Peripheral tissue blood flow monitoring methods, devices, systems, equipment, media and products

Abstract

The present invention discloses a peripheral tissue blood flow monitoring method, device, system, equipment, medium and product, which belongs to the field of medical data processing technology. The method includes: obtaining the actual autocorrelation data combination of the peripheral tissue blood flow of the target object under the current sliding window, simulating the autocorrelation data generation process of the peripheral tissue blood flow based on a predetermined simulation model, and obtaining a theoretical autocorrelation data combination; determining the first root mean square error relative mean for the autocorrelation data based on the actual autocorrelation data combination and the theoretical autocorrelation data combination; determining the predetermined blood flow index data combination corresponding to the actual autocorrelation data combination, and determining the second root mean square error relative mean based on the predetermined blood flow index data combination; determining the state description data of the peripheral tissue blood flow based on the first root mean square error relative mean and the second root mean square error relative mean. The embodiment of the present invention can improve the universality of the peripheral tissue blood flow monitoring method based on the diffusion correlation spectroscopy technology.

Description

Technical Field

[0001] The present invention relates to the field of medical data processing technology, and in particular to a peripheral tissue blood flow monitoring method, device, system, equipment, medium and product. Background Art

[0002] Currently, end-to-end learning network models that map the autocorrelation of light intensity data to peripheral tissue blood flow typically analyze the autocorrelation of newly acquired light intensity data to obtain corresponding peripheral tissue blood flow predictions. The accuracy of peripheral tissue blood flow determined by this technical solution depends on the accuracy of the end-to-end learning model architecture and the accuracy and comprehensiveness of the training sample set. Due to the limited comprehensiveness of the training sample set, existing technical solutions for determining peripheral tissue blood flow based on end-to-end learning models suffer from at least poor generalization and applicability.

[0003] Therefore, it is necessary to provide a universal peripheral tissue blood flow monitoring method to accurately determine the monitoring results of peripheral tissue blood flow of various target objects. Summary of the Invention

[0004] The present invention provides a peripheral tissue blood flow monitoring method, device, system, equipment, medium and product to solve the problem of poor universality of the existing peripheral tissue blood flow monitoring method based on diffusion correlation spectroscopy technology.

[0005] According to one aspect of the present invention, a method for monitoring peripheral tissue blood flow is provided, the method comprising:

[0006] Acquiring an actual autocorrelation data combination of the peripheral tissue blood flow of the target object in a current sliding window, simulating a process of generating autocorrelation data of the peripheral tissue blood flow based on a predetermined simulation model, and obtaining a theoretical autocorrelation data combination of the peripheral tissue blood flow of the target object in the current sliding window;

[0007] Determining a first root mean square error relative mean value for the autocorrelation data based on the actual autocorrelation data combination and the theoretical autocorrelation data combination;

[0008] Determining a predetermined blood flow index data combination corresponding to the actual autocorrelation data combination, and determining a second root mean square error relative mean value for the predetermined blood flow index data based on the predetermined blood flow index data combination;

[0009] The peripheral tissue blood flow state description data is determined according to the first root mean square error relative mean and the second root mean square error relative mean.

[0010] According to another aspect of the present invention, a peripheral tissue blood flow monitoring system is provided, comprising:

[0011] an acquisition device for acquiring a combination of actual autocorrelation data of blood flow in peripheral tissues of a target object;

[0012] A processor is configured to execute the peripheral tissue blood flow monitoring method described in any embodiment.

[0013] According to another aspect of the present invention, there is provided a peripheral tissue blood flow monitoring device, comprising:

[0014] A first autocorrelation module is used to obtain an actual autocorrelation data combination of blood flow in peripheral tissues of a target object;

[0015] A second autocorrelation module is used to simulate the generation process of peripheral tissue autocorrelation data based on a predetermined simulation model to obtain a theoretical autocorrelation data combination;

[0016] A first mean module is used to determine a first root mean square error relative mean value for the autocorrelation data based on the actual autocorrelation data combination and the theoretical autocorrelation data combination;

[0017] A second mean value module is used to determine predetermined blood flow index data corresponding to the actual autocorrelation data combination, and a second root mean square error relative mean value of the predetermined blood flow index data;

[0018] The state description data determination module is used to determine the state description data of the peripheral tissue blood flow according to the first relative mean value of the root mean square error and the second relative mean value of the root mean square error.

