Ground vibration field PGA attenuation relation statistical analysis method and equipment

Abstract

The embodiment of the invention provides a statistical analysis method and device for a PGA attenuation relation of an earthquake site, and the method comprises the steps: constructing an earthquake source model based on a change rule of an earthquake motion parameter along with an earthquake magnitude; constructing a propagation path model based on the change rule of the seismic oscillation parameters along with the distance; constructing a site condition model based on the change rule of the seismic oscillation parameters along with the local site conditions; constructing a seismic oscillation attenuation relation model based on the seismic source model, the propagation path model and the site condition model; and carrying out seismic oscillation field PGA attenuation relation statistical analysis based on the seismic oscillation attenuation relation model. According to the invention, the attenuation relation can be obtained through direct statistics of the strong vibration data of the seismic field.

Description

Technical Field

[0001] The present disclosure relates to the field of earthquake monitoring technology, and in particular to a method, device, equipment and medium for statistical analysis of PGA attenuation relationship of earthquake motion sites. Background Art

[0002] In geological engineering, attenuation relationships are usually used to analyze seismic motion behavior, but attenuation relationships are constantly evolving, and seismic motion parameters gradually develop from the earliest earthquake intensity to peak acceleration, response spectrum, etc.

[0003] From the perspective of national attenuation relationship research, due to the rich historical earthquake intensity data, the research results on earthquake intensity attenuation relationship are also quite rich. Therefore, the national ground motion parameter attenuation relationship is obtained by converting the earthquake intensity of the local area with the intensity and ground motion parameter attenuation relationship of the reference area, that is, the conversion method. At present, the mapping method is often used for conversion, that is, on the basis of determining the ground motion attenuation relationship partition, the ground motion intensity attenuation relationship of each partition is established; the designated area is selected as the reference area, the ground motion attenuation relationship of the area is established, and the appropriate reference area ground motion attenuation relationship is selected, and the ground motion parameter attenuation relationship of each partition is obtained by the conversion method. In the conversion process, some strong earthquake data are also used for control. Huo Junrong (1989) used the relatively new strong earthquake data in the western United States (mostly analog records) to determine the attenuation relationship of the horizontal and vertical peaks and response spectra in the western United States, and based on the difference in the intensity attenuation relationship between China and the United States, the attenuation relationship of the four ground motion partitions in my country was established by conversion correction. Yu Yanxiang and Wang Suyun (2006) used strong earthquake data (mainly digital records) and broadband velocity records in the western United States to regress the horizontal long-period attenuation relationship in the western United States, and used the intensity attenuation relationship formed in the process of compiling the fourth-generation map. Through the conversion and correction of the above two attenuation relationships, the response spectrum attenuation relationship of the two earthquake motion zones in the east and west of my country was established. Xiao Liang (2011) used the data of the US NGA strong earthquake database and, based on Yu Yanxiang and Wang Suyun (2006), used a piecewise linear model to regress the horizontal long-period attenuation relationship in the western United States. Then, the intensity attenuation relationship of the Chinese sub-zones established in the process of compiling the fifth-generation map was used, and the response spectrum attenuation relationship of the four earthquake motion zones in my country was obtained through conversion and correction. Xiao Liang and Yu Yanxiang (2021) used the recently supplemented broadband velocity bedrock records in the western United States to correct the long-period (period > 1.2s) part of Xiao Liang's (2011) results, and obtained the attenuation relationship of the horizontal long-period response spectrum of my country's regional divisions (period to 10s) through intensity conversion. It was issued as a recommended appendix in the "Outline for Regional Earthquake Safety Assessment (Trial)" (China Earthquake Administration, 2019) in the earthquake system, referred to as the Regional Assessment 2019 Model. These sets of attenuation relationships are widely used in actual engineering seismic fortification because they are oriented to larger seismic motion divisions.

[0004] my country started large-scale strong earthquake observations relatively late. Although strong earthquake observation data have been rapidly accumulated in recent years, most areas of the country are still not large enough to directly use strong earthquake data to obtain the attenuation relationship. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for statistical analysis of PGA attenuation relationship of earthquake field, aiming to directly obtain the attenuation relationship through statistics of strong vibration data of the earthquake field.

