Lower urinary tract function monitoring method, system, equipment and medium

Abstract

The invention provides a lower urinary tract function monitoring method, system and device and a medium, and belongs to the technical field of lower urinary tract function monitoring. The method comprises the following steps: constructing a training set and a test set by using preprocessed data capable of reflecting whether the lower urinary tract function of a human body is normal or not; and constructing a BP neural network, training a BP neural network model based on the constructed training set, and testing a BP neural network model training result based on the constructed test set to obtain a trained BP neural network model. And inputting to-be-predicted data capable of reflecting whether the lower urinary tract function of the human body is normal or not into the trained BP neural network model for monitoring the lower urinary tract function of the human body to obtain a monitoring result of the lower urinary tract function of the human body. Compared with the prior art, the problems that a catheter needs to be inserted into a human body, a patient cannot operate by himself or herself, and lagged clinical examination cannot effectively prevent diseases in the aspect of lower urinary tract dysfunction are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of lower urinary tract function monitoring, and in particular relates to a lower urinary tract function monitoring method, system, equipment and medium. Background Art

[0002] LUTD (Lower Urinary Tract Dysfunction) is a series of diseases with abnormalities in the lower urinary tract, such as the bladder or urethra. With the increasing aging of the population, the prevalence of lower urinary tract dysfunction is increasing. Patients with lower urinary tract dysfunction are prone to depression and anxiety. If not diagnosed and treated in time, it will lead to severe renal failure, which seriously threatens the life and health of patients. It not only has a negative impact on the quality of life of patients, but also increases the burden on society.

[0003] At present, the diagnosis of LUTD in clinical practice mostly relies on professional medical equipment such as urodynamic analyzers, which are not only invasive (need to insert a catheter into the human body), but also need to be operated by hospital professionals, and patients cannot operate it themselves. In order to obtain data on human urine flow rate and human bladder pressure values ​​to reflect whether the lower urinary tract function is normal, there are many shortcomings in terms of safety, comfort and testing costs, and delayed clinical examinations cannot effectively prevent diseases related to lower urinary tract dysfunction. Summary of the invention

[0004] The purpose of the present invention is to provide a lower urinary tract function monitoring method, system, equipment and medium, which are used to solve the problems in the prior art that a catheter needs to be inserted into the human body, the patient cannot operate it by himself, and the delayed clinical examination cannot effectively prevent diseases related to lower urinary tract dysfunction.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for monitoring lower urinary tract function, comprising the following steps: Acquire data that can reflect whether the lower urinary tract function of the human body is normal, and pre-process the acquired data that can reflect whether the lower urinary tract function of the human body is normal; The training set and the test set are constructed using the preprocessed data that can reflect whether the lower urinary tract function of the human body is normal; Constructing a BP neural network, training the BP neural network model based on the constructed training set, and testing the training result of the BP neural network model based on the constructed test set to obtain a trained BP neural network model; The data to be predicted that can reflect whether the lower urinary tract function of the human body is normal is input into the trained BP neural network model to monitor the lower urinary tract function of the human body, and the monitoring result of the lower urinary tract function of the human body is obtained.

[0006] A further improvement of the present invention is that, when obtaining data that can reflect whether the lower urinary tract function of the human body is normal through millimeter wave radar simulation, the millimeter wave radar measurement parameters are adjusted based on the actual water flow rate and the water flow rate measured by the millimeter wave radar, and then the millimeter wave radar is used to obtain data that can reflect whether the lower urinary tract function of the human body is normal.

[0007] A further improvement of the present invention is that the millimeter wave radar measurement parameters include starting frequency, frequency modulation slope, idle time, transmission signal starting time, analog-to-digital converter starting time, sampling frequency, analog-to-digital converter samples, slope end time, receiving gain, number of chirps per frame and time from transmission to end of the transmission signal.

[0008] A further improvement of the present invention is that the data that can reflect whether the lower urinary tract function of the human body is normal includes human urine flow rate and human bladder pressure value data.

[0009] A further improvement of the present invention is that the preprocessing refers to removing outliers, supplementing missing values ​​and denoising the data obtained that can reflect whether the lower urinary tract function of the human body is normal, and there is no order of operation for removing outliers, supplementing missing values ​​and denoising.

[0010] A further improvement of the present invention is that the obtained human lower urinary tract function monitoring results are evaluated using mean absolute error, mean square error, mean absolute percentage error and determination coefficient.

[0011] A further improvement of the present invention is that 80% of the pre-processed data that can reflect whether the lower urinary tract function of the human body is normal is used as a training set, and 20% is used as a test set.

[0012] In a second aspect, the present invention provides a lower urinary tract function monitoring system, including a data acquisition module, a data preprocessing module, a training set and a test set construction module, a BP neural network model training module and a human lower urinary tract function monitoring module; The data acquisition module is used to acquire data that can reflect whether the lower urinary tract function of the human body is normal; The data preprocessing module is used to preprocess the obtained data that can reflect whether the lower urinary tract function of the human body is normal; The training set and test set construction module is used to construct a training set and a test set using preprocessed data that can reflect whether the lower urinary tract function of the human body is normal; The BP neural network model training module is used to construct a BP neural network, train the BP neural network model based on the constructed training set, and test the BP neural network model training result based on the constructed test set to obtain a trained BP neural network model; The human lower urinary tract function monitoring module is used to input the data to be predicted that can reflect whether the human lower urinary tract function is normal into the trained BP neural network model to perform human lower urinary tract function monitoring and obtain the human lower urinary tract function monitoring results.

[0013] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the lower urinary tract function monitoring method introduced above when executing the computer program.

[0014] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the lower urinary tract function monitoring method introduced above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention belongs to an improved invention. Compared with the existing lower urinary tract function monitoring method, the lower urinary tract function monitoring method proposed by the present invention inputs the data to be predicted that can reflect whether the lower urinary tract function of the human body is normal into the trained BP neural network model to monitor the lower urinary tract function of the human body, and obtains the monitoring result of the lower urinary tract function of the human body. It can be seen that after the BP neural network model is trained, the present invention can input the data to be predicted that can reflect whether the lower urinary tract function of the human body is normal into the trained BP neural network model to monitor the lower urinary tract function of the human body, and determine whether the lower urinary tract function of the human body is normal by comparing the monitoring result of the lower urinary tract function of the human body with the data of the actual normal lower urinary tract function of the human body, and whether it is necessary to go to the hospital for diagnosis and treatment, thereby effectively preventing diseases related to abnormal lower urinary tract function, and effectively solving the problem in the prior art that a catheter needs to be inserted into the human body, the patient cannot operate it by himself, and the delayed clinical examination cannot effectively prevent diseases related to abnormal lower urinary tract function. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the method for monitoring lower urinary tract function of the present invention; Figure 2 is a schematic diagram of a lower urinary tract function monitoring system of the present invention; Figure 3 A schematic diagram of a device for calculating water flow velocity using millimeter wave radar according to the present invention; Figure 4 A schematic diagram of a bladder device simulating a human body according to the present invention; Figure 5A schematic diagram of the experimental process of the present invention using a bladder device simulating a human body; Figure 6 A curve diagram of human urine flow rate simulated by the present invention; Figure 7 A comparison result diagram of the bladder pressure value predicted by the present invention and the actual bladder pressure value; Figure 8 It is a structural schematic diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0017] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0018] The lower urinary tract function monitoring method proposed in the present invention inputs the data to be predicted that can reflect whether the lower urinary tract function of the human body is normal into the trained BP neural network model to monitor the lower urinary tract function of the human body, and obtains the monitoring result of the lower urinary tract function of the human body. Compared with the prior art, the present invention effectively solves the problem that a urinary catheter needs to be inserted into the human body, the patient cannot operate it by himself, and the delayed clinical examination cannot effectively prevent diseases related to lower urinary tract dysfunction.

[0019] Embodiment 1: The flow chart of the method for monitoring lower urinary tract function of the present invention is as follows Figure 1 As shown, the lower urinary tract function monitoring method of the present invention comprises the following steps: S1. Acquire data that can reflect whether the lower urinary tract function of a human body is normal, and pre-process the acquired data that can reflect whether the lower urinary tract function of a human body is normal.

[0020] S2. Use the preprocessed data that can reflect whether the lower urinary tract function of the human body is normal to construct a training set and a test set.

[0021] S3. Construct a BP neural network, train the BP neural network model based on the constructed training set, and test the training result of the BP neural network model based on the constructed test set to obtain a trained BP neural network model.

[0022] S4. Input the data to be predicted that can reflect whether the lower urinary tract function of the human body is normal into the trained BP neural network model to monitor the lower urinary tract function of the human body, and obtain the monitoring result of the lower urinary tract function of the human body.

[0023] Embodiment 2: The schematic diagram of the lower urinary tract function monitoring system of the present invention is as follows Figure 2As shown, the lower urinary tract function monitoring system of the present invention includes a data acquisition module, a data preprocessing module, a training set and test set construction module, a BP neural network model training module and a human lower urinary tract function monitoring module.

