Pregnancy-related monitoring system based on electrochemical sensing technology

Abstract

The invention discloses a pregnancy-related monitoring system based on an electrochemical sensing technology, and the system comprises a wearable device which is used for obtaining hormone detection data of a target monitoring user; the server is connected to the wearable device and comprises a parameter analysis module and a scheme generation module, and the parameter analysis module is used for obtaining the hormone detection data and obtaining ovulation cycle parameters of the target monitoring user through analysis according to the hormone detection data; and the scheme generation module is used for generating a pregnancy-related guidance scheme corresponding to the target monitoring user according to a pregnancy-related target corresponding to the target monitoring user and the ovulation cycle parameter. Therefore, more intelligent and efficient pregnancy-related monitoring and guidance services can be realized, the monitoring accuracy of pregnancy-related indexes is improved, and better use experience is provided for users.

Description

Technical Field

[0001] The present invention relates to the technical field of pregnancy data monitoring, and in particular to a pregnancy-related monitoring system based on electrochemical sensing technology. Background Art

[0002] At present, when women are taking contraceptive measures or preparing for pregnancy, they mainly rely on traditional methods such as basal body temperature measurement and ovulation test strips to monitor their ovulation cycle. These methods have problems such as low monitoring accuracy, susceptibility to external factors, cumbersome operation and the need for manual recording and analysis of data, inability to monitor changes in multiple hormone levels in real time, and lack of personalized health management recommendations. Therefore, there are defects and they need to be improved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a pregnancy-related monitoring system based on electrochemical sensing technology, which can realize more intelligent and efficient pregnancy-related monitoring and guidance services, improve the monitoring accuracy of pregnancy-related indicators, and provide users with a better user experience.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a pregnancy-related monitoring system based on electrochemical sensing technology, the system comprising:

[0005] Wearable devices used to obtain hormone test data of target monitoring users;

[0006] The server is connected to the wearable device and includes a parameter analysis module and a solution generation module, wherein:

[0007] The parameter analysis module is used to obtain the hormone detection data, and analyze and obtain the ovulation cycle parameters of the target monitored user according to the hormone detection data;

[0008] The program generation module is used to generate a pregnancy-related guidance program corresponding to the target monitoring user according to the pregnancy-related goals corresponding to the target monitoring user and the ovulation cycle parameters.

[0009] As an optional embodiment, the wearable device includes a flexible base layer, a flexible printed circuit board and an electrochemical sensor arranged in the flexible base layer; a data processing circuit and a communication circuit are arranged on the flexible printed circuit board; the electrochemical sensor and the data processing circuit are used to acquire and analyze the hormone detection data, and the communication circuit is used to send the hormone detection data to the server.

[0010] As an optional embodiment, the hormone detection data includes luteinizing hormone data, estradiol data and progesterone level data; the electrochemical sensor includes a working electrode, a reference electrode and an auxiliary electrode; the working electrode adopts a gold electrode with a diameter of 3 mm, and a self-assembled monolayer is formed on the surface by 3-mercaptopropionic acid, and a specific antibody is covalently linked after EDC / NHS activation, and the specific antibody includes anti-luteinizing hormone antibody, anti-estradiol antibody and anti-progesterone antibody; the reference electrode is an Ag / AgCl electrode with a diameter of 2 mm and a potential stability of ±1 mV; the auxiliary electrode is a platinum electrode with a diameter of 2 mm; the working electrode, the reference electrode and the auxiliary electrode are connected to the flexible printed circuit board through a conductive silver paste, and the resistivity of the conductive silver paste is not more than 1.0×10 -6 Ω·m.

[0011] As an optional implementation, the parameter analysis module includes a data preprocessing unit, a hormone concentration analysis unit and a cycle prediction unit, wherein:

[0012] The data preprocessing unit is used to denoise the hormone detection data using a wavelet transform algorithm to obtain denoised detection data;

[0013] The hormone concentration analysis unit is used to scan the denoised detection data using linear scanning voltammetry to obtain hormone concentration data;

[0014] The cycle prediction unit is used to input the hormone concentration data into a trained cycle prediction model to obtain the output ovulation cycle parameters.