[0006] The technical solution of the present disclosure is as follows:

[0007] In a first aspect, an embodiment of the present disclosure provides a method for statistically analyzing the PGA attenuation relationship of a seismic field, including:

[0008] Based on the variation of earthquake motion parameters with earthquake magnitude, a source model is constructed;

[0009] Based on the variation of ground motion parameters with distance, a propagation path model is constructed;

[0010] Based on the variation of ground motion parameters with local site conditions, a site condition model is constructed;

[0011] Based on the earthquake source model, propagation path model and site condition model, a seismic motion attenuation relationship model is constructed;

[0012] Based on the earthquake attenuation relationship model, a statistical analysis of the PGA attenuation relationship of earthquake sites is conducted.

[0013] In a possible implementation, in the method provided by the embodiment of the present invention, the source model formula is expressed as:

[0014]

[0015] Where M is the magnitude, a1, a2, b1 and b2 are regression coefficients, and Mc is the cutoff magnitude.

[0016] In a possible implementation, in the method provided in the embodiment of the present invention, the propagation path model formula is expressed as:

[0017] f(R)=-(cM+d)lg(R+h 0 )

[0018] Among them, R is the distance parameter, M is the magnitude, c, d and h 0 is the regression coefficient.

[0019] In a possible implementation, in the method provided in the embodiment of the present invention, the site condition model formula is expressed as:

[0020] f(site)=f(rock)+s 1 S 1 +s 2 S 2 +…

[0021] Where f(rock) is the model of bedrock site; S 1 and S 2 Respectively represent the categories of various sites. When the site is of the corresponding category, the item is 1, otherwise it is 0; s 1 ,s 2, ...are regression coefficients.

[0022] In a possible implementation, in the method provided in the embodiment of the present invention, the source model formula is also expressed as:

[0023] f(R)=-clg[R+dexp(eM)]

[0024] Where R is the distance parameter, M is the magnitude, and c, d and e are regression coefficients.

[0025] In a possible implementation, in the method provided in the embodiment of the present invention, the field source model formula is also expressed as:

[0026] f(M)=a+bM+cM 2

[0027] Among them, M is the magnitude, a, b and c are regression coefficients, and c is taken as a negative value.

[0028] Among them, the earthquake motion attenuation relationship model formula is expressed as:

[0029] lgY=f(M)+f(R)+f(site)+ε, where f(M) is the source model, f(R) is the propagation path model, f(site) is the site condition model, and ε is a random term with a mean of 0 and a standard deviation of σ.

[0030] In a second aspect, the present disclosure also provides a device for statistically analyzing the PGA attenuation relationship of a seismic field, including:

[0031] A source model building module is used to build a source model based on the variation of ground motion parameters with earthquake magnitude;

[0032] A propagation path model building module is used to build a propagation path model based on the variation of ground motion parameters with distance;

[0033] A site condition model building module is used to build a site condition model based on the variation of ground motion parameters with local site conditions;

[0034] A seismic motion attenuation relationship model building module is used to build a seismic motion attenuation relationship model based on a source model, a propagation path model and a site condition model;

[0035] The statistical analysis module is used to perform statistical analysis of the PGA attenuation relationship of the earthquake site based on the earthquake attenuation relationship model.

[0036] In a third aspect, an embodiment of the present disclosure further provides an electronic device, including:

[0037] processor;

[0038] a memory for storing processor-executable instructions;

[0039] The processor is configured to execute instructions to implement the method of the first aspect.

[0040] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method of the first aspect when executed by a processor.

[0041] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, comprising a computer program / instructions, wherein the computer program / instructions implement the method of the first aspect when executed by a processor.

[0042] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0043] In the embodiments of the present disclosure, a source model is constructed based on the variation law of earthquake motion parameters with magnitude; a propagation path model is constructed based on the variation law of earthquake motion parameters with distance; a site condition model is constructed based on the variation law of earthquake motion parameters with local site conditions; a seismic motion attenuation relationship model is constructed based on the source model, the propagation path model and the site condition model; and a statistical analysis of the seismic site PGA attenuation relationship is performed based on the seismic motion attenuation relationship model. Through the present invention, the attenuation relationship can be directly obtained by statistically analyzing the strong vibration data of the earthquake field.

[0044] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0046] Figure 1 A schematic diagram of a flow chart of a method for statistically analyzing the PGA attenuation relationship of a ground motion site provided by an embodiment of the present disclosure;

[0047] Figure 2 A schematic diagram of the structure of a device for statistically analyzing the PGA attenuation relationship of a seismic field provided by an embodiment of the present disclosure;

[0048] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0049] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0050] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0051] Figure 1 This is a flow chart of a method for statistically analyzing the PGA attenuation relationship of a ground motion site provided by the first embodiment of the present disclosure, such as Figure 1 As shown, the method for statistical analysis of the PGA attenuation relationship of ground motion sites may include the following steps:

[0052] S101, constructing a source model based on the variation of earthquake motion parameters with earthquake magnitude.

