresponding to the historical position coordinate. Optionally, the coordinate value range of the grid unit may also be determined according to the central coordinate of each grid unit, so that the grid unit in which each historical measurement data is located is determined according to the coordinate value range, which is not limited in this embodiment.
In this embodiment, a correspondence between the historical measurement data and the grid unit is established by calculating the lightning historical position coordinates of the historical measurement data, so that the correspondence is used for performing optimization training of the lightning distribution probability value.
When the server builds the corresponding relation between the parameters in the fingerprint database and the sensors, the initial distribution parameters of the sensors can be optimized in a mode of building an optimized objective function, so that the target distribution parameters of the sensors are obtained, and the corresponding relation between the target distribution parameters and the sensors is built. In an embodiment, as shown in fig. 5, the counting and analyzing the historical measurement data in each grid cell to obtain a corresponding relationship between the grid cell, the identifier of the sensor, and the parameter of the sensor includes:
s501, carrying out probability distribution analysis on historical measurement data in each grid unit, and determining initial probability distribution parameters of sensors in each grid unit.
In this embodiment, the server performs probability distribution feature analysis on the measurement data of each sensor in each grid cell to determine which probability distribution the measurement data of each sensor conforms to, optionally, if the measurement data of the sensor conforms to a normal distribution, i.e., p (d)i|L)～N(μ,σ2) Then the initial probability distribution parameters for that sensor are determined to be μ and σ, where μ is the mathematical expectation, σ is the mean square error, diFor the distance data corresponding to the ith sensor, the server may optionally assign initial values to μ and σ.
S502, substituting the initial probability distribution parameters of the sensors in each network unit into a preset optimization objective function to carry out maximum value solution, and obtaining the objective parameters corresponding to the sensors.
The preset optimization objective function can be expressed as:
wherein, L represents the position coordinate of the lightning, and D represents the data collected by the sensor array when the lightning occurs. The server will have p (d) with initial distribution parametersiL) is substituted into the above-mentioned optimization objective function to perform calculation solution, that is, a set of target distribution parameters is determined for each grid unit, so that the target probability is maximized. Illustratively, a set of target distribution parameters may be represented as [ mu ]1σ1，μ2σ2，...，μnσn]。
S503, establishing a corresponding relation among the grid units, the identifiers of the sensors and the parameters of the sensors according to the identifiers of the sensors in the grid units and the target parameters of the sensors in the grid units.
In this embodiment, the server determines, through the above steps, a target parameter set corresponding to the maximum target probability of each grid cell, where the target parameter set includes target parameters corresponding to each sensor, and establishes a correspondence between the grid cell, the sensor identifier, and the target parameters corresponding to the sensor according to the target parameters determined in the above steps, and optionally, the server may store the correspondence in the fingerprint database.
In this embodiment, the server optimizes the initial distribution parameters of each sensor in each grid unit by constructing an optimization objective function, so that the distribution probability of the lightning position of each grid unit reaches the maximum value, and the accuracy and precision of determining the lightning position by a probability distribution method are improved.
In constructing the fingerprint database, it is necessary to determine an effective detection area of the sensor array, and perform grid division on the effective detection area, in an embodiment, as shown in fig. 6, a target detection space corresponding to each sensor in the sensor array is divided to obtain a grid unit, where the method includes:
s601, drawing effective detection areas of the sensors by taking the centers of the sensors as circle centers and the effective detection radiuses of the sensors according to the arrangement mode of the sensor array.
The arrangement mode of the sensor array can be any arrangement, and can also be a sequential arrangement.
In this embodiment, the server determines the effective detection radius r of each sensor according to the arrangement of the sensor array, and draws an effective circular area of each sensor with each sensor as a center of a circle and r as a radius. Where the effective circular areas of the sensors may intersect.
S602, according to the effective detection areas of the sensors, determining a union area of the effective detection areas of all the sensors in the detection array, and determining the minimum circumscribed rectangle of the union area as a target detection space.
