CN116956637B - Method for detecting robustness of coverage surface of fire extinguishing bomb - Google Patents
Method for detecting robustness of coverage surface of fire extinguishing bomb Download PDFInfo
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Abstract
The invention relates to the technical field of detection of coverage of fire extinguishing bomb, and discloses a method for detecting robustness of the coverage of the fire extinguishing bomb, which comprises the following steps: detonating fire extinguishing bomb in the target area, and counting the component concentration of the fire extinguishing bomb in unit volume in each subarea; solving distribution expectancy and variance of distance parameters according to the constructed fire extinguishing bomb explosion probability distribution model, and constructing an applicability function; carrying out model parameter robustness optimization solution according to the constructed applicability function; and calculating to obtain the coverage area of the extinguishing bomb by using the optimal extinguishing bomb explosion probability distribution model. According to the method, the actual probability density of the component concentration of the extinguishing bomb in unit volume in different subareas is added with random disturbance probability, the actual probability density of robustness is obtained, model parameter solving is carried out, a extinguishing bomb explosion probability distribution model with higher robustness is constructed, expected concentration of the extinguishing bomb component in unit volume in different areas is obtained through model estimation, and the coverage robustness detection of the extinguishing bomb is realized.
Description
Technical Field
The invention relates to the technical field of detection of coverage of fire extinguishing bomb, in particular to a method for detecting robustness of the coverage of the fire extinguishing bomb.
Background
The forest fire has the characteristics of strong burst, rapid development, difficult suppression and the like, and can cause great damage to the ecological environment once the forest fire occurs. Along with the development of technology, people have increasingly recognized forest fires, a plurality of fireproof devices have been developed, and more advanced technologies are introduced into the fields of forest fire prevention and fire fighting. Fire extinguishing bombs play an important role as the main equipment for forest fire prevention. The coverage area is an important index when detecting the fire extinguishing bomb as an important parameter of the fire extinguishing bomb, but because the fire extinguishing bomb has high manufacturing cost, the fire extinguishing bomb cannot be detected in a large scale, and the estimated deviation of the fire extinguishing bomb in the forest fire extinguishing process is very easy to cause. Aiming at the problem, the invention provides a method for detecting the robustness of the coverage of the fire extinguishing bomb.
Disclosure of Invention
In view of the above, the invention provides a method for detecting robustness of coverage of fire extinguishing bomb, which aims at: 1) Dividing the distance from the explosion center point of the fire extinguishing bomb to obtain different subareas, obtaining actual probability densities of the constituent concentrations of the fire extinguishing bomb in unit volume of the different subareas, representing the probability densities of the constituent concentrations of the fire extinguishing bomb in unit volume corresponding to the different distances after the explosion of the fire extinguishing bomb by poisson distribution, obtaining an estimated probability density function, taking the accuracy of the model parameters to be solved as a target, constructing an applicability function, obtaining an optimal fire extinguishing bomb explosion probability distribution model by solving the model parameters to be solved, further obtaining the expected concentrations of the constituent of the fire extinguishing bomb in unit volume under different areas by estimation under the condition of no detection, and counting the area of the subareas effectively covered by the fire extinguishing bomb to realize the coverage detection of the fire extinguishing bomb; 2) The actual probability density of the component concentration of the fire extinguishing bomb in unit volume in different subareas is added with random disturbance probability to obtain actual probability density of robustness, estimated probability density corresponding to different parameters of the fire extinguishing bomb to be solved is compared with the actual probability density of the robustness to obtain robust variation probability of different groups of parameters of the model to be solved, variation processing of the parameters of the model to be solved is carried out based on the robust variation probability and the applicability function value, solution space of parameter solution is expanded, diversity of solution results is improved, model parameters with better performance are obtained by solving, the smaller the robust variation probability is, the constructed fire extinguishing bomb explosion probability distribution model has higher robustness, and further stronger robust fire extinguishing bomb coverage detection is realized.
The invention provides a method for detecting robustness of a coverage surface of a fire extinguishing bomb, which comprises the following steps:
s1: detonating fire extinguishing bomb in the target area, subdividing the target area into a plurality of subareas, and counting the component concentration of the fire extinguishing bomb in unit volume in each subarea;
s2: constructing a fire extinguishing bomb explosion probability distribution model, wherein the model takes the distance from an explosion center point as input and takes the probability density corresponding to the component concentration of the fire extinguishing bomb in unit volume as output;
s3: solving the distribution expectation and variance of the distance parameter according to the constructed fire extinguishing bomb explosion probability distribution model, and constructing an fitness function based on the distribution expectation and variance of the distance parameter;
s4: carrying out model parameter robustness optimization solution according to the constructed applicability function to obtain an optimal fire extinguishing bomb blasting probability distribution model;
s5: and calculating to obtain the coverage area of the extinguishing bomb by using the optimal extinguishing bomb explosion probability distribution model.