[0015] As an optional implementation, the data preprocessing unit uses the db4 wavelet basis function to perform 5-layer wavelet decomposition on the hormone detection data to obtain the wavelet coefficients of each layer, processes the wavelet coefficients of each layer through a soft threshold function, and finally uses the processed wavelet coefficients to reconstruct the signal to obtain the denoised detection data; the cycle prediction model is based on a support vector machine algorithm, and uses a radial basis kernel function (RBF) for nonlinear mapping, the kernel function parameter γ is set to 0.01, and the penalty factor C is set to 100; during the training process of the cycle prediction model, the hormone level data for 30 consecutive days are first divided into a training set and a validation set in a ratio of 8:2, and the training data is input into the model after being standardized; the input features of the cycle prediction model include daily luteinizing hormone, estradiol and progesterone concentration values ​​and their first-order differences, and the output is the prediction result of ovulation time; the cycle prediction model optimizes the model parameters through cross-validation, and finally achieves a prediction accuracy of not less than 93% on the validation set, and can warn the ovulation time 36-48 hours in advance.

[0016] As an optional implementation, the server further includes an encryption module, which is used to perform the following steps:

[0017] Acquire a plurality of the wavelet coefficients obtained by the data preprocessing unit processing the hormone detection data to obtain wavelet transform data;

[0018] Obtaining a vector product of a scanning range and a scanning rate of the hormone concentration analysis unit to obtain a scanning parameter;

[0019] Based on a preset data value mapping rule, generating a corresponding encryption key according to the wavelet transform data and the scanning parameters;

[0020] Encrypting the hormone concentration data and the ovulation cycle parameters according to the encryption key to obtain encrypted data;

[0021] The encrypted data is sent to a user terminal device corresponding to the target monitored user; the user terminal device is used to decrypt the encrypted data based on the wavelet transform data and the scanning parameters to obtain the hormone concentration data and the ovulation cycle parameters.

[0022] As an optional implementation manner, the user terminal device is used to decrypt the encrypted data based on the wavelet transform data and the scanning parameters to obtain the hormone concentration data and the ovulation cycle parameters. The specific steps include:

[0023] communicating with the wearable device to obtain at least one historical hormone detection data;

[0024] Obtaining historical hormone concentration data corresponding to the historical hormone detection data from the server;

[0025] Inputting the historical hormone detection data and the historical hormone concentration data into a trained parameter prediction neural network to obtain output predicted wavelet transform data and scanning parameters; the parameter prediction neural network is trained by a training data set including a plurality of training hormone data and corresponding wavelet transform data and scanning parameter annotations;

[0026] Generate a corresponding decryption key according to the data value mapping rule, the wavelet transform data and the scanning parameter;

[0027] The encrypted data is decrypted according to the decryption key to obtain the hormone concentration data and the ovulation cycle parameters.

[0028] As an optional implementation, the plan generation module includes a plan recommendation unit and a nutrition database, wherein:

[0029] The nutrition database is used to store a plurality of preset guidance programs and guidance programs of a plurality of historical users;

[0030] The plan recommendation unit is used to obtain the pregnancy-related goals corresponding to the target monitoring user, and generate a pregnancy-related guidance plan corresponding to the target monitoring user based on the pregnancy-related goals, the ovulation cycle parameters and the guidance plan; the pregnancy-related goals are contraceptive goals or pregnancy preparation goals; the pregnancy-related guidance plan includes a diet plan and an exercise plan.

[0031] As an optional implementation manner, the specific steps of the plan recommendation unit generating a pregnancy-related guidance plan corresponding to the target monitoring user according to the pregnancy-related goal, the ovulation cycle parameter and the guidance plan include:

[0032] According to the pregnancy-related goal, screening out a plurality of adaptation schemes from the plurality of preset guidance schemes;

[0033] Screening out a plurality of similar users from the plurality of historical users according to the pregnancy-related goal and the ovulation cycle parameter;

[0034] Inputting the pregnancy-related goal and the ovulation cycle parameter into a trained scenario prediction neural network to obtain an output prediction scenario; the scenario prediction neural network is an LSTM network;

[0035] For each of the adaptation solutions, calculate the average of the similarities between the adaptation solution and the guidance solutions of all the similar users to obtain the historical similarity;

[0036] Calculating the similarity between the adaptation scheme and the prediction scheme to obtain the prediction similarity;

[0037] Calculate the weighted average of the historical similarity and the predicted similarity to obtain the solution priority corresponding to the adaptation solution;

[0038] Filter out the adaptation schemes whose scheme priorities are greater than the priority threshold from all the adaptation schemes to obtain multiple preferred schemes;

[0039] The intersection of the prediction scheme and the multiple preferred schemes is calculated to obtain a pregnancy-related guidance scheme corresponding to the target monitored user.