[0019] According to another aspect of the present invention, an electronic device is provided, comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the peripheral tissue blood flow monitoring method described in any embodiment of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the peripheral tissue blood flow monitoring method described in any embodiment of the present invention when executed.

[0024] According to another aspect of the present invention, a computer program product is provided. The computer program product comprises a computer program. When the computer program is executed by a processor, the computer program implements the peripheral tissue blood flow monitoring method according to any one of the embodiments.

[0025] The technical solution of the embodiment of the present invention uses the first root mean square error relative mean to characterize the relative error of the actual autocorrelation data compared with the theoretical autocorrelation data; uses the second root mean square error relative mean to characterize the relative error of the predetermined blood flow index data itself; since the predetermined blood flow index data combination is determined based on the actual autocorrelation data combination, the state description data of the peripheral tissue blood flow is determined based on the first root mean square error relative mean and the second root mean square error relative mean, which can enable the state description data to carry the relative error information of different data processing stages, thereby enabling the state description data to have higher accuracy.

[0026] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] 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.

[0028] Figure 1 is a flow chart of a peripheral tissue blood flow monitoring method provided according to an embodiment of the present invention;

[0029] Figure 2 is another flow chart of a peripheral tissue blood flow monitoring method provided according to an embodiment of the present invention;

[0030] Figure 3 is another flow chart of a peripheral tissue blood flow monitoring method provided according to an embodiment of the present invention;

[0031] Figure 4 is a schematic structural diagram of a peripheral tissue blood flow monitoring system provided according to an embodiment of the present invention;

[0032] Figure 5 is a schematic structural diagram of a peripheral tissue blood flow monitoring device provided according to an embodiment of the present invention;

[0033] Figure 6 It is a structural diagram of an electronic device for implementing the peripheral tissue blood flow monitoring method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions 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 embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the description 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 numbers 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 clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] Diffuse Correlation Spectroscopy (DCS) is a non-invasive optical technique. The raw data collected is typically a signal of light intensity variation over time, referred to as intensity data or intensity time series. Autocorrelation analysis of the intensity data is then performed to obtain the autocorrelation function g2(τ), as follows:

[0037]

[0038] in, For light intensity, For time, is the delay time, Indicates time average.

[0039] The autocorrelation function g2(τ) is a function of the delay time These data points are called autocorrelation data and are usually presented in the form of a table or curve.

[0040] For example, real-time autocorrelation data collection of peripheral tissue blood flow in a target subject is performed over a 10-minute period. If the sampling frequency is 10 Hz, then a total of 6,000 autocorrelation function curves, or 6,000 autocorrelation data points, are collected during these 10 minutes. Each piece of autocorrelation data corresponds to a predetermined blood flow indicator.

[0041] This embodiment uses diffusion correlation spectroscopy technology to monitor peripheral tissue blood flow in real time.

[0042] Figure 1 This is a flow chart of a peripheral tissue blood flow monitoring method provided by an embodiment of the present invention. This embodiment is applicable to the case of determining the monitoring results of the peripheral tissue blood flow of a target object in real time. The method can be executed by a peripheral tissue blood flow monitoring device. The peripheral tissue blood flow monitoring device can be implemented in the form of hardware and / or software. The peripheral tissue blood flow monitoring device can be configured in a processor. Figure 1 As shown, the method includes:

[0043] S110. Obtain an actual autocorrelation data combination of the peripheral tissue blood flow of the target object in the current sliding window, simulate the autocorrelation data generation process of the peripheral tissue blood flow based on a predetermined simulation model, and obtain a theoretical autocorrelation data combination of the peripheral tissue blood flow of the target object in the current sliding window.

