Intelligent infusion cabinet data monitoring and processing system and method based on Internet of Things

Abstract

The invention relates to the technical field of data monitoring, and provides an intelligent infusion cabinet data monitoring and processing system and method based on the Internet of Things, and a signal collection module which is used for collecting a static radio frequency signal in an intelligent infusion cabinet through liquid drop radio frequency equipment, extracting an electromagnetic wave signal of the static radio frequency signal, obtaining a first signal, and transmitting the first signal to a second signal; acquiring a dynamic radio-frequency signal in the intelligent infusion cabinet by using a droplet ultrasonic system, and extracting an electromagnetic wave signal of the dynamic radio-frequency signal to obtain a second signal; the dripping speed calculation module is used for calculating the infusion dripping speed corresponding to the intelligent infusion cabinet; the infusion state analysis module is used for analyzing the infusion state corresponding to the intelligent infusion cabinet; the suitability analysis module is used for analyzing the medicine suitability corresponding to the intelligent infusion cabinet; the performance analysis module is used for analyzing the operation performance corresponding to the operation hardware; and the monitoring interaction module is used for setting a monitoring interaction page corresponding to the intelligent infusion cabinet. The accuracy of data monitoring of the intelligent infusion cabinet is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring, and in particular to a data monitoring and processing system and method for an intelligent infusion cabinet based on the Internet of Things. Background Art

[0002] The IoT smart infusion cabinet is a smart medical device that combines IoT technology to manage and monitor the infusion process. This smart infusion cabinet can usually track the infusion status, provide remote monitoring, alarm function, drug management and other features. In order to improve the efficiency and safety of the infusion process, it is necessary to monitor and process the data of the smart infusion cabinet, so as to quickly find the corresponding problems.

[0003] The existing intelligent infusion cabinet data monitoring and processing adopts the photoelectric sensing method. This method places the transmitting end and the receiving end on both sides of the infusion dropper. When the receiving end detects the light change, the electrical signal blocking the transmitting part and the receiving part will change instantly. This change can convert the optical signal into an electrical signal for detection by the relevant circuit, thereby monitoring the dripping rate of the infusion, such as the dripping rate is too fast, too slow or even the needle is stuck. This method is very accurate for dripping rate monitoring, but there is no way to monitor the total weight and remaining weight, which leads to the data monitoring of the intelligent infusion cabinet is not comprehensive enough, causing interference to the subsequent infusion processing. Summary of the invention

[0004] The present invention provides a data monitoring and processing system and method for an intelligent infusion cabinet based on the Internet of Things, the main purpose of which is to improve the accuracy of data monitoring and processing of the intelligent infusion cabinet.

[0005] To achieve the above purpose, the present invention provides a data monitoring and processing system based on the Internet of Things intelligent infusion cabinet, including: a signal acquisition module, a drip rate calculation module, an infusion state analysis module, an adaptability analysis module, a performance analysis module, and a monitoring and display module.

[0006] The signal acquisition module is used to configure a droplet radio frequency device in the intelligent infusion cabinet, use the droplet radio frequency device to collect static radio frequency signals in the intelligent infusion cabinet, extract electromagnetic wave signals of the static radio frequency signals, and obtain a first signal, and use the droplet ultrasonic system to collect dynamic radio frequency signals in the intelligent infusion cabinet, extract electromagnetic wave signals of the dynamic radio frequency signals, and obtain a second signal;

[0007] The drip rate calculation module is used to perform interference elimination processing on the first signal and the second signal respectively to obtain a first target signal and a second target signal, and calculate the device distance between the signal transmitting device and the infusion device in the intelligent infusion cabinet, and calculate the static phase value of the intelligent infusion cabinet in combination with the first target signal and the device distance, and calculate the dynamic phase value of the intelligent infusion cabinet in combination with the second target signal and the device distance, and calculate the infusion drip rate corresponding to the intelligent infusion cabinet in combination with the static phase value and the dynamic phase value;

[0008] The infusion status analysis module is used to query the infusion target corresponding to the smart infusion cabinet, and analyze the infusion status corresponding to the smart infusion cabinet in combination with the infusion drip rate and the infusion target;

[0009] The adaptability analysis module is used to detect the environmental parameters inside the smart infusion cabinet, and analyze the compatibility of the drug corresponding to the smart infusion cabinet in combination with the infusion drug data and the environmental parameters;

[0010] The performance analysis module is used to query the operating hardware corresponding to the intelligent infusion cabinet, collect hardware operating data corresponding to the operating hardware, and analyze the operating performance corresponding to the operating hardware according to the hardware operating data;

[0011] The monitoring interaction module is used to set a monitoring interaction page corresponding to the smart infusion cabinet in combination with the infusion status, the drug compatibility and the operating performance.

[0012] Optionally, extracting the electromagnetic wave signal of the static radio frequency signal to obtain the first signal includes:

[0013] Draw a time domain waveform diagram corresponding to the static radio frequency signal, and perform Fourier transform on the static radio frequency signal to obtain a radio frequency signal spectrum;

[0014] Extracting features from the time domain waveform to obtain radio frequency time domain features, and performing spectrum analysis on the radio frequency signal spectrum to obtain spectrum information;

[0015] Determining, according to the spectrum information, radio frequency frequency domain characteristics corresponding to the static radio frequency signal;

[0016] The electromagnetic wave signal of the static radio frequency signal is extracted by combining the radio frequency time domain feature and the radio frequency frequency domain feature to obtain a first signal.

[0017] Optionally, performing interference removal processing on the first signal and the second signal respectively to obtain a first target signal and a second target signal includes:

[0018] Performing noise reduction processing on the first signal and the second signal respectively to obtain a first noise reduction signal and a second noise reduction signal;

[0019] Calculating a first frequency domain complex number corresponding to the first noise reduction signal, and calculating a second frequency domain complex number corresponding to the second noise reduction signal;

[0020] Combining the first frequency domain complex number and the second frequency domain complex number, respectively decomposing the first noise reduction signal and the second noise reduction signal to obtain a first decomposed signal and a second decomposed signal;

[0021] Performing interference detection on the first decomposed signal and the second decomposed signal respectively to obtain a first interference signal and a second interference signal;

[0022] The first interference signal and the second interference signal are respectively eliminated to obtain a first target signal and a second target signal.

