A method, device, electronic device and storage medium for detecting the period of carbon isotope shift

Abstract

The present invention provides a method, device, electronic device and storage medium for detecting the carbon isotope offset period. The method includes: processing the original test data of carbon isotopes by using a sliding window to obtain smoothed data; calculating the difference between the original test data and the smoothed data, and obtaining a set of significant point sequences based on the difference and the quartile difference method; presetting a maximum interval threshold, and sequentially judging the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold to obtain a significant offset interval; corresponding the significant offset interval to the original test data and the smoothed data to obtain the carbon isotope offset period. The present application uses sliding window threshold processing to accurately, efficiently and generally identify carbon isotope offsets. It is an objective and highly adaptable automated detection method with better robustness, and can improve the accuracy and consistency of CIEs boundary recognition.

Description

Technical Field

[0001] The present invention belongs to the technical fields of artificial intelligence and geoscience big data, and particularly relates to a method, a device, an electronic device and a storage medium for detecting the period of carbon isotope offset. Background Art

[0002] Carbon isotope excursions (CIEs) are important landmark events in geological history and are usually closely related to environmental disasters such as global climate change, mass extinctions, and oceanic anoxic events (OAEs). CIEs reflect major disturbances in the Earth's carbon cycle at different times, and these changes are recorded by the δ 13 C values of carbonates and organic matter in sediments as key chemical signals in the geological record. The change in δ 13 C represents processes such as the production, burial, and oxidation of organic carbon and can reveal the carbon exchange between the atmosphere and the ocean. Studying CIEs not only helps scientists reveal the evolution laws of paleoclimate and paleoecosystems but also enables the identification of the profound impacts of carbon cycle perturbation events on the global ecosystem.

[0003] Carbon isotope excursion events can provide important correlation tools for chronostratigraphy, especially in dealing with the causal relationships of paleoclimate transitions and species extinctions. By comparing the changes in δ 13 C values in different regions worldwide, CIEs provide a basis for the precise correlation of stratigraphic relationships, thus enabling a more consistent comparison of the geological histories of different regions. In addition, the definition of CIEs is crucial for calculating the duration of these events and their lag or lead relationships with environmental factors. For example, the occurrence time and duration of carbon isotope excursions often have a certain time difference from important environmental events such as climate change, ocean environmental change, and biological extinction events, which provides a deeper perspective for revealing the driving mechanisms of climate change.

[0004] Currently, there are several deficiencies in the methods for defining the boundaries of carbon isotope excursions, mainly manifested as the lack of standardization and strong subjectivity of the methods. Traditional practices usually rely on visually estimating the inflection point positions in isotope data (i.e., the "visual method"), which can lead to inconsistent delineation of CIEs boundaries due to the different experiences of researchers. Many change point detection methods used in research, such as Bayesian analysis, piecewise regression, and sequential t-tests, can identify significant changes in isotope curves to a certain extent, but these methods show limitations when dealing with data with high noise or subtle changes. For example, in datasets with large sedimentation rate variations and obvious regional differences, these methods may be difficult to accurately capture the start and end times of CIEs, resulting in biases in the estimation of the duration. Therefore, this application anticipates a method for detecting the period of carbon isotope offset. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides a method, a system, a device and a storage medium for detecting the carbon isotope offset period, so as to solve the technical problem that the start and end times of CIEs cannot be accurately and objectively captured in the existing related technologies.

[0006] In a first aspect, an embodiment of the present invention provides a method for detecting the carbon isotope offset period, including the following steps:

[0007] Processing the original test data of carbon isotope by using a sliding window to obtain smoothed data;

[0008] Calculating the difference between the original test data and the smoothed data, and obtaining a set of significant point sequences based on the difference and the quartile difference method;

[0009] Presetting a maximum interval threshold, and sequentially judging the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold to obtain a significant offset interval;

[0010] Mapping the significant offset interval to the original test data and the smoothed data to obtain the carbon isotope offset period.