[0044] The current sliding window width is configurable and can be set by the user according to specific circumstances, for example, 2 minutes, 5 minutes, 10 minutes, etc.

[0045] The actual autocorrelation data combination is obtained by an acquisition device, which may be a diffusion spectrum correlation device. The theoretical autocorrelation data combination is obtained by simulation and can therefore be regarded as a standard autocorrelation data combination.

[0046] The actual autocorrelation data combination corresponds to the same time range as the theoretical autocorrelation data combination, so the two are aligned in time.

[0047] S120 . Determine a first relative mean of root mean square errors of the autocorrelation data based on the actual autocorrelation data combination and the theoretical autocorrelation data combination.

[0048] The RMS error relative to the mean can be used to assess measurement error because it reflects the proportion of the error relative to the overall data. It compares the RMS error to the mean of the data, providing a measure of relative error.

[0049] Specifically, the root mean square error of the corresponding autocorrelation data between the actual autocorrelation data combination and the theoretical autocorrelation data combination is determined; the mean of all theoretical autocorrelation data in the theoretical autocorrelation data combination is determined; and the ratio between the root mean square error and the mean is used as the first root mean square error relative mean for the autocorrelation data. The specific formula is as follows:

[0050]

[0051] in, is the actual autocorrelation data, is the theoretical autocorrelation data, is the mean of all theoretical autocorrelation data in the theoretical autocorrelation data combination, n is the number of actual autocorrelation data included in the actual autocorrelation data combination, and is also the number of theoretical autocorrelation data included in the theoretical autocorrelation data combination.

[0052] S130: Determine a predetermined blood flow index data combination corresponding to the actual autocorrelation data combination, and determine a second root mean square error relative mean value for the predetermined blood flow index data based on the predetermined blood flow index data combination.

[0053] After the actual autocorrelation data combination is determined, the existing technology is used to determine the predetermined blood flow index data corresponding to each actual autocorrelation data in the actual autocorrelation data combination. The predetermined blood flow index data can be blood flow data or blood flow index data.

[0054] Specifically, the second root mean square error relative mean for the predetermined blood flow index data is determined by the following formula:

[0055]

[0056] in, is the predetermined blood flow index data, is the mean of all predetermined blood flow indicator data in the predetermined blood flow indicator data combination, and n is the number of predetermined blood flow indicator data included in the predetermined blood flow indicator data combination. Obviously, the number of actual autocorrelation data included in the actual autocorrelation data combination is the same as the number of predetermined blood flow indicator data included in the predetermined blood flow indicator data combination. This is because one actual autocorrelation data can determine one predetermined blood flow indicator data.

[0057] S140 . Determine peripheral tissue blood flow state description data according to the first root mean square error relative mean and the second root mean square error relative mean.

[0058] The state description data can be used to determine the state of peripheral tissue blood flow, such as abnormal, fluctuating, stable, etc.

[0059] In one embodiment, a weighted sum of the first root mean square error relative to the mean and the second root mean square error relative to the mean is used as the state description data of the peripheral tissue blood flow.

[0060] Specifically, Among them, the sum of the weighted coefficients of the first root mean square error relative to the mean and the second root mean square error relative to the mean is 1, that is, . is the first weighting coefficient, which is used to represent the relative mean of the first root mean square error ( ) in the state description data C; is the second weighting coefficient, which is used to express the relative mean of the second root mean square error ( ) in the status description data C.

[0061] Since the first root mean square error relative mean is used to reflect the relative error of the actual autocorrelation data compared with the theoretical autocorrelation data; the second root mean square error relative mean is used to reflect the relative error of the predetermined blood flow index data itself; in addition, the predetermined blood flow index data combination is determined based on the actual autocorrelation data combination, and therefore the weighted sum of the first root mean square error relative mean and the second root mean square error relative mean is used as the state description data of the peripheral tissue blood flow, thereby realizing the weighted sum of the relative errors in different data processing stages, and using the weighted sum as the state description data of the peripheral tissue blood flow, it can ensure that the state description data has high accuracy and is suitable for state monitoring of peripheral tissue blood flow in various scenarios, such as daily monitoring of peripheral tissue blood flow, intraoperative monitoring of peripheral tissue blood flow, etc.