As a further improvement of the present invention:
optionally, the step S1 detonates the fire extinguishing bomb in the target area and subdivides the target area into a plurality of sub-areas, including:
detonating fire extinguishing bomb at the center of the target area, and subdividing the target area into a plurality of subareas, wherein the subarea division flow is as follows:
s11: with the center of the target area as the center point, respectively with the radiusDividing into K circular regions, wherein ∈>For the set region radius, the kth circular region is +.>，/>The radius of adjacent circular areas satisfies the following equation:
；
wherein:
the radius of the kth circular area with the center of the target area as the center point, +.>；
S12: dividing to obtain a kth sub-region:
；
wherein:
represents the kth sub-region;
represents the k-th circular area of division, < >>Is 0;
represents the region from the kth circle +.>Middle gouging the (k-1) th circular regionIs defined by the remaining area of the substrate;
s13: constructing and obtaining a subarea set:where K represents the total number of divided sub-regions.
Optionally, counting the concentration of the fire extinguishing bomb component in unit volume in each subarea in the step S1 includes:
counting the concentration of the extinguishing bomb component per unit volume in each sub-zone, wherein the kth sub-zoneThe formula for calculating the concentration of the components of the extinguishing bomb in unit volume is as follows:
；
wherein:
represents the kth subregion->H represents the effective height of the action of the fire extinguishing bomb components; in the embodiment of the invention, H is set to be 1 meter;
representing the mass of fire extinguishing bomb components detected in the kth sub-zone;
represents the kth subregion->Is a concentration of the extinguishing bomb component per unit volume.
Optionally, constructing a fire extinguishing bomb explosion probability distribution model in the step S2, including:
constructing a fire extinguishing bomb explosion probability distribution model, wherein the fire extinguishing bomb explosion probability distribution model takes the distance from an explosion center point as input and takes the probability density of the component concentration of the fire extinguishing bomb in unit volume as output, and the representation form of the fire extinguishing bomb explosion probability distribution model is as follows:
；
wherein:
to be solved forModel parameters of the solution;
the radius of the kth circular area, namely the distance from the kth sub-area to the explosion center point;
representing +.>Probability density of constituent concentration per unit volume of fire extinguishing bomb at the location;
representing a Gamma function; in the embodiment of the present invention, < > a->；
According to the actual component concentration of the extinguishing bomb in unit volume of the extinguishing bomb acquired in the step S1, constructing and obtaining the center point of distance explosionActual probability density of constituent concentration of extinguishing bomb per unit volume of location:
；
wherein:
distance from the explosion center point->Actual probability density of the concentration of the fire extinguishing bomb component per unit volume of the location.
Optionally, in the step S3, the calculating the distribution expectation and variance of the distance parameter according to the constructed fire extinguishing bomb explosion probability distribution model includes:
solving the distribution expectation and variance of the distance parameters according to the constructed fire extinguishing bomb explosion probability distribution model, wherein the distribution expectation of the distance parameters is as follows:
；
wherein:
representing a distribution desire of the distance parameter R;
the variance of the distance parameter is:
；
wherein:
representing the variance of the distance parameter R.
Optionally, constructing the applicability function based on the distribution expectancy and variance of the distance parameter in the step S3 includes:
constructing an applicability function based on the distribution expectations and variances of the distance parameters, wherein the constructed applicability function is as follows:
；
wherein:
for the constructed applicability function, the smaller the applicability function is, the more accurate the estimated parameters are.
