Iot-based centralized monitoring system for a compounding center

Abstract

The application discloses a centralized monitoring system of a static preparation center based on an internet of things, relates to the field of data acquisition and monitoring control, and solves the problem of insufficient monitoring accuracy of an existing monitoring system, and comprises the following modules: a data acquisition module that acquires monitoring data and image information; a data storage module that evaluates and stores the data range of the monitoring data, acquires layout image information and equipment image information, constructs a three-dimensional model, and stores in combination with the running state of the equipment; a data analysis module that acquires the environment standard of the static preparation center, analyzes the environment parameters of the static preparation center, and constructs an environment parameter model; the running state of the equipment of the static preparation center is integrated with the environment parameter model to construct a static preparation safety model; an early warning module that performs early warning judgment on the static preparation center; and a visualization module that visualizes the static preparation center, so that the monitoring accuracy can be effectively improved, and the problem of abnormal response lag can be reduced.

Description

Technical Field

[0001] This invention belongs to the field of static compounding center monitoring, and more specifically relates to data acquisition and monitoring control, specifically a centralized monitoring system for static compounding centers based on the Internet of Things. Background Technology

[0002] The existing centralized monitoring system for static compounding centers has the following specific shortcomings when performing monitoring:

[0003] 1. Existing centralized monitoring systems for static compounding centers typically monitor the main facilities of the static compounding center. The monitoring scope is not comprehensive enough, and it is impossible to make accurate predictions about the static compounding center, making it difficult to guarantee the static compounding safety of the static compounding center.

[0004] 2. Existing monitoring systems lack usability of monitoring data, often only providing early warnings to the static compounding center by simply comparing the monitoring data with standard data; the accuracy of data monitoring is insufficient, the data lag is serious, and it is difficult to respond to abnormal situations in the static compounding center in a timely manner.

[0005] 3. Existing centralized monitoring systems for sterile compounding centers achieve centralized monitoring of the centers by monitoring data. However, the data has low identification accuracy, making it difficult for non-professionals to monitor the sterile compounding centers. Furthermore, the systems have high personnel costs and low applicability.

[0006] Therefore, we propose a centralized monitoring system for static distribution centers based on the Internet of Things. Summary of the Invention

[0007] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a centralized monitoring system for static distribution centers based on the Internet of Things, and this invention aims to improve the accuracy of monitoring.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: a centralized monitoring system for static distribution centers based on the Internet of Things, the specific working process of each module is as follows:

[0009] Data acquisition module: monitors the environmental parameters of the static mixing center and obtains the monitoring data of the environmental parameters; records the layout image information and equipment images of the static mixing center;

[0010] Data storage module: Performs error analysis on environmental parameter monitoring data, filters the monitoring data based on the analysis results, evaluates the data range based on the filtered data, and stores the monitoring data and the data range;

[0011] The spatial layout of the static assembly center is analyzed based on the layout image information to construct a 3D model; the equipment images are analyzed to determine the operating status of the equipment; and the 3D model of the static assembly center and the equipment operating status are stored.

[0012] Data Analysis Module: Obtain environmental standards for the static compounding center; analyze the environmental parameters of the static compounding center by combining monitoring data and data range, and construct an environmental parameter model; integrate the equipment operation status of the static compounding center with the environmental parameter model to construct a static compounding safety model;

[0013] Early warning module: Acquires real-time data from the static compounding center, and makes early warning judgments based on the real-time data and the static compounding safety model;

[0014] Visualization module: Based on the 3D model of the static mixing center, the static mixing center is visualized, and data annotation of the static mixing center is performed by combining the monitoring data of equipment operation status and environmental parameters.

[0015] Furthermore, the data acquisition module collects data, specifically as follows:

[0016] Environmental parameters of the static compounding center are monitored, including differential pressure, temperature, and cleanliness. The types of monitored environmental parameters are denoted as 'as', and the monitoring data are denoted as 'jcs1' to 'jcs'. as ; among which jcs as Let represent the monitoring data of the bs represent the number of monitoring data points for each environmental parameter according to the monitoring time sequence; then, jcs represent the monitoring data for each type of environmental parameter. a (1) to jcs a (bs); jcs a (bs) represents the monitoring data of the a-th environmental parameter at time bs;

[0017] Images of the facility layout are acquired to obtain layout image information; the appearance and status of each facility are recorded to obtain equipment image information.