In this embodiment, effective detection areas of the sensors may be partially overlapped or not overlapped, in order to ensure that the target detection space includes the effective detection areas of all the sensors, the server may first determine a union area of the effective detection areas of all the sensors, further, in order to construct the two-dimensional detection space model, the server may determine the target detection space by making a circumscribed rectangle in the union area of the effective detection areas, and optionally, in order to ensure the effectiveness of the target detection space, the server may select an area included in the minimum circumscribed rectangle as the target detection space.
And S603, carrying out grid division on the target detection space according to a preset division step length to obtain a plurality of grid units.
In this embodiment, the server divides the target detection space according to the division step length, where the target detection space is a detection space model in a two-dimensional coordinate that is constructed in advance, and optionally, the server may determine a horizontal division step length and a vertical division step length, and divide the target detection space to obtain a plurality of equally divided grid units; or the server can determine a dividing step length, and divide the target detection space from the transverse direction and the longitudinal direction respectively according to the dividing step length to obtain a plurality of equally divided grid units; alternatively, the server may divide the target detection space according to a plurality of division steps to obtain a plurality of unequally-divided grid cells, and the grid cells may be represented by G ═ { G ═ for example1,g2,…,gmAnd meanwhile, determining vertex coordinates of each grid unit according to a two-dimensional coordinate system, which is not limited in this embodiment.
In this embodiment, the server determines to perform the target detection space according to the sensor array and the effective detection area of each sensor, and performs grid cell division on the target detection space, and the division method is simple and effective.
Optionally, when the server constructs the fingerprint database, it needs to determine target distribution parameters corresponding to each sensor in each grid unit, so as to measure and determine the lightning location by using a naive bayes method based on probability distribution, in an embodiment, the performing probability distribution analysis on historical measurement data in each grid unit to determine initial probability distribution parameters of the sensors in each network unit includes:
and fitting the measurement data of each sensor in each grid unit by adopting normal distribution to determine the initial probability distribution parameters of the sensors in each grid unit.
In this embodiment, the server may analyze the distribution characteristics of the measurement data of each sensor according to the historical measurement data, for example, if the measurement data of each sensor conforms to the normal distribution characteristics, the measurement data of each sensor in each grid unit is fitted by using a normal distribution fitting algorithm. It should be noted that, if the measurement data of each sensor conforms to other distribution characteristics, for example, exponential distribution, logarithmic distribution, etc., the measurement data of each sensor in each grid unit is fitted by using a corresponding distribution fitting algorithm to obtain an initial probability distribution parameter of the sensor in each grid unit, which is not limited in this embodiment.
In this embodiment, the server performs distribution feature analysis on the measurement data of each sensor, thereby performing distribution fitting, determining the distribution parameters of each sensor, and using the naive bayes method based on probability distribution to avoid the problem that the lightning position measurement result of the sensor is inaccurate due to the low measurement accuracy.
Optionally, after determining the target parameters of the sensors, the server preliminarily calculates the distribution probability of the sensors in each grid unit, and the server solves the distribution probability of the lightning position of the current grid unit according to the distribution probability of the sensors, in an embodiment, as shown in fig. 7, the calculating the distribution probability of the lightning position in each grid unit according to the measurement data and the target parameters of the sensors includes:
s701, calculating the distribution probability of each sensor in each grid unit by using a normal distribution probability calculation method according to the measured data and the target parameters of each sensor.
In this embodiment, optionally, the server may calculate the distribution probability of each sensor in each grid unit by using a normal distribution fitting algorithm. It should be noted that, here, a normal distribution fitting algorithm is determined to be used by primarily determining the distribution characteristics of the measurement data, and if the distribution characteristics of the measurement data conform to other distribution algorithms, such as exponential distribution and lognormal distribution, the corresponding exponential distribution fitting algorithm and lognormal distribution fitting algorithm are used to perform corresponding calculation, which is not limited in this embodiment.
