A method, apparatus, equipment and storage medium for testing sample stability

Abstract

The present invention discloses a method, device, equipment and storage medium for detecting sample stability, which obtains the spectral data of the sample, wherein the spectral data is a two-dimensional array of light intensity values ​​in two dimensions of sampling time and sampling position, extracts the maximum light intensity value at the sampling moment for each sampling moment in the two-dimensional array, determines the target sampling position corresponding to the maximum light intensity value, constructs an array sequence including multiple groups of sampling moment-target sampling positions, calculates the correlation coefficient of the array sequence, and determines the stability of the sample based on the correlation coefficient and the correlation coefficient of historical spectral data of historical samples that have been determined to be stable. The present invention comprehensively and coherently describes the changes of the sample, does not ignore the features within each height range or time range, performs a correlation test on the array sequence of sampling moment-target sampling position, and compares it with the correlation coefficient of the stable sample, thereby improving the accuracy of the determination, saving the time and cost of manual determination, and avoiding subjective errors.

Description

Technical Field

[0001] The present invention relates to sample detection technology, and in particular to a sample stability detection method, device, equipment and storage medium. Background Art

[0002] Emulsions are widely used in pharmaceuticals, cosmetics, food, coatings, pesticides, petroleum and wastewater treatment. The stability of an emulsion refers to its ability to maintain its physical, chemical, biological and microbiological properties. It has always been a topic of concern to researchers, manufacturers and consumers. Determining the stability of an emulsion is also a necessary step in the product development process.

[0003] The current measurement method is to use multiple light scattering technology, that is, to place the emulsion sample in a spectrometer, penetrate the emulsion sample with a light beam, collect the transmitted light through the sample and the scattered light scattered by the sample material, and reflect the flow changes of the material in the sample through the change of light intensity value. Since the result is the light intensity value in two dimensions of time and space, the tester will generally take the value within a specific height range and time range, calculate the average value, change rate and other aggregate indicators to summarize the changes in the sample, and then manually judge the stability of the sample based on the results.

[0004] In this process, the aggregate calculation will erase the details of the changes in time and space, making the final stability result less accurate. In addition, the obtained aggregate results still need to be further judged by the testers based on their experience, which still requires time for manual judgment and has a certain degree of human subjectivity. Summary of the invention

[0005] The present invention provides a sample stability detection method, device, equipment and storage medium to improve the accuracy of stability determination and reduce costs.

[0006] In a first aspect, the present invention provides a method for detecting sample stability, comprising:

[0007] Acquire spectral data of the sample, wherein the spectral data is a two-dimensional array of light intensity values ​​in two dimensions of sampling time and sampling position;

[0008] For each sampling moment in the two-dimensional array, extracting the maximum light intensity value at the sampling moment;

[0009] Determine the target sampling position corresponding to the maximum light intensity value, and construct an array sequence including multiple groups of sampling moments-target sampling positions;

[0010] Calculate the correlation coefficient of the array sequence;

[0011] The stability of the sample is determined based on the correlation coefficient of the array sequence and the correlation coefficient of the historical spectral data of the historical sample that has been determined to be stable.

[0012] Optionally, for each sampling moment in the two-dimensional array, extracting the maximum light intensity value at the sampling moment includes:

[0013] For each sampling moment in the two-dimensional array, percentile conversion is performed on a light intensity value sequence including a plurality of light intensity values ​​at the sampling moment to obtain a percentile sequence of the light intensity value sequence at the sampling moment;

[0014] The light intensity value corresponding to the maximum percentile in the percentile sequence is taken as the maximum light intensity value at the sampling moment.

[0015] Optionally, percentile conversion is performed on a light intensity value sequence including a plurality of light intensity values ​​at the sampling moment to obtain a percentile sequence of the light intensity value sequence at the sampling moment, including:

[0016] Sorting the light intensity values ​​in the light intensity value sequence in ascending order to obtain a sorted sequence;

[0017] Determining a ranking value of each light intensity value in the ranking sequence;

[0018] The ranking value of each light intensity value is divided by the total number of sampling positions at the sampling moment to obtain the percentile sequence of the light intensity value sequence at the sampling moment.