[0018] Furthermore, the monitoring data is filtered, as follows:

[0019] Obtain the operational standard information of the environmental parameters of the static compounding center; classify the environmental parameters according to the operational standard information; extract the monitoring data of the environmental parameters according to the classification results; filter the monitoring data of the environmental parameters according to the operational standard information to obtain the monitoring data that meets the standard; calculate the data range of the static compounding center under the standard limit.

[0020] The limits of each environmental parameter are obtained based on the operating standard information, and the environmental parameters are divided into regional parameters and boundary parameters based on the limits of the environmental parameters.

[0021] The types of regional parameters are denoted as cs, and the types of boundary parameters are denoted as ds; the sum of the types of regional parameters and boundary parameters equals the types of environmental parameters monitored, cs + ds = bs; the standard for the value of the regional parameter is obtained, denoted as bzz ± bdz; where bzz is the baseline value of the regional parameter, and bdz is the fluctuation value of the regional parameter; the standard for the value of the boundary parameter is obtained, denoted as jxz, where jxz is the boundary value of the boundary parameter.

[0022] Furthermore, the data range for the static compounding center under the standard limitations is calculated as follows:

[0023] Based on the types of regional parameters (cs), environmental parameter monitoring data are extracted to obtain regional parameter data (qyc). c (b); qyc c (b) represents the monitoring data of the c-th regional parameter at time b;

[0024] The regional parameter data is filtered according to the criteria for selecting regional parameters to obtain the filtered regional parameter values;

[0025] Filter the value qsx based on the region parameter. c (b) Combining regional parameter data qyc c (b) Perform calculations to obtain the mean of the regional parameter data, which is denoted as the regional parameter mean;

[0026] The difference between the regional parameter mean and the regional parameter data is calculated, and the fluctuation of the regional parameter data is calculated based on the difference between the regional parameter mean and the regional parameter data to obtain the regional parameter fluctuation value qfd. c ;

[0027] The data range of the regional parameter is obtained by considering the mean and fluctuation values ​​of the regional parameter, and is denoted as the regional parameter range qyf. c qyf c =qjz c +qfd c ;

[0028] Based on the types of boundary parameters (ds), environmental parameter monitoring data are extracted to obtain boundary parameter data (jxc). d (b); jxc d (b) represents the monitoring data of the d-th boundary parameter at time b;

[0029] The boundary parameter data is filtered according to the criteria for boundary parameter values ​​to obtain the boundary parameter filter value jsx. d (b); For boundary parameter data that meets the value criteria, the boundary parameter filter value is assigned to 1; for data that does not meet the criteria, the value is assigned to 0;

[0030] Filter values ​​based on boundary parameters (jsx)d (b) Combining the boundary parameter data jxc d (b) Perform calculations to obtain the mean of the limit parameter data, denoted as the limit parameter mean jjz;

[0031] The calculation results of the regional parameters and the boundary parameters are saved to obtain the data range of the monitoring data.

[0032] Furthermore, the spatial layout of the static compounding center is analyzed, as follows:

[0033] Based on the layout image information, the facility layout of the static compounding center is obtained, the facility layout of the static compounding center is converted into three-dimensional coordinate points, and the static compounding center is represented in three-dimensional coordinates based on the three-dimensional coordinate points.

[0034] Obtain device image information and perform image analysis on each device based on the device image information; obtain device images and obtain image pixels from the device images, and represent each pixel by (i, j); obtain the number of pixels in the horizontal direction of the image, denoted as is; obtain the number of pixels in the vertical direction of the image, denoted as js; then i∈[1, is], j∈[1, js];

[0035] For each pixel, obtain its pixel value, denoted as xsz(i,j). Starting from pixel (1,1), traverse the image, using xsz(1,1) as a comparison value. If the pixel value xsz(i,j) is equal to xsz(1,1), the pixel value remains unchanged; otherwise, the pixel value is changed to 0. Based on the portion of the pixel value that is equal to xsz(1,1), determine the continuity of the image and divide it into multiple sub-images. Obtain the vertices of the pixels in each sub-image and determine the pixel range of the sub-image based on the vertices, denoted as (xi,xj) to (di,dj).