Alternatively, the distribution function of a normal distribution may be as follows:
the corresponding distribution probability calculation formula is as follows:
in the present embodiment, the distribution probability p (d) of the frequency of the distance data of each sensor in each grid cell is calculated using the distance data d of the sensor, the number of times t the distance data appears, and the determined target distribution parameters μ, σ of the sensor as input parametersiL), in particular, μ is the mathematical expectation and σ is the mean square error.
S702, calculating the distribution probability of all the sensors in each grid unit by multiplication to obtain the distribution probability of the lightning position in each grid unit.
In this embodiment, the server needs to calculate the distribution probability of the lightning position in each grid cell in the case of acquiring the sensor measurement data, and optionally, in the case of acquiring the sensor measurement data, the distribution probability of the lightning position in each grid cell may be represented as P (L | D), and the calculation manner of P (L | D) may be represented as:
wherein L represents the position coordinates of lightning, D represents the data collected by the sensor array when lightning occurs, and p (D) is the prior probability of the sensor data, independent of position, which can be considered as a constant. P (l) is the prior probability of the location, which can also be considered constant due to the large uncertainty of the motion of the lightning. Then, the way P (L | D) is calculated can be expressed as:
P(L|D)＝P(D|L)
wherein P (D | L) refers to the distribution probability of the measured data in each grid cell under the condition that the lightning position is known, and the server performs a multiplication calculation on the distribution probabilities of all the sensors of each grid cell to obtain P (D | L) through calculation, and exemplarily, the multiplication calculation formula is as follows:
wherein n is the number of sensors in the grid cell.
Thus, the server can determine P (L | D), i.e., the distribution probability of the lightning location in each grid cell, by obtaining P (D | L) by multiplying the distribution probabilities of all the sensors in each grid cell.
In this embodiment, the server determines an equivalent model for calculating the distribution probability of the lightning position in each grid unit by analyzing the distribution probability of the lightning position in each grid unit, so that the distribution probability of the lightning position in each grid unit can be effectively calculated according to the measurement data, and the measurement result of the lightning position calculated according to the distribution probability is accurate.
The server calculates the coordinates of the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit, which are obtained through optimization, and in one embodiment, as shown in fig. 8, the calculating of the coordinates of the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit includes:
s801, arranging the distribution probability of the lightning positions in each grid unit from large to small, and acquiring a preset number of grid units ranked at the front as target grid units.
In this embodiment, the server arranges the distribution probabilities of the lightning positions in each grid cell from large to small, and determines a preset number of target grid cells ranked at the top, for example, determines the first 10 grid cells as target grid cells; optionally, after the server arranges the distribution probabilities of the lightning positions in the grid cells from large to small, the grid cells larger than a preset probability threshold may be determined as target grid cells according to the preset probability threshold, for example, the grid cells with the distribution probability value larger than 75% are determined as the target grid cells.
S802, determining the center coordinates of each target grid cell according to the coordinates of each target grid cell.
In this embodiment, when performing mesh division, the server may obtain vertex coordinates of each mesh unit, and at this time, the server needs to calculate the center coordinates of each mesh unit according to the vertex coordinates, where the center coordinates may be calculated by taking an average of two vertex coordinates respectively scattered in the horizontal direction and the vertical direction as the center coordinates of the current mesh unit, and exemplarily, the four vertex coordinates of the mesh unit are (x1, y1), (x2, y1), (x1, y2), (x2, y2), and the center coordinates of the mesh unit are (| x1-x2|/2, | y1-y2 |/2).
And S803, carrying out weighted average on the central coordinates of each target grid unit and the distribution probability of each target grid unit, and determining the lightning position.