[0062] In one embodiment, a measurement site identifier is obtained, and configuration information corresponding to the measurement site identifier is determined, the configuration information including a first weighting coefficient, a second weighting coefficient, and a constant, wherein the constant is greater than or equal to zero, and both the first weighting coefficient and the second weighting coefficient are greater than zero; state description data of the target object is determined based on the first coefficient, the second coefficient, the constant, the first root mean square error relative mean, and the second root mean square error relative mean. Specifically, .in, and are the first weighting coefficient and the second weighting coefficient respectively, is a constant. Finally, the same abnormal data range can be used to determine whether there is abnormality in peripheral tissue blood flow in different parts.

[0063] The technical solution provided by an embodiment of the present invention uses a first root mean square error relative mean to characterize the relative error of actual autocorrelation data compared to theoretical autocorrelation data; and uses a second root mean square error relative mean to characterize the relative error of the predetermined blood flow index data itself; since the predetermined blood flow index data combination is determined based on the actual autocorrelation data combination, the state description data of the peripheral tissue blood flow is determined based on the first root mean square error relative mean and the second root mean square error relative mean, so that the state description data can carry relative error information of different data processing stages, thereby making the state description data have higher accuracy.

[0064] Figure 2 This is another flow chart of the peripheral tissue blood flow monitoring method provided by the embodiment of the present invention. This embodiment refines the acquisition process of the theoretical autocorrelation data combination based on the above embodiment. Figure 2 As shown, the method includes:

[0065] S210. Obtain an actual autocorrelation data combination of the target object's peripheral tissue blood flow in the current sliding window, simulate the autocorrelation data generation process of the peripheral tissue blood flow based on a predetermined simulation model, and obtain a theoretical autocorrelation data combination of the target object's peripheral tissue blood flow in the current sliding window, wherein the predetermined simulation model is a Monte Carlo model, and the boundary information used in the simulation process is limited by a semi-infinite model.

[0066] This embodiment ensures the accuracy of boundary information through a semi-infinite model; by combining the Monte Carlo model with the semi-infinite model to simulate the generation process of peripheral tissue blood flow autocorrelation data, the theoretical autocorrelation data combination of the peripheral tissue blood flow of the target object in the current sliding window is obtained, thereby ensuring the accuracy of the theoretical autocorrelation data combination.

[0067] The Monte Carlo model is a numerical simulation method based on random sampling that can be used to simulate the propagation of light in scattering media, such as biological tissue. It tracks the random motion of large numbers of photons to statistically analyze the distribution and behavior of light in the medium, making it an important tool for studying the interaction between light and tissue.

[0068] The Monte Carlo model in this embodiment requires at least the absorption coefficient, reduced scattering coefficient, Brownian diffusion coefficient, source-detector spacing, anisotropy factor, number of photons, medium geometry, and medium boundary conditions. It should be noted that some of these parameters can be configured as defaults, such as the anisotropy factor (optionally set to 0.9); others, such as source-detector spacing, require specific configuration based on the device model.

[0069] The medium geometry and medium boundary conditions in this embodiment are defined by a semi-infinite model.

[0070] The semi-infinite model used in this example is a simplified model commonly used in the fields of optics and biomedicine. It is used to describe the propagation of light in a medium. It assumes that the medium is infinitely extended in directions perpendicular to the surface (such as the z-axis) and infinitely extended in directions parallel to the surface (such as the x- and y-axes). The surface of the medium (z = 0) is usually assumed to be flat. At the surface, the reflection and refraction behavior of light can be described using convenient conditions. The optical properties of the medium (such as the absorption coefficient, reduced scattering coefficient, and anisotropy factor) are uniformly distributed.