[0036] The sub-image pixels are evaluated based on the pixel range to obtain the pixel evaluation value szz;

[0037] For each sub-image, traverse the sub-image starting from (xi, xj) to determine the continuity of the images within the sub-image; and calculate the image pixel evaluation value based on the continuity.

[0038] The three-dimensional coordinates of the static matching center are mapped based on the image pixel evaluation values. The static matching center is then identified and divided using the pixel evaluation values: the number of sub-images is obtained, denoted as zs; the pixel evaluation value of each sub-image is obtained, denoted as szz(z); the corresponding parts of the static matching center are extracted based on the sub-images, and the corresponding parts of the static matching center are identified using the pixel evaluation values ​​of the sub-images.

[0039] Furthermore, a static configuration safety model is constructed, as follows:

[0040] Based on the environmental standards of the static compounding center, the monitoring data and data range of environmental parameters, a multi-level early warning system for environmental parameters is constructed. The monitoring data and data range of environmental parameters are analyzed, the environmental standards of the static compounding center are analyzed, and an environmental parameter model is constructed.

[0041] The operating status of the equipment in the static mixing center is analyzed, and the operating status is combined with the environmental parameter model to construct a static mixing safety model.

[0042] Furthermore, an environmental parameter model is constructed, as follows:

[0043] Obtain the mean and fluctuation values ​​of the regional parameters, determine the standard values ​​for the regional parameters, and calculate the consistency value qxf based on the mean, fluctuation values, and standard values. c ;

[0044] Obtain the limit values ​​and mean values ​​of the limit parameters; calculate the consistency value of the limit parameters based on the limit values ​​and the mean value of the limit parameters, and obtain the limit consistency value jxf. d ;

[0045] The environmental parameter model is constructed based on the boundary matching value and the regional matching value; thus, the environmental parameter model hjc is obtained.

[0046] Furthermore, the equipment operating status is combined with the environmental parameter model, as follows:

[0047] Image recognition is used to assess the operational status of the equipment in the sterile compounding center. Image pixels of the equipment are acquired, and these pixels are compared with their corresponding pixel evaluation values. If a discrepancy is found, the affected area is identified as abnormal. Abnormal areas are extracted and analyzed, including whether water stains are present, whether the equipment has been reset after use, and whether medical waste remains on the worktable. This analysis yields a safety value (SBA). If no abnormalities are found, the safety value is set to 0; otherwise, it is set to 1.

[0048] By combining environmental parameter models with equipment safety values, a static configuration safety model is constructed.

[0049] Furthermore, early warning assessments are conducted for static compounding centers, as detailed below:

[0050] The real-time data of the static mixing center is acquired, and the real-time data of the static mixing center is substituted into the static mixing safety model for calculation to obtain the static mixing safety value jpaq;

[0051] If jpaq = 0, then the static mixing center is considered to be operating normally.

[0052] If 0 < jpaq ≤ 2, the operation status of the static mixing center will be monitored again immediately. The monitoring results will be substituted into the static mixing safety model. If the static mixing safety value does not increase, a safety warning will be issued to the static mixing center. If the static mixing safety value increases, a safety warning will be issued to the static mixing center, and the data will be transmitted to the management personnel.

[0053] If jpaq > 2, issue a safety warning to the static compounding center and transmit the data to the management personnel to urge them to handle the static compounding center.

[0054] Furthermore, the static compounding center is visualized, as follows:

[0055] A 3D model of the static compounding center is used to visualize the center, resulting in a visual model. Monitoring data on equipment operation and environmental parameters are acquired and directly connected to the visual model. For each piece of equipment, its safety value is obtained and marked at the corresponding location on the visual model. Based on the environmental parameter monitoring data, the calculation results for each environmental parameter are obtained and highlighted in the visual model. The static compounding center management personnel can comprehensively monitor the center's operation using the calculated environmental parameters and equipment safety values.