[0011] Further, the process of processing the original test data of carbon isotope by using a sliding window to obtain smoothed data is as follows:

[0012] Smoothing the original test data through a sliding window, and taking the mean value of its adjacent points as the original value for each data point, where the number of adjacent points is the size of the sliding window:

[0013]

[0014] where w is the size of the sliding window, i is the index of the data point, s i is the smoothed data sequence, and x j is the jth value in the original test data sequence, ranging from i - [w / 2] to i + [w / 2];

[0015] The original test data is where x i is the ith data point, and N is the total number of sample points.

[0016] Further, when processing the original test data of carbon isotope by using a sliding window, at the start and end of the original test data sequence, the ring filling method is adopted, taking the front window value of the first data as the end of the sequence and the back window value of the last data as the start of the sequence.

[0017] Further, calculating the difference between the original test data and the smoothed data, and obtaining the trend slope of the smoothed data based on the adjacent differences;

[0018] Based on the preset offset threshold and trend change threshold of the quarterback difference method, if the difference is greater than the offset threshold, there is a significant offset at this point, which is marked as a significant offset point; if the trend slope is greater than the trend change threshold, the change trend at this point is significant, which is marked as a significant change trend point;

[0019] Take all the points that satisfy the offset significance and trend change significance as a set to obtain a set of significant points, and sort the set of significant points to obtain a set of significant point sequences.

[0020] Furthermore, the difference between the original test data and the smoothed data is:

[0021] o i = x i - s i

[0022] where, o i represents the difference between the original data point x i and the smoothed data point s i . If o i is a positive offset, the data is higher than the trend; if o i is a negative offset, the data is lower than the trend;

[0023] The trend slope of the smoothed data is:

[0024] slope i = s i - s i-1

[0025] where, slope i represents the trend slope of the i-th data point, s i is the i-th value of the smoothed data, and s i-1 is the previous value of the i-th smoothed data.

[0026] Furthermore, the set of significant point sequences is:

[0027]

[0028] where, {i||o i |> θ offset} is the set of significant offset points that satisfy the offset threshold, {i||slope i |> θ slope} is the set of significant change points that satisfy the slope threshold, and the sort function is used to sort the merged points in index order.

[0029] Furthermore, the process of obtaining the significant offset interval is:

[0030] Preset a maximum interval threshold max_gap, and successively judge the distances between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold max_gap. If the distance between two adjacent offset points is less than or equal to the maximum interval, it is used as the starting point of the same interval. If the distance between two adjacent offset points is greater than the maximum interval, it is used as the end point of the interval; until all adjacent offset points in the set of significant point sequences are judged, a continuous interval R is obtained k :

[0031] R k ={(P start ,P end )|P end -P start <=max_gap}

[0032] where P start is the starting point of the interval, and P end is the end point of the interval.

[0033] In a second aspect, an embodiment of the present invention provides a system for detecting the carbon isotope offset period, including:

[0034] A preprocessing unit, configured to:

[0035] Process the original test data of carbon isotopes by using a sliding window to obtain smoothed data;

[0036] An operation unit, configured to:

[0037] Calculate the difference between the original test data and the smoothed data, and obtain a set of significant point sequences based on the difference and the quartile difference method;

[0038] A judgment unit, configured to:

[0039] Preset a maximum interval threshold, and successively judge the distances between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold to obtain a significant offset interval;

[0040] An output unit, configured to:

[0041] Correspond the significant offset interval to the original test data and the smoothed data to obtain the carbon isotope offset period.

[0042] In a third aspect, an embodiment of the present disclosure provides an electronic device, which includes:

[0043] At least one processor; and,

[0044] A memory communicatively connected to the at least one processor; wherein,

[0045] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for detecting the carbon isotope offset period described above.

[0046] In a fourth aspect, an embodiment of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for detecting the carbon isotope offset period described above.

[0047] Some other optional features and technical effects of the embodiments of the present invention are described below and some can be understood by reading this article.

[0048] Compared with the prior art, the present invention has the following beneficial technical effects:

[0049] The present invention provides a method for detecting the carbon isotope offset period, including the following steps: processing the original test data of carbon isotopes by using a sliding window to obtain smoothed data; calculating the difference between the original test data and the smoothed data, and obtaining a set of significant point sequences based on the difference and the quartile difference method; presetting a maximum interval threshold, and sequentially judging the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold to obtain a significant offset interval; corresponding the significant offset interval to the original test data and the smoothed data to obtain the carbon isotope offset period. The present application uses sliding window threshold processing to accurately, efficiently, and generally identify the carbon isotope offset, which is an objective and highly adaptable automated detection method with better robustness, and can improve the accuracy and consistency of CIEs boundary recognition. Description of the Drawings

[0050] Figure 1 Shows a schematic flowchart of a method for detecting the carbon isotope offset period according to an embodiment of the present disclosure;

[0051] Figure 2 Shows one of the offset interval recognition results according to an embodiment of the present disclosure;

[0052] Figure 3 Shows another offset interval recognition result according to an embodiment of the present disclosure;

[0053] Figure 4 Shows yet another offset interval recognition result according to an embodiment of the present disclosure;

[0054] Figure 5 Shows a device for detecting the carbon isotope offset period according to an embodiment of the present disclosure. Detailed Description of the Embodiments

[0055] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0056] The following describes the embodiments of the present disclosure through specific specific examples. Those skilled in the art can easily understand the other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present disclosure.

[0057] It should be noted that the following describes various aspects of the embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. Additionally, this device and / or this method can be implemented using other structures and / or functionality in addition to one or more of the aspects described herein.

[0058] It should also be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present disclosure schematically. The drawings only show the components related to the present disclosure and are not drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and proportion of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0059] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0060] Figure 1 A flowchart 100 of a method for detecting the carbon isotope offset period disclosed in the first embodiment of the present invention is shown. As Figure 1 shown, the embodiments of the present disclosure include the following steps:

[0061] In step S101, a sliding window is used to process the original test data of carbon isotopes to obtain smoothed data;

[0062] Specifically, the process of using a sliding window to process the original test data of carbon isotopes to obtain smoothed data is as follows:

[0063] Smooth the original test data through a sliding window. For each data point, take the mean of its adjacent points as the original value, where the number of adjacent points is the size of the sliding window:

[0064]

[0065] where w is the size of the sliding window, i is the index of the data point, and s i is the smoothed data sequence, and x j is the j-th value in the original test data sequence, ranging from i - [w / 2] to i + [w / 2];

[0066] It should be noted that the original test data is where x i is the i-th data point, and N is the total number of sample points.

[0067] It should be further noted that the sliding window smoothing technique reduces the fluctuations or noise in the data by averaging each data point in the original test data and its adjacent points, thereby obtaining a smoother data sequence. That is to say, in the embodiments of the present disclosure, a window of a fixed size, that is, a group of adjacent data points, is slid on the data. For each position within the window, the mean of all points within the window is calculated, and this mean is used as the new data value for that position. In this way, a data sequence smoother than the original data can be obtained, which can effectively reduce the random fluctuations in the data.

[0068] In some embodiments, when using a sliding window to process the original test data of carbon isotopes, at the beginning and end of the original test data sequence, the ring filling method is used, taking the pre-window value of the first data as the end of the sequence and the post-window value of the last data as the start of the sequence. Specifically, when performing sliding window smoothing, assume that the data sequence is periodic, or only for the data near the boundary. This means that when the window slides to the beginning of the data, it will "wrap around" to the end of the data to obtain the missing adjacent points, and vice versa.

[0069] Next, go to step S102;

[0070] At step S102, calculate the difference between the original test data and the smoothed data, and obtain a set of significant point sequences based on the difference and the quartile difference method;

[0071] Specifically, calculate the difference between the original test data and the smoothed data, and obtain the trend slope of the smoothed data based on the adjacent differences;

[0072] Based on the preset offset threshold and trend change threshold of the quarterback difference method, if the difference is greater than the offset threshold, there is a significant offset at this point, which is marked as a significant offset point; if the trend slope is greater than the trend change threshold, the change trend at this point is significant, which is marked as a significant change trend point.

[0073] All points that satisfy the significance of the offset and the significance of the trend change are used as a set to obtain a set of significant points, and the set of significant points is sorted to obtain a set of significant point sequences.