[0053] The earthquake source model describes the change of earthquake motion parameters with earthquake magnitude. Early studies often used the linear term of earthquake magnitude to describe the growth of earthquake motion parameters with earthquake magnitude. In this case, it is assumed that as long as the earthquake magnitude increases, the earthquake motion parameters will increase linearly. The formula is as follows:

[0054] f(M)=a+bM

[0055] Where M is the magnitude, and a and b are regression coefficients.

[0056] However, for the commonly used peak or high-frequency response spectrum values, these parameters do not increase infinitely with the increase of magnitude, but there is a saturation upper limit, which is the so-called magnitude saturation effect. Therefore, in order to reflect the magnitude saturation effect, many studies have made corresponding improvements based on the linear model. One of the commonly used improvements is to introduce the form of quadratic terms to describe it. In this model, as the magnitude increases, the growth rate of the seismic motion parameters gradually decreases. The formula is expressed as follows:

[0057] f(M)=a+bM+cM 2 Where M is the magnitude, a, b and c are regression coefficients, and c is usually a negative value.

[0058] Another way to consider the magnitude saturation effect is to divide the magnitudes into different segments. The formula is as follows:

[0059]

[0060] Where M is the magnitude, a1, a2, b1 and b2 are regression coefficients, and Mc is the cutoff magnitude.

[0061] S102, constructing a propagation path model based on the variation pattern of earthquake motion parameters with distance.

[0062] The propagation path model describes the variation of seismic parameters with distance. For commonly used seismic parameters such as peak or response spectrum values, they generally show a downward trend with the increase of propagation distance, which is the so-called distance attenuation. Early studies were limited to the number of strong earthquake data, and there were few studies on the near-field distance saturation effect of large earthquakes, and constant scale factors were often used. This model avoids the problem of taking the logarithm at a distance of 0 and is suitable for small and medium magnitude earthquakes. The formula is shown below:

[0063] f(R))=-clg(R+h 0 )

[0064]

[0065] Where R is the distance parameter, h0 is the scale factor, and c and h0 are regression coefficients.

[0066] As the understanding of earthquake motion characteristics gradually improves, researchers have realized that the attenuation of high-frequency earthquake motion in the near field is significantly slower than that in the mid- and far-field regions, which is the saturation effect of the near field of a large earthquake. A common method is to change the scale factor to a saturation factor that increases exponentially with the magnitude M, as shown in the following formula:

[0067] f(R)=-clg[R+dexp(eM)]

[0068] Where R is the distance parameter, M is the magnitude, and c, d, and e are regression coefficients.

[0069] As shown in the above formula, the near area with slow attenuation for large magnitudes is significantly larger than that for small and medium magnitudes. Another way is to write the exponential attenuation term of distance as a linear function of magnitude, where the term that changes with magnitude M acts outside the logarithmic term. In this case, the attenuation curves of different magnitudes are no longer parallel, but have a bell-shaped shape, and the larger the magnitude, the slower the curve decays. The formula is as follows:

[0070] f(R)=-(cM+d)lg(R+h 0 )

[0071] Where R is the distance parameter, M is the magnitude, and c, d and h0 are regression coefficients.

[0072] S103, constructing a site condition model based on the variation of the seismic parameters with the local site conditions.

[0073] The site condition model describes the variation of seismic parameters with local site conditions. Generally speaking, for bedrock sites and soil sites on the free surface at the same magnitude-distance, the seismic parameters of the two sites are somewhat different and should not be mixed.

[0074] In order to reflect the influencing factors of the site, a simple processing method is to use only the records of the target site conditions and discard the records of other site conditions when performing attenuation relationship regression. This method assumes that the relationship between the change of seismic parameters with magnitude and distance in different site classifications is different. Its limitation is that there must be a sufficient amount of data in each type of site condition.

[0075] Another treatment method is to use factors to relate the seismic parameters between different site conditions, as shown in the following formula. This model uses bedrock seismic motion as a reference and assumes that the results of different sites should be multiplied by a certain adjustment factor compared to the bedrock.