[0056] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0057] 1. Conduct comprehensive monitoring of the static compounding center, including various environmental data, equipment layout, and equipment images; enhance the accuracy of monitoring the static compounding center based on monitoring data from all aspects; promptly detect abnormalities in the static compounding center to ensure static compounding safety.

[0058] 2. Establish a buffer zone for the monitoring data of the static mixing center, process the fluctuation of each data based on the daily monitoring data, and make accurate predictions of the data values ​​by combining the standard values ​​of each data; at the same time, use the daily monitoring data as a buffer zone to provide early warnings for the monitoring data; reduce the dangers of static mixing.

[0059] 3. Visualize the centralized monitoring system of the sterile compounding center, and use visualization software to intuitively represent abnormal monitoring data, thereby reducing the monitoring difficulty of the sterile compounding center and enhancing the applicability of the monitoring system. Attached Figure Description

[0060] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0061] Figure 1 This is an overall system block diagram of the present invention;

[0062] Figure 2 This is a schematic diagram of the data range in this invention;

[0063] Figure 3 This is a schematic diagram of data processing in this invention; Detailed Implementation

[0064] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0065] Example 1

[0066] Please see Figure 1 This invention pertains to data acquisition and monitoring control, and provides a technical solution: a centralized monitoring system for static distribution centers based on the Internet of Things, comprising a data acquisition module, a data storage module, a data analysis module, an early warning module, a visualization module, and a server. The data acquisition module, data storage module, data analysis module, early warning module, and visualization module are respectively connected to the server, and the server controls the data acquisition module, data storage module, data analysis module, early warning module, and visualization module respectively.

[0067] Data acquisition module: monitors the environmental parameters of the static mixing center and obtains the monitoring data of the environmental parameters; records the layout image information and equipment images of the static mixing center;

[0068] The specific workflow of the data acquisition module is as follows:

[0069] Multidimensional sensors are deployed in the static compounding center to monitor environmental parameters, including but not limited to: measuring the differential pressure using a differential pressure sensor, monitoring the temperature using a temperature sensor, and monitoring the cleanliness of the static compounding center using a particle counter; the types of monitored environmental parameters are denoted as 'as'; and the monitoring data for these environmental parameters are denoted as jcs1 to jcs. as ; among which jcs as Let represent the monitoring data of the bs represent the number of monitoring data points for each environmental parameter according to the monitoring time sequence; then, jcs represent the monitoring data for each type of environmental parameter. a (1) to jcsa (bs); jcs a (bs) represents the monitoring data of the a-th environmental parameter at time bs;

[0070] It should be noted that by designing a front-end circuit for data acquisition and analog-to-digital conversion to connect with the sensor, this module can acquire values ​​in a minimum time interval of 100ms. That is, for the monitoring sequence of environmental parameters, the time interval between two adjacent monitoring data is 100ms. By accurately sampling the data at 100ms intervals, the accuracy and real-time performance of data sampling are ensured.

[0071] The layout of the facilities is captured by a depth camera to obtain layout image information; the appearance and status of each facility are recorded to obtain equipment image information.

[0072] It should be noted that: facility layout refers to the spatial location of the equipment in the static assembly center; facility appearance and condition refers to the shape, size, and color of the facility; facility condition covers the operation of the facility and whether there are any faults or abnormalities.

[0073] Data storage module: Performs error analysis on environmental parameter monitoring data, filters the monitoring data based on the analysis results, evaluates the data range based on the filtered data, and stores the monitoring data and the data range;

[0074] The spatial layout of the static assembly center is analyzed based on the layout image information to construct a 3D model; the equipment images are analyzed to determine the operating status of the equipment; and the 3D model of the static assembly center and the equipment operating status are stored.