[0071] The absorption coefficient describes the intensity of light absorption in a medium. It represents the probability of absorption per unit path length and is expressed as the inverse of the unit length. The reduced scattering coefficient is an important parameter describing the scattering behavior of light when propagating in a scattering medium. It represents the effective probability of light scattering per unit path length and is usually expressed as the inverse of the unit length. The Brownian diffusion coefficient is a physical quantity that describes the random diffusion behavior of particles in a fluid due to Brownian motion. It reflects the average squared displacement of particles due to thermal motion per unit time and is usually expressed per unit area per unit time. The anisotropy factor describes the directionality of light scattering in a scattering medium. It reflects the forward or backward propagation direction of photons in a scattering event. The source-detector spacing refers to the physical distance between the light source (the location where light is emitted) and the detector (the location where light is received).

[0072] Because the absorption coefficient, reduced scattering coefficient, Brownian diffusion coefficient, and light source-detector spacing at the boundary are configurable, users can configure the relevant parameters of the semi-infinite model based on the specific parameters of the autocorrelation diffusion spectroscopy device being used. This allows for a customized semi-infinite model for each autocorrelation diffusion spectroscopy device. This ensures that the Monte Carlo model corresponding to each autocorrelation diffusion spectroscopy device can determine an accurate theoretical autocorrelation data combination.

[0073] S220 : Determine a first root mean square error relative mean value for the autocorrelation data based on the actual autocorrelation data combination and the theoretical autocorrelation data combination.

[0074] S230: Determine a predetermined blood flow index data combination corresponding to the actual autocorrelation data combination, and determine a second root mean square error relative mean value for the predetermined blood flow index data based on the predetermined blood flow index data combination.

[0075] S240 . Determine peripheral tissue blood flow state description data according to the first root mean square error relative mean and the second root mean square error relative mean.

[0076] The embodiment of the present invention adopts a Monte Carlo model combined with a semi-infinite model to simulate the light intensity data generation process of peripheral tissue blood flow, and obtains the theoretical autocorrelation data combination of the peripheral tissue blood flow of the target object under the current sliding window. The accuracy of the theoretical autocorrelation data combination can be guaranteed, thereby ensuring the accuracy of the relative mean of the first root mean square error determined based on the actual autocorrelation data combination and the theoretical autocorrelation data combination.

[0077] Figure 3 This is another flow chart of the peripheral tissue blood flow monitoring method provided by an embodiment of the present invention. This embodiment adds an abnormal prompt information display step based on the above embodiment. Figure 3 As shown, the method includes:

[0078] S310. Obtain an actual autocorrelation data combination of the target object's peripheral tissue blood flow in the current sliding window, simulate the autocorrelation data generation process of the peripheral tissue blood flow based on a predetermined simulation model, and obtain a theoretical autocorrelation data combination of the target object's peripheral tissue blood flow in the current sliding window.

[0079] S320 : Determine a first root mean square error relative mean value for the autocorrelation data based on the actual autocorrelation data combination and the theoretical autocorrelation data combination.

[0080] S330: Determine a predetermined blood flow index data combination corresponding to the actual autocorrelation data combination, and determine a second root mean square error relative mean value for the predetermined blood flow index data based on the predetermined blood flow index data combination.

[0081] S340: Determine peripheral tissue blood flow state description data according to the first root mean square error relative mean and the second root mean square error relative mean.

[0082] S350: If the status description data is within the predetermined abnormal data range, output abnormal prompt information.

[0083] For example, four state description data intervals are pre-created: the first interval is 85-100, corresponding to an abnormal state indicator; the second interval is 65-85, corresponding to a fluctuating state indicator; the third interval is 40-65, corresponding to a slightly fluctuating state indicator; and the fourth interval is greater than 0 and less than 40, corresponding to a stable state indicator. After the state description data (C) is determined, it is determined whether it is in the first interval. If so, an abnormal prompt message, i.e., an abnormal state indicator, is output.