In this embodiment, after determining the center coordinates of the target grid cell, the server may calculate the position coordinates of the lightning from the distribution probability of the target grid cell and the center coordinates of the target grid cell. Illustratively, there are 3 target grid cells, and the center coordinate corresponding to each target grid cell is (x)1,y1)、(x2,y2)、(x3,y3) The distribution probability corresponding to each target grid cell is P1、P2、P3The position coordinates (X, Y) of the lightning can be calculated as follows:
in the embodiment, the lightning coordinate position is calculated through the distribution probability of the lightning position of each grid unit and the coordinate of each target unit, the distance data collected by the sensor is not relied on, and the calculated lightning position result is more accurate.
To better explain the above method, as shown in fig. 9, the present embodiment provides a lightning location measurement method, which specifically includes:
s101, drawing an effective detection area of each sensor by taking the center of each sensor as a circle center and the effective detection radius of each sensor according to the arrangement mode of the sensor array;
s102, determining a union region of the effective detection regions of all the sensors in the detection array according to the effective detection region of each sensor, and determining the minimum circumscribed rectangle of the union region as a target detection space;
s103, carrying out grid division on a target detection space of the sensor array according to a preset division step length to obtain a plurality of grid units;
s104, calculating lightning historical position coordinates corresponding to the historical measurement data according to the historical measurement data, and determining a grid unit where the historical measurement data are located according to the lightning historical position coordinates;
s105, carrying out probability distribution analysis on historical measurement data in each grid unit, and determining initial probability distribution parameters of sensors in each grid unit;
s106, substituting the initial probability distribution parameters of the sensors in each network unit into a preset optimization objective function to carry out maximum value solution to obtain objective parameters corresponding to the sensors;
s107, establishing a corresponding relation among the grid cells, the identifiers of the sensors and the parameters of the sensors according to the identifiers of the sensors in the grid cells and the target parameters of the sensors, and using the corresponding relation as a fingerprint database;
s108, collecting lightning measurement data through a sensor array;
s109, inquiring target parameters of each sensor in each grid unit from a preset fingerprint database according to the identifier of each sensor in the sensor array;
s110, calculating the distribution probability of each sensor in each grid unit by using a normal distribution probability calculation method according to the measured data and the target parameters of each sensor;
s111, calculating the distribution probability of all sensors in each grid unit by multiplication to obtain the distribution probability of lightning positions in each grid unit;
s112, arranging the distribution probability of the lightning positions in each grid unit from large to small, and acquiring a preset number of grid units with the top rank as target grid units;
s113, determining the central coordinates of each target grid cell according to the coordinate range of each target grid cell;
s114, carrying out weighted average on the center coordinates of each target grid unit and the distribution probability of each target grid unit, and determining the lightning position.
In the embodiment, the server determines the distribution probability of the lightning position in each grid unit through the fingerprint database, so that the measurement result of the position coordinate of the lightning is more accurate based on the distribution probability of the lightning position in each grid unit, the method does not directly calculate the lightning position coordinate according to the measurement data acquired by each sensor and does not depend on the measurement data acquired by each sensor, the problem that the measurement result of the lightning position is inaccurate due to low measurement precision of the sensor is solved, and the problem that the acquired measurement data cannot be solved due to different measurement reference systems of the sensors is also solved.
The implementation principle and technical effect of the lightning position measuring method provided by the above embodiment are similar to those of the above embodiment, and are not described again here.
It should be understood that although the various steps in the flow charts of fig. 2-9 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-9 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 10, there is provided a lightning position measuring apparatus including: the device comprises an acquisition module 01, a division module 02, a first calculation module 03 and a second calculation module 04, wherein:
the acquisition module 01 is used for acquiring the distance between each sensor in the sensor array and lightning to obtain measurement data;
the dividing module 02 is used for acquiring grid units obtained after the target detection space corresponding to each sensor in the sensor array is divided;
the first calculation module 03 is configured to obtain a target parameter of each sensor in each grid unit according to an identifier of each sensor in the sensor array, and calculate a distribution probability of a lightning position in each grid unit according to measurement data and the target parameter of each sensor;
and the second calculation module 04 is used for determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit. .