[0075] The specific workflow of the data storage module is as follows:

[0076] Obtain the operational standard information of the environmental parameters of the static compounding center; classify the environmental parameters according to the operational standard information; extract the monitoring data of the environmental parameters according to the classification results; filter the monitoring data of the environmental parameters according to the operational standard information to obtain the monitoring data that meets the standard; calculate the data range of the static compounding center under the standard limit.

[0077] The limits of each environmental parameter are obtained based on the operating standard information, and the environmental parameters are divided into regional parameters and boundary parameters based on the limits of the environmental parameters.

[0078] It should be noted that: regional parameters refer to environmental parameters whose values ​​fluctuate within a certain range, such as a pressure difference of 5-10 Pa between adjacent clean areas; and boundary parameters refer to environmental parameters whose values ​​are below or above a certain threshold, such as a total bacterial count ≤10 CFU / cm³. 2By dividing environmental parameters, the accuracy of calculation results is enhanced, and calculation errors caused by data selection are prevented.

[0079] Please see Figure 2 The types of regional parameters are denoted as cs, and the types of boundary parameters are denoted as ds. The sum of the types of regional parameters and boundary parameters equals the types of environmental parameters being monitored, cs + ds = bs. The standard for the value of the regional parameter is obtained, denoted as bzz ± bdz. Where bzz is the baseline value of the regional parameter and bdz is the fluctuation value of the regional parameter. The standard for the value of the boundary parameter is obtained, denoted as jxz, where jxz is the boundary value of the boundary parameter.

[0080] Based on the types of regional parameters (cs), environmental parameter monitoring data are extracted to obtain regional parameter data (qyc). c (b); qyc c (b) represents the monitoring data of the c-th regional parameter at time b;

[0081] The fluctuation value represents the error in the value of the regional parameter. If bzz represents temperature, then bdz ranges from [0, 0.1], and the specific value is determined with reference to the existing standard error.

[0082] The regional parameter data is filtered according to the criteria for selecting regional parameters to obtain the filtered regional parameter value qsx;

[0083] ;

[0084] Among them: qsx c (b) represents the region parameter selection value of the c-th region parameter at time b.

[0085] Filter the value qsx based on the region parameter. c (b) Combining regional parameter data qyc c (b) Perform calculations to obtain the mean of the regional parameter data, denoted as the mean of the regional parameter qjz;

[0086] ;

[0087] Among them: qjz c This represents the mean value of the regional parameters for the c-th type of region.

[0088] The difference between the regional parameter data and the mean of the regional parameter is calculated. The fluctuation of the regional parameter data is calculated based on the difference between the mean of the regional parameter and the regional parameter data, and the fluctuation value of the regional parameter qfd is obtained.

[0089] ;

[0090] Among them: qfd cThis represents the floating value of the region parameter for the c-th type of region parameter.

[0091] The data range of the regional parameter is obtained by considering the mean and fluctuation values ​​of the regional parameter, and is denoted as the regional parameter range qyf. c qyf c =qjz c +qfd c ;

[0092] Based on the types of boundary parameters (ds), environmental parameter monitoring data are extracted to obtain boundary parameter data (jxc). d (b); jxc d (b) represents the monitoring data of the d-th boundary parameter at time b;

[0093] The boundary parameter data is filtered according to the criteria for boundary parameter values ​​to obtain the boundary parameter filter value jsx. d (b); For boundary parameter data that meets the value criteria, the boundary parameter filter value is assigned to 1; for data that does not meet the criteria, the value is assigned to 0;

[0094] Filter values ​​based on boundary parameters (jsx) d (b) Combining the boundary parameter data jxc d (b) Perform calculations to obtain the mean of the limit parameter data, denoted as the limit parameter mean jjz;

[0095] ;

[0096] Among them: jjz d Express the mean of the boundary parameters for the d-th type of boundary parameter;

[0097] It should be noted that the boundary parameter is a monotonic parameter. Under the condition of meeting the standard, the larger the difference between the parameter value and the boundary value, the more the parameter meets the standard. Therefore, only the mean of the boundary parameter is needed as the parameter threshold to judge the parameