Multi-parameter space-time movement comparison test method for operation effect area

Abstract

The invention discloses an operation effect area multi-parameter space-time movement comparison test method, which comprises the following steps: acquiring work monitoring data of an artificial precipitation enhancement area, and preprocessing the work monitoring data; performing comparison parameter selection according to the work monitoring data to obtain multiple comparison parameters, and selecting a catalyst diffusion transmission scheme according to an operation mode to obtain an operation influence area; selecting an area which is not influenced by the catalytic operation according to the operation influence area to obtain a comparison removal area, and carrying out comparison test on the operation influence area and the comparison area according to the multiple movable comparison parameters to obtain a comparison test formula; and performing post-operation effect evaluation by adopting the increased rainfall according to the comparison test value to obtain an operation effect. The method not only can improve the precision of the multi-parameter space-time movement comparison test of the operation effect area, but also has better interpretability, and can be directly applied to a multi-parameter space-time movement comparison test system of the operation effect area.

Description

Technical Field

[0001] The present invention relates to the field of comparative inspection, and in particular to a method for comparative inspection of multi-parameter spatiotemporal movement of an operation effect area. Background Art

[0002] In recent years, with the continuous development of the national economy, society and industrialization, while it has brought convenience to people, it has also brought some negative effects. Meteorological disasters such as drought, rainstorms and strong winds have occurred frequently. In order to solve these problems, the demand for artificial rainmaking to resist drought, artificial hail prevention and artificial rain reduction has continued to increase, and artificial weather modification has received more widespread attention. With the increasing number of artificial catalytic operations, the unavoidable problem is that we need to know whether the operation is effective. However, the effect inspection after the operation is also a difficulty in artificial weather modification.

[0003] The effect test after the operation can be divided into physical test, statistical test and numerical simulation. Statistical test is a basic effect test method, including randomized and non-randomized schemes. The more famous randomized experiments include Israel's random cloud seeding experiment and the United States snow enhancement experiment. To use statistical tests to evaluate the effect after the operation, random experiments should be conducted, and statistical data should be used in random experiments. However, since the randomized experiment has a long test cycle and requires a large number of test samples, it is difficult to exclude uncertain factors such as natural clouds. Therefore, the operation method in actual business is usually non-randomized; for numerical simulation tests, model tests are to add cloud catalysis schemes to the model schemes to evaluate the effect after the operation, which depends on the accuracy of the numerical model; physical tests are to obtain the effect of the operation by studying the changes in physical parameters in the cloud precipitation process after the cloud is affected. For example, some studies have observed cloud trenches or strong echo bands formed by a large number of small ice crystals after cloud seeding on satellites and radars, but physical tests cannot directly give quantitative results, and are not very applicable when quantitative results are needed in many cases. Therefore, Kou Dai invented a multi-parameter spatiotemporal mobile comparison test method for the effect area to improve the accuracy, timeliness and precision of the effect test. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-parameter spatiotemporal movement comparison inspection method for an operation effect area.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] Collecting working monitoring data of the artificial rainfall increase area and preprocessing the working monitoring data;

[0008] By performing comparison parameter selection on the work monitoring data, a plurality of comparison parameters are obtained, a catalyst diffusion transmission scheme is selected according to the operation mode, and an operation influence area is obtained;

[0009] Selecting an area not affected by the catalytic operation according to the operation influence area to obtain a comparison area, and performing a comparison test on the operation influence area and the comparison area according to the multiple comparison parameters moved to obtain a comparison test value;

[0010] The rainfall increase amount is used to conduct a post-operation effect evaluation based on the comparative test value to obtain the operation effect.