[0076] f(site)=f(rock)+s 1 S 1 +s 2 S 2 +…

[0077] Where f(rock) is the model of bedrock site; S 1 and S 2 Respectively represent the categories of various sites. When the site is of the corresponding category, the item is 1, otherwise it is 0; s 1 ,s 2 , ... are regression coefficients. The advantage of this method is that the data of soil layer and bedrock can be used to build the model, which expands the data set; its limitation is that the relationship between the seismic parameters of different site classifications needs to be determined in advance, and when a constant factor is used, that is, the seismic attenuation curves of the soil layer and bedrock are parallel, this assumption is inconsistent with the inelastic effect of the soil.

[0078] S104, constructing a seismic motion attenuation relationship model based on the earthquake source model, the propagation path model and the site condition model. The seismic motion attenuation relationship model formula is expressed as:

[0079] lgY=f(M)+f(R)+f(site)+ε

[0080] Among them, f(M) is the earthquake source model, f(R) is the propagation path model, f(site) is the site condition model, and ε is a random term with mean 0 and standard deviation σ.

[0081] S105, based on the earthquake motion attenuation relationship model, statistical analysis of the earthquake motion site PGA attenuation relationship is performed.

[0082] In order to solve the problem of parallel seismic attenuation curves of soil and bedrock sites, some studies added magnitude, distance and amplitude related terms to the soil effect factor and used V S30 (equivalent shear wave velocity within 30m of the surface) is used as an independent variable, and the next generation attenuation model (NGA) attenuation relationship often uses this model. For example, the site model used by ASK14 in the NGA-west2 database is as follows:

[0083]

[0084] In the formula, v S30 = Reference ground motion parameter value when v = 1100m / s lin v indicates that the amplification effect can be regarded as linear S30 The remaining terms are regression coefficients and vary with the period.

[0085] The above model makes different v S30 The amplification effect of is related to the amplitude of the response spectrum of the reference bedrock surface. S30 More than v lin It then manifests itself as linear soil amplification, and in weaker sites it manifests itself as nonlinear soil effects, which is relatively complex overall. At present, most engineering applications in my country use the attenuation relationship of bedrock to estimate seismic parameters, and then use drilling data to analyze the dynamic response of the soil layer, or use the seismic parameter conversion table of different sites to estimate the seismic parameters of other sites. Generally, the attenuation relationship of the soil layer is not used directly.

[0086] Based on the same inventive concept, the embodiment of the present disclosure also provides a device for statistically analyzing the PGA attenuation relationship of a seismic field. Figure 2 As shown, the ground motion field PGA attenuation relationship statistical analysis device 200 includes:

[0087] A source model building module 210 is used to build a source model based on the variation of ground motion parameters with earthquake magnitude;

[0088] A propagation path model building module 220, for building a propagation path model based on the variation of ground motion parameters with distance;

[0089] A site condition model building module 230 is used to build a site condition model based on the variation of ground motion parameters with local site conditions;

[0090] A seismic motion attenuation relationship model building module 240 is used to build a seismic motion attenuation relationship model based on a source model, a propagation path model and a site condition model;

[0091] The statistical analysis module 250 is used to perform statistical analysis on the PGA attenuation relationship of the earthquake site based on the earthquake attenuation relationship model.

[0092] The specific implementation method and technical effect of the device provided in the embodiment of the present disclosure are similar to those of the above-mentioned method embodiment, and will not be repeated here.

[0093] In addition, combined Figure 1-Figure 2 The battery capacity prediction method and device described in the embodiments of the present application can be implemented by an electronic device. Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0094] like Figure 3 As shown, the electronic device 1000 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1008 to a random access memory (RAM) 1003 to implement the battery capacity prediction method of the embodiment described in the present disclosure. In the RAM 1003, various programs and data required for the operation of the electronic device 1000 are also stored. The processing device 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0095] Typically, the following devices may be connected to the I / O interface 1005: an input device 1006 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1007 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1008 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the electronic device 1000 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 1000 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0096] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart, thereby implementing the voice control method as described above. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 1009, or installed from the storage device 1008, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0097] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0098] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0099] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0100] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0101] A flow proportional valve is provided on the second oil supply circuit of the first hydraulic cylinder in the dual hydraulic cylinder system;

[0102] When the dual hydraulic cylinder system is in operation, the second oil supply circuit of the first hydraulic cylinder is closed, and oil is supplied through the regular oil supply circuit of the first hydraulic cylinder;

[0103] Real-time monitoring of the displacement deviation of the first hydraulic cylinder piston rod and the second hydraulic cylinder piston rod in a dual hydraulic cylinder system;

[0104] When it is detected that the displacement stroke deviation between the piston rod of the first hydraulic cylinder and the piston rod of the second hydraulic cylinder is greater than a preset displacement stroke deviation threshold range, the conventional oil supply circuit of the first hydraulic cylinder is closed and the second oil supply circuit is opened;

[0105] After the second oil supply circuit is opened, the oil supply to the first hydraulic cylinder is adjusted through the flow proportional valve to adjust the displacement stroke of the piston rod of the first hydraulic cylinder, thereby realizing the correction control of the displacement stroke of the piston rod of the first hydraulic cylinder and the piston rod of the second hydraulic cylinder.