[0040] As an optional implementation, screening out a plurality of similar users from the plurality of historical users according to the pregnancy-related goal and the ovulation cycle parameter includes:

[0041] For each of the historical users, user data corresponding to the historical user is obtained; the user data includes historical pregnancy-related goals and historical ovulation cycle parameters corresponding to multiple historical time points;

[0042] Based on a preset data stability screening algorithm, stable data is screened out from the user data; the stable data includes historical pregnancy-related goals and historical ovulation cycle parameters that remain stable at multiple historical time points;

[0043] Calculating current user data consisting of the pregnancy-related goal and the ovulation cycle parameters;

[0044] Calculating the similarity between the current user data and the stable data to obtain the user similarity corresponding to the historical user;

[0045] Users whose user similarity is greater than a similarity threshold are screened out from all the historical users to obtain a plurality of similar users.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] It can be seen that the present invention can conveniently and directly monitor the user's hormone data based on wearable devices and analyze based on the server to determine the ovulation cycle parameters and guidance plans, thereby realizing more intelligent and efficient pregnancy-related monitoring and guidance services, improving the monitoring accuracy of pregnancy-related indicators, and giving users a better user experience. 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 It is a structural schematic diagram of a pregnancy-related monitoring system based on electrochemical sensing technology disclosed in an embodiment of the present invention. Figure 2 It is a schematic diagram of the structure of an electrochemical sensor disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] 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 are within the scope of protection of the present invention.

[0051] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or ends.

[0052] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0053] The present invention aims to provide an innovative pregnancy-related monitoring system based on electrochemical sensing technology, which can conveniently and directly monitor the user's hormone data based on wearable devices and analyze based on a server to determine ovulation cycle parameters and guidance plans, thereby achieving more intelligent and efficient pregnancy-related monitoring and guidance services, improving the monitoring accuracy of pregnancy-related indicators, and giving users a better user experience.

[0054] For details, please refer to Figure 1 , Figure 1 Schematic diagram of a pregnancy-related monitoring system based on electrochemical sensing technology disclosed in an embodiment of the present invention. Figure 1 As shown, the pregnancy-related monitoring system based on electrochemical sensing technology includes at least a wearable device and a server, wherein the wearable device can be worn on the user to obtain hormone detection data of the target monitored user, and the server is connected to the wearable device and includes a parameter analysis module and a solution generation module, wherein:

[0055] The parameter analysis module is used to obtain hormone test data, and analyze the ovulation cycle parameters of the target monitored user based on the hormone test data;

[0056] The program generation module is used to generate a pregnancy-related guidance program corresponding to the target monitoring user according to the pregnancy-related goals and ovulation cycle parameters corresponding to the target monitoring user.

[0057] As an optional embodiment, the wearable device includes a flexible base layer, a flexible printed circuit board, and an electrochemical sensor arranged in the flexible base layer; a data processing circuit and a communication circuit are arranged on the flexible printed circuit board; the electrochemical sensor and the data processing circuit are used to acquire and analyze hormone detection data, and the communication circuit is used to send the hormone detection data to a server.

[0058] Specifically, the flexible base layer is made of medical grade silicone material with a thickness of 0.8-1.2mm to ensure wearing comfort and biocompatibility. The electrochemical sensor is embedded in the flexible base layer and has a contact area of ​​10-15mm with the skin. 2 . The flexible printed circuit board is U-shaped with a bending angle of 120±5°, which is used to realize the electrical interconnection of various components. Specifically, the data processing unit in the data processing circuit adopts a low-power microprocessor with a main frequency of 48MHz and a built-in 4MB flash memory, which is responsible for data acquisition and preprocessing. Specifically, the communication circuit adopts the Bluetooth 5.0 protocol, the working frequency band is 2.4GHz, and the transmission distance is not less than 10 meters, realizing data transmission with the mobile terminal.