Optionally, in the step S4, model parameter robustness optimization solution is performed based on the applicability function, so as to obtain an optimal fire extinguishing bomb explosion probability distribution model, which includes:
carrying out robust optimization solving on model parameters to be solved in the fire extinguishing bomb explosion probability distribution model based on the applicability function, and constructing an optimal fire extinguishing bomb explosion probability distribution model based on the model parameters obtained by solving, wherein the robust optimization solving process comprises the following steps:
s41: initializing two groups of binary coding representations with length U, and respectively taking the binary coding representations as model parameters to be solvedBinary coded representation of (a); and performing random transformation on each group of binary coded representation to obtain model parameters to be solved +.>Model parameters to be solved->The H different sets of initial binary codes of (c) represent the results:
；
；
wherein:
representing model parameters to be solved->The h group of initial binary codes of (a) represents the result;
representing model parameters to be solved->The h group of initial binary codes of (a) represents the result;
；
s42: setting the current iteration number of the algorithm as t and the maximum iteration number as Max, and setting the parameters of the h group to-be-solved model obtained by the t iteration asWherein the initial value of t is 0;
s43: each group of model parameters to be solvedConverting into decimal system, inputting into applicability function to obtain a group of model parameters +.>Applicability function value->Wherein->Expressing the applicability function value of the h group of model parameters to be solved obtained by the t-th iteration;
s44: performing nonlinear transformation on the applicability function value of each group of model parameters to be solved:
；
；
wherein:
representing an adjustment parameter for adjusting the applicability function value obtained by the t-th iteration;
function value representing applicability->Is a non-linear transformation result of (a);
s45: generating robustness variation probabilities of each group of model parameters to be solved:
；
wherein:
representing model parameters to be solved->Decimal transformation results of (2);
representation->Random disturbance probability of>；
Representing +.>Actual probability density of constituent concentration of extinguishing bomb per unit volume of location;
representing model parameters to be solved->Robustness variation probability of (c);
s46: calculating the variation percentage of each group of model parameters to be solved:
；
wherein:
representing model parameters to be solved->The percentage of variation of (2);
s47: varying probabilities in terms of robustnessModel parameters to be solved->Performing mutation, wherein the mutation process comprises the following steps: respectively select->Middle->The coding results of the binary coding positions are exchanged, and U represents the length of the binary coding representing results;
taking the model parameters to be solved after the mutation treatment or the model parameters to be solved without the mutation treatment as the model parameters to be solved after the t+1st iterationRecording the model parameters to be solved with the lowest applicability function value after the t+1st iteration, and enabling +.>Returning to step S43 until t=max;
s48: according to the recorded Max groups of model parameters to be solved with the lowest applicability function value obtained by Max iterations, respectively calculating the robustness variation probability of each group of model parameters to be solved, and selecting the model parameters to be solved with the lowest robustness variation probability as the model parameters obtained by final solvingAnd use model parameters->And constructing an optimal fire extinguishing bomb explosion probability distribution model.
Optionally, in the step S5, detecting by using an optimal fire extinguishing bomb explosion probability distribution model to obtain a fire extinguishing bomb coverage area, including:
and calculating to obtain the fire extinguishing bomb coverage area by using the optimal fire extinguishing bomb explosion probability distribution model, wherein the fire extinguishing bomb coverage area calculation flow combining the optimal fire extinguishing bomb explosion probability distribution model is as follows:
generating robust probability density of constituent concentration of fire extinguishing bullet in unit volume of different subareas by using optimal fire extinguishing bullet explosion probability distribution model, wherein subareasThe robustness probability density of the component concentration of the extinguishing bomb per unit volume is as follows；
Generating sub-regionsDesired concentration of extinguishing bomb component per unit volume +.>Wherein M represents the total mass of the fire extinguishing bomb components; in the embodiment of the invention, the total mass of the fire extinguishing bomb components is the filling amount of the fire extinguishing agent in the fire extinguishing bomb;
if subareasIf the desired concentration of the extinguishing bomb component per unit volume is higher than the preset threshold value, the extinguishing bomb is indicated to be in the sub-area +.>Performing effective coverage;
and counting the total area of the subareas effectively covered by the fire extinguishing bomb to obtain the coverage area of the fire extinguishing bomb.
In order to solve the above-described problems, the present invention provides an electronic apparatus including:
a memory storing at least one instruction;
the communication interface is used for realizing the communication of the electronic equipment; and
And the processor executes the instructions stored in the memory to realize the method for detecting the robustness of the coverage of the fire extinguishing bomb.
In order to solve the above-mentioned problems, the present invention also provides a computer readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the fire extinguishing bomb coverage robustness detection method described above.
Compared with the prior art, the invention provides a method for detecting the robustness of the coverage of the fire extinguishing bomb, which has the following advantages:
firstly, the scheme provides a fire extinguishing bomb coverage detection method, according to a constructed fire extinguishing bomb explosion probability distribution model, the distribution expectation and variance of distance parameters are solved, wherein the distribution expectation of the distance parameters is as follows:
；
wherein:
representing a distribution desire of the distance parameter R;
the variance of the distance parameter is:
；
wherein:
square for representing distance parameter RAnd (3) difference. Constructing an applicability function based on the distribution expectations and variances of the distance parameters, wherein the constructed applicability function is as follows:
；
wherein:
is a function of the applicability of the construction. And carrying out robust optimization solution on model parameters to be solved in the fire extinguishing bomb explosion probability distribution model based on the applicability function, and constructing an optimal fire extinguishing bomb explosion probability distribution model based on the model parameters obtained by the solution. And calculating to obtain the fire extinguishing bomb coverage area by using the optimal fire extinguishing bomb explosion probability distribution model, wherein the fire extinguishing bomb coverage area calculation flow combining the optimal fire extinguishing bomb explosion probability distribution model is as follows: generating robust probability density of constituent concentration of fire extinguishing bomb in unit volume of different subareas by using optimal fire extinguishing bomb explosion probability distribution model, wherein subareas +.>The probability density of the robustness of the concentration of the constituents of the extinguishing bomb per unit volume is +.>The method comprises the steps of carrying out a first treatment on the surface of the Generating subregion->Desired concentration of extinguishing bomb component per unit volume +.>Wherein M represents the total mass of the fire extinguishing bomb components; if subareasIf the desired concentration of the extinguishing bomb component per unit volume is higher than the preset threshold value, the extinguishing bomb is indicated to be in the sub-area +.>Performing effective coverage; statistical fire suppressionThe total area of the subareas effectively covered by the cartridges yields the fire extinguishing cartridge coverage area. According to the scheme, different subareas are obtained according to the distance from the explosion center point of the fire extinguishing bomb, the actual probability density of the fire extinguishing bomb component concentration in unit volume of the different subareas is obtained, the probability density of the fire extinguishing bomb component concentration in unit volume corresponding to the different distances after the explosion of the fire extinguishing bomb is represented by poisson distribution, an estimated probability density function is obtained, the accuracy of the model parameters to be solved is taken as a target, an applicability function is built, the model parameters to be solved are solved to obtain an optimal fire extinguishing bomb explosion probability distribution model, further the expected concentration of the fire extinguishing bomb component in unit volume in the different areas is estimated under the condition of no detection, and the subarea area effectively covered by the fire extinguishing bomb is counted, so that the coverage detection of the fire extinguishing bomb is realized.