[0023] Optionally, calculating a first frequency domain complex number corresponding to the first noise reduction signal includes:

[0024] Extracting a sequence index and a sequence amplitude value corresponding to the first noise reduction signal, and identifying a time series corresponding to the first noise reduction signal;

[0025] Calculating a signal length corresponding to the first noise reduction signal according to the time series;

[0026] In combination with the time series, the signal length, the sequence index and the sequence amplitude value, the first frequency domain complex number corresponding to the first noise reduction signal is calculated by the following formula:

[0027]

[0028] Among them, A represents the first frequency domain complex number corresponding to the first denoised signal, b represents the starting sequence of the time series, B represents the signal length, D(b) represents the sequence amplitude value of the bth time series, e represents a natural constant, which is approximately equal to 2.71828, i represents an imaginary unit, and d represents a sequence index.

[0029] Optionally, the combining the first target signal and the device distance to calculate the static phase value of the intelligent infusion cabinet includes:

[0030] Analyzing a signal frequency corresponding to the first target signal to obtain a first signal frequency;

[0031] Calculating the signal wave velocity corresponding to the first target signal according to the first signal frequency to obtain a first signal wave velocity;

[0032] Combining the device distance and the first signal wave speed, the static phase value of the intelligent infusion cabinet is calculated by the following formula:

[0033]

[0034] Wherein, E1 represents the static phase value of the intelligent infusion cabinet, F represents the device distance, and ω1 represents the first signal wave velocity.

[0035] Optionally, the combining the second target signal and the device distance to calculate the dynamic phase value of the intelligent infusion cabinet includes:

[0036] measuring the infusion interval corresponding to the infusion device, querying the signal wave velocity of the second target signal in the liquid, and obtaining the second signal wave velocity;

[0037] Combining the infusion interval, the device distance and the second signal wave speed, the dynamic phase value of the intelligent infusion cabinet is calculated by the following formula:

[0038]

[0039] Among them, E2 represents the dynamic phase value of the intelligent infusion cabinet, F represents the device distance, ω2 represents the second signal wave velocity, and d represents the infusion interval.

[0040] Optionally, combining the infusion drug data and the environmental parameters to analyze the drug compatibility corresponding to the smart infusion cabinet includes:

[0041] Identify the drug name in the infusion drug data, and query the pharmaceutical index and index standard parameters corresponding to the drug name;

[0042] extracting drug-related parameters from the environmental parameters according to the pharmaceutical indicators;

[0043] In combination with the pharmaceutical index, clustering the index standard parameters and the drug association parameters is performed to obtain cluster index parameters;

[0044] Calculating the parameter difference value of each category in the clustering indicator parameter, and calculating the indicator weight corresponding to the pharmaceutical indicator;

[0045] Calculating the environmental adaptability corresponding to the drug name according to the indicator weight and the parameter difference value;

[0046] According to the environmental adaptability, the drug adaptability corresponding to the intelligent infusion cabinet is analyzed.

[0047] Optionally, extracting drug-related parameters from the environmental parameters according to the pharmaceutical indicators includes:

[0048] Analyze the environmental factors corresponding to the environmental parameters and extract the index elements in the pharmaceutical index;

[0049] Vectorizing the indicator elements and the environmental factors respectively to obtain element vectors and factor vectors;

[0050] Calculating the vector correlation between the element vector and the factor vector;

[0051] When the vector correlation degree is greater than a preset correlation degree, determining the associated environmental factors among the environmental factors;

[0052] According to the associated environmental factors, drug associated parameters are extracted from the environmental parameters.

[0053] Optionally, analyzing the operating performance corresponding to the operating hardware according to the hardware operating data includes:

[0054] Performing data cleaning on the hardware operation data to obtain target operation data;

[0055] Analyzing data variables corresponding to the target operation data, and extracting variable values ​​in the target operation data;

[0056] Combining the data variables and the variable values, constructing a visualization chart of the target operation data;

[0057] mining chart information corresponding to the visualization chart;

[0058] The operating performance corresponding to the operating hardware is analyzed according to the chart information.

[0059] A data monitoring and processing method for an intelligent infusion cabinet based on the Internet of Things, characterized in that the method comprises:

[0060] A droplet radio frequency device is configured in the intelligent infusion cabinet, and the static radio frequency signal in the intelligent infusion cabinet is collected by using the droplet radio frequency device, and the electromagnetic wave signal of the static radio frequency signal is extracted to obtain a first signal, and the dynamic radio frequency signal in the intelligent infusion cabinet is collected by using the droplet ultrasonic system, and the electromagnetic wave signal of the dynamic radio frequency signal is extracted to obtain a second signal;

[0061] Perform interference elimination processing on the first signal and the second signal respectively to obtain a first target signal and a second target signal, and calculate the device distance between the signal transmitting device and the infusion device in the intelligent infusion cabinet, and calculate the static phase value of the intelligent infusion cabinet in combination with the first target signal and the device distance, and calculate the dynamic phase value of the intelligent infusion cabinet in combination with the second target signal and the device distance, and calculate the infusion drip rate corresponding to the intelligent infusion cabinet in combination with the static phase value and the dynamic phase value;

[0062] Querying the infusion target corresponding to the smart infusion cabinet, and analyzing the infusion state corresponding to the smart infusion cabinet in combination with the infusion drip rate and the infusion target;

[0063] Detecting the environmental parameters inside the smart infusion cabinet, and analyzing the compatibility of the drug corresponding to the smart infusion cabinet in combination with the infusion drug data and the environmental parameters;

[0064] Querying the operating hardware corresponding to the intelligent infusion cabinet, collecting hardware operating data corresponding to the operating hardware, and analyzing the operating performance corresponding to the operating hardware according to the hardware operating data;

[0065] In combination with the infusion status, the drug compatibility and the operating performance, a monitoring interaction page corresponding to the smart infusion cabinet is set.