[0011] Furthermore, the method of obtaining multiple comparison parameters by performing comparison parameter selection through the work monitoring data includes:

[0012] Obtain the parameters of the operation effect according to the work monitoring data, and calculate the correlation degree of the parameters to the operation effect:

[0013]

[0014] The bth parameter is h b , parameter h b The correlation degree of the operation effect is The parameter h at the sth moment b h b (s), the parameter h at the s+1th moment b h b (s+1), the number of parameters is N, the parameter h b The probability of occurrence is p(h b ), the sign function is sign(·), the parameter h b The actual operation effect after the change is Parameter h b The unchanged forecasting job effect is Parameter h b The weight is η b , parameter h b The error is σ b , the step function is δ|·|;

[0015] The parameters with a correlation degree of operation effect greater than 0.3172 are taken as candidate parameters, and the linear correlation between the candidate parameters is calculated:

[0016]

[0017] The average value of the bth candidate parameter is The vth candidate parameter is h v , the average value of the vth candidate parameter is Candidate parameter h b and candidate parameter h vThe linear correlation is γ(h b ,h v ), candidate parameter h b and candidate parameter h v The difference distance is Candidate parameter h b and candidate parameter h v The deviation is Δh b,v , similarity capacity parameter is ρ;

[0018] Calculate the change information between candidate parameters:

[0019]

[0020] The candidate parameter h b and candidate parameter h v The change information degree is ε b,v , the number of windows is p, and the deviation is Δh b,v The probability of occurrence is p(Δh b,v );

[0021] Calculate the degree of change of candidate parameters:

[0022]

[0023] The candidate parameter h b The importance of The scaling factor is ζ, and the heritability coefficient is The error value is δ;

[0024] The candidate parameters with importance greater than 0.137 are output as multiple comparison parameters.

[0025] Furthermore, the method for obtaining the operation impact zone includes:

[0026] Acquire the operation area according to the catalyst diffusion transmission scheme, and calculate the change values ​​of multiple comparison parameters of the operation area;

[0027] Δu i,b =u io (s)-u i,b (s+j)

[0028] The bth multiple comparison parameter of the i-th operation area is u i,b , the change value of the b-th multiple comparison parameters in the i-th operation area is Δu i,b , the initial value of the bth multiple comparison parameter in the ith operation area at the sth time is u io (s), the bth multiple comparison parameter of the ith operation area at the s+jth time is u i,b (s+j);

[0029] The operation areas with multiple comparison parameter change values ​​greater than 0.241 are selected as candidate impact areas, and primary division is performed according to the criterion that the deviation of multiple comparison parameter change values ​​is less than 0.216 to obtain primary areas;

[0030] Calculate the effect correlation of multiple comparison parameters:

[0031]

[0032] The external interference of the b-th multiple comparison parameters in the i-th primary area is B i,b , the number of multiple comparison parameters is N 1 , multiple comparison parameters u i,b The probability of occurrence is p(u i,b ), the importance weight of the b-th multiple comparison parameter in the ith primary region is τ i,b , multiple comparison parameters u i,b The effect correlation is

[0033] According to the effect correlation of multiple comparison parameters, the effect correlation matrix is ​​constructed, the primary area is input into the convolutional neural network classification model, and the convolution layer output is calculated:

[0034]

[0035] The convolutional layer output of the ath region is D a , the activation function is θ, the output bias is M, the convolution kernel size is Q, the classification boundary vector is d, the penalty coefficient is λ, and the numerical error of the zth comparison parameter is ε b , the diagonal matrix is ​​Σ, the effect correlation matrix of multiple comparison parameters in the ath primary region is Z, transposed to T, the effect correlation matrix of comparison parameters in different primary regions is R, and the effect correlation matrix of different primary regions in the first y columns is S y , the first y columns of the diagonal matrix are Σ y , the effect correlation matrix within the first y columns of the primary region is R y , the inverse matrix of the diagonal matrix is ​​∑ -1 , the number of primary regions is m;

[0036] Calculation output categories:

[0037]

[0038] The output of the fully connected layer in region a is f a , the category of the ath output is X a , the maximum pooling function value of the ath region is W a , the moving length is ω, the pooling size is β, and the number of outputs is

[0039] The output of the convolutional neural network classification model is classified according to the category and output as the job impact area.

[0040] Furthermore, a method for obtaining a comparison area by selecting an area not affected by the catalytic operation according to the operation impact area includes:

[0041] Under the same weather influencing system, an area that is perpendicular to the system movement direction, parallel to the operation influencing area, and not affected by the catalytic operation is selected as a comparison area with a range close to the location of the influencing area and the same shape and area.