[0024] The data acquisition module is used to acquire data that can reflect whether the lower urinary tract function of the human body is normal.

[0025] The data preprocessing module is used to preprocess the obtained data that can reflect whether the lower urinary tract function of the human body is normal.

[0026] The training set and test set construction module is used to construct the training set and the test set using the preprocessed data that can reflect whether the lower urinary tract function of the human body is normal.

[0027] The BP neural network model training module is used to construct a BP neural network, train the BP neural network model based on the constructed training set, and test the BP neural network model training result based on the constructed test set to obtain a trained BP neural network model.

[0028] The human lower urinary tract function monitoring module is used to input the data to be predicted that can reflect whether the human lower urinary tract function is normal into the trained BP neural network model to monitor the human lower urinary tract function and obtain the human lower urinary tract function monitoring results.

[0029] Embodiment 3: S1. Acquire data that can reflect whether the lower urinary tract function of a human body is normal, and pre-process the acquired data that can reflect whether the lower urinary tract function of a human body is normal.

[0030] In this step, the millimeter-wave radar is used to obtain data that can reflect whether the lower urinary tract function of the human body is normal. When the millimeter-wave radar is used to obtain data that can reflect whether the lower urinary tract function of the human body is normal, the millimeter-wave radar measurement parameters are adjusted based on the actual water flow rate and the water flow rate measured by the millimeter-wave radar, and then the millimeter-wave radar is used to obtain data that can reflect whether the lower urinary tract function of the human body is normal.

[0031] The data that can reflect whether the lower urinary tract function of the human body is normal include human urine flow rate and human bladder pressure value data. The millimeter wave radar measurement parameters include starting frequency, frequency modulation slope, idle time, transmission signal start time, analog digital converter start time, sampling frequency, analog digital converter sample, slope end time, receiving gain, number of chirps per frame and the time from transmission to end of the transmission signal.

[0032] Preprocessing refers to the removal of outliers, supplementation of missing values ​​and denoising (such as data cleaning and smoothing techniques) of the data obtained that can reflect whether the lower urinary tract function of the human body is normal. There is no order of operation for the removal of outliers, supplementation of missing values ​​and denoising.

[0033] Abnormal values ​​refer to data of other objects measured by the millimeter-wave radar, rather than data that can reflect whether the lower urinary tract function of the human body is normal. Missing values ​​refer to the position deviation during the water flow velocity measurement, which may exceed the measurement range of the millimeter-wave radar and cannot fully measure the data that can reflect whether the lower urinary tract function of the human body is normal.

[0034] Specifically, in this step, the present embodiment first adjusts the millimeter wave radar parameters based on the actual water flow velocity and the water flow velocity measured by the millimeter wave radar, and then uses the millimeter wave radar to measure the urine flow rate and bladder pressure value in the bladder device simulating the human body. The schematic diagram of the device for calculating the water flow velocity using the millimeter wave radar in this embodiment is shown in FIG. Figure 3 As shown, the millimeter wave radar (also called radar) is used to measure the flow rate of water (also called water flow). The schematic diagram of the bladder device simulating the human body in this embodiment is as follows Figure 4 As shown in the figure, in the bladder device simulating the human body, the flow meter is installed at the outlet of the pipe water flow to evaluate the accuracy and stability of the millimeter wave radar (also called radar) measurement. The pressure gauge is installed at the outlet of the bucket to monitor the pressure change of the bladder using the pressure gauge. The pressurized water bag is a simulated bladder, and the pressurized ball is used to pressurize the simulated bladder to achieve drainage.

[0035] Table 1 is the comparison results of two experiments on the actual water flow velocity and the water flow velocity measured by millimeter wave radar.

[0036] Table 1 Comparison of actual water flow velocity and water flow velocity measured by millimeter wave radar

[0037] It can be seen from Table 1 that the difference between the actual water flow velocity and the water flow velocity measured by the millimeter wave radar is within an acceptable range, indicating that the set radar measurement parameters are reasonable and the accuracy of the radar measurement results is also guaranteed. The radar measurement parameters set in this embodiment are shown in Table 2, where ADC represents an analog-to-digital converter, ADC start time represents the start time of converting the received analog signal into a digital signal, ADC sample represents a series of discrete samples of an analog signal obtained by sampling the analog signal at a fixed time interval (for example), and the number of chirps represents the number of chirps contained in each frame of the transmitted signal.