[0066] The present invention uses the droplet radio frequency device to collect static radio frequency signals in the smart infusion cabinet and uses the droplet ultrasonic system to collect dynamic radio frequency signals in the smart infusion cabinet, so as to obtain static and dynamic radio frequency signals corresponding to the smart infusion cabinet, that is, ultrasonic signals under no infusion and infusion conditions, so as to facilitate the subsequent calculation of the infusion drip rate. The present invention performs interference elimination processing on the first signal and the second signal respectively, which can reduce the interference in the signal, thereby improving the quality of the signal, thereby improving the calculation accuracy of the subsequent static phase value and the dynamic phase value. The present invention combines the infusion drip rate and the infusion target to analyze the infusion state corresponding to the smart infusion cabinet, so as to obtain the current infusion progress of the smart infusion cabinet, for example, the current medicine has been half completed, which is convenient for the subsequent smart infusion cabinet. The setting of the corresponding monitoring interaction page, the present invention analyzes the drug compatibility corresponding to the smart infusion cabinet by combining the infusion drug data and the environmental parameters, so as to analyze the degree of compatibility between the drug in the smart infusion cabinet and the environment, and facilitate the subsequent adjustment of the internal parameters of the smart infusion cabinet to improve the safety and effectiveness of the drug. The present invention can understand the performance of the operating hardware by analyzing the operating performance corresponding to the hardware operating data, and provide a basis for the subsequent analysis of the operating stability corresponding to the smart infusion cabinet. The present invention sets the monitoring interaction page corresponding to the smart infusion cabinet by combining the infusion status, the drug compatibility and the operating performance, so that the smart infusion cabinet can better manage the infusion process, ensure drug safety and treatment effectiveness, and improve medical quality. Therefore, the data monitoring and processing system and method based on the smart infusion cabinet of the Internet of Things provided in the embodiment of the present invention can improve the accuracy of data monitoring of the smart infusion cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1A functional module diagram of a data monitoring and processing system for an intelligent infusion cabinet based on the Internet of Things provided by one embodiment of the present invention;

[0068] Figure 2 A flowchart of a data monitoring and processing method for an intelligent infusion cabinet based on the Internet of Things is provided in one embodiment of the present invention.

[0069] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 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.

[0071] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0072] In fact, the server-side device deployed by the data monitoring and processing system for the smart infusion cabinet based on the Internet of Things may be composed of one or more devices. The above-mentioned data monitoring and processing system for the smart infusion cabinet based on the Internet of Things can be implemented as: business instance, virtual machine, hardware device. For example, the data monitoring and processing system for the smart infusion cabinet based on the Internet of Things can be implemented as a business instance deployed on one or more devices in the cloud node. In simple terms, the live broadcast service system can be understood as a software deployed on the cloud node, which is used to provide services based on the data monitoring and processing of the smart infusion cabinet based on the Internet of Things for each user terminal. Alternatively, the data monitoring and processing system for the smart infusion cabinet based on the Internet of Things can also be implemented as a virtual machine deployed on one or more devices in the cloud node. The virtual machine is installed with application software for managing each user terminal. Alternatively, the data monitoring and processing system for the smart infusion cabinet based on the Internet of Things can also be implemented as a server consisting of many hardware devices of the same or different types, and one or more hardware devices are set to provide data monitoring and processing services based on the smart infusion cabinet based on the Internet of Things for each user terminal.

[0073] In terms of implementation, the data monitoring and processing system for the smart infusion cabinet based on the Internet of Things and the user end are adapted to each other. That is, the data monitoring and processing system for the smart infusion cabinet based on the Internet of Things is an application installed on the cloud service platform, and the user end is a client that establishes a communication connection with the application; or the data monitoring and processing system for the smart infusion cabinet based on the Internet of Things is implemented as a website, and the user end is implemented as a web page; or the data monitoring and processing system for the smart infusion cabinet based on the Internet of Things is implemented as a cloud service platform, and the user end is implemented as a small program in the instant messaging application.

[0074] Reference Figure 1 , which is a functional module diagram of a data monitoring and processing system for an intelligent infusion cabinet based on the Internet of Things provided by one embodiment of the present invention.

[0075] The data monitoring and processing system 100 based on the Internet of Things intelligent infusion cabinet of the present invention can be set in the cloud server. In terms of implementation, it can be used as one or more service devices, or it can be installed as an application on the cloud (such as the server of the live broadcast service operator, server cluster, etc.), or it can be developed as a website. According to the functions implemented, the data monitoring and processing system 100 based on the Internet of Things intelligent infusion cabinet includes a signal acquisition module 101, a drip rate calculation module 102, an infusion state analysis module 103, an adaptability analysis module 104, a performance analysis module 105 and a monitoring and display module 106.

[0076] In the embodiment of the present invention, in the tracking of data monitoring and processing based on the smart infusion cabinet of the Internet of Things, each of the above modules can be independently implemented and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules connected to it. In the data monitoring and processing system based on the smart infusion cabinet of the Internet of Things provided by the embodiment of the present invention, the scope of application of the data monitoring and processing architecture based on the smart infusion cabinet of the Internet of Things can be adjusted by adding modules and directly calling them without modifying the program code, so as to achieve cluster-based horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the data monitoring and processing system based on the smart infusion cabinet of the Internet of Things. In actual applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0077] In the following, in conjunction with specific embodiments, each component and specific workflow of the data monitoring and processing system for the intelligent infusion cabinet based on the Internet of Things are described respectively.

[0078] The signal acquisition module 101 is used to configure a droplet radio frequency device in the smart infusion cabinet, use the droplet radio frequency device to collect static radio frequency signals in the smart infusion cabinet, extract the electromagnetic wave signals of the static radio frequency signals, and obtain a first signal, use the droplet ultrasonic system to collect dynamic radio frequency signals in the smart infusion cabinet, extract the electromagnetic wave signals of the dynamic radio frequency signals, and obtain a second signal.

[0079] The present invention uses the droplet radio frequency device to collect static radio frequency signals in the smart infusion cabinet and uses the droplet ultrasonic system to collect dynamic radio frequency signals in the smart infusion cabinet, so as to obtain static and dynamic radio frequency signals corresponding to the smart infusion cabinet, that is, ultrasonic signals under no infusion and infusion conditions, so as to facilitate the subsequent calculation of the infusion drip rate, wherein the smart infusion cabinet is a medical infusion device with intelligent functions, the droplet radio frequency device is a system for detecting tiny droplets through ultrasonic technology, and is composed of a radio frequency receiver, the static radio frequency signal is a radio frequency signal collected by the droplet radio frequency device when there is no infusion in the smart infusion cabinet, the electromagnetic wave signal is a signal corresponding to the electromagnetic wave within a specific frequency range in the radio frequency signal, and the dynamic radio frequency signal is a radio frequency signal corresponding to the infusion in the smart infusion cabinet collected by the droplet radio frequency device. Optionally, the extraction of the electromagnetic wave signal of the dynamic radio frequency signal is the same as the extraction principle of the static radio frequency signal, which will not be elaborated here.