[0084] In one embodiment, based on a pre-established correspondence between intervals and state identifiers, the interval within which the current state description data resides is determined, the state identifier corresponding to the interval is used as the state identifier corresponding to the current state description data, and the state identifier is output. The state identifier is one of an abnormal state identifier, a fluctuating state identifier, a slightly fluctuating state identifier, and a stable state identifier.

[0085] In order to improve the alarm effect, when the status mark is an abnormal status mark, the abnormal status mark is displayed in a flashing form and a predetermined alarm ringtone is output at the same time to attract the user's attention faster and better.

[0086] In one embodiment, when the state description data is detected to be within a target range, the corresponding blood flow timing information is updated, where the target range may include at least one of the first interval, the second interval, the third interval, and the fourth interval. This embodiment facilitates determining the duration or proportion of time that the target subject is in the target state. For example, if the target range includes the first interval, the user can intuitively determine the duration or proportion of time that the target subject is in an abnormal state.

[0087] The technical solution provided by the embodiment of the present invention determines whether the target object is currently abnormal by determining whether the state description data is within the predetermined abnormal data range; and outputs an abnormal state identifier when the state description data is within the predetermined abnormal data range.

[0088] Figure 4 The peripheral tissue blood flow abnormality monitoring system provided by the embodiment of the present invention. Figure 4 As shown, the system includes:

[0089] The acquisition device 100 is used to acquire actual autocorrelation data of blood flow in peripheral tissues of the target object;

[0090] The processor 11 is configured to execute the peripheral tissue blood flow monitoring method described in any of the aforementioned embodiments.

[0091] In one embodiment, the acquisition device 100 is an existing diffuse correlation spectroscopy device. The processor 11 is an electronic device that is not a diffuse correlation spectroscopy device and is connected to the acquisition device to obtain the actual autocorrelation data of the peripheral tissue blood flow of the target object collected by the acquisition device in real time.

[0092] In one embodiment, the processor is a processor of a diffuse correlation spectroscopy device. That is, the computer program corresponding to the peripheral tissue blood flow monitoring method described in any embodiment of the present invention can be added to the processor of the diffuse correlation spectroscopy device to obtain the peripheral tissue blood flow monitoring system described in this embodiment.

[0093] It should be noted that the peripheral tissue blood flow monitoring method executed by the processor can be found in the aforementioned embodiment, which will not be described in detail in this embodiment.

[0094] The technical solution provided by the embodiment of the present invention combines a device for collecting actual autocorrelation data of the peripheral tissue blood flow of the target object with a processor that can execute the peripheral tissue blood flow monitoring method described in the aforementioned embodiment to obtain a peripheral tissue blood flow monitoring system. The peripheral tissue blood flow monitoring system can complete non-invasive and real-time monitoring of the peripheral tissue blood flow status.

[0095] Figure 5 Schematic diagram of the structure of the peripheral tissue blood flow monitoring device provided by the embodiment of the present invention. Figure 5As shown, the device includes:

[0096] A first autocorrelation module 51 is used to obtain an actual autocorrelation data combination of the peripheral tissue blood flow of the target object;

[0097] A second autocorrelation module 52 is configured to simulate a generation process of peripheral tissue autocorrelation data based on a predetermined simulation model to obtain a theoretical autocorrelation data combination;

[0098] A first mean module 53 is configured to determine a first root mean square error relative mean value for the autocorrelation data based on the actual autocorrelation data combination and the theoretical autocorrelation data combination;

[0099] A second mean value module 54 is used to determine predetermined blood flow index data corresponding to the actual autocorrelation data combination, and a second root mean square error relative mean value of the predetermined blood flow index data;

[0100] The state description data determining module 55 is configured to determine the state description data of the peripheral tissue blood flow according to the first relative mean value of the root mean square error and the second relative mean value of the root mean square error.

[0101] In one embodiment, the predetermined simulation model is a Monte Carlo model, and boundary information used in the simulation process is defined by a semi-infinite model.