[0038] Table 2. Measurement parameters of millimeter wave radar

[0039] In this step, the process of using millimeter wave radar to simulate the urine flow rate and bladder pressure value in the human bladder device is described as follows: This step uses a bladder device that simulates the human body. The schematic diagram of the experimental process is as follows Figure 5 As shown, open the pressure valve, inject a certain amount of water into the water bag, place the water bag into the pressure bag, squeeze the whole to simulate the bladder force process, and place the millimeter wave radar component vertically opposite to the radial emission direction of the water flow, and adjust the millimeter wave radar acquisition system to the ready state. During the measurement process, human subjects use different pressure squeezes to simulate the effect of urinating from the bladder. The liquid collector is placed under the simulated bladder outlet to collect the discharged liquid for reuse. During this process, the millimeter wave radar and the pressure gauge are turned on at the same time, and the maximum value of the pressure gauge during the value change process is recorded.

[0040] In order to simulate the bladder pressure and urination time of the real human urination process, different squeezing forces and times were selected during the experiment. By changing the squeezing force and time, urination data in different situations were obtained for more detailed and comprehensive research and analysis. Figure 6 is a graph of simulated human urine flow rate. Figure 6 It can be seen that the water flow velocity changes with the number of frames (time), which is consistent with the speed change during human urination.

[0041] S2. Use the preprocessed data that can reflect whether the lower urinary tract function of the human body is normal to construct a training set and a test set.

[0042] The preprocessed data that can reflect whether the lower urinary tract function of the human body is normal is used to construct a training set and a test set. 80% of the preprocessed data that can reflect whether the lower urinary tract function of the human body is normal is used as a training set, and 20% is used as a test set.

[0043] As another implementation, 80% and 20% in this step can be adjusted according to actual needs, for example, 90% of the preprocessed data that can reflect whether the lower urinary tract function of the human body is normal is used as a training set, and 10% is used as a test set.

[0044] S3. Construct a BP neural network, train the BP neural network model based on the constructed training set, and test the training result of the BP neural network model based on the constructed test set to obtain a trained BP neural network model.

[0045] A BP neural network is constructed, the BP neural network model is trained based on the constructed training set, and the training result of the BP neural network model is tested based on the constructed test set to obtain a trained BP neural network model.

[0046] S4. Input the data to be predicted that can reflect whether the lower urinary tract function of the human body is normal into the trained BP neural network model to monitor the lower urinary tract function of the human body, and obtain the monitoring result of the lower urinary tract function of the human body.

[0047] The data to be predicted that can reflect whether the lower urinary tract function of the human body is normal is input into the trained BP neural network model to monitor the lower urinary tract function of the human body, and the monitoring result of the lower urinary tract function of the human body is obtained.

[0048] In this step, the obtained human lower urinary tract function monitoring results are evaluated using mean absolute error, mean square error, mean absolute percentage error and coefficient of determination. The coefficient of determination is used to evaluate the degree of fit of the model to the data. The closer the coefficient of determination is to 1, the stronger the explanatory power of the model.

[0049] In order to verify the effectiveness of the lower urinary tract function monitoring method proposed in the present invention, this embodiment uses 87.5% of the acquired human urine flow rate and human bladder pressure value data as a training set and 12.5% ​​as a test set, and uses the trained BP neural network to predict the maximum bladder pressure. The prediction results are shown in Table 3.

[0050] Table 3 Prediction results of maximum bladder pressure by BP neural network

[0051] The first four columns of data in Table 3 are the initial, maximum and final urine flow rates and the true maximum bladder pressure values ​​collected by the millimeter wave radar, and the last column of data is the maximum bladder pressure value predicted by the BP neural network model. By comparing the true maximum bladder pressure and the predicted maximum bladder pressure in Table 3, and evaluating the prediction results based on the true maximum bladder pressure and the predicted maximum bladder pressure, the prediction result evaluation scores are as follows: A. The mean absolute error is 0.01909; B. The mean square error is 0.0005886; C. The mean absolute percentage error is 2.831%, D. The coefficient of determination is 0.7142. It can be seen that the error between the actual maximum bladder pressure and the maximum bladder pressure predicted by the BP neural network is not large, which proves the effectiveness of the lower urinary tract function monitoring method proposed in the present invention.

[0052] The comparison results of the human lower urinary tract function monitoring results (predicted bladder pressure value) of the present invention and the actual bladder pressure value are as follows: Figure 7 As shown, the blue line is the actual bladder pressure value, and the yellow line is the predicted bladder pressure value. Figure 7 It can be seen that the human lower urinary tract function monitoring results (predicted bladder pressure values) are not much different from the actual bladder pressure value curve, and when evaluating the obtained human lower urinary tract function monitoring results, the determination coefficient is 0.7142, indicating that the trained BP neural network model can output a variance of 71.42%, and the error result is small.