[0080] As an embodiment of the present invention, the extraction of the electromagnetic wave signal of the static RF signal to obtain the first signal includes: drawing a time domain waveform diagram corresponding to the static RF signal, performing Fourier transform on the static RF signal to obtain an RF signal spectrum, performing feature extraction on the time domain waveform diagram to obtain RF time domain features, and performing spectrum analysis on the RF signal spectrum to obtain spectrum information, determining the RF frequency domain features corresponding to the static RF signal based on the spectrum information, and extracting the electromagnetic wave signal of the static RF signal by combining the RF time domain features and the RF frequency domain features to obtain the first signal.

[0081] Among them, the time domain waveform is a chart of the static RF signal changing with time, the RF signal spectrum is a chart of the distribution of the static RF signal in the frequency domain, the RF time domain feature is the feature of the static RF signal changing over time, such as amplitude, the spectrum information is the descriptive information in the RF signal spectrum, such as frequency distribution information, and the RF frequency domain feature is the specific properties and characteristics exhibited by the static RF signal in the frequency domain.

[0082] Optionally, the time domain waveform corresponding to the static RF signal can be realized by a software simulation method, by converting the static RF signal into a digital signal, and using electromagnetic field simulation software (such as ADS, CST MicrowaveStudio) in combination with the digital signal to draw the time domain waveform corresponding to the static RF signal; the feature extraction of the time domain waveform can be realized by an autocorrelation function, and by calculating the autocorrelation function of the signal, the periodic information and delay and other characteristics of the signal can be extracted; the spectrum analysis of the RF signal spectrum can be realized by a spectrum analysis tool, and the spectrum analysis tool is compiled by JAVA language; the extraction of the electromagnetic wave signal of the static RF signal can be matched with the RF time domain characteristics and the RF frequency domain characteristics and the characteristics of the known electromagnetic wave type, so as to extract the electromagnetic wave signal of the static RF signal and obtain the first signal.

[0083] The drip rate calculation module 102 is used for the drip rate calculation module, and is used to perform interference elimination processing on the first signal and the second signal respectively to obtain a first target signal and a second target signal, and calculate the device distance between the signal transmitting device and the infusion device in the intelligent infusion cabinet, and calculate the static phase value of the intelligent infusion cabinet in combination with the first target signal and the device distance, and calculate the dynamic phase value of the intelligent infusion cabinet in combination with the second target signal and the device distance, and calculate the infusion drip rate corresponding to the intelligent infusion cabinet in combination with the static phase value and the dynamic phase value.

[0084] The present invention performs interference elimination processing on the first signal and the second signal respectively, which can reduce the interference in the signal, thereby improving the quality of the signal, thereby improving the calculation accuracy of the subsequent static phase value and the dynamic phase value, wherein the first target signal and the second target signal are the signals corresponding to the first signal and the second signal after the signals are optimized, respectively, and the device distance represents the distance between the transmitting device and the infusion device, that is, the transmission distance of the first target signal. Furthermore, the device distance between the signal transmitting device and the infusion device in the intelligent infusion cabinet can be achieved through the Euclidean distance algorithm.

[0085] As an embodiment of the present invention, the interference removal processing of the first signal and the second signal respectively to obtain the first target signal and the second target signal includes: performing noise reduction processing on the first signal and the second signal respectively to obtain the first noise reduction signal and the second noise reduction signal, calculating the first frequency domain complex number corresponding to the first noise reduction signal, and calculating the second frequency domain complex number corresponding to the second noise reduction signal, combining the first frequency domain complex number and the second frequency domain complex number, respectively decomposing the first noise reduction signal and the second noise reduction signal to obtain the first decomposed signal and the second decomposed signal, respectively performing interference detection on the first decomposed signal and the second decomposed signal to obtain the first interference signal and the second interference signal, and respectively removing the first interference signal and the second interference signal to obtain the first target signal and the second target signal.

[0086] Among them, the first frequency domain complex number and the second frequency domain complex number are respectively the complex values ​​of the first noise reduction signal and the second noise reduction signal in the frequency domain, that is, the energy distribution of each signal at different frequencies, the first decomposed signal and the second decomposed signal are respectively the first noise reduction signal and the second noise reduction signal decomposed into shorter subsequence signals, and the first interference signal and the second interference signal are respectively the interference signals existing in the first decomposed signal and the second decomposed signal.

[0087] Furthermore, the noise reduction processing of the first signal and the second signal can be achieved through a low-pass filter; the calculation principle of the second frequency domain complex number is the same as that of the first frequency domain complex number, which will not be elaborated here; the decomposition processing of the first noise reduction signal and the second noise reduction signal can be achieved through the wavelet transform method; the interference detection of the first decomposed signal and the second decomposed signal can be achieved through an interference detection algorithm, and the interference detection algorithm can be edited through a programming language; the elimination processing of the first interference signal and the second interference signal can be achieved through frequency domain filtering.

[0088] Optionally, as an optional embodiment of the present invention, the calculating the first frequency domain complex number corresponding to the first denoised signal includes: extracting a sequence index and a sequence amplitude value corresponding to the first denoised signal, identifying a time series corresponding to the first denoised signal, calculating a signal length corresponding to the first denoised signal according to the time series, and combining the time series, the signal length, the sequence index and the sequence amplitude value to calculate the first frequency domain complex number corresponding to the first denoised signal by the following formula:

[0089]

[0090] Among them, A represents the first frequency domain complex number corresponding to the first denoised signal, b represents the starting sequence of the time series, B represents the signal length, D(b) represents the sequence amplitude value of the bth time series, e represents a natural constant, which is approximately equal to 2.71828, i represents an imaginary unit, and d represents a sequence index.

[0091] Among them, the time series is the time sequence corresponding to the first denoised signal, and the signal length represents the continuity of the first denoised signal in time, space or other dimensions. Furthermore, the sequence index and sequence amplitude value corresponding to the first denoised signal can be implemented by signal processing library functions, such as signal processing tools or programming library functions, such as MATLAB language library; the identification of the time series corresponding to the first denoised signal can be implemented by Octave tools; the signal length corresponding to the first denoised signal can be obtained by calculating the difference between the starting sequence and the last sequence in the time series.