[0059] As an optional embodiment, the hormone detection data includes luteinizing hormone data, estradiol data and progesterone level data.

[0060] Specifically, Figure 2 As shown, the electrochemical sensor (120) adopts a three-electrode structure, including a working electrode (121), a reference electrode (122) and an auxiliary electrode (123). The working electrode (121) is a gold electrode with a diameter of 3 mm, and its surface is modified with a 3-mercaptopropionic acid self-assembled monolayer and a specific antibody in sequence. The specific antibodies are covalently linked to the surface of the working electrode after being activated by EDC / NHS, including anti-luteinizing hormone antibodies, anti-estradiol antibodies and anti-progesterone antibodies. The reference electrode (122) adopts an Ag / AgCl electrode with a diameter of 2 mm and a potential stability of ±1 mV. The auxiliary electrode (123) is a platinum electrode with a diameter of 2 mm. The three electrodes are arranged in an equilateral triangle with an electrode spacing of 5 mm, and the working electrode is located directly above. The three electrodes are connected to the flexible printed circuit through a conductive silver paste, and the resistivity of the conductive silver paste is not greater than 1.0×10 -6 Ω·m.

[0061] As an optional embodiment, the parameter analysis module includes a data preprocessing unit, a hormone concentration analysis unit and a cycle prediction unit, wherein:

[0062] The data preprocessing unit is used to denoise the hormone detection data using a wavelet transform algorithm to obtain denoised detection data;

[0063] The hormone concentration analysis unit is used to scan the denoised detection data using linear scanning voltammetry to obtain hormone concentration data;

[0064] The cycle prediction unit is used to input hormone concentration data into the trained cycle prediction model to obtain output ovulation cycle parameters.

[0065] As an optional embodiment, the data preprocessing unit uses the db4 wavelet basis function to perform 5-layer wavelet decomposition on the hormone detection data to obtain the wavelet coefficients of each layer, processes the wavelet coefficients of each layer through a soft threshold function, and finally uses the processed wavelet coefficients to reconstruct the signal to obtain the denoised detection data; the cycle prediction model is based on the support vector machine algorithm, and uses the radial basis kernel function (RBF) for nonlinear mapping, the kernel function parameter γ is set to 0.01, and the penalty factor C is set to 100; during the training process of the cycle prediction model, the hormone level data for 30 consecutive days are first divided into a training set and a validation set in a ratio of 8:2, and the training data is input into the model after being standardized; the input features of the cycle prediction model include daily luteinizing hormone, estradiol and progesterone concentration values ​​and their first-order differences, and the output is the prediction result of ovulation time; the cycle prediction model optimizes the model parameters through cross-validation, and finally achieves a prediction accuracy of not less than 93% on the validation set, and can warn the ovulation time 36-48 hours in advance.

[0066] As an optional embodiment, the server further includes an encryption module, which is used to perform the following steps:

[0067] Acquire multiple wavelet coefficients obtained by processing hormone detection data by a data preprocessing unit to obtain wavelet transform data;

[0068] Obtaining the vector product of the scanning range and the scanning rate performed by the hormone concentration analysis unit to obtain scanning parameters;

[0069] Based on the preset data value mapping rules, the corresponding encryption key is generated according to the wavelet transform data and the scanning parameters;

[0070] Encrypting the hormone concentration data and ovulation cycle parameters according to the encryption key to obtain encrypted data;

[0071] The encrypted data is sent to the user terminal device corresponding to the target monitored user; the user terminal device is used to decrypt the encrypted data based on the wavelet transform data and the scanning parameters to obtain the hormone concentration data and the ovulation cycle parameters.