[0019] Optionally, taking the light intensity value corresponding to the maximum percentile in the percentile sequence as the maximum light intensity value at the sampling moment includes:

[0020] Selecting a percentile with a percentile equal to 1 from the percentile sequence as the maximum percentile;

[0021] The light intensity value corresponding to the percentile with a percentile equal to 1 is taken as the maximum light intensity value at the sampling moment.

[0022] Optionally, the Pearson correlation coefficient calculation formula is used to calculate the correlation coefficient of the array sequence, and the calculation formula is as follows:

[0023]

[0024] Among them, ρ1 is the Pearson correlation coefficient of the array sequence, t i is the sampling time of the i-th array in the array sequence, is the average value of each sampling moment in the array sequence, h tag-i is the target sampling position of the i-th array in the array sequence, is the average value of each target sampling position in the array sequence.

[0025] Optionally, before acquiring the spectral data of the sample, the following steps are also included:

[0026] Acquire historical spectral data, wherein the historical spectral data is a two-dimensional array of light intensity values ​​in two dimensions: sampling time and sampling position;

[0027] For each sampling moment in the two-dimensional array of the historical spectral data, extracting the maximum light intensity value at the sampling moment;

[0028] Determine the target sampling position corresponding to the maximum light intensity value, and construct an array sequence including multiple sets of sampling time-target sampling positions;

[0029] Calculate the correlation coefficient of the array sequence and obtain the correlation coefficient of the historical spectral data.

[0030] Optionally, determining the stability of the sample based on the correlation coefficient of the array sequence and the correlation coefficient of historical spectral data of historical samples that have been determined to be stable includes:

[0031] Calculating an average value of correlation coefficients of a plurality of the historical spectral data;

[0032] Comparing the correlation coefficient of the spectral data of the sample with the average value;

[0033] When the correlation coefficient of the array sequence is less than or equal to the correlation coefficient of the historical spectrum data, the sample is determined to be stable;

[0034] When the correlation coefficient of the array sequence is greater than the correlation coefficient of the historical spectrum data, the sample is determined to be unstable.

[0035] In a second aspect, the present invention further provides a sample stability detection device, comprising:

[0036] A spectral data acquisition module, used to acquire spectral data of the sample, wherein the spectral data is a two-dimensional array of light intensity values ​​in two dimensions: sampling time and sampling position;

[0037] A maximum light intensity value extraction module, used for extracting the maximum light intensity value at each sampling moment in the two-dimensional array;

[0038] An array sequence construction module, used to determine the target sampling position corresponding to the maximum light intensity value, and construct an array sequence including multiple groups of sampling moments-target sampling positions;

[0039] A correlation coefficient calculation module, used to calculate the correlation coefficient of the array sequence;

[0040] The stability determination module is used to determine the stability of the sample based on the correlation coefficient of the array sequence and the correlation coefficient of the historical spectral data of the historical sample determined to be stable.

[0041] In a third aspect, the present invention further provides an electronic device, comprising:

[0042] one or more processors;

[0043] A storage device for storing one or more programs;

[0044] When the one or more programs are executed by the one or more processors, the one or more processors implement the sample stability detection method provided in the first aspect of the present invention.

[0045] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the sample stability detection method provided in the first aspect of the present invention.

[0046] The sample stability detection method provided by the present invention obtains the spectral data of the sample, the spectral data is a two-dimensional array of light intensity values ​​in two dimensions of sampling time and sampling position, extracts the maximum light intensity value at the sampling moment for each sampling moment in the two-dimensional array, determines the target sampling position corresponding to the maximum light intensity value, and constructs an array sequence including multiple groups of sampling moments-target sampling positions, calculates the correlation coefficient of the array sequence, and determines the stability of the sample based on the correlation coefficient of the array sequence and the correlation coefficient of the historical spectral data of the historical sample that has been determined to be stable. Since the distribution of substances in the sample is spatially coherent, the present invention can summarize the aggregation position of substances in the sample by extracting the maximum light intensity value at each sampling moment and determining the target sampling position corresponding to the maximum light intensity value as a section of the sample with the highest light intensity value, and simplifies the two-dimensional change problem of the sample in space and time into the change trend of the aggregation of substances in the sample over time, and can comprehensively and coherently describe the changes of the sample, without ignoring the characteristics of each height range or time range, thereby improving the accuracy of stability determination. In addition, the present invention performs a correlation test on the array sequence of sampling time-target sampling position, and the obtained correlation coefficient objectively evaluates the degree of correlation between the sample material aggregation position and the sampling time. By comparing with the correlation coefficient of the existing stable sample, it automatically draws a conclusion on whether the sample is stable, and refers to the judgment results in historical detection, which further improves the accuracy of stability judgment. At the same time, there is no need for manual judgment, which saves the time and cost of manual judgment and avoids subjective errors.