Meanwhile, the scheme provides a robust model solving method, robust optimization solving is carried out on model parameters to be solved in a fire extinguishing bomb explosion probability distribution model based on an applicability function, and an optimal fire extinguishing bomb explosion probability distribution model is constructed based on the model parameters obtained by solving, wherein the robust optimization solving flow is as follows: initializing two groups of binary coding representations with length U, and respectively taking the binary coding representations as model parameters to be solvedBinary coded representation of (a); and performing random transformation on each group of binary coded representation to obtain model parameters to be solved +.>Model parameters to be solved->The H different sets of initial binary codes of (c) represent the results: />；
；
Wherein:
representing model parameters to be solved->The h group of initial binary codes of (a) represents the result; />Representing model parameters to be solved->The h group of initial binary codes of (a) represents the result; />The method comprises the steps of carrying out a first treatment on the surface of the Setting the current iteration number of the algorithm as t and the maximum iteration number as Max, and obtaining an h group of model parameters to be solved by the t iteration as +.>Wherein the initial value of t is 0; the parameters of each group of models to be solved are +.>Converting into decimal system, inputting into applicability function to obtain a group of model parameters +.>Applicability function value->Wherein->Expressing the applicability function value of the h group of model parameters to be solved obtained by the t-th iteration; performing nonlinear transformation on the applicability function value of each group of model parameters to be solved:
；
；
wherein:
representing an adjustment parameter for adjusting the applicability function value obtained by the t-th iteration; />Function value representing applicability->Is a non-linear transformation result of (a); generating robustness variation probabilities of each group of model parameters to be solved:
；
wherein:
representing model parameters to be solved->Decimal transformation results of (2); />Representation->Random disturbance probability of>；/>Representing +.>Actual probability density of constituent concentration of extinguishing bomb per unit volume of position；/>Representing model parameters to be solved->Robustness variation probability of (c); calculating the variation percentage of each group of model parameters to be solved:
；
wherein:
representing model parameters to be solved->The percentage of variation of (2); according to the probability of robust variation->Model parameters to be solved->Performing mutation, wherein the mutation process comprises the following steps: respectively select->In (a)The coding results of the binary coding positions are exchanged, and U represents the length of the binary coding representing results; taking the model parameters to be solved after the mutation treatment or the model parameters to be solved without the mutation treatment as the model parameters to be solved after the t+1st iteration +.>Recording the model parameters to be solved with the lowest applicability function value after the t+1st iteration, and enabling +.>Until t=max; according to the recorded Max groups of model parameters to be solved with the lowest application degree function value obtained by Max iterations, respectively calculating the robustness variation probability of each group of model parameters to be solved, and selecting the model parameters to be solved with the lowest robustness variation probability as the model parameters obtained by final solving>And use model parameters->And constructing an optimal fire extinguishing bomb explosion probability distribution model. According to the scheme, the actual probability density of the constituent concentration of the fire extinguishing bomb in unit volume in different subareas is added with random disturbance probability to obtain actual probability density of robustness, the estimated probability density corresponding to different parameters of the fire extinguishing bomb to be solved is compared with the actual probability density of robustness to obtain robust variation probability of different groups of parameters of the model to be solved, variation processing of the parameters of the model to be solved is carried out based on the robust variation probability and the applicability function value, the solution space of parameter solution is expanded, the diversity of solution results is improved, the model parameters with better performance are obtained by solving, the smaller the robust variation probability is, the constructed fire extinguishing bomb explosion probability distribution model is higher in robustness, and further the fire extinguishing bomb coverage detection with stronger robustness is realized.