[0072] As an optional embodiment, the user terminal device is used to decrypt the encrypted data based on the wavelet transform data and the scanning parameters to obtain the hormone concentration data and the ovulation cycle parameters. The specific steps include:

[0073] communicating with the wearable device to obtain at least one historical hormone test data;

[0074] Obtain historical hormone concentration data corresponding to historical hormone test data from the server;

[0075] Inputting historical hormone test data and historical hormone concentration data into a trained parameter prediction neural network to obtain output predicted wavelet transform data and scanning parameters; the parameter prediction neural network is trained by a training data set including a plurality of training hormone data and corresponding wavelet transform data and scanning parameter annotations;

[0076] Generate a corresponding decryption key according to data value mapping rules, wavelet transform data and scanning parameters;

[0077] The encrypted data is decrypted according to the decryption key to obtain hormone concentration data and ovulation cycle parameters.

[0078] As an optional embodiment, the plan generation module includes a plan recommendation unit and a nutrition database, wherein:

[0079] The nutrition database is used to store a plurality of preset guidance programs and guidance programs of a plurality of historical users;

[0080] The plan recommendation unit is used to obtain the pregnancy-related goals corresponding to the target monitoring user, and generate the pregnancy-related guidance plan corresponding to the target monitoring user according to the pregnancy-related goals, ovulation cycle parameters and guidance plan; the pregnancy-related goals are contraceptive goals or pregnancy preparation goals; the pregnancy-related guidance plan includes a diet plan and an exercise plan.

[0081] As an optional embodiment, the specific steps of the plan recommendation unit generating a pregnancy-related guidance plan corresponding to the target monitoring user according to the pregnancy-related goals, ovulation cycle parameters and guidance plan include:

[0082] According to pregnancy-related goals, multiple adaptive plans are screened out from multiple preset guidance plans;

[0083] According to pregnancy-related goals and ovulation cycle parameters, multiple similar users are screened out from multiple historical users;

[0084] The pregnancy-related goals and ovulation cycle parameters are input into the trained plan prediction neural network to obtain the output prediction plan; the plan prediction neural network is an LSTM network;

[0085] For each adaptation scheme, the average similarity between the adaptation scheme and the guidance schemes of all similar users is calculated to obtain the historical similarity;

[0086] Calculate the similarity between the adaptation scheme and the prediction scheme to obtain the prediction similarity;

[0087] Calculate the weighted average of the historical similarity and the predicted similarity to obtain the solution priority corresponding to the adaptation solution;

[0088] Filter out adaptation schemes whose scheme priorities are greater than a priority threshold from all adaptation schemes to obtain multiple preferred schemes;

[0089] The intersection of the predicted scheme and multiple preferred schemes is calculated to obtain the pregnancy-related guidance scheme corresponding to the target monitored user.

[0090] As an optional embodiment, multiple similar users are screened out from multiple historical users according to pregnancy-related goals and ovulation cycle parameters, including:

[0091] For each historical user, obtain user data corresponding to the historical user; the user data includes historical pregnancy-related goals and historical ovulation cycle parameters corresponding to multiple historical time points;

[0092] Based on the preset data stability screening algorithm, stable data is screened out from the user data; stable data includes historical pregnancy-related goals and historical ovulation cycle parameters that remain stable at multiple historical time points;

[0093] Calculate current user data consisting of pregnancy related goals and ovulation cycle parameters;

[0094] Calculate the similarity between the current user data and the stable data to obtain the user similarity corresponding to the historical user;

[0095] Users whose user similarity is greater than a similarity threshold are screened out from all historical users to obtain multiple similar users.

[0096] In a specific embodiment, when the user wears the device, the electrochemical sensor recognizes the hormone molecules in the sweat through specific antibodies and generates corresponding electrochemical signals. After the initial processing of these signals by the data processing unit, they are transmitted to the mobile application in the server through the communication circuit. The data analysis module of the application reduces noise and analyzes the signal to obtain accurate hormone concentration values. The algorithm engine predicts the ovulation cycle based on historical data and current hormone levels. Finally, the solution generation module pushes personalized diet and exercise recommendations to the user-end device based on the user's goals (contraception or preparation for pregnancy) and current physiological state.

[0097] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0099] The apparatus, device, non-volatile computer-readable storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding apparatus, device, and non-volatile computer storage medium will not be repeated here.