[0053] Embodiment 4: See also Figure 8 As shown, the present invention also provides an electronic device 100 for a lower urinary tract function monitoring method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0054] The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the lower urinary tract function monitoring method described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0055] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0056] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for evaluating the storage life of an electronic device, and the processor 102 can execute the plurality of instructions to implement: Acquire data that can reflect whether the lower urinary tract function of the human body is normal, and pre-process the acquired data that can reflect whether the lower urinary tract function of the human body is normal; The training set and the test set are constructed using the preprocessed data that can reflect whether the lower urinary tract function of the human body is normal; Constructing a BP neural network, training the BP neural network model based on the constructed training set, and testing the training result of the BP neural network model based on the constructed test set to obtain a trained BP neural network model; The data to be predicted that can reflect whether the lower urinary tract function of the human body is normal is input into the trained BP neural network model to monitor the lower urinary tract function of the human body, and the monitoring result of the lower urinary tract function of the human body is obtained.

[0057] Embodiment 5: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0058] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] 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.

[0061] 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.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring lower urinary tract function, characterized in that: The following steps are involved: Acquire data that can reflect whether the lower urinary tract function of the human body is normal, and pre-process the acquired data that can reflect whether the lower urinary tract function of the human body is normal; The training set and the test set are constructed using the preprocessed data that can reflect whether the lower urinary tract function of the human body is normal; Constructing a BP neural network, training the BP neural network model based on the constructed training set, and testing the training result of the BP neural network model based on the constructed test set to obtain a trained BP neural network model; The data to be predicted that can reflect whether the lower urinary tract function of the human body is normal is input into the trained BP neural network model to monitor the lower urinary tract function of the human body, and the monitoring result of the lower urinary tract function of the human body is obtained.

2. The method for monitoring lower urinary tract function according to claim 1, characterized in that: When obtaining data that can reflect whether the lower urinary tract function of the human body is normal through the millimeter wave radar, the millimeter wave radar measurement parameters are adjusted based on the actual water flow rate and the water flow rate measured by the millimeter wave radar, and then the millimeter wave radar is used to obtain data that can reflect whether the lower urinary tract function of the human body is normal.

3. The method for monitoring lower urinary tract function according to claim 2, characterized in that: The millimeter wave radar measurement parameters include starting frequency, frequency modulation slope, idle time, transmission signal start time, analog-to-digital converter start time, sampling frequency, analog-to-digital converter samples, slope end time, receiving gain, number of chirps per frame and time from transmission to end of transmission signal.

4. The method for monitoring lower urinary tract function according to claim 1 or 2, characterized in that: The data that can reflect whether the lower urinary tract function of the human body is normal include the human urine flow rate and the human bladder pressure value data.

5. The method for monitoring lower urinary tract function according to claim 1, characterized in that: The preprocessing refers to removing outliers, supplementing missing values ​​and denoising the data obtained to reflect whether the lower urinary tract function of the human body is normal. There is no order of operation for removing outliers, supplementing missing values ​​and denoising.

6. The method for monitoring lower urinary tract function according to claim 1, characterized in that: The obtained human lower urinary tract function monitoring results were evaluated using mean absolute error, mean square error, mean absolute percentage error and coefficient of determination.

7. The method for monitoring lower urinary tract function according to claim 1, characterized in that: 80% of the preprocessed data that can reflect whether the lower urinary tract function of the human body is normal is used as a training set, and 20% is used as a test set.

8. A lower urinary tract function monitoring system, characterized in that: It includes data acquisition module, data preprocessing module, training set and test set construction module, BP neural network model training module and human lower urinary tract function monitoring module; The data acquisition module is used to acquire data that can reflect whether the lower urinary tract function of the human body is normal; The data preprocessing module is used to preprocess the obtained data that can reflect whether the lower urinary tract function of the human body is normal; The training set and test set construction module is used to construct a training set and a test set using preprocessed data that can reflect whether the lower urinary tract function of the human body is normal; The BP neural network model training module is used to construct a BP neural network, train the BP neural network model based on the constructed training set, and test the BP neural network model training result based on the constructed test set to obtain a trained BP neural network model; The human lower urinary tract function monitoring module is used to input the data to be predicted that can reflect whether the human lower urinary tract function is normal into the trained BP neural network model to perform human lower urinary tract function monitoring and obtain the human lower urinary tract function monitoring results.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the lower urinary tract function monitoring method according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the lower urinary tract function monitoring method according to any one of claims 1 to 7 are implemented.

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