[0074] It should be noted that in the embodiments of the present disclosure, the quarterback difference method (IQR) is a method for measuring the degree of data dispersion. IQR is the difference between the 75th percentile (Q3) and the 25th percentile (Q1) of the data:

[0075] IQR = Q3 - Q1

[0076] If a data point exceeds Q3 + 1.5 × IQR, then this point is considered an outlier. Therefore, the offset threshold θ offset and the trend change threshold θ slope can be set according to this rule respectively.

[0077] In some embodiments, the difference between the original test data and the smoothed data is:

[0078] o i = x i - s i

[0079] where o i represents the difference between the original data point x i and the smoothed data point s i . If o i is a positive offset, then this data is higher than the trend; if o i is a negative offset, then this data is lower than the trend.

[0080] The trend slope of the smoothed data is:

[0081] slope i = s i - s i-1

[0082] where slope i represents the trend slope of the i-th data point, s i is the i-th value of the smoothed data, and s i-1 is the previous value of the i-th smoothed data.

[0083] In some embodiments, the set of significant point sequences is:

[0084]

[0085] Among them, {i||o i |>θ offset} is the set of significant offset points that satisfy the offset threshold, and {i||slope i |>θ slope} is the set of significant change points that satisfy the slope threshold. The sort function is used to sort the merged points in index order.

[0086] Next, go to step S103;

[0087] At step S103, a preset maximum interval threshold is set, and based on the maximum interval threshold, the distances between two adjacent offset points in the set of significant point sequences are sequentially judged to obtain significant offset intervals;

[0088] Specifically, the process of obtaining the significant offset intervals is as follows:

[0089] Preset a maximum interval threshold max_gap. Based on the maximum interval threshold max_gap, sequentially judge the distances between two adjacent offset points in the set of significant point sequences. If the distance between two adjacent offset points is less than or equal to the maximum interval, it is used as the starting point of the same interval. If the distance between two adjacent offset points is greater than the maximum interval, it is used as the end point of this interval; until all adjacent offset points in the set of significant point sequences are judged, a continuous interval R k :

[0090] R k ={(P start ,P end )|P end -P start <=max_gap}

[0091] Among them, P start is the starting point of the interval, and P end is the end point of the interval.

[0092] Next, go to step S104;

[0093] At step S104, the significant offset intervals are mapped to the original test data and the smoothed data to obtain the carbon isotope offset periods.

[0094] Specifically, when the original test data and the smoothed data are plotted on the same graph, the significant offset intervals are mapped to the original test data and the smoothed data and highlighted. The highlighted area is the significant offset interval, and the significant offset interval is the carbon isotope offset period. Figure 2It shows only one offset interval recognition result of an embodiment of the present disclosure, that is, a single-offset recognition result, and the shaded part is the offset interval; Figure 3 It shows the interval recognition result including positive and negative offsets in an embodiment of the present disclosure, and the shaded part is the offset interval, Figure 4 It shows the recognition result including continuous positive and negative offsets and an offset interval that suddenly occurs after stabilization in an embodiment of the present disclosure, and the shaded part is the offset interval.

[0095] The second embodiment of the present invention also provides a system for detecting the offset period of carbon isotopes, including:

[0096] A preprocessing unit, configured to:

[0097] Use a sliding window to process the original test data of carbon isotopes to obtain smoothed data;

[0098] An operation unit, configured to:

[0099] Calculate the difference between the original test data and the smoothed data, and obtain a set of significant point sequences based on the difference and the quartile difference method;

[0100] A judgment unit, configured to:

[0101] Preset a maximum interval threshold, and sequentially judge the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold to obtain a significant offset interval;

[0102] An output unit, configured to:

[0103] Correspond the significant offset interval to the original test data and the smoothed data to obtain the offset period of carbon isotopes.

[0104] The third embodiment of the present invention also provides an electronic device, which includes:

[0105] At least one processor; and,

[0106] A memory communicatively connected to the at least one processor; wherein,

[0107] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for detecting the offset period of carbon isotopes in any of the foregoing embodiments.

[0108] The fourth embodiment of the present invention also provides a non-transitory computer-readable storage medium, and the non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the method for detecting the offset period of carbon isotopes in any of the foregoing embodiments.