[0100] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0101] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0102] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0103] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0104] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0105] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0108] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0109] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0110] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0111] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0112] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0113] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0114] Finally, it should be noted that the pregnancy-related monitoring system based on electrochemical sensing technology disclosed in the embodiment of the present invention is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A pregnancy-related monitoring system based on electrochemical sensing technology, characterized in that: The system comprises: Wearable devices used to obtain hormone test data of target monitoring users; The server is connected to the wearable device and includes a parameter analysis module and a solution generation module, wherein: The parameter analysis module is used to obtain the hormone detection data, and analyze and obtain the ovulation cycle parameters of the target monitored user according to the hormone detection data; The program generation module is used to generate a pregnancy-related guidance program corresponding to the target monitoring user according to the pregnancy-related goals corresponding to the target monitoring user and the ovulation cycle parameters.

2. The pregnancy-related monitoring system based on electrochemical sensing technology according to claim 1, characterized in that: The wearable device includes a flexible base layer, a flexible printed circuit board and an electrochemical sensor arranged in the flexible base layer; a data processing circuit and a communication circuit are arranged on the flexible printed circuit board; the electrochemical sensor and the data processing circuit are used to acquire and analyze the hormone detection data, and the communication circuit is used to send the hormone detection data to the server.

3. The pregnancy-related monitoring system based on electrochemical sensing technology according to claim 2, characterized in that: The hormone detection data includes luteinizing hormone data, estradiol data and progesterone level data; the electrochemical sensor includes a working electrode, a reference electrode and an auxiliary electrode; the working electrode adopts a gold electrode with a diameter of 3 mm, the surface of which forms a self-assembled monolayer through 3-mercaptopropionic acid, and is covalently linked to specific antibodies after EDC / NHS activation, and the specific antibodies include anti-luteinizing hormone antibodies, anti-estradiol antibodies and anti-progesterone antibodies; the reference electrode is an Ag / AgCl electrode with a diameter of 2 mm and a potential stability of ±1 mV; the auxiliary electrode is a platinum electrode with a diameter of 2 mm; the working electrode, the reference electrode and the auxiliary electrode are connected to the flexible printed circuit board through conductive silver paste, and the resistivity of the conductive silver paste is not more than 1.0×10 -6 Ω·m.

4. The pregnancy-related monitoring system based on electrochemical sensing technology according to claim 1, characterized in that: The parameter analysis module includes a data preprocessing unit, a hormone concentration analysis unit and a cycle prediction unit, wherein: The data preprocessing unit is used to denoise the hormone detection data using a wavelet transform algorithm to obtain denoised detection data; The hormone concentration analysis unit is used to scan the denoised detection data using linear scanning voltammetry to obtain hormone concentration data; The cycle prediction unit is used to input the hormone concentration data into a trained cycle prediction model to obtain the output ovulation cycle parameters.

5. The pregnancy-related monitoring system based on electrochemical sensing technology according to claim 4, characterized in that: The data preprocessing unit uses the db4 wavelet basis function to perform 5-layer wavelet decomposition on the hormone detection data to obtain the wavelet coefficients of each layer, processes the wavelet coefficients of each layer through a soft threshold function, and finally uses the processed wavelet coefficients to reconstruct the signal to obtain the denoised detection data; the cycle prediction model is based on a support vector machine algorithm, and uses a radial basis kernel function (RBF) for nonlinear mapping, the kernel function parameter γ is set to 0.01, and the penalty factor C is set to 100; during the training process of the cycle prediction model, the hormone level data for 30 consecutive days are first divided into a training set and a validation set in a ratio of 8:2, and the training data is input into the model after being standardized; the input features of the cycle prediction model include daily luteinizing hormone, estradiol and progesterone concentration values ​​and their first-order differences, and the output is the prediction result of ovulation time; the cycle prediction model optimizes the model parameters through cross-validation, and finally achieves a prediction accuracy of not less than 93% on the validation set, and can warn the ovulation time 36-48 hours in advance.