[0042] Furthermore, the method for obtaining the comparative test formula includes:

[0043] Calculate the dynamic comparison values ​​of multiple comparison parameters of the operation impact area and multiple comparison parameters of the comparison area over time:

[0044]

[0045] The operation impact area is c and the comparison area is c 1 The bth multi-parameter comparison of the operation influence area at the sth time is u c,b (s), the bth multi-parameter comparison in the operation influence area at the s+jth time is u c,b (s+j), the bth multiple comparison parameter of the comparison area at the sth time is The bth multi-parameter comparison in the comparison area at the s+jth time is The dynamic comparison values ​​of multiple comparison parameters of the operation impact area and multiple comparison parameters of the comparison area over time are The number of multiple comparison parameters in the operation impact area is n c , the number of multiple comparison parameters in the comparison area is

[0046] In a very short time, multiple comparison parameters of the operation impact area and multiple comparison parameters of the comparison area are used as dynamic comparison values ​​over time as a comparison test formula.

[0047] Furthermore, the method of evaluating the effect of the operation after using the rainfall increase amount according to the comparative test value includes:

[0048] Calculate the average rainfall values ​​in the operation impact area and the comparison area:

[0049]

[0050] The average hourly rainfall in the region is The hourly rainfall observed by an automatic station in the region is P i , the number of stations that observed rainfall at the current time The total number of automatic rain gauges H;

[0051] According to the rainfall value, the rainfall comparison test value of the operation impact area and the comparison area is calculated, and the rainfall increase rate is calculated:

[0052]

[0053] Calculate the amount of rainfall increase:

[0054]

[0055] The area of ​​the affected area at a certain moment is V, and the amount of rainfall increase is G;

[0056] When the comparison test value is greater than 1, the effect of the operation is analyzed by analyzing the changing trends of the parameters of the impact area and the comparison area before and after the operation.

[0057] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0058] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0059] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0060] The beneficial effects of the present invention are:

[0061] The present invention is a multi-parameter spatiotemporal movement comparison inspection method for operation effect area. Compared with the prior art, the present invention has the following technical effects:

[0062] The present invention can improve the accuracy of multi-parameter spatiotemporal movement comparison and inspection in the operation effect area through preprocessing, comparison parameter selection, obtaining the operation influence area, obtaining the comparison area, obtaining the comparison inspection value and post-operation effect evaluation steps, thereby improving the precision of multi-parameter spatiotemporal movement comparison and inspection in the operation effect area, optimizing the multi-parameter spatiotemporal movement comparison and inspection in the operation effect area, greatly saving resources and improving work efficiency, and can realize automatic comparison and inspection of multi-parameter spatiotemporal movement in the operation effect area, and perform post-operation effect evaluation on the multi-parameter spatiotemporal movement comparison and inspection in the operation effect area in real time, which is of great significance for the automatic comparison and inspection of multi-parameter spatiotemporal movement in the operation effect area, can adapt to the automatic comparison and inspection of multi-parameter spatiotemporal movement in operation effect areas of different standards and the automatic comparison and inspection of multi-parameter spatiotemporal movement in different operation effect areas, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of the steps of a multi-parameter spatiotemporal movement comparison inspection method for an operation effect area of ​​the present invention;

[0064] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0065] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0066] The present invention provides a method for comparing and inspecting multi-parameter spatiotemporal movement of an operation effect area, comprising the following steps:

[0067] like Figure 1 As shown, in this embodiment, the following steps are included:

[0068] Collecting working monitoring data of the artificial rainfall increase area and preprocessing the working monitoring data;

[0069] In the actual evaluation, taking an aircraft catalytic operation in the XXX area on March 19, 2017 as an example, take-off time: 10:25; landing time: 11:43; operation catalytic time: 10:41-11:14; operation catalytic height is about 3700 meters; operation catalytic concentration: a total of 750g of silver iodide was spread, and the horizontal moving wind speed and direction of the 3700m spreading layer on that day was about 10m / s, 233°;

[0070] The CAPPI at different radar heights and rainfall were selected as parameters for calculation to see the effect of cloud seeding operation;