[0109] The fifth embodiment of the present invention further provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to execute the method for detecting the carbon isotope offset period in any of the foregoing embodiments.

[0110] The sixth embodiment of the present invention further provides a computer program, which includes program instructions that, when executed by a computer, cause the computer to execute the method for detecting the carbon isotope offset period in any of the foregoing embodiments.

[0111] Figure 5 A schematic diagram showing a method that can implement the embodiments of the present invention or a device 1000 that can implement the embodiments of the present invention is shown. In some embodiments, it may include more or fewer devices than shown. In some embodiments, it can be implemented using a single or multiple devices. In some embodiments, it can be implemented using cloud or distributed devices.

[0112] As Figure 5 shown, the device 1000 includes a processor 1001, which can perform various appropriate operations and processes according to the programs and / or data stored in the read-only memory (ROM) 1002 or the programs and / or data loaded from the storage section 1008 into the random access memory (RAM) 1003. The processor 1001 can be a multi-core processor or can include multiple processors. In some embodiments, the processor 1001 can include a general main processor and one or more special coprocessors, such as a central processing unit (CPU), a graphics processing unit (GPU), a neural network processing unit (NPU), a digital signal processor (DSP), and so on. In the RAM 1003, various programs and data required for the operation of the device 1000 are also stored. The processor 1001, the ROM 1002, and the RAM 1003 are connected to each other through a bus 1004. The input / output (I / O) interface 1005 is also connected to the bus 1004.

[0113] The above-mentioned processor and memory are jointly used to execute the program stored in the memory. When the program is executed by a computer, it can implement the methods, steps, or functions described in the above embodiments.

[0114] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, a touch screen, etc.; an output section 1007 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed so that a computer program read therefrom can be installed into the storage section 1008 as needed. Figure 5 Only some components are schematically shown, and it does not mean that the device 1000 only includes Figure 5 the components shown.

[0115] The systems, devices, modules or units illustrated in the above embodiments can be implemented by a computer or its associated components. The computer can be, for example, a mobile terminal, a smart phone, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a personal digital assistant, a media player, a navigation device, a game console, a tablet computer, a wearable device, a smart TV, an Internet of Things system, a smart home, an industrial computer, a server, or a combination thereof.

[0116] Although not shown, in an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program / instructions are stored, and when the computer program / instructions are executed by a processor, the method for detecting the carbon isotope offset period described in the embodiment is implemented.

[0117] The storage medium in the embodiment of the present invention includes permanent and non-permanent, removable and non-removable articles that can implement information storage by any method or technology. Examples of 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0118] Although not shown, an embodiment of the present invention also provides a computer program product, including: a computer program / instructions, and when the computer program / instructions are executed by a processor, the method for detecting the carbon isotope offset period described in the embodiment is implemented.

[0119] The methods, programs, systems, devices, etc. of the embodiments of the present invention can be executed or implemented in a single or multiple networked computers, and can also be practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks can be executed by remote processing devices connected through a communication network.

[0120] Those skilled in the art should understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, those skilled in the art can conceive that the implementation of the functional modules / units or controllers and related method steps illustrated in the above embodiments can be achieved in a software, hardware, or a combination of software and hardware manner.

[0121] Unless explicitly stated, the actions or steps of the methods and programs recorded according to the embodiments of the present invention do not necessarily have to be executed in a specific order and can still achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0122] In this article, multiple embodiments of the present invention have been described. However, for the sake of brevity, the descriptions of each embodiment are not exhaustive, and the same or similar features or parts between the various embodiments may be omitted. In this article, "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean applicable to at least one embodiment or example according to the present invention, rather than all embodiments. The above terms do not necessarily refer to the same embodiment or example. Without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0123] The exemplary systems and methods of the present invention have been specifically shown and described with reference to the above embodiments, which are only examples of the best modes for implementing the systems and methods. Those skilled in the art can understand that various changes can be made to the embodiments of the systems and methods described herein when implementing the systems and / or methods without departing from the spirit and scope of the present invention defined in the appended claims.