[0047] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flow chart of a sample stability detection method provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of the structure of a sample stability detection device provided by an embodiment of the present invention;

[0051] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0052] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention 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 invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0055] Figure 1This is a flow chart of a sample stability detection method provided by an embodiment of the present invention. This embodiment can be applied to detecting sample stability. The method can be performed by a sample stability detection device provided by an embodiment of the present invention. The device can be implemented by software and / or hardware, and is usually configured in an electronic device, such as Figure 1 As shown, the sample stability detection method comprises the following steps:

[0056] S101. Acquire spectral data of a sample, where the spectral data is a two-dimensional array of light intensity values ​​in two dimensions: sampling time and sampling position.

[0057] In the embodiment of the present invention, the sample to be tested can be an emulsion or paste in the cosmetics field, or an emulsion or paste in other fields, which is not limited in the embodiment of the present invention. The inspector puts the sample to be tested into the multiple light scattering spectral stability analyzer, sets a timed detection, collects spectral signals from the sample from top to bottom each time, and ends after a period of time to generate the spectral data of the sample. The spectral data is a two-dimensional array (t i ,h j ; a ij ), where t i is the i-th sampling moment, h j is the jth sampling position (or sampling height), a ij is the light intensity value at the jth sampling position at the ith sampling time. Then the spectral data of the sample is exported from the multiple light scattering spectral stability analysis instrument.

[0058] S102. For each sampling moment in the two-dimensional array, extract the maximum light intensity value at the sampling moment.

[0059] After obtaining the spectral data in the form of a two-dimensional array, for each sampling time t in the two-dimensional array i , the sampling time t i All the light intensity values ​​under are extracted to obtain a light intensity value sequence a i Among them, the light intensity value sequence a i Indicates that at sampling time t i The light intensity value at each sampling position is extracted. i The maximum value of the species is obtained, and the sampling time t is obtained i The maximum light intensity value a under im .

[0060] Exemplarily, in some embodiments of the present invention, step S102 includes the following sub-steps:

[0061] S1021. For each sampling moment in the two-dimensional array, percentile conversion is performed on a light intensity value sequence including a plurality of light intensity values ​​at the sampling moment to obtain a percentile sequence of the light intensity value sequence at the sampling moment.

[0062] Exemplarily, for each sampling time t in the two-dimensional array i , for the sampling time t i The light intensity value sequence a includes multiple light intensity values i Perform percentile conversion to obtain the sampling time t i The percentile sequence P of the light intensity value sequence under i .

[0063] For example, the light intensity value sequence a i The light intensity values ​​in are sorted in ascending order to obtain the sorted sequence a i ', determine the sorting sequence a i ', and then sort the sequence a i The ranking value of each light intensity value in ' is divided by the sampling time t i The total number of sampling positions N is obtained, and the sampling time t i The light intensity value sequence a i The percentile sequence P i .

[0064] S1022. Taking the light intensity value corresponding to the maximum percentile in the percentile sequence as the maximum light intensity value at the sampling moment.

[0065] In the embodiment of the present invention, the percentile sequence P i The light intensity value corresponding to the maximum percentile in is taken as the maximum light intensity value a at the sampling time im . Since the sorting sequence a i The last ranking value is equal to the sampling time t i The total number of downsampling positions N, the sorting values ​​of the remaining elements are all less than the sampling time t i The total number of downsampled positions N, therefore, the percentile sequence P i The maximum percentile in the sorted sequence a i The last digit, the corresponding light intensity value is also the sampling time t i The maximum light intensity value a under im .