[0092] The present invention calculates the static phase value of the intelligent infusion cabinet by combining the first target signal and the device distance, so as to understand the phase angle corresponding to the radio frequency signal waveform when there is no infusion in the intelligent infusion cabinet, and then understand the phase position of the radio frequency signal, and combine it with the subsequent dynamic phase value to calculate the infusion drip rate corresponding to the intelligent infusion cabinet.

[0093] As an embodiment of the present invention, the combining of the first target signal and the device distance to calculate the static phase value of the intelligent infusion cabinet includes: analyzing the signal frequency corresponding to the first target signal to obtain the first signal frequency, calculating the signal wave velocity corresponding to the first target signal according to the first signal frequency to obtain the first signal wave velocity, combining the device distance and the first signal wave velocity, and calculating the static phase value of the intelligent infusion cabinet by the following formula:

[0094]

[0095] Wherein, E1 represents the static phase value of the intelligent infusion cabinet, F represents the device distance, and ω1 represents the first signal wave velocity.

[0096] Among them, the infusion device is an instrument used for infusion in the intelligent infusion cabinet, the signal frequency represents the number of times the signal corresponding to the first target signal is repeated, and the signal wave speed represents the propagation speed corresponding to the first target signal.

[0097] Furthermore, the analysis of the signal frequency corresponding to the first target signal can be achieved by a radio frequency spectrum analyzer; the signal wave velocity calculation formula corresponding to the first target signal is: speed of light / signal frequency.

[0098] The present invention calculates the dynamic phase value of the intelligent infusion cabinet by combining the second target signal and the droplet radio frequency device, so as to understand the phase angle corresponding to the radio frequency signal waveform of the intelligent infusion cabinet during infusion.

[0099] As an embodiment of the present invention, the combining of the second target signal and the device distance to calculate the dynamic phase value of the intelligent infusion cabinet includes: measuring the infusion interval corresponding to the infusion device, querying the signal wave velocity of the second target signal in the liquid, obtaining the second signal wave velocity, combining the infusion interval, the device distance and the second signal wave velocity, and calculating the dynamic phase value of the intelligent infusion cabinet by the following formula:

[0100]

[0101] Among them, E2 represents the dynamic phase value of the intelligent infusion cabinet, F represents the device distance, ω2 represents the second signal wave velocity, and d represents the infusion interval.

[0102] Among them, the infusion interval represents the length of the liquid in the infusion device and the transmission distance of the radio frequency signal in the liquid. Furthermore, the measurement of the infusion interval corresponding to the infusion device can be achieved by a laser rangefinder; the signal wave velocity of the second target signal in the liquid can be obtained by querying the database of electromagnetic wave propagation speed.

[0103] The present invention calculates the infusion drip rate corresponding to the intelligent infusion cabinet by combining the static phase value and the dynamic phase value, and can obtain the infusion speed corresponding to the intelligent infusion cabinet, so as to facilitate the subsequent analysis of the infusion state of the intelligent infusion cabinet. Furthermore, the calculation formula of the infusion drip rate corresponding to the intelligent infusion cabinet is 60 / (the static phase value and the dynamic phase value).

[0104] The infusion status analysis module 103 is used to query the infusion target corresponding to the smart infusion cabinet, and analyze the infusion status corresponding to the smart infusion cabinet in combination with the infusion drip rate and the infusion target.

[0105] The present invention analyzes the infusion status corresponding to the smart infusion cabinet by combining the infusion drip rate and the infusion target, and can obtain the current infusion progress of the smart infusion cabinet, for example, the current medicine is half completed, which is convenient for the subsequent setting of the monitoring interaction page corresponding to the smart infusion cabinet, wherein the infusion target is the infusion purpose corresponding to the smart infusion cabinet, such as the amount of supplemented medicine or the amount of infusion. Optionally, the analysis steps of the infusion status corresponding to the smart infusion cabinet are: querying the medicine amount and infusion time of the infusion medicine, calculating the infusion amount corresponding to the infusion medicine according to the infusion drip rate and the infusion time, calculating the remaining infusion amount in combination with the medicine amount and the infusion amount, determining the total infusion amount of the smart infusion cabinet according to the infusion target, and determining the infusion status corresponding to the smart infusion cabinet in combination with the remaining infusion amount and the total infusion.

[0106] The adaptability analysis module 104 is used to detect the environmental parameters inside the smart infusion cabinet, and analyze the compatibility of the drug corresponding to the smart infusion cabinet in combination with the infusion drug data and the environmental parameters.

[0107] The present invention analyzes the compatibility of the drugs corresponding to the smart infusion cabinet by combining the infusion drug data and the environmental parameters, so as to analyze the degree of compatibility between the drugs in the smart infusion cabinet and the environment, and facilitate the subsequent adjustment of the internal parameters of the smart infusion cabinet to improve the safety and effectiveness of the drugs, wherein the environmental parameters are the environmental information inside the smart infusion cabinet, such as temperature. Furthermore, the detection of the environmental parameters inside the smart infusion cabinet can be achieved by a detection instrument, such as a thermometer.

[0108] As an embodiment of the present invention, the combination of the infusion drug data and the environmental parameters to analyze the drug adaptability corresponding to the smart infusion cabinet includes: identifying the drug name in the infusion drug data, querying the pharmaceutical indicators and indicator standard parameters corresponding to the drug name, extracting drug-related parameters from the environmental parameters based on the pharmaceutical indicators, clustering the indicator standard parameters and the drug-related parameters in combination with the pharmaceutical indicators to obtain clustering indicator parameters, calculating the parameter difference value of each category in the clustering indicator parameters, and calculating the indicator weight corresponding to the pharmaceutical indicator, calculating the environmental adaptability corresponding to the drug name based on the indicator weight and the parameter difference value, and analyzing the drug adaptability corresponding to the smart infusion cabinet based on the environmental adaptability.

[0109] Among them, the pharmaceutical index is an index that affects the drug corresponding to the drug name, the index standard parameter is the reference standard value corresponding to the pharmaceutical index, the drug-related parameter is a parameter in the environmental parameter that has a direct impact on the drug, the clustering index parameter is a set of parameters obtained by classifying the parameters of the same index in the index standard parameter and the drug-related parameter, and the environmental adaptability indicates the degree of compatibility between the drug corresponding to the drug name and the environment.