[0071] By performing comparison parameter selection on the work monitoring data, a plurality of comparison parameters are obtained, a catalyst diffusion transmission scheme is selected according to the operation mode, and an operation influence area is obtained;

[0072] In the actual assessment, multiple comparison parameters included hourly ground precipitation, hourly radar combined reflectivity, CAPPI at different altitudes, echo top height, echo volume, maximum reflectivity, vertical accumulated liquid water content, precipitation flux, optical thickness, cloud top height, cloud top temperature, cloud particles, and ice crystal concentration; 4 operational impact areas were selected;

[0073] Selecting an area not affected by the catalytic operation according to the operation influence area to obtain a comparison area, and performing a comparison test on the operation influence area and the comparison area according to the multiple comparison parameters moved to obtain a comparison test value;

[0074] In the actual evaluation, four comparison areas were selected;

[0075] The rainfall increase amount is used to conduct a post-operation effect evaluation based on the comparative test value to obtain the operation effect.

[0076] In this embodiment, the method of obtaining multiple comparison parameters by performing comparison parameter selection through the work monitoring data includes:

[0077] Obtain the parameters of the operation effect according to the work monitoring data, and calculate the correlation degree of the parameters to the operation effect:

[0078]

[0079] The bth parameter is h b , parameter h b The correlation degree of the operation effect is The parameter h at the sth moment b h b (s), the parameter h at the s+1th moment b h b (s+1), the number of parameters is N, the parameter h b The probability of occurrence is p(h b ), the sign function is sign(·), the parameter h b The actual operation effect after the change is Parameter h b The unchanged forecasting job effect is Parameter h b The weight is η b , parameter h b The error is σ b , the step function is δ|·|;

[0080] The parameters with a correlation degree of operation effect greater than 0.3172 are taken as candidate parameters, and the linear correlation between the candidate parameters is calculated:

[0081]

[0082] The average value of the bth candidate parameter is The vth candidate parameter is h v , the average value of the vth candidate parameter is Candidate parameter h b and candidate parameter h v The linear correlation is γ(h b ,h v ), candidate parameter h b and candidate parameter h v The difference distance is Candidate parameter h b and candidate parameter h v The deviation is Δh b,v , similarity capacity parameter is ρ;

[0083] Calculate the change information between candidate parameters:

[0084]

[0085] The candidate parameter h b and candidate parameter h v The change information degree is ε b,v , the number of windows is p, and the deviation is Δh b,v The probability of occurrence is p(Δh b,v );

[0086] Calculate the degree of change of candidate parameters:

[0087]

[0088] The candidate parameter h b The importance of The scaling factor is ζ, and the heritability coefficient is The error value is δ;

[0089] The candidate parameters with importance greater than 0.137 are output as multiple comparison parameters.

[0090] In this embodiment, the method for obtaining the operation influence area includes:

[0091] Acquire the operation area according to the catalyst diffusion transmission scheme, and calculate the change values ​​of multiple comparison parameters of the operation area;

[0092] Δu i,b =u io (s)-u i,b (s+j)

[0093] The bth multiple comparison parameter of the i-th operation area is u i,b , the change value of the b-th multiple comparison parameters in the i-th operation area is Δu i,b , the initial value of the bth multiple comparison parameter in the ith operation area at the sth time is u io (s), the bth multiple comparison parameter of the ith operation area at the s+jth time is u i,b (s+j);

[0094] The operation areas with multiple comparison parameter change values ​​greater than 0.241 are selected as candidate impact areas, and primary division is performed according to the criterion that the deviation of multiple comparison parameter change values ​​is less than 0.216 to obtain primary areas;

[0095] Calculate the effect correlation of multiple comparison parameters:

[0096]

[0097] The external interference of the b-th multiple comparison parameters in the i-th primary area is B i,b , the number of multiple comparison parameters is N 1 , multiple comparison parameters u i,b The probability of occurrence is p(u i,b ), the importance weight of the b-th multiple comparison parameter in the ith primary region is τ i,b , multiple comparison parameters u i,b The effect correlation is

[0098] According to the effect correlation of multiple comparison parameters, the effect correlation matrix is ​​constructed, the primary area is input into the convolutional neural network classification model, and the convolution layer output is calculated:

[0099]

[0100] The convolutional layer output of the ath region is D a , the activation function is θ, the output bias is M, the convolution kernel size is Q, the classification boundary vector is d, the penalty coefficient is λ, and the numerical error of the zth comparison parameter is ε b , the diagonal matrix is ​​Σ, the effect correlation matrix of multiple comparison parameters in the ath primary region is Z, transposed to T, the effect correlation matrix of comparison parameters in different primary regions is R, and the effect correlation matrix of different primary regions in the first y columns is S y , the first y columns of the diagonal matrix are Σ y , the effect correlation matrix within the first y columns of the primary region is R y , the inverse matrix of the diagonal matrix is ​​∑ -1 , the number of primary regions is

[0101] Calculation output categories:

[0102]

[0103] The output of the fully connected layer in region a is f a , the category of the ath output is χ a , the maximum pooling function value of the ath region is W a , the moving length is ω, the pooling size is β, and the number of outputs is

[0104] The output of the convolutional neural network classification model is classified according to the category and output as the job impact area.

[0105] In this embodiment, the method of obtaining a comparison area by selecting an area not affected by the catalytic operation according to the operation impact area includes:

[0106] Under the same weather influencing system, an area that is perpendicular to the system movement direction, parallel to the operation influencing area, and not affected by the catalytic operation is selected as a comparison area with a range close to the location of the influencing area and the same shape and area.

[0107] In this embodiment, the method for obtaining the comparison test formula includes:

[0108] Calculate the dynamic comparison values ​​of multiple comparison parameters of the operation impact area and multiple comparison parameters of the comparison area over time:

[0109]

[0110] The operation impact area is c and the comparison area is c 1 The bth multi-parameter comparison of the operation influence area at the sth time is u c,b (s), the bth multi-parameter comparison in the operation influence area at the s+jth time is u c,b (s+j), the bth multiple comparison parameter of the comparison area at the sth time is The bth multi-parameter comparison in the comparison area at the s+jth time is The dynamic comparison values ​​of multiple comparison parameters of the operation impact area and multiple comparison parameters of the comparison area over time are The number of multiple comparison parameters in the operation impact area is n c , the number of multiple comparison parameters in the comparison area is

[0111] In a very short time, multiple comparison parameters of the operation impact area and multiple comparison parameters of the comparison area are used as the dynamic comparison values ​​over time as the comparison test formula;

[0112] In the actual evaluation, the K value was significantly less than 1 before the operation; as the operation began, the K value increased significantly, and at 11:41, the ratio of rainfall in the operation impact area and the comparison area, K value was greater than 1, K was 1.2933; 2 hours after the operation, the K value reached a maximum of 2.0565, and then began to decline, and at 13:41, the K value was still greater than 1; 4 hours after the operation, there was no rainfall station in the impact area and the comparison area, and the K value was 0; the above analysis of rainfall using the regional moving multi-parameter comparative analysis method shows that this operation catalyzed the operation impact area to produce a certain rainfall-increasing effect.

[0113] In this embodiment, the method of evaluating the effect of the operation after using the rainfall increase amount according to the comparative test value includes:

[0114] Calculate the average rainfall values ​​in the operation impact area and the comparison area:

[0115]

[0116] The average hourly rainfall in the region is The hourly rainfall observed by an automatic station in the region is P i , the number of stations that observed rainfall at the current time The total number of automatic rain gauges H;

[0117] According to the rainfall value, the rainfall comparison test value of the operation impact area and the comparison area is calculated, and the rainfall increase rate is calculated:

[0118]

[0119] Calculate the amount of rainfall increase:

[0120]

[0121] The area of ​​the affected area at a certain moment is V, and the amount of rainfall increase is G;

[0122] When the comparison test value is greater than 1, the effect of the operation is analyzed by analyzing the change trend of the parameters of the impact area and the comparison area before and after the operation;

[0123] In the actual evaluation, the rainfall increase rates after the operation were approximately 0.520, 0.514, and 0.106, respectively; the average rainfall values ​​in the affected areas were approximately 0.056, 0.0473, 0.0422, and 0, respectively. The total precipitation in 1 hour after the catalytic operation was calculated to be 1.968×105m 3 ; The total precipitation 2 hours after the operation was 2.361×105m 3 ; The total precipitation in 3 hours after the operation was 1.191×105m 3 ; Total precipitation 4 hours after operation is 0m 3 ;

[0124] The total amount of rainfall increase in the affected area is 1.787×105m 3 .