[0124] In addition, the methods, systems, devices, and media for detecting the carbon isotope offset period according to the present invention can also be implemented in the following manner:

[0125] (1) A method for detecting the carbon isotope offset period, characterized by comprising the following steps:

[0126] Process the original test data of carbon isotopes using a sliding window to obtain smoothed data;

[0127] Calculate the difference between the original test data and the smoothed data, and obtain a set of significant point sequences based on the difference and the quartile difference method;

[0128] Preset a maximum interval threshold, and successively judge the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold to obtain a significant offset interval;

[0129] Correspond the significant offset interval to the original test data and the smoothed data to obtain the carbon isotope offset period.

[0130] (2) According to the method for detecting the carbon isotope offset period described in (1), characterized in that the process of using a sliding window to process the original test data of carbon isotope to obtain smoothed data is as follows:

[0131] Smooth the original test data through a sliding window. For each data point, take the mean value of its adjacent points as the original value, where the number of adjacent points is the size of the sliding window:

[0132]

[0133] Among them, w is the size of the sliding window, i is the index of the data point, and s i is the smoothed data sequence, and x j is the jth value in the original test data sequence, ranging from i - [w / 2] to i + [w / 2];

[0134] The original test data is where x i is the ith data point, and N is the total number of sample points.

[0135] (3) According to the method for detecting the carbon isotope offset period described in (2), characterized in that when using a sliding window to process the original test data of carbon isotope, at the beginning and end of the original test data sequence, the ring filling method is adopted, taking the pre-window value of the first data as the end of the sequence and the post-window value of the last data as the start of the sequence.

[0136] (4) According to the method for detecting the carbon isotope offset period described in (1), characterized in that calculate the difference between the original test data and the smoothed data, and obtain the trend slope of the smoothed data based on the adjacent differences;

[0137] Based on the quartile difference method, preset an offset threshold and a trend change threshold. If the difference is greater than the offset threshold, then this point has a significant offset and is marked as a significant offset point; if the trend slope is greater than the trend change threshold, then the change trend of this point is significant and is marked as a significant change trend point;

[0138] All points that satisfy the significance of the offset and the significance of the trend change are taken as a set to obtain a set of significant points, and the set of significant points is sorted to obtain a set of significant point sequences.

[0139] (5) According to the method for detecting the carbon isotope offset period described in (4), it is characterized in that the difference between the original test data and the smoothed data is:

[0140] o i = x i - s i

[0141] where, o i represents the difference between the original data point x i and the smoothed data point s i . If o i is a positive offset, then this data is higher than the trend; if o i is a negative offset, then this data is lower than the trend;

[0142] The trend slope of the smoothed data is:

[0143] slope i = s i - s i-1

[0144] where, slope i represents the trend slope of the i-th data point, s i is the i-th value of the smoothed data, and s i-1 is the previous value of the i-th smoothed data.

[0145] (6) According to the method for detecting the carbon isotope offset period described in (4), it is characterized in that the set of significant point sequences is:

[0146]

[0147] where, {i||o i |> θ offset} is the set of significant offset points that satisfy the offset threshold, and {i||slope i |> θ slope} is the set of significant change points that satisfy the slope threshold. The sort function is used to sort the merged points in index order.

[0148] (7) According to the method for detecting the carbon isotope offset period described in (1), it is characterized in that the process of obtaining the significant offset interval is:

[0149] Preset a maximum interval threshold max_gap, and sequentially determine the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold max_gap. If the distance between two adjacent offset points is less than or equal to the maximum interval, it is used as the starting point of the same interval. If the distance between two adjacent offset points is greater than the maximum interval, it is used as the end point of the interval; until all adjacent offset points in the set of significant point sequences are judged to obtain a continuous interval R k :

[0150] R k ={(P start ,P end )|P end -P start <=max_gap}

[0151] wherein, P start is the starting point of the interval, and P end is the end point of the interval.