Drawings
Fig. 1 is a schematic flow chart of a method for detecting robustness of a coverage of a fire extinguishing bomb according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an electronic device for implementing a method for detecting robustness of a coverage area of a fire extinguishing bomb according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a method for detecting robustness of a coverage surface of a fire extinguishing bomb. The execution subject of the fire extinguishing bomb coverage robustness detection method includes, but is not limited to, at least one of a server, a terminal and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the fire extinguishing bomb coverage robustness detection method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1
S1: detonating fire extinguishing bomb in the target area and subdividing the target area into a plurality of subareas, and counting the component concentration of the fire extinguishing bomb in unit volume in each subarea.
In the step S1, the fire extinguishing bomb is detonated in the target area and the target area is subdivided into a plurality of subareas, and the method comprises the following steps:
detonating fire extinguishing bomb at the center of the target area, and subdividing the target area into a plurality of subareas, wherein the subarea division flow is as follows:
s11: with the center of the target area as the center point, respectively with the radiusDividing into K circular regions, wherein ∈>For the set region radius, the kth circular region is +.>，/>The radius of adjacent circular areas satisfies the following equation:
；
wherein:
the radius of the kth circular area with the center of the target area as the center point, +.>；
S12: dividing to obtain a kth sub-region:
；
wherein:
represents the kth sub-region;
represents the k-th circular area of division, < >>Is 0;
represents the region from the kth circle +.>Middle-planed k-1 th circular region +.>Is defined by the remaining area of the substrate;
s13: constructing and obtaining a subarea set:where K represents the total number of divided sub-regions.
And in the step S1, counting the component concentration of the extinguishing bomb in unit volume in each subarea, wherein the method comprises the following steps:
counting the concentration of the extinguishing bomb component per unit volume in each sub-zone, wherein the kth sub-zoneThe formula for calculating the concentration of the components of the extinguishing bomb in unit volume is as follows:
；
wherein:
represents the kth subregion->H represents the effective height of the action of the fire extinguishing bomb components; in the embodiment of the invention, H is set to be 1 meter;
representing the mass of fire extinguishing bomb components detected in the kth sub-zone;
represents the kth subregion->Is a concentration of the extinguishing bomb component per unit volume.
S2: and constructing a fire extinguishing bomb explosion probability distribution model, wherein the distance from an explosion center point is taken as input, and the probability density corresponding to the component concentration of the fire extinguishing bomb in unit volume is taken as output.
And S2, constructing a fire extinguishing bomb explosion probability distribution model, which comprises the following steps:
constructing a fire extinguishing bomb explosion probability distribution model, wherein the fire extinguishing bomb explosion probability distribution model takes the distance from an explosion center point as input and takes the probability density of the component concentration of the fire extinguishing bomb in unit volume as output, and the representation form of the fire extinguishing bomb explosion probability distribution model is as follows:
；
wherein:
the method comprises the steps of obtaining model parameters to be solved;
the radius of the kth circular area, namely the distance from the kth sub-area to the explosion center point;
representing +.>Probability density of constituent concentration per unit volume of fire extinguishing bomb at the location;
representing a Gamma function; in the embodiment of the present invention, < > a->；
According to the actual component concentration of the extinguishing bomb in unit volume of the extinguishing bomb acquired in the step S1, constructing and obtaining the center point of distance explosionActual probability density of constituent concentration of extinguishing bomb per unit volume of location:
；
wherein:
distance from the explosion center point->Actual probability density of the concentration of the fire extinguishing bomb component per unit volume of the location.
S3: and solving the distribution expectation and variance of the distance parameters according to the constructed fire extinguishing bomb explosion probability distribution model, and constructing the fitness function based on the distribution expectation and variance of the distance parameters.
In the step S3, according to the constructed fire extinguishing bomb explosion probability distribution model, the distribution expectation and variance of the distance parameter are solved, and the method comprises the following steps:
solving the distribution expectation and variance of the distance parameters according to the constructed fire extinguishing bomb explosion probability distribution model, wherein the distribution expectation of the distance parameters is as follows:
；
wherein:
representing a distribution desire of the distance parameter R;
the variance of the distance parameter is:
；
wherein:
representing the variance of the distance parameter R.
And in the step S3, constructing an applicability function based on the distribution expectation and variance of the distance parameters, wherein the method comprises the following steps:
constructing an applicability function based on the distribution expectations and variances of the distance parameters, wherein the constructed applicability function is as follows:
；
wherein:
for the constructed applicability function, the smaller the applicability function is, the more accurate the estimated parameters are.
S4: and carrying out model parameter robustness optimization solution according to the constructed applicability function to obtain the optimal fire extinguishing bomb explosion probability distribution model.