[0110] Furthermore, the identification of drug names in the infusion drug data can be achieved through OCR recognition technology, and the pharmaceutical indicators and indicator standard parameters corresponding to the drug names can be queried from the Internet through human-computer interaction; the clustering processing of the indicator standard parameters and the drug-related parameters can be achieved through a clustering algorithm, such as a hierarchical clustering algorithm; the calculation of the indicator weights corresponding to the pharmaceutical indicators can be achieved through a hierarchical analysis method, by constructing a judgment matrix of the pharmaceutical indicators, calculating the eigenvectors corresponding to the judgment matrix, normalizing the eigenvectors to obtain the target vectors, and calculating the ratios between the target vectors to obtain the indicator weights of each pharmaceutical indicator; the environmental adaptability corresponding to the drug name can be obtained by multiplying the indicator weights and the corresponding parameter difference values ​​and summing them; the drug adaptability corresponding to the intelligent infusion cabinet can be analyzed by the high and low environmental adaptability, for example, if the environmental adaptability is 90, the drug adaptability is high.

[0111] Optionally, as an optional embodiment of the present invention, extracting drug-associated parameters from the environmental parameters based on the pharmaceutical indicators includes: analyzing the environmental factors corresponding to the environmental parameters, extracting indicator elements in the pharmaceutical indicators, vectorizing the indicator elements and the environmental factors respectively to obtain element vectors and factor vectors, calculating the vector correlation between the element vector and the factor vector, determining the associated environmental factors in the environmental factors when the vector correlation is greater than a preset correlation, and extracting drug-associated parameters from the environmental parameters based on the associated environmental factors.

[0112] Among them, the environmental factor is the environmental factor in the environmental parameters, such as humidity, the indicator element is the indicator key character in the pharmaceutical indicator, the element vector and the factor vector are the expression vectors corresponding to the indicator element and the environmental factor respectively, the vector correlation degree represents the degree of correlation between the element vector and the factor vector, and the preset correlation degree is the reference value of the vector correlation degree, which can be 0.8, and can also be set according to the actual application scenario.

[0113] Furthermore, the analysis of the environmental factors corresponding to the environmental parameters can be achieved through factor analysis tools, such as IBM SPSS Statistics, SAS and other tools; the extraction of indicator elements in the pharmaceutical indicators can be achieved through text mining tools, such as TextBlob tools; the vectorization processing of the indicator elements and the environmental factors can be achieved through the word2vec algorithm; the calculation of the vector correlation between the element vector and the factor vector can be achieved through the Pearson correlation coefficient algorithm; the extraction of drug-related parameters from the environmental parameters can be achieved through an extraction function, and the extraction function is compiled by a programming language.

[0114] The performance analysis module 105 is used to query the operating hardware corresponding to the intelligent infusion cabinet, collect hardware operating data corresponding to the operating hardware, and analyze the operating performance corresponding to the operating hardware based on the hardware operating data.

[0115] By analyzing the operating performance corresponding to the hardware operating data, the present invention can understand the performance corresponding to the operating hardware, and provide a basis for the subsequent analysis of the operating stability corresponding to the intelligent infusion cabinet, wherein the operating performance is the hardware performance corresponding to the operating hardware, and further, the collection of hardware operating data corresponding to the operating hardware can be achieved through log records corresponding to the hardware.

[0116] As an embodiment of the present invention, analyzing the operating performance corresponding to the operating hardware based on the hardware operating data includes: performing data cleaning on the hardware operating data to obtain target operating data, analyzing data variables corresponding to the target operating data, and extracting variable values ​​in the target operating data, combining the data variables and the variable values ​​to construct a visualization chart of the target operating data, mining chart information corresponding to the visualization chart, and analyzing the operating performance corresponding to the operating hardware based on the chart information.

[0117] Among them, the data variable is the data feature corresponding to the target operating data, the variable value is the numerical value corresponding to the data variable in the target operating data, the visualization chart is a chart of the target operating data constructed by a corresponding visualization method, and the chart information is the content expressed in the visualization chart, such as the trend of an image.

[0118] Furthermore, data cleaning of the hardware operation data can be achieved through the box plot method; analysis of data variables corresponding to the target operation data can be achieved through the principal component analysis method; construction of visualization charts of the target operation data can be achieved through the Visio drawing tool; mining of chart information corresponding to the visualization chart can be achieved through corresponding models, such as regression models and ARIMA models; analysis of the operation performance corresponding to the operation hardware can obtain the hardware performance information through the chart information, such as processor speed, memory size and storage capacity, etc., and compare the hardware performance information with the corresponding performance standard performance to understand the difference between the performances and analyze the operation performance corresponding to the operation hardware.

[0119] The monitoring interaction module 106 is used to set a monitoring interaction page corresponding to the smart infusion cabinet in combination with the infusion status, the drug compatibility and the operating performance.

[0120] The present invention sets a monitoring interaction page corresponding to the smart infusion cabinet by combining the infusion status, the drug compatibility and the operating performance, so that the smart infusion cabinet can better manage the infusion process, ensure drug safety and treatment effectiveness, and improve medical quality. Furthermore, the monitoring interaction page corresponding to the smart infusion cabinet can be input into a GUI design tool by combining the infusion status, the drug compatibility and the operating performance, and the input content is visualized through the GUI design tool to set the monitoring interaction page corresponding to the smart infusion cabinet.

[0121] The present invention uses the droplet radio frequency device to collect static radio frequency signals in the smart infusion cabinet and uses the droplet ultrasonic system to collect dynamic radio frequency signals in the smart infusion cabinet, so as to obtain static and dynamic radio frequency signals corresponding to the smart infusion cabinet, that is, ultrasonic signals under no infusion and infusion conditions, so as to facilitate the subsequent calculation of the infusion drip rate. The present invention performs interference elimination processing on the first signal and the second signal respectively, which can reduce the interference in the signal, thereby improving the quality of the signal, thereby improving the calculation accuracy of the subsequent static phase value and the dynamic phase value. The present invention combines the infusion drip rate and the infusion target to analyze the infusion state corresponding to the smart infusion cabinet, so as to obtain the current infusion progress of the smart infusion cabinet, for example, the current medicine has been half completed, which is convenient for the subsequent smart infusion cabinet. The setting of the corresponding monitoring interaction page, the present invention analyzes the drug compatibility corresponding to the smart infusion cabinet by combining the infusion drug data and the environmental parameters, so as to analyze the degree of compatibility between the drug in the smart infusion cabinet and the environment, and facilitate the subsequent adjustment of the internal parameters of the smart infusion cabinet to improve the safety and effectiveness of the drug. The present invention can understand the performance of the operating hardware by analyzing the operating performance corresponding to the hardware operating data, and provide a basis for the subsequent analysis of the operating stability corresponding to the smart infusion cabinet. The present invention sets the monitoring interaction page corresponding to the smart infusion cabinet by combining the infusion status, the drug compatibility and the operating performance, so that the smart infusion cabinet can better manage the infusion process, ensure drug safety and treatment effectiveness, and improve medical quality. Therefore, the data monitoring and processing system and method based on the smart infusion cabinet of the Internet of Things provided in the embodiment of the present invention can improve the accuracy of data monitoring of the smart infusion cabinet.