In one embodiment, the grid cells are obtained by a pre-constructed fingerprint database; as shown in fig. 11, the lightning position measuring apparatus further includes a building module 05, which calculates lightning historical position coordinates corresponding to each historical measurement data according to each historical measurement data, and determines a grid unit where each historical measurement data is located according to each lightning historical position coordinate; and counting and analyzing historical measurement data in each grid unit to obtain the corresponding relation among the grid units, the identifiers of the sensors and the parameters of the sensors.
In an embodiment, the building module 05 is specifically configured to calculate lightning historical position coordinates of each historical measurement data by using a triangulation method according to each historical measurement data; and determining the grid unit where the historical measurement data are located according to the coordinates of the historical positions of the lightning and the coordinate range of the grid unit.
In an embodiment, the building module 05 is specifically configured to perform probability distribution analysis on historical measurement data in each grid unit, and determine an initial probability distribution parameter of a sensor in each grid unit; substituting the initial probability distribution parameters of the sensors in each network unit into a preset optimization objective function to carry out maximum value solution to obtain target parameters corresponding to the sensors; and establishing a corresponding relation among the grid cells, the identifiers of the sensors and the parameters of the sensors according to the identifiers of the sensors in the grid cells and the target parameters of the sensors in the grid cells.
In an embodiment, the dividing module 02 is specifically configured to draw the effective detection area of each sensor by taking the center of each sensor as a circle center and the effective detection radius of each sensor according to the arrangement manner of the sensor array; determining a union region of the effective detection regions of all the sensors in the detection array according to the effective detection region of each sensor, and determining the minimum circumscribed rectangle of the union region as a target detection space; and carrying out grid division on the target detection space according to a preset division step length to obtain a plurality of grid units.
In an embodiment, the building module 05 is specifically configured to fit the measurement data of each sensor in each grid unit by using normal distribution, and determine an initial probability distribution parameter of the sensor in each grid unit.
In an embodiment, the first calculating module 03 is specifically configured to calculate the distribution probability of each sensor in each grid unit by using a normal distribution probability calculating method according to the measurement data and the target parameter of each sensor; and performing multiplication calculation on the distribution probability of all the sensors in each grid unit to obtain the distribution probability of the lightning position in each grid unit.
In an embodiment, the second calculating module 04 is specifically configured to arrange the distribution probabilities of the lightning positions in each grid unit in a descending order, and obtain a preset number of grid units ranked at the top as target grid units; determining the center coordinates of each target grid cell according to the coordinates of each target grid cell; and carrying out weighted average on the central coordinates of each target grid unit and the distribution probability of each target grid unit to determine the lightning position.
For specific limitations of the lightning position measuring device, reference may be made to the above limitations of the lightning position measuring method, which are not described in detail here. The various modules in the lightning position measuring device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 12. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a lightning location measurement method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 12 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring the distance between each sensor in the sensor array and lightning to obtain measurement data;
acquiring grid units obtained after dividing target detection spaces corresponding to sensors in a sensor array;
acquiring target parameters of each sensor in each grid unit according to the identifier of each sensor in the sensor array, and calculating the distribution probability of the lightning position in each grid unit according to the measured data and the target parameters of each sensor;
and determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit.
The implementation principle and technical effect of the computer device provided by the above embodiment are similar to those of the above method embodiment, and are not described herein again.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring the distance between each sensor in the sensor array and lightning to obtain measurement data;
acquiring grid units obtained after dividing target detection spaces corresponding to sensors in a sensor array;
acquiring target parameters of each sensor in each grid unit according to the identifier of each sensor in the sensor array, and calculating the distribution probability of the lightning position in each grid unit according to the measured data and the target parameters of each sensor;
and determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A lightning location measurement method, characterized in that the method comprises:
acquiring the distance between each sensor in the sensor array and lightning to obtain measurement data;
acquiring grid units obtained after dividing target detection spaces corresponding to the sensors in the sensor array;
acquiring target parameters of the sensors in each grid unit according to the identifiers of the sensors in the sensor array, and calculating the distribution probability of lightning positions in each grid unit according to the measurement data and the target parameters of the sensors;
and determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit.