[0106] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps described in the above embodiments.

[0107] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0108] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0109] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0110] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0111] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] The battery capacity prediction method mentioned in the embodiment of the present invention includes:

[0113] In the embodiment of the present disclosure, a fully charged battery to be tested is first discharged at a constant current, and the discharge amount is determined, then the open circuit voltage of the battery to be tested is measured, and the remaining battery capacity of the current battery is predicted based on the corresponding relationship between the open circuit voltage and the remaining battery capacity, and finally the battery capacity of the battery to be tested is determined based on the remaining capacity and the discharge amount. A battery capacity prediction scheme is provided, which uses the pre-fitted corresponding relationship between the open circuit voltage and the remaining battery capacity, and can accurately predict the battery capacity of the battery to be tested with only one discharge, effectively shortening the process time of the capacity test, ensuring continuous production, and saving generation energy consumption.

[0114] 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.

[0115] 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 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] 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.

[0117] 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.

[0118] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0119] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A statistical analysis method for the PGA attenuation relationship of ground motion sites, characterized in that: include: Based on the variation of earthquake motion parameters with earthquake magnitude, a source model is constructed; Based on the variation of ground motion parameters with distance, a propagation path model is constructed; Based on the variation of ground motion parameters with local site conditions, a site condition model is constructed; Based on the earthquake source model, propagation path model and site condition model, construct a seismic motion attenuation relationship model; Based on the earthquake motion attenuation relationship model, a statistical analysis of the earthquake site PGA attenuation relationship is performed.

2. The method for statistical analysis of ground motion PGA attenuation relationship according to claim 1, characterized in that: The earthquake source model formula is expressed as: Where M is the magnitude, a1, a2, b1 and b2 are regression coefficients, and Mc is the cutoff magnitude.

3. The method for statistical analysis of ground motion PGA attenuation relationship according to claim 1, characterized in that: The propagation path model formula is expressed as: f(R)=-(cM+d)lg(R+h0) Among them, R is the distance parameter, M is the magnitude, and c, d and h0 are regression coefficients.

4. The method for statistical analysis of ground motion PGA attenuation relationship according to claim 1, characterized in that: The site condition model formula is expressed as: f(site)=f(rock)+s1S1+s2S2+… Where f(rock) is the model of bedrock site; S1 and S2 represent the categories of various sites respectively. When the site belongs to the corresponding category, the item is 1, otherwise it is 0; s1, s2, ... are regression coefficients.

5. The method for statistical analysis of ground motion PGA attenuation relationship according to claim 1, characterized in that: The earthquake source model formula is also expressed as: f(R)=-clg[R+dexp(eM)] Where R is the distance parameter, M is the magnitude, and c, d, and e are regression coefficients.

6. The method for statistical analysis of ground motion PGA attenuation relationship according to claim 1, characterized in that: The field source model formula is also expressed as: f(M)=a+bM+cM 2 Among them, M is the magnitude, a, b and c are regression coefficients, and c is taken as a negative value.

7. The method for statistical analysis of ground motion PGA attenuation relationship according to claim 1, characterized in that: The earthquake motion attenuation relationship model formula is expressed as: lgY=f(M)+f(R)+f(site)+ε Among them, f(M) is the earthquake source model, f(R) is the propagation path model, f(site) is the site condition model, and ε is a random term with mean 0 and standard deviation σ.

8. A device for statistical analysis of PGA attenuation relationship of ground motion, characterized in that: include: A source model building module is used to build a source model based on the variation of ground motion parameters with earthquake magnitude; A propagation path model building module is used to build a propagation path model based on the variation of ground motion parameters with distance; A site condition model building module is used to build a site condition model based on the variation of ground motion parameters with local site conditions; A seismic motion attenuation relationship model construction module, used to construct a seismic motion attenuation relationship model based on the earthquake source model, the propagation path model and the site condition model; The statistical analysis module is used to perform statistical analysis on the PGA attenuation relationship of the earthquake site based on the earthquake attenuation relationship model.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the battery capacity prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the lithium-ion battery life prediction method according to any one of claims 1 to 7 is implemented.

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