6. The pregnancy-related monitoring system based on electrochemical sensing technology according to claim 5, characterized in that: The server also includes an encryption module for performing the following steps: Acquire a plurality of the wavelet coefficients obtained by the data preprocessing unit processing the hormone detection data to obtain wavelet transform data; Obtaining a vector product of a scanning range and a scanning rate of the hormone concentration analysis unit to obtain a scanning parameter; Based on a preset data value mapping rule, generating a corresponding encryption key according to the wavelet transform data and the scanning parameters; Encrypting the hormone concentration data and the ovulation cycle parameters according to the encryption key to obtain encrypted data; Sending the encrypted data to a user terminal device corresponding to the target monitored user; The user terminal device is used to decrypt the encrypted data based on the wavelet transform data and the scanning parameters to obtain the hormone concentration data and the ovulation cycle parameters.

7. The pregnancy-related monitoring system based on electrochemical sensing technology according to claim 4, characterized in that: The specific steps of the user terminal device being used to decrypt the encrypted data based on the wavelet transform data and the scanning parameters to obtain the hormone concentration data and the ovulation cycle parameters include: communicating with the wearable device to obtain at least one historical hormone detection data; Obtaining historical hormone concentration data corresponding to the historical hormone detection data from the server; Inputting the historical hormone detection data and the historical hormone concentration data into a trained parameter prediction neural network to obtain output predicted wavelet transform data and scanning parameters; the parameter prediction neural network is trained by a training data set including a plurality of training hormone data and corresponding wavelet transform data and scanning parameter annotations; Generate a corresponding decryption key according to the data value mapping rule, the wavelet transform data and the scanning parameter; The encrypted data is decrypted according to the decryption key to obtain the hormone concentration data and the ovulation cycle parameters.

8. The pregnancy-related monitoring system based on electrochemical sensing technology according to claim 1, characterized in that: The scheme generation module includes a scheme recommendation unit and a nutrition database, wherein: The nutrition database is used to store a plurality of preset guidance programs and guidance programs of a plurality of historical users; The plan recommendation unit is used to obtain the pregnancy-related goals corresponding to the target monitoring user, and generate a pregnancy-related guidance plan corresponding to the target monitoring user based on the pregnancy-related goals, the ovulation cycle parameters and the guidance plan; the pregnancy-related goals are contraceptive goals or pregnancy preparation goals; the pregnancy-related guidance plan includes a diet plan and an exercise plan.

9. The pregnancy-related monitoring system based on electrochemical sensing technology according to claim 8, characterized in that: The specific steps of the plan recommendation unit generating a pregnancy-related guidance plan corresponding to the target monitoring user according to the pregnancy-related goal, the ovulation cycle parameter and the guidance plan include: According to the pregnancy-related goal, screening out a plurality of adaptation schemes from the plurality of preset guidance schemes; Screening out a plurality of similar users from the plurality of historical users according to the pregnancy-related goal and the ovulation cycle parameter; Inputting the pregnancy-related goal and the ovulation cycle parameter into a trained scenario prediction neural network to obtain an output prediction scenario; the scenario prediction neural network is an LSTM network; For each of the adaptation solutions, calculate the average of the similarities between the adaptation solution and the guidance solutions of all the similar users to obtain the historical similarity; Calculating the similarity between the adaptation scheme and the prediction scheme to obtain the prediction similarity; Calculate the weighted average of the historical similarity and the predicted similarity to obtain the solution priority corresponding to the adaptation solution; Filter out the adaptation schemes whose scheme priorities are greater than the priority threshold from all the adaptation schemes to obtain multiple preferred schemes; The intersection of the prediction scheme and the multiple preferred schemes is calculated to obtain a pregnancy-related guidance scheme corresponding to the target monitored user.

10. The pregnancy-related monitoring system based on electrochemical sensing technology according to claim 9, characterized in that: The step of screening out a plurality of similar users from the plurality of historical users according to the pregnancy-related goal and the ovulation cycle parameter comprises: For each of the historical users, user data corresponding to the historical user is obtained; the user data includes historical pregnancy-related goals and historical ovulation cycle parameters corresponding to multiple historical time points; Based on a preset data stability screening algorithm, stable data is screened out from the user data; the stable data includes historical pregnancy-related goals and historical ovulation cycle parameters that remain stable at multiple historical time points; Calculating current user data consisting of the pregnancy-related goal and the ovulation cycle parameters; Calculating the similarity between the current user data and the stable data to obtain the user similarity corresponding to the historical user; Users whose user similarity is greater than a similarity threshold are screened out from all the historical users to obtain a plurality of similar users.

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