[0152] (8) A system for detecting the carbon isotope offset period, based on the method for detecting the carbon isotope offset period according to any one of (1) to (7), includes:

[0153] A preprocessing unit, configured to:

[0154] Use a sliding window to process the original test data of carbon isotopes to obtain smoothed data;

[0155] An operation unit, configured to:

[0156] Calculate the difference between the original test data and the smoothed data, and obtain a set of significant point sequences based on the difference and the quartile difference method;

[0157] A judgment unit, configured to:

[0158] Preset a maximum interval threshold, and sequentially judge the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold to obtain a significant offset interval;

[0159] An output unit, configured to:

[0160] Correspond the significant offset interval to the original test data and the smoothed data to obtain the carbon isotope offset period.

[0161] (9) According to the preprocessing unit described in (8), it is further configured to:

[0162] The process of using a sliding window to process the original test data of carbon isotopes to obtain smoothed data is:

[0163] Smoothing the original test data through a sliding window. For each data point, the mean of its adjacent points is used as the original value, where the number of adjacent points is the size of the sliding window:

[0164]

[0165] where w is the size of the sliding window, i is the index of the data point, and s i is the smoothed data sequence, and x j is the jth value in the original test data sequence, ranging from i - [w / 2] to i + [w / 2];

[0166] The original test data is where x i is the ith data point and N is the total number of sample points.

[0167] (10) The preprocessing unit according to (8) is further configured to:

[0168] When processing the original test data of carbon isotopes using a sliding window, at the start and end of the original test data sequence, the ring filling method is adopted, taking the pre-window value of the first data as the end of the sequence and the post-window value of the last data as the start of the sequence.

[0169] (11) The arithmetic unit according to (8) is further configured to:

[0170] Calculate the difference between the original test data and the smoothed data, and obtain the trend slope of the smoothed data based on the adjacent differences;

[0171] Based on the preset offset threshold and trend change threshold by the quartile difference method, if the difference is greater than the offset threshold, then this point has a significant offset and is marked as a significant offset point; if the trend slope is greater than the trend change threshold, then the change trend of this point is significant and is marked as a significant change trend point;

[0172] Take all the points that satisfy the offset significance and trend change significance as a set to obtain a set of significant points, sort the set of significant points, and obtain a set of significant point sequences.

[0173] (12) The arithmetic unit according to (8) is further configured to:

[0174] Process the difference between the original test data and the smoothed data:

[0175] o i = x i - s i

[0176] where o iRepresents the original data point x i The difference from the smoothed data point s i If o i Is a positive offset, then the data is above the trend. If o i Is a negative offset, then the data is below the trend;

[0177] The trend slope of the smoothed data is:

[0178] slope i = s i - s i-1

[0179] Where slope i Represents the trend slope of the i-th data point, and s i Is the i-th value of the smoothed data, and s i-1 Is the previous value of the i-th smoothed data.

[0180] (13) The arithmetic unit according to (8) is further configured to:

[0181] Output the set of significant point sequences:

[0182]

[0183] Where, {i||o i |> θ offset} is the set of significant offset points that satisfy the offset threshold, and {i||slope i |> θ slope} is the set of significant change points that satisfy the slope threshold. The sort function is used to sort the merged points in index order.

[0184] (14) The judgment unit according to (8) is further configured to:

[0185] Preset the maximum interval threshold max_gap, and successively judge the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold max_gap. If the distance between two adjacent offset points is less than or equal to the maximum interval, it is used as the starting point of the same interval. If the distance between two adjacent offset points is greater than the maximum interval, it is used as the end point of the interval; until all adjacent offset points in the set of significant point sequences are judged, the continuous interval R k :

[0186] R k = {(P start , P end )|P end - P start <= max_gap}

[0187] Where, Pstart is the starting point of the interval, P end is the end point of the interval.

[0188] (15) An electronic device, characterized in that the electronic device comprises:

[0189] at least one processor; and,

[0190] a memory communicatively connected to the at least one processor; wherein,

[0191] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for detecting the carbon isotope offset period according to any one of (1) to (14).

[0192] (16) A non-transitory computer-readable storage medium, characterized in that the non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the method for detecting the carbon isotope offset period according to any one of (1) to (14).