[0066] In some embodiments of the present invention, specifically, due to the sorting sequence a i The last ranking value is equal to the sampling time t i The total number of downsampled positions N, and the percentile is the sorted sequence a i The ranking value of each light intensity value in ' is divided by the sampling time t iThe total number of downsampling positions N, that is, the percentile sequence P i The last percentile in is 100%, so we can get the percentile sequence P from i The percentile with a percentile equal to 1 is selected as the maximum percentile. Then, the light intensity value corresponding to the percentile with a percentile equal to 1 is taken as the sampling time t i The maximum light intensity value a under im .

[0067] S103, determining the target sampling position corresponding to the maximum light intensity value, and constructing an array sequence including multiple groups of sampling time-target sampling positions.

[0068] As mentioned above, the light intensity value sequence a i Indicates that at sampling time t i Under this condition, the light intensity value at each sampling position, that is, each light intensity value corresponds to a sampling position. Then, when determining the sampling time t i The maximum light intensity value a under im After that, determine the target sampling position h corresponding to the maximum light intensity value tag-i Therefore, at each sampling time t i Corresponding to a target sampling position h tag-i , forming an array of sampling time-target sampling position, the arrays of sampling time-target sampling position of all sampling positions constitute an array sequence (t i ,h tag-i ).

[0069] S104. Calculate the correlation coefficient of the array sequence.

[0070] In the embodiment of the present invention, the array sequence (t i ,h tag-i ), the correlation coefficient is a quantity that studies the linear correlation degree between variables. In the embodiment of the present invention, the correlation between the sampling time and the target sampling position is studied. Exemplarily, in the embodiment of the present invention, the correlation coefficient may be a Pearson correlation coefficient, a Spearman correlation coefficient, a Kendall correlation coefficient, etc., which is not limited in the embodiment of the present invention.

[0071] For example, to calculate the array sequence (t i ,h tag-i ) as an example, the calculation formula is as follows:

[0072]

[0073] Among them, ρ1 is the Pearson correlation coefficient of the array sequence, t i is the sampling time of the i-th array in the array sequence, is the average value of each sampling moment in the array sequence, htag-i is the target sampling position of the i-th array in the array sequence, is the average value of each target sampling position in the array sequence.

[0074] S105. Determine the stability of the sample based on the correlation coefficient of the array sequence and the correlation coefficient of the historical spectrum data of the historical sample determined to be stable.

[0075] In an embodiment of the present invention, correlation coefficients of historical spectral data of multiple historical samples that have been determined to be stable are obtained in advance. After the correlation coefficient of the array sequence of the sample to be tested is calculated, the stability of the sample is determined based on the correlation coefficient of the array sequence and the correlation coefficient of the historical spectral data of the historical sample that has been determined to be stable.

[0076] Exemplarily, the correlation coefficients of the historical spectral data of multiple historical samples can be calculated in advance, and the process of calculating the correlation coefficients of the historical spectral data is similar to the process of calculating the correlation coefficients of the spectral data of the sample to be detected in the aforementioned embodiment. Exemplarily, the historical spectral data of the historical samples are obtained in advance from the multiple light scattering spectral stability analyzer, and the historical spectral data is a two-dimensional array of light intensity values ​​in two dimensions of sampling time and sampling position. For each sampling moment in the two-dimensional array of the historical spectral data, the maximum light intensity value at the sampling moment is extracted, the target sampling position corresponding to the maximum light intensity value is determined, and an array sequence including multiple groups of sampling moments-target sampling positions is constructed, and the correlation coefficients of the array sequence are calculated to obtain the correlation coefficients of the historical spectral data. The specific process of calculating the correlation coefficient has been described in detail in the aforementioned embodiment, and the embodiment of the present invention will not be repeated here.