[0122] Reference Figure 2 FIG. 1 is a flow chart of a method for monitoring and processing data of an intelligent infusion cabinet based on the Internet of Things according to an embodiment of the present invention. In this embodiment, the method for monitoring and processing data of an intelligent infusion cabinet based on the Internet of Things includes:

[0123] A droplet radio frequency device is configured in the intelligent infusion cabinet, and the static radio frequency signal in the intelligent infusion cabinet is collected by using the droplet radio frequency device, and the electromagnetic wave signal of the static radio frequency signal is extracted to obtain a first signal, and the dynamic radio frequency signal in the intelligent infusion cabinet is collected by using the droplet ultrasonic system, and the electromagnetic wave signal of the dynamic radio frequency signal is extracted to obtain a second signal;

[0124] Perform interference elimination processing on the first signal and the second signal respectively to obtain a first target signal and a second target signal, and calculate the device distance between the signal transmitting device and the infusion device in the intelligent infusion cabinet, and calculate the static phase value of the intelligent infusion cabinet in combination with the first target signal and the device distance, and calculate the dynamic phase value of the intelligent infusion cabinet in combination with the second target signal and the device distance, and calculate the infusion drip rate corresponding to the intelligent infusion cabinet in combination with the static phase value and the dynamic phase value;

[0125] Querying the infusion target corresponding to the smart infusion cabinet, and analyzing the infusion state corresponding to the smart infusion cabinet in combination with the infusion drip rate and the infusion target;

[0126] Detecting the environmental parameters inside the smart infusion cabinet, and analyzing the compatibility of the drug corresponding to the smart infusion cabinet in combination with the infusion drug data and the environmental parameters;

[0127] Querying the operating hardware corresponding to the intelligent infusion cabinet, collecting hardware operating data corresponding to the operating hardware, and analyzing the operating performance corresponding to the operating hardware according to the hardware operating data;

[0128] In combination with the infusion status, the drug compatibility and the operating performance, a monitoring interaction page corresponding to the smart infusion cabinet is set.

[0129] In the several embodiments provided by the present invention, it should be understood that the provided system and method can be implemented in other ways. For example, the system embodiment described above is only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0130] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0131] Finally, it should be noted that the above embodiments are 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 preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A data monitoring and processing system for intelligent infusion cabinets based on the Internet of Things, characterized in that: The data monitoring and processing system includes: a signal acquisition module, a drip rate calculation module, an infusion state analysis module, a compatibility analysis module, a performance analysis module, and a monitoring and display module. The signal acquisition module is used to configure a droplet radio frequency device in the intelligent infusion cabinet, use the droplet radio frequency device to collect static radio frequency signals in the intelligent infusion cabinet, extract electromagnetic wave signals of the static radio frequency signals, and obtain a first signal, and use the droplet ultrasonic system to collect dynamic radio frequency signals in the intelligent infusion cabinet, extract electromagnetic wave signals of the dynamic radio frequency signals, and obtain a second signal; The drip rate calculation module is used to perform interference elimination processing on the first signal and the second signal respectively to obtain a first target signal and a second target signal, and calculate the device distance between the signal transmitting device and the infusion device in the intelligent infusion cabinet, and calculate the static phase value of the intelligent infusion cabinet in combination with the first target signal and the device distance, and calculate the dynamic phase value of the intelligent infusion cabinet in combination with the second target signal and the device distance, and calculate the infusion drip rate corresponding to the intelligent infusion cabinet in combination with the static phase value and the dynamic phase value; The infusion status analysis module is used to query the infusion target corresponding to the smart infusion cabinet, and analyze the infusion status corresponding to the smart infusion cabinet in combination with the infusion drip rate and the infusion target; The adaptability analysis module is used to detect the environmental parameters inside the smart infusion cabinet, and analyze the compatibility of the drug corresponding to the smart infusion cabinet in combination with the infusion drug data and the environmental parameters; The performance analysis module is used to query the operating hardware corresponding to the intelligent infusion cabinet, collect hardware operating data corresponding to the operating hardware, and analyze the operating performance corresponding to the operating hardware according to the hardware operating data; The monitoring interaction module is used to set a monitoring interaction page corresponding to the smart infusion cabinet in combination with the infusion status, the drug compatibility and the operating performance.

2. The data monitoring and processing system based on the Internet of Things intelligent infusion cabinet according to claim 1 is characterized in that: The step of extracting the electromagnetic wave signal of the static radio frequency signal to obtain a first signal includes: Draw a time domain waveform diagram corresponding to the static radio frequency signal, and perform Fourier transform on the static radio frequency signal to obtain a radio frequency signal spectrum; Extracting features from the time domain waveform to obtain radio frequency time domain features, and performing spectrum analysis on the radio frequency signal spectrum to obtain spectrum information; Determining, according to the spectrum information, radio frequency frequency domain characteristics corresponding to the static radio frequency signal; The electromagnetic wave signal of the static radio frequency signal is extracted by combining the radio frequency time domain feature and the radio frequency frequency domain feature to obtain a first signal.

3. The data monitoring and processing system based on the Internet of Things intelligent infusion cabinet according to claim 1 is characterized in that: The performing interference elimination processing on the first signal and the second signal respectively to obtain a first target signal and a second target signal includes: Performing noise reduction processing on the first signal and the second signal respectively to obtain a first noise reduction signal and a second noise reduction signal; Calculating a first frequency domain complex number corresponding to the first noise reduction signal, and calculating a second frequency domain complex number corresponding to the second noise reduction signal; Combining the first frequency domain complex number and the second frequency domain complex number, respectively decomposing the first noise reduction signal and the second noise reduction signal to obtain a first decomposed signal and a second decomposed signal; Performing interference detection on the first decomposed signal and the second decomposed signal respectively to obtain a first interference signal and a second interference signal; The first interference signal and the second interference signal are respectively eliminated to obtain a first target signal and a second target signal.