[0125] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0126] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0127] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0128] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a multi-parameter spatiotemporal movement comparison inspection device for the operation effect area at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the aforementioned multi-parameter spatiotemporal movement comparison inspection methods for the operation effect area.

[0129] The above application Figure 1The multi-parameter spatiotemporal movement comparison inspection method of the operation effect area disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0130] The electronic device may also perform Figure 1 A multi-parameter space-time moving comparison test method for operation effect area is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0131] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, any one of the aforementioned multi-parameter spatiotemporal movement comparison inspection methods for the working effect area is executed.

[0132] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0136] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0137] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0138] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0139] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0140] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0141] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-parameter spatiotemporal movement comparison inspection method for operation effect area, characterized in that: The following steps are involved: Collecting working monitoring data of the artificial rainfall increase area and preprocessing the working monitoring data; By performing comparison parameter selection on the work monitoring data, a plurality of comparison parameters are obtained, a catalyst diffusion transmission scheme is selected according to the operation mode, and an operation influence area is obtained; Selecting an area not affected by the catalytic operation according to the operation influence area to obtain a comparison area, and performing a comparison test on the operation influence area and the comparison area according to the multiple comparison parameters moved to obtain a comparison test value; The rainfall increase amount is used to conduct a post-operation effect evaluation based on the comparative test value to obtain the operation effect.

2. According to claim 1, a multi-parameter spatiotemporal movement comparison inspection method for operation effect area is characterized in that: The method of obtaining a plurality of comparison parameters by performing comparison parameter selection through the work monitoring data includes: Obtain the parameters of the operation effect according to the work monitoring data, and calculate the correlation degree of the parameters to the operation effect: The bth parameter is h b , parameter h b The correlation degree of the operation effect is The parameter h at the sth moment b h b (s), the parameter h at the s+1th moment b h b (s+1), the number of parameters is N, the parameter h b The probability of occurrence is p(h b ), the sign function is sign(·), the parameter h b The actual operation effect after the change is Parameter h b The unchanged forecasting job effect is Parameter h b The weight is η b , parameter h b The error is σ b , the step function is δ|·|; The parameters with a correlation degree of operation effect greater than 0.3172 are taken as candidate parameters, and the linear correlation between the candidate parameters is calculated: The average value of the bth candidate parameter is The vth candidate parameter is h v , the average value of the vth candidate parameter is Candidate parameter h b and candidate parameter h v The linear correlation is γ(h b ,h v ), candidate parameter h b and candidate parameter h v The difference distance is Candidate parameter h b and candidate parameter h v The deviation is Δh b,v , similarity capacity parameter is ρ; Calculate the change information between candidate parameters: The candidate parameter h b and candidate parameter h v The change information degree is ε b,v , the number of windows is p, and the deviation is Δh b,v The probability of occurrence is p(Δh b,v ); Calculate the degree of change of candidate parameters: The candidate parameter h b The importance of The scaling factor is ζ, and the heritability coefficient is The error value is δ; The candidate parameters with importance greater than 0.137 are output as multiple comparison parameters.