2. The method according to claim 1, characterized in that the grid cells are obtained in particular by means of a pre-constructed fingerprint database;
the construction mode of the fingerprint database comprises the following steps:
calculating lightning historical position coordinates corresponding to the historical measurement data according to historical measurement data, and determining a grid unit where the historical measurement data are located according to the lightning historical position coordinates;
and counting and analyzing historical measurement data in each grid unit to obtain the corresponding relation among the grid units, the identifier of the sensor and the parameters of the sensor.
3. The method of claim 2, wherein the calculating lightning historical position coordinates corresponding to each historical measurement data according to the historical measurement data and determining the grid cell in which each historical measurement data is located according to each lightning historical position coordinate comprises:
calculating the lightning historical position coordinates of each historical measurement data by using a triangulation method according to each historical measurement data;
and determining the grid cell in which each historical measurement data is located according to the coordinates of each lightning historical position and the coordinate range of each grid cell.
4. The method of claim 2, wherein said counting and analyzing historical measurement data in each of said grid cells to obtain a correspondence between grid cells, sensor identifications, and sensor parameters comprises:
carrying out probability distribution analysis on historical measurement data in each grid unit to determine initial probability distribution parameters of sensors in each grid unit;
substituting the initial probability distribution parameters of the sensors in each network unit into a preset optimization objective function to carry out maximum value solution to obtain target parameters corresponding to the sensors;
and establishing a corresponding relation among the grid units, the identifiers of the sensors and the parameters of the sensors according to the identifiers of the sensors in the grid units and the target parameters of the sensors in the grid units.
5. The method according to any one of claims 1 to 4, wherein the dividing the target detection space corresponding to each sensor in the sensor array to obtain the grid cells comprises:
determining an effective detection area of each sensor by taking the center of each sensor as a circle center and the effective detection radius of each sensor according to the arrangement mode of the sensor array;
determining a union region of the effective detection regions of all the sensors in the detection array according to the effective detection region of each sensor, and determining the minimum circumscribed rectangle of the union region as the target detection space;
and carrying out grid division on the target detection space according to a preset division step length to obtain a plurality of grid units.
6. The method of any of claims 1-4, wherein said calculating a probability of distribution of lightning locations in each of said grid cells based on said measurement data and a target parameter of each of said sensors comprises:
calculating the distribution probability of each sensor in each grid unit by using a normal distribution probability calculation method according to the measurement data and the target parameters of each sensor;
and performing multiplication calculation on the distribution probability of all the sensors in each grid unit to obtain the distribution probability of the lightning position in each grid unit.
7. The method of any of claims 1-6, wherein said determining the lightning location based on the probability of the distribution of the lightning location in each of the grid cells and the coordinates of each of the grid cells comprises:
arranging the distribution probability of the lightning positions in each grid unit from large to small, and acquiring a preset number of grid units which are ranked in the front as target grid units;
determining the center coordinates of each target grid cell according to the coordinates of each target grid cell;
and carrying out weighted average on the central coordinates of each target grid unit and the distribution probability of each target grid unit to determine the lightning position.
8. A lightning position measuring apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring the distance between each sensor in the sensor array and lightning to obtain measurement data;
the dividing module is used for acquiring grid units obtained after the target detection space corresponding to each sensor in the sensor array is divided;
the first calculation module is used for acquiring target parameters of the sensors in each grid unit according to the identifications of the sensors in the sensor array, and calculating the distribution probability of lightning positions in each grid unit according to the measurement data and the target parameters of the sensors;
and the second calculation module is used for determining the lightning position according to the distribution probability of the lightning position in each grid unit and the coordinates of each grid unit.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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