And in the step S4, model parameter robustness optimization solution is carried out based on the applicability function to obtain an optimal fire extinguishing bomb explosion probability distribution model, which comprises the following steps:
carrying out robust optimization solving on model parameters to be solved in the fire extinguishing bomb explosion probability distribution model based on the applicability function, and constructing an optimal fire extinguishing bomb explosion probability distribution model based on the model parameters obtained by solving, wherein the robust optimization solving process comprises the following steps:
s41: initializing two groups of binary coding representations with length U, and respectively taking the binary coding representations as model parameters to be solvedBinary coded representation of (a); and performing random transformation on each group of binary coded representation to obtain model parameters to be solved +.>Model parameters to be solved->The H different sets of initial binary codes of (c) represent the results:
；
；
wherein:
representing model parameters to be solved->The h group of initial binary codes of (a) represents the result;
representing model parameters to be solved->The h group of initial binary codes of (a) represents the result;
；
s42: setting the current iteration number of the algorithm as t and the maximum iteration number as Max, and setting the parameters of the h group to-be-solved model obtained by the t iteration asWherein the initial value of t is 0;
s43: each group of model parameters to be solvedConverting into decimal system, inputting into applicability function to obtain a group of model parameters +.>Applicability function value->Wherein->Expressing the applicability function value of the h group of model parameters to be solved obtained by the t-th iteration;
s44: performing nonlinear transformation on the applicability function value of each group of model parameters to be solved:
；
；
wherein:
representing an adjustment parameter for adjusting the applicability function value obtained by the t-th iteration;
function value representing applicability->Is a non-linear transformation result of (a);
s45: generating robustness variation probabilities of each group of model parameters to be solved:
；
wherein:
representing model parameters to be solved->Decimal transformation results of (2);
representation->Random disturbance probability of>；/>
Representing +.>Actual probability density of constituent concentration of extinguishing bomb per unit volume of location;
representing model parameters to be solved->Robustness variation probability of (c);
s46: calculating the variation percentage of each group of model parameters to be solved:
；
wherein:
representing model parameters to be solved->The percentage of variation of (2);
s47: varying probabilities in terms of robustnessModel parameters to be solved->Performing mutation, wherein the mutation process comprises the following steps: respectively select->Middle->The coding results of the binary coding positions are exchanged, and U represents the length of the binary coding representing results;
taking the model parameters to be solved after the mutation treatment or the model parameters to be solved without the mutation treatment as the model parameters to be solved after the t+1st iterationRecording the model parameters to be solved with the lowest applicability function value after the t+1st iteration, and enabling +.>Returning to step S43 until t=max;
s48: according to the recorded Max groups of model parameters to be solved with the lowest applicability function value obtained by Max iterations, respectively calculating the robustness variation probability of each group of model parameters to be solved, and selecting the model parameters to be solved with the lowest robustness variation probability as the model parameters obtained by final solvingAnd use model parameters->And constructing an optimal fire extinguishing bomb explosion probability distribution model.
S5: and calculating to obtain the coverage area of the extinguishing bomb by using the optimal extinguishing bomb explosion probability distribution model.
And S5, detecting by using an optimal fire extinguishing bomb explosion probability distribution model to obtain a fire extinguishing bomb coverage area, wherein the method comprises the following steps of:
and calculating to obtain the fire extinguishing bomb coverage area by using the optimal fire extinguishing bomb explosion probability distribution model, wherein the fire extinguishing bomb coverage area calculation flow combining the optimal fire extinguishing bomb explosion probability distribution model is as follows:
generating robust probability density of constituent concentration of fire extinguishing bullet in unit volume of different subareas by using optimal fire extinguishing bullet explosion probability distribution model, wherein subareasThe robustness probability density of the component concentration of the extinguishing bomb per unit volume is as follows；
Generating sub-regionsChinese billDesired concentration of fire extinguishing bomb component in bit volume +.>Wherein M represents the total mass of the fire extinguishing bomb components; in the embodiment of the invention, the total mass of the fire extinguishing bomb components is the filling amount of the fire extinguishing agent in the fire extinguishing bomb;
if subareasIf the desired concentration of the extinguishing bomb component per unit volume is higher than the preset threshold value, the extinguishing bomb is indicated to be in the sub-area +.>Performing effective coverage;
and counting the total area of the subareas effectively covered by the fire extinguishing bomb to obtain the coverage area of the fire extinguishing bomb.
Example 2:
fig. 2 is a schematic structural diagram of an electronic device for implementing a method for detecting robustness of a coverage area of a fire extinguishing bomb according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication interface 13 and a bus, and may further comprise a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects the respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules (a program 12 for realizing the fire extinguishing bomb coverage robustness detection, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process the data.