3. According to claim 1, a multi-parameter spatiotemporal movement comparison inspection method for operation effect area is characterized in that: The method for obtaining the operation influence area comprises: Acquire the operation area according to the catalyst diffusion transmission scheme, and calculate the change values ​​of multiple comparison parameters of the operation area; Δu i,b =u io (s)-u i,b (s+j) The bth multiple comparison parameter of the i-th operation area is u i,b , the change value of the b-th multiple comparison parameters in the i-th operation area is Δu i,b , the initial value of the bth multiple comparison parameter in the ith operation area at the sth time is u io (s), the bth multiple comparison parameter of the ith operation area at the s+jth time is u i,b (s+j); The operation areas with multiple comparison parameter change values ​​greater than 0.241 are selected as candidate impact areas, and primary division is performed according to the criterion that the deviation of multiple comparison parameter change values ​​is less than 0.216 to obtain primary areas; Calculate the effect correlation of multiple comparison parameters: The external interference of the b-th multiple comparison parameters in the i-th primary area is B i,b , the number of multiple comparison parameters is N1, and the number of multiple comparison parameters u i,b The probability of occurrence is p(u i,b ), the importance weight of the b-th multiple comparison parameter in the ith primary region is τ i,b , multiple comparison parameters u i,b The effect correlation is According to the effect correlation of multiple comparison parameters, the effect correlation matrix is ​​constructed, the primary area is input into the convolutional neural network classification model, and the convolution layer output is calculated: The convolutional layer output of the ath region is D a , the activation function is θ, the output bias is M, the convolution kernel size is Q, the classification boundary vector is d, the penalty coefficient is λ, and the numerical error of the zth comparison parameter is ε b , the diagonal matrix is ​​Σ, the effect correlation matrix of multiple comparison parameters in the ath primary region is Z, transposed to T, the effect correlation matrix of comparison parameters in different primary regions is R, and the effect correlation matrix of different primary regions in the first y columns is S y , the first y columns of the diagonal matrix are Σ y , the effect correlation matrix within the first y columns of the primary region is R y , the inverse matrix of the diagonal matrix is ​​Σ -1 , the number of primary regions is m; Calculation output categories: The output of the fully connected layer in region a is f a , the category of the ath output is χ a , the maximum pooling function value of the ath region is W a , the moving length is ω, the pooling size is β, and the number of outputs is m1; The output of the convolutional neural network classification model is classified according to the category and output as the job impact area.

4. According to claim 1, a multi-parameter spatiotemporal movement comparison inspection method for operation effect area is characterized in that: The method of obtaining a comparison area by selecting an area not affected by catalytic operation according to the operation impact area includes: Under the same weather influencing system, an area that is perpendicular to the system movement direction, parallel to the operation influencing area, and not affected by the catalytic operation is selected as a comparison area with a range close to the location of the influencing area and the same shape and area.

5. According to claim 1, a multi-parameter spatiotemporal movement comparison inspection method for operation effect area is characterized in that: The method for obtaining the comparative test formula includes: Calculate the dynamic comparison values ​​of multiple comparison parameters of the operation impact area and multiple comparison parameters of the comparison area over time: The operation influence area is c, the comparison area is c1, and the bth multi-parameter comparison of the operation influence area at the sth time is u c,b (s), the bth multi-parameter comparison in the operation influence area at the s+jth time is u c,b (s+j), the bth multiple comparison parameter of the comparison area at the sth time is The bth multi-parameter comparison in the comparison area at the s+jth time is The dynamic comparison values ​​of multiple comparison parameters of the operation impact area and multiple comparison parameters of the comparison area over time are The number of multiple comparison parameters in the operation impact area is n c , the number of multiple comparison parameters in the comparison area is In a very short time, multiple comparison parameters of the operation impact area and multiple comparison parameters of the comparison area are used as dynamic comparison values ​​over time as a comparison test formula.

6. According to claim 1, a multi-parameter spatiotemporal movement comparison inspection method for operation effect area is characterized in that: The method of evaluating the effect of the operation after using the rainfall increase amount according to the comparative test value includes: Calculate the average rainfall values ​​in the operation impact area and the comparison area: The average hourly rainfall in the region is The hourly rainfall observed by an automatic station in the region is P i , the number of stations that observed rainfall at the current time The total number of automatic rain gauges H; According to the rainfall value, the rainfall comparison test value of the operation impact area and the comparison area is calculated, and the rainfall increase rate is calculated: Calculate the amount of rainfall increase: The area of ​​the affected area at a certain moment is V, and the amount of rainfall increase is G; When the comparison test value is greater than 1, the effect of the operation is analyzed by analyzing the changing trends of the parameters of the impact area and the comparison area before and after the operation.

7. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute the method according to any one of claims 1 to 6.

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