[0077] In an embodiment of the present invention, the correlation coefficients of the historical spectral data of multiple historical samples can be stored in a memory. The correlation coefficients of the historical spectral data of multiple historical samples in the memory can be called later, and the average value of the correlation coefficients of the historical spectral data of multiple historical samples can be calculated. Then the correlation coefficient ρ1 of the spectral data of the sample to be tested is compared with the average value Compare, when the correlation coefficient ρ1 of the array sequence is less than or equal to the correlation coefficient of the historical spectral data , it means that the sample tested this time is less likely to change over time than the previously identified stable sample, so the sample is determined to be stable; when the correlation coefficient ρ1 of the array sequence is greater than the correlation coefficient of the historical spectral data , it means that the sample tested this time has a tendency to change over time more than the previously identified stable sample, so the sample is judged to be unstable.

[0078] The sample stability detection method provided by the embodiment of the present invention obtains the spectral data of the sample, and the spectral data is a two-dimensional array of light intensity values ​​in two dimensions of sampling time and sampling position. For each sampling moment in the two-dimensional array, the maximum light intensity value at the sampling moment is extracted, the target sampling position corresponding to the maximum light intensity value is determined, and an array sequence including multiple groups of sampling moments-target sampling positions is constructed, and the correlation coefficient of the array sequence is calculated. The stability of the sample is determined based on the correlation coefficient of the array sequence and the correlation coefficient of the historical spectral data of the historical samples that have been determined to be stable. Since the distribution of substances in the sample is coherent in space, the present invention can summarize the aggregation position of substances in the sample by extracting the maximum light intensity value at each sampling moment and determining the target sampling position corresponding to the maximum light intensity value as a section of the area with the highest light intensity value of the sample, simplifying the two-dimensional change problem of the sample in space and time into the change trend of the aggregation of substances in the sample over time, and can comprehensively and coherently describe the changes of the sample, without ignoring the characteristics of each height range or time range, thereby improving the accuracy of stability determination. In addition, the present invention performs a correlation test on the array sequence of sampling time-target sampling position, and the obtained correlation coefficient objectively evaluates the degree of correlation between the sample material aggregation position and the sampling time. By comparing with the correlation coefficient of the existing stable sample, it automatically draws a conclusion on whether the sample is stable, and refers to the judgment results in historical detection, which further improves the accuracy of stability judgment. At the same time, there is no need for manual judgment, which saves the time and cost of manual judgment and avoids subjective errors.

[0079] The embodiment of the present invention also provides a sample stability detection device, Figure 2 A schematic diagram of the structure of a sample stability detection device provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the sample stability detection device comprises:

[0080] A spectral data acquisition module 201 is used to acquire spectral data of a sample, wherein the spectral data is a two-dimensional array of light intensity values ​​in two dimensions: sampling time and sampling position;

[0081] A maximum light intensity value extraction module 202, for extracting the maximum light intensity value at each sampling moment in the two-dimensional array;

[0082] An array sequence construction module 203 is used to determine the target sampling position corresponding to the maximum light intensity value, and to construct an array sequence including multiple groups of sampling time-target sampling positions;

[0083] A correlation coefficient calculation module 204, used to calculate the correlation coefficient of the array sequence;

[0084] The stability determination module 205 is used to determine the stability of the sample based on the correlation coefficient of the array sequence and the correlation coefficient of the historical spectral data of the historical sample determined to be stable.

[0085] In some embodiments of the present invention, the maximum light intensity value extraction module 202 includes:

[0086] A percentile conversion submodule, for performing percentile conversion on a light intensity value sequence including a plurality of light intensity values ​​at each sampling moment in the two-dimensional array, to obtain a percentile sequence of the light intensity value sequence at the sampling moment;

[0087] The maximum light intensity value determination submodule is used to take the light intensity value corresponding to the maximum percentile in the percentile sequence as the maximum light intensity value at the sampling moment.

[0088] In some embodiments of the present invention, the percentile conversion submodule includes:

[0089] A sorting unit, used for sorting the light intensity values ​​in the light intensity value sequence in ascending order to obtain a sorted sequence;

[0090] A ranking value determination unit, used to determine the ranking value of each light intensity value in the ranking sequence;

[0091] The percentile calculation unit is used to divide the ranking value of each light intensity value by the total number of sampling positions at the sampling moment to obtain the percentile sequence of the light intensity value sequence at the sampling moment.