The communication interface 13 may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices and to enable connection communication between internal components of the electronic device.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 2 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 2 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
detonating fire extinguishing bomb in the target area, subdividing the target area into a plurality of subareas, and counting the component concentration of the fire extinguishing bomb in unit volume in each subarea;
constructing a fire extinguishing bomb explosion probability distribution model;
solving the distribution expectation and variance of the distance parameter according to the constructed fire extinguishing bomb explosion probability distribution model, and constructing an fitness function based on the distribution expectation and variance of the distance parameter;
carrying out model parameter robustness optimization solution according to the constructed applicability function to obtain an optimal fire extinguishing bomb blasting probability distribution model;
and calculating to obtain the coverage area of the extinguishing bomb by using the optimal extinguishing bomb explosion probability distribution model.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.
It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method 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, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (7)
1. A method for detecting robustness of a coverage area of a fire extinguishing bomb, the method comprising:
s1: detonating fire extinguishing bomb in the target area, subdividing the target area into a plurality of subareas, and counting the component concentration of the fire extinguishing bomb in unit volume in each subarea;
s2: constructing a fire extinguishing bomb explosion probability distribution model, wherein the model takes the distance from an explosion center point as input and takes the probability density corresponding to the component concentration of the fire extinguishing bomb in unit volume as output; comprising the following steps:
constructing a fire extinguishing bomb explosion probability distribution model, wherein the fire extinguishing bomb explosion probability distribution model takes the distance from an explosion center point as input and takes the probability density of the component concentration of the fire extinguishing bomb in unit volume as output, and the representation form of the fire extinguishing bomb explosion probability distribution model is as follows:
；
wherein:
the method comprises the steps of obtaining model parameters to be solved;
the radius of the kth circular area, namely the distance from the kth sub-area to the explosion center point;
representing +.>Probability density of constituent concentration per unit volume of fire extinguishing bomb at the location;
representing a Gamma function;
according to the actual component concentration of the extinguishing bomb in unit volume of the extinguishing bomb acquired in the step S1, constructing and obtaining the center point of distance explosionActual probability density of constituent concentration of extinguishing bomb per unit volume of location:
；
wherein:
distance from the explosion center point->Actual probability density of constituent concentration of extinguishing bomb per unit volume of location;
s3: solving the distribution expectation and variance of the distance parameter according to the constructed fire extinguishing bomb explosion probability distribution model, and constructing an fitness function based on the distribution expectation and variance of the distance parameter;
s4: carrying out model parameter robustness optimization solution according to the constructed applicability function to obtain an optimal fire extinguishing bomb blasting probability distribution model;
s5: and calculating to obtain the coverage area of the extinguishing bomb by using the optimal extinguishing bomb explosion probability distribution model.
2. A method of detecting robustness of a fire extinguishing bomb coverage as claimed in claim 1, wherein said step S1 of detonating the fire extinguishing bomb in the target area and subdividing the target area into a plurality of sub-areas comprises:
detonating fire extinguishing bomb at the center of the target area, and subdividing the target area into a plurality of subareas, wherein the subarea division flow is as follows:
s11: with the center of the target area as the center point, respectively with the radiusDividing into K circular regions, wherein ∈>For the set region radius, the kth circular region is +.>，/>The radius of adjacent circular areas satisfies the following equation:
；
wherein:
the radius of the kth circular area with the center of the target area as the center point, +.>；
S12: dividing to obtain a kth sub-region:
；
wherein:
represents the kth sub-region;
represents the k-th circular area of division, < >>Is 0;
represents the region from the kth circle +.>Middle-planed k-1 th circular region +.>Is defined by the remaining area of the substrate;
s13: constructing and obtaining a subarea set:where K represents the total number of divided sub-regions.
3. A method for detecting the robustness of a fire extinguishing bomb coverage as claimed in claim 2, wherein in step S1, the statistics of the concentration of fire extinguishing bomb components per unit volume in each sub-area include:
counting the concentration of the extinguishing bomb component per unit volume in each sub-zone, wherein the kth sub-zoneThe formula for calculating the concentration of the components of the extinguishing bomb in unit volume is as follows:
；
wherein:
represents the kth subregion->H represents the effective height of the action of the fire extinguishing bomb components;
representing the mass of fire extinguishing bomb components detected in the kth sub-zone;
represents the kth subregion->Is a concentration of the extinguishing bomb component per unit volume.
4. The method for detecting robustness of a fire extinguishing bomb coverage area according to claim 1, wherein in the step S3, the distribution expectation and variance of the distance parameter are solved according to the constructed fire extinguishing bomb explosion probability distribution model, and the method comprises the following steps:
solving the distribution expectation and variance of the distance parameters according to the constructed fire extinguishing bomb explosion probability distribution model, wherein the distribution expectation of the distance parameters is as follows:
；
wherein:
representing a distribution desire of the distance parameter R;
the variance of the distance parameter is:
；
wherein:
representing the variance of the distance parameter R.