Claims

1. A method for detecting the period of carbon isotope offset, characterized in that It includes the following steps: Use a sliding window to process the original test data of carbon isotopes to obtain smoothed data; Calculate the difference between the original test data and the smoothed data, and obtain a set of significant point sequences based on the difference and the quartile difference method; Preset a maximum interval threshold, and sequentially judge the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold to obtain a significant offset interval; Correspond the significant offset interval to the original test data and the smoothed data to obtain the carbon isotope offset period.

2. The method for detecting the carbon isotope shift period according to claim 1, wherein The process of using a sliding window to process the original test data of carbon isotopes to obtain smoothed data is as follows: Smooth the original test data through a sliding window. For each data point, use the mean value of its adjacent points as the original value, where the number of adjacent points is the size of the sliding window: where w is the size of the sliding window, i is the index of the data point, s i is the smoothed data sequence, x j is the j-th value in the original experimental data sequence, ranging from i - [w / 2] to i + [w / 2]; The original test data is where x i is the i-th data point and N is the total number of sample points.

3. The method for detecting the carbon isotope shift period according to claim 2, characterized in that, When using a sliding window to process the original test data of carbon isotopes, at the start and end of the original test data sequence, use the ring filling method, use the front window value of the first data as the end of the sequence, and use the back window value of the last data as the start of the sequence.

4. The method for detecting the period of carbon isotope shift according to claim 1, wherein Calculate the difference between the original test data and the smoothed data, and obtain the trend slope of the smoothed data based on the adjacent differences; Preset an offset threshold and a trend change threshold based on the quartile difference method. If the difference is greater than the offset threshold, then this point has a significant offset and is marked as a significant offset point; if the trend slope is greater than the trend change threshold, then the change trend of this point is significant and is marked as a significant change trend point; Take all the points that meet the offset significance and trend change significance as a set to obtain a set of significant points, and sort the set of significant points to obtain a set of significant point sequences.

5. The method for detecting the period of carbon isotope shift according to claim 4, characterized in that, The difference between the original test data and the smoothed data is: o i = x i - s i Among them, o i represents the difference between the original data point x i and the smoothed data point s i . If o i is a positive offset, the data is above the trend. If o i is a negative offset, the data is below the trend; The trend slope of the smoothed data is: slope i = s i -s i-1 Among them, slope i represents the trend slope of the i-th data point, s i is the i-th value of the smoothed data, s i-1 is the previous value of the i-th smoothed data.

6. The method for detecting the carbon isotope offset period according to claim 4, wherein The set of significant point sequences is: Among them, {i||o i |>θ offset} is a set of significant offset points that meet the offset threshold, {i||slope i |>θ slope} is a set of significant change points that meet the slope threshold, and the sort function is used to sort the merged points in index order.

7. The method for detecting the period of carbon isotope shift according to claim 1, wherein The process of obtaining the significant offset interval is: Preset a maximum interval threshold max_gap, and sequentially judge the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold max_gap. If the distance between two adjacent offset points is less than or equal to the maximum interval, then it is used as the starting point of the same interval. If the distance between two adjacent offset points is greater than the maximum interval, then it is used as the end point of this interval; Until all adjacent offset points of the significant point sequence set are judged to be completed, a continuous interval R is obtained k : R k = {(P start , P end ) | P end - P start <= max_gap} Among them, P start is the starting point of the interval, and P end is the ending point of the interval.

8. An apparatus for detecting the period of carbon isotope offset, characterized in that, Based on the method for detecting the carbon isotope offset period according to any one of claims 1-7, the device includes: A preprocessing unit, configured to: Use a sliding window to process the original test data of carbon isotopes to obtain smoothed data; An operation unit, configured to: Calculate the difference between the original test data and the smoothed data, and obtain a set of significant point sequences based on the difference and the quartile difference method; A judgment unit, configured to: Preset a maximum interval threshold, and sequentially judge the distance between two adjacent offset points in the set of significant point sequences based on the maximum interval threshold to obtain a significant offset interval; An output unit, configured to: Correspond the significant offset interval to the original test data and the smoothed data to obtain the carbon isotope offset period.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for detecting the carbon isotope offset period according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the method for detecting the carbon isotope offset period according to any one of claims 1 to 7.

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