[0092] In some embodiments of the present invention, the maximum light intensity value determination submodule includes:

[0093] a maximum percentile determination unit, configured to select a percentile with a percentile equal to 1 from the percentile sequence as the maximum percentile;

[0094] The maximum light intensity value determining unit is used to take the light intensity value corresponding to the percentile with the percentile equal to 1 as the maximum light intensity value at the sampling moment.

[0095] In some embodiments of the present invention, the Pearson correlation coefficient calculation formula is used to calculate the correlation coefficient of the array sequence, and the calculation formula is as follows:

[0096]

[0097] Among them, ρ1 is the Pearson correlation coefficient of the array sequence, t i is the sampling time of the i-th array in the array sequence, is the average value of each sampling moment in the array sequence, h tag-i is the target sampling position of the i-th array in the array sequence, is the average value of each target sampling position in the array sequence.

[0098] In some embodiments of the present invention, the sample stability detection device further comprises:

[0099] A historical data acquisition module, used for acquiring historical spectral data before acquiring spectral data of a sample, wherein the historical spectral data is a two-dimensional array of light intensity values ​​in two dimensions of sampling time and sampling position;

[0100] A historical maximum light intensity value extraction module, used for extracting the maximum light intensity value at each sampling moment in the two-dimensional array of the historical spectral data;

[0101] A historical array sequence construction module is used to determine the target sampling position corresponding to the maximum light intensity value and construct an array sequence including multiple sets of sampling time-target sampling positions;

[0102] The historical correlation coefficient calculation module is used to calculate the correlation coefficient of the array sequence and obtain the correlation coefficient of the historical spectral data.

[0103] In some embodiments of the present invention, the stability determination module 205 includes:

[0104] An average value calculation submodule, used for calculating the average value of the correlation coefficients of the plurality of historical spectral data;

[0105] A comparison submodule, used for comparing the correlation coefficient of the spectral data of the sample with the average value;

[0106] A first determination submodule, configured to determine that the sample is stable when the correlation coefficient of the array sequence is less than or equal to the correlation coefficient of the historical spectrum data;

[0107] The second determination submodule is used to determine that the sample is unstable when the correlation coefficient of the array sequence is greater than the correlation coefficient of the historical spectrum data.

[0108] The above-mentioned sample stability detection device can execute the sample stability detection method provided by the aforementioned embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the sample stability detection method.

[0109] Figure 3A schematic diagram of the structure of an electronic device provided for an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0110] like Figure 3 As shown, the electronic device includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0111] A number of components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 performs the various methods and processes described above, such as a sample stability detection method.

[0113] In some embodiments, the sample stability detection method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the sample stability detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the sample stability detection method in any other appropriate manner (e.g., by means of firmware).

[0114] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0116] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. 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.

[0117] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0118] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by 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), a blockchain network, and the Internet.

[0119] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0120] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the sample stability detection method provided in any embodiment of the present application.

[0121] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can 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 the case of a remote computer, the remote computer can 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 can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0122] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0123] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting sample stability, characterized in that: include: Acquire spectral data of the sample, wherein the spectral data is a two-dimensional array of light intensity values ​​in two dimensions of sampling time and sampling position; For each sampling moment in the two-dimensional array, extracting the maximum light intensity value at the sampling moment; Determine the target sampling position corresponding to the maximum light intensity value, and construct an array sequence including multiple groups of sampling moments-target sampling positions; The Pearson correlation coefficient calculation formula is used to calculate the correlation coefficient of the array sequence; Determining the stability of the sample based on the correlation coefficient of the array sequence and the correlation coefficient of historical spectral data of historical samples that have been determined to be stable; Determining the stability of the sample based on the correlation coefficient of the array sequence and the correlation coefficient of the historical spectral data of the historical sample determined to be stable includes: Calculating an average value of correlation coefficients of a plurality of the historical spectral data; Comparing the correlation coefficient of the spectral data of the sample with the average value; When the correlation coefficient of the array sequence is less than or equal to the correlation coefficient of the historical spectrum data, the sample is determined to be stable; When the correlation coefficient of the array sequence is greater than the correlation coefficient of the historical spectrum data, the sample is determined to be unstable.