5. The method for detecting robustness of coverage of fire extinguishing bomb according to claim 4, wherein the constructing the applicability function based on the distribution expectancy and variance of the distance parameter in the step S3 includes:
constructing an applicability function based on the distribution expectations and variances of the distance parameters, wherein the constructed applicability function is as follows:
；
wherein:
is a function of the applicability of the construction.
6. The method for detecting the robustness of the coverage of the fire extinguishing bomb according to claim 1, wherein in the step S4, the model parameter robustness optimization solution is performed based on the applicability function, so as to obtain an optimal fire extinguishing bomb explosion probability distribution model, and the method comprises the following steps:
carrying out robust optimization solving on model parameters to be solved in the fire extinguishing bomb explosion probability distribution model based on the applicability function, and constructing an optimal fire extinguishing bomb explosion probability distribution model based on the model parameters obtained by solving, wherein the robust optimization solving process comprises the following steps:
s41: initializing two groups of binary coding representations with length U, and respectively taking the binary coding representations as model parameters to be solvedBinary coded representation of (a); and performing random transformation on each group of binary coded representation to obtain model parameters to be solved +.>Model parameters to be solved->The H different sets of initial binary codes of (c) represent the results:
；
；
wherein:
representing model parameters to be solved->The h group of initial binary codes of (a) represents the result;
representing model parameters to be solved->The h group of initial binary codes of (a) represents the result;
；
s42: setting the current iteration number of the algorithm as t and the maximum iteration number as Max, and setting the parameters of the h group to-be-solved model obtained by the t iteration asWherein the initial value of t is 0;
s43: each group of model parameters to be solvedConverting into decimal system, inputting into applicability function to obtain a group of model parameters +.>Applicability function value->Wherein->Expressing the applicability function value of the h group of model parameters to be solved obtained by the t-th iteration;
s44: performing nonlinear transformation on the applicability function value of each group of model parameters to be solved:
；
；
wherein:
representing an adjustment parameter for adjusting the applicability function value obtained by the t-th iteration;
function value representing applicability->Is a non-linear transformation result of (a);
s45: generating robustness variation probabilities of each group of model parameters to be solved:
；
wherein:
representing waitingSolution model parameters->Decimal transformation results of (2);
representation->Random disturbance probability of>；
Representing +.>Actual probability density of constituent concentration of extinguishing bomb per unit volume of location;
representing model parameters to be solved->Robustness variation probability of (c);
s46: calculating the variation percentage of each group of model parameters to be solved:
；
wherein:
representing model parameters to be solved->The percentage of variation of (2);
s47: varying probabilities in terms of robustnessModel parameters to be solved->Performing mutation, wherein the mutation process comprises the following steps: respectively select->Middle->The coding results of the binary coding positions are exchanged, and U represents the length of the binary coding representing results;
taking the model parameters to be solved after the mutation treatment or the model parameters to be solved without the mutation treatment as the model parameters to be solved after the t+1st iterationRecording the model parameters to be solved with the lowest applicability function value after the t+1st iteration, and enabling +.>Returning to step S43 until t=max;
s48: according to the recorded Max groups of model parameters to be solved with the lowest applicability function value obtained by Max iterations, respectively calculating the robustness variation probability of each group of model parameters to be solved, and selecting the model parameters to be solved with the lowest robustness variation probability as the model parameters obtained by final solvingAnd use model parameters->And constructing an optimal fire extinguishing bomb explosion probability distribution model.
7. The method for detecting robustness of fire extinguishing bomb coverage area according to claim 6, wherein the step S5 of detecting the fire extinguishing bomb coverage area by using an optimal fire extinguishing bomb explosion probability distribution model comprises the steps of:
and calculating to obtain the fire extinguishing bomb coverage area by using the optimal fire extinguishing bomb explosion probability distribution model, wherein the fire extinguishing bomb coverage area calculation flow combining the optimal fire extinguishing bomb explosion probability distribution model is as follows:
generating robust probability density of constituent concentration of fire extinguishing bullet in unit volume of different subareas by using optimal fire extinguishing bullet explosion probability distribution model, wherein subareasThe probability density of the robustness of the concentration of the constituents of the extinguishing bomb per unit volume is +.>；
Generating sub-regionsDesired concentration of extinguishing bomb component per unit volume +.>Wherein M represents the total mass of the fire extinguishing bomb components;
if subareasIf the desired concentration of the extinguishing bomb component per unit volume is higher than the preset threshold value, the extinguishing bomb is indicated to be in the sub-area +.>Performing effective coverage;
and counting the total area of the subareas effectively covered by the fire extinguishing bomb to obtain the coverage area of the fire extinguishing bomb.
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