4. A data monitoring and processing system based on the Internet of Things intelligent infusion cabinet as claimed in claim 3, characterized in that: The calculating a first frequency domain complex number corresponding to the first noise reduction signal includes: Extracting a sequence index and a sequence amplitude value corresponding to the first noise reduction signal, and identifying a time series corresponding to the first noise reduction signal; Calculating a signal length corresponding to the first noise reduction signal according to the time series; In combination with the time series, the signal length, the sequence index and the sequence amplitude value, the first frequency domain complex number corresponding to the first noise reduction signal is calculated by the following formula: Among them, A represents the first frequency domain complex number corresponding to the first denoised signal, b represents the starting sequence of the time series, B represents the signal length, D(b) represents the sequence amplitude value of the bth time series, e represents a natural constant, which is approximately equal to 2.71828, i represents an imaginary unit, and d represents a sequence index.

5. The data monitoring and processing system based on the Internet of Things intelligent infusion cabinet according to claim 1 is characterized in that: The combining the first target signal and the device distance to calculate the static phase value of the intelligent infusion cabinet includes: Analyzing a signal frequency corresponding to the first target signal to obtain a first signal frequency; Calculating the signal wave velocity corresponding to the first target signal according to the first signal frequency to obtain a first signal wave velocity; Combining the device distance and the first signal wave speed, the static phase value of the intelligent infusion cabinet is calculated by the following formula: Wherein, E1 represents the static phase value of the intelligent infusion cabinet, F represents the device distance, and ω1 represents the first signal wave velocity.

6. The data monitoring and processing system based on the Internet of Things intelligent infusion cabinet according to claim 1 is characterized in that: The combining the second target signal and the device distance to calculate the dynamic phase value of the intelligent infusion cabinet includes: measuring the infusion interval corresponding to the infusion device, querying the signal wave velocity of the second target signal in the liquid, and obtaining the second signal wave velocity; Combining the infusion interval, the device distance and the second signal wave speed, the dynamic phase value of the intelligent infusion cabinet is calculated by the following formula: Among them, E2 represents the dynamic phase value of the intelligent infusion cabinet, F represents the device distance, ω2 represents the second signal wave velocity, and d represents the infusion interval.

7. The data monitoring and processing system based on the Internet of Things intelligent infusion cabinet according to claim 1 is characterized in that: The combining the infusion drug data and the environmental parameters to analyze the drug compatibility corresponding to the smart infusion cabinet includes: Identify the drug name in the infusion drug data, and query the pharmaceutical index and index standard parameters corresponding to the drug name; extracting drug-related parameters from the environmental parameters according to the pharmaceutical indicators; In combination with the pharmaceutical index, clustering the index standard parameters and the drug association parameters is performed to obtain cluster index parameters; Calculating the parameter difference value of each category in the clustering indicator parameter, and calculating the indicator weight corresponding to the pharmaceutical indicator; Calculating the environmental adaptability corresponding to the drug name according to the indicator weight and the parameter difference value; According to the environmental adaptability, the drug adaptability corresponding to the intelligent infusion cabinet is analyzed.

8. The data monitoring and processing system based on the Internet of Things intelligent infusion cabinet according to claim 7 is characterized in that: The step of extracting drug-related parameters from the environmental parameters according to the pharmaceutical indicators includes: Analyze the environmental factors corresponding to the environmental parameters and extract the index elements in the pharmaceutical index; Vectorizing the indicator elements and the environmental factors respectively to obtain element vectors and factor vectors; Calculating the vector correlation between the element vector and the factor vector; When the vector correlation degree is greater than a preset correlation degree, determining the associated environmental factors among the environmental factors; According to the associated environmental factors, drug associated parameters are extracted from the environmental parameters.

9. The data monitoring and processing system based on the Internet of Things intelligent infusion cabinet according to claim 1 is characterized in that: The analyzing the operation performance corresponding to the operation hardware according to the hardware operation data includes: Performing data cleaning on the hardware operation data to obtain target operation data; Analyzing data variables corresponding to the target operation data, and extracting variable values ​​in the target operation data; Combining the data variables and the variable values, constructing a visualization chart of the target operation data; mining chart information corresponding to the visualization chart; The operating performance corresponding to the operating hardware is analyzed according to the chart information.

10. A data monitoring and processing method for an intelligent infusion cabinet based on the Internet of Things, characterized in that: The method comprises: A droplet radio frequency device is configured in the intelligent infusion cabinet, and the static radio frequency signal in the intelligent infusion cabinet is collected by using the droplet radio frequency device, and the electromagnetic wave signal of the static radio frequency signal is extracted to obtain a first signal, and the dynamic radio frequency signal in the intelligent infusion cabinet is collected by using the droplet ultrasonic system, and the electromagnetic wave signal of the dynamic radio frequency signal is extracted to obtain a second signal; Perform interference elimination processing on the first signal and the second signal respectively to obtain a first target signal and a second target signal, and calculate the device distance between the signal transmitting device and the infusion device in the intelligent infusion cabinet, and calculate the static phase value of the intelligent infusion cabinet in combination with the first target signal and the device distance, and calculate the dynamic phase value of the intelligent infusion cabinet in combination with the second target signal and the device distance, and calculate the infusion drip rate corresponding to the intelligent infusion cabinet in combination with the static phase value and the dynamic phase value; Querying the infusion target corresponding to the smart infusion cabinet, and analyzing the infusion state corresponding to the smart infusion cabinet in combination with the infusion drip rate and the infusion target; Detecting the environmental parameters inside the smart infusion cabinet, and analyzing the compatibility of the drug corresponding to the smart infusion cabinet in combination with the infusion drug data and the environmental parameters; Querying the operating hardware corresponding to the intelligent infusion cabinet, collecting hardware operating data corresponding to the operating hardware, and analyzing the operating performance corresponding to the operating hardware according to the hardware operating data; In combination with the infusion status, the drug compatibility and the operating performance, a monitoring interaction page corresponding to the smart infusion cabinet is set.

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