2. The sample stability detection method according to claim 1, characterized in that: For each sampling moment in the two-dimensional array, extracting the maximum light intensity value at the sampling moment includes: For each sampling moment in the two-dimensional array, percentile conversion is performed on a light intensity value sequence including a plurality of light intensity values ​​at the sampling moment to obtain a percentile sequence of the light intensity value sequence at the sampling moment; The light intensity value corresponding to the maximum percentile in the percentile sequence is taken as the maximum light intensity value at the sampling moment.

3. The sample stability detection method according to claim 2, characterized in that: Performing percentile conversion on a light intensity value sequence including a plurality of light intensity values ​​at the sampling moment to obtain a percentile sequence of the light intensity value sequence at the sampling moment includes: Sorting the light intensity values ​​in the light intensity value sequence in ascending order to obtain a sorted sequence; Determining a ranking value of each light intensity value in the ranking sequence; The ranking value of each light intensity value is divided by the total number of sampling positions at the sampling moment to obtain the percentile sequence of the light intensity value sequence at the sampling moment.

4. The sample stability detection method according to claim 2, characterized in that: Taking the light intensity value corresponding to the maximum percentile in the percentile sequence as the maximum light intensity value at the sampling time, including: Selecting a percentile with a percentile equal to 1 from the percentile sequence as the maximum percentile; The light intensity value corresponding to the percentile with a percentile equal to 1 is taken as the maximum light intensity value at the sampling moment.

5. The sample stability detection method according to any one of claims 1 to 4, characterized in that: The Pearson correlation coefficient calculation formula is used to calculate the correlation coefficient of the array sequence, and the calculation formula is as follows: ； in, is the Pearson correlation coefficient of the array sequence, is the sampling time of the i-th array in the array sequence, is the average value of each sampling moment in the array sequence, is the first array of target sampling positions, is the average value of each target sampling position in the array sequence.

6. The sample stability detection method according to any one of claims 1 to 4, characterized in that: Before acquiring the spectral data of the sample, it also includes: Acquire historical spectral data, which is a two-dimensional array of light intensity values ​​in two dimensions: sampling time and sampling position; For each sampling moment in the two-dimensional array of the historical spectral data, extracting the maximum light intensity value at the sampling moment; Determine the target sampling position corresponding to the maximum light intensity value, and construct an array sequence including multiple sets of sampling time-target sampling positions; Calculate the correlation coefficient of the array sequence and obtain the correlation coefficient of the historical spectral data.

7. A sample stability detection device, characterized in that: include: A spectral data acquisition module, used to acquire spectral data of the sample, wherein the spectral data is a two-dimensional array of light intensity values ​​in two dimensions: sampling time and sampling position; A maximum light intensity value extraction module, used for extracting the maximum light intensity value at each sampling moment in the two-dimensional array; An array sequence construction module, used to determine the target sampling position corresponding to the maximum light intensity value, and construct an array sequence including multiple groups of sampling moments-target sampling positions; A correlation coefficient calculation module, used to calculate the correlation coefficient of the array sequence using a Pearson correlation coefficient calculation formula; A stability determination module, used for determining the stability of the sample based on the correlation coefficient of the array sequence and the correlation coefficient of the historical spectrum data of the historical sample determined to be stable; The stability determination module includes: An average value calculation submodule, used for calculating the average value of the correlation coefficients of the plurality of historical spectral data; A comparison submodule, used for comparing the correlation coefficient of the spectral data of the sample with the average value; A first determination submodule, configured to determine that the sample is stable when the correlation coefficient of the array sequence is less than or equal to the correlation coefficient of the historical spectrum data; The second determination submodule is used to determine that the sample is unstable when the correlation coefficient of the array sequence is greater than the correlation coefficient of the historical spectrum data.

8. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the sample stability detection method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the sample stability detection method as described in any one of claims 1 to 6 is implemented.

Images