[0102] In one embodiment, the status description data determination module 55 is configured to:

[0103] A weighted sum of the first root mean square error relative mean and the second root mean square error relative mean is used as the state description data of the peripheral tissue blood flow.

[0104] In one embodiment, a display module is further included, wherein the display module is used to:

[0105] If the state description data is within a predetermined abnormal data range, abnormal prompt information is output.

[0106] In one embodiment, the first averaging module 53 is configured to:

[0107] Determining a root mean square error of corresponding autocorrelation data between the actual autocorrelation data combination and the theoretical autocorrelation data combination;

[0108] Determining the mean of all theoretical autocorrelation data in the theoretical autocorrelation data combination;

[0109] The ratio between the root mean square error and the mean is used as a first root mean square error relative mean for the autocorrelation data.

[0110] The technical solution provided by the embodiment of the present invention uses a first root mean square error relative mean to represent the relative error of actual autocorrelation data compared to theoretical autocorrelation data; and uses a second root mean square error relative mean to represent the relative error of the predetermined blood flow index data itself; since the predetermined blood flow index data combination is determined based on the actual autocorrelation data combination, the state description data of the peripheral tissue blood flow is determined based on the first root mean square error relative mean and the second root mean square error relative mean, so that the state description data can carry relative error information of different data processing stages, thereby making the state description data have higher accuracy.

[0111] The peripheral tissue blood flow monitoring device provided in the embodiment of the present invention can execute the peripheral tissue blood flow monitoring method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0112] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. 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 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 claimed herein.

[0113] like Figure 6 As shown, electronic device 10 includes at least one processor 11 and memory, such as read-only memory (ROM) 12 and random access memory (RAM) 13, communicatively connected to at least one processor 11. The memory stores computer programs executable by the at least one processor. Processor 11 can perform various appropriate actions and processes based on the computer programs stored in ROM 12 or loaded from storage unit 18 into RAM 13. RAM 13 can also store various programs and data required for the operation of electronic device 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0114] Multiple components in the electronic device 10 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 magnetic 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 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0115] The processor 11 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the peripheral tissue blood flow monitoring method.

[0116] In some embodiments, the peripheral tissue blood flow monitoring method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the peripheral tissue blood flow monitoring method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the peripheral tissue blood flow monitoring method in any other appropriate manner (e.g., via firmware).

[0117] Various embodiments of the systems and techniques described above 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes 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.

[0118] 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, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0119] 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 conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0121] The systems and techniques described herein can be implemented in a computing system that includes back-end 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 front-end components (e.g., a user computer with a graphical user interface or 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 back-end components, middleware components, or front-end components. The components of the system can 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.

[0122] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0123] An embodiment of the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the peripheral tissue blood flow monitoring method provided in any embodiment of the present application.

[0124] During implementation, the computer program product may be written in one or more programming languages ​​or a combination thereof to perform the operations of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0126] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for monitoring peripheral tissue blood flow, characterized in that: The method includes: Acquiring an actual autocorrelation data combination of the peripheral tissue blood flow of the target object in a current sliding window, simulating a process of generating autocorrelation data of the peripheral tissue blood flow based on a predetermined simulation model, and obtaining a theoretical autocorrelation data combination of the peripheral tissue blood flow of the target object in the current sliding window; Determining a first relative mean of root mean square errors for autocorrelation data based on the actual autocorrelation data combination and the theoretical autocorrelation data combination, comprising: determining a root mean square error of corresponding autocorrelation data between the actual autocorrelation data combination and the theoretical autocorrelation data combination; determining a mean of all theoretical autocorrelation data in the theoretical autocorrelation data combination; and using a ratio between the root mean square error and the mean of all theoretical autocorrelation data in the theoretical autocorrelation data combination as the first relative mean square error for the autocorrelation data; Determining a predetermined blood flow index data combination corresponding to the actual autocorrelation data combination, and determining a second root mean square error relative mean value for the predetermined blood flow index data based on the predetermined blood flow index data combination; Determining the state description data of the peripheral tissue blood flow according to the first relative mean value of the root mean square error and the second relative mean value of the root mean square error, comprising: taking a weighted sum of the first relative mean value of the root mean square error and the second relative mean value of the root mean square error as the state description data of the peripheral tissue blood flow; The first root mean square error relative mean is determined by the following formula: ； in, is the actual autocorrelation data in the actual autocorrelation data combination, is the theoretical autocorrelation data, is the mean value of all the theoretical autocorrelation data in the theoretical autocorrelation data combination, and n is the number of the actual autocorrelation data included in the actual autocorrelation data combination; The second root mean square error relative mean is determined by the following formula: ； in, is the predetermined blood flow index data, is the mean value of all the predetermined blood flow indicator data in the predetermined blood flow indicator data combination, and n is the number of the predetermined blood flow indicator data included in the predetermined blood flow indicator data combination; The weighted sum is determined by the following formula: ； in, , is the first weighting coefficient, is the second weighting coefficient.

2. The method according to claim 1, characterized in that The predetermined simulation model is a Monte Carlo model, and boundary information used in the simulation process is defined by a semi-infinite model.

3. The method according to claim 1, characterized in that After determining the peripheral tissue blood flow state description data according to the first root mean square error relative mean and the second root mean square error relative mean, the method further includes: If the state description data is within a predetermined abnormal data range, abnormal prompt information is output.

4. A peripheral tissue blood flow monitoring system, characterized in that: include: an acquisition device for acquiring a combination of actual autocorrelation data of blood flow in peripheral tissues of a target object; A processor is configured to execute the peripheral tissue blood flow monitoring method described in any one of claims 1-3.

5. A peripheral tissue blood flow monitoring device, characterized in that: include: A first autocorrelation module is used to obtain an actual autocorrelation data combination of blood flow in peripheral tissues of a target object; A second autocorrelation module is used to simulate the generation process of peripheral tissue autocorrelation data based on a predetermined simulation model to obtain a theoretical autocorrelation data combination; A first mean module is configured to determine a first root mean square error relative mean for autocorrelation data based on the actual autocorrelation data combination and the theoretical autocorrelation data combination, comprising: determining a root mean square error of corresponding autocorrelation data between the actual autocorrelation data combination and the theoretical autocorrelation data combination; determining a mean of all theoretical autocorrelation data in the theoretical autocorrelation data combination; and using a ratio between the root mean square error and the mean of all theoretical autocorrelation data in the theoretical autocorrelation data combination as the first root mean square error relative mean for the autocorrelation data; A second mean value module is used to determine predetermined blood flow index data corresponding to the actual autocorrelation data combination, and a second root mean square error relative mean value of the predetermined blood flow index data; a state description data determination module, configured to determine the state description data of the peripheral tissue blood flow based on the first relative mean value of the root mean square error and the second relative mean value of the root mean square error, comprising: taking a weighted sum of the first relative mean value of the root mean square error and the second relative mean value of the root mean square error as the state description data of the peripheral tissue blood flow; The first mean value module determines the first root mean square error relative mean value by the following formula: ； in, is the actual autocorrelation data in the actual autocorrelation data combination, is the theoretical autocorrelation data, is the mean value of all the theoretical autocorrelation data in the theoretical autocorrelation data combination, and n is the number of the actual autocorrelation data included in the actual autocorrelation data combination; The second mean module determines the second root mean square error relative mean by the following formula: ； in, is the predetermined blood flow index data, is the mean value of all the predetermined blood flow indicator data in the predetermined blood flow indicator data combination, and n is the number of the predetermined blood flow indicator data included in the predetermined blood flow indicator data combination; The state description data determination module determines the weighted sum using the following formula: ； in, , is the first weighting coefficient, is the second weighting coefficient.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the peripheral tissue blood flow monitoring method according to any one of claims 1 to 3.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the peripheral tissue blood flow monitoring method according to any one of claims 1 to 3 when executed.

8. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the peripheral tissue blood flow monitoring method according to any one of claims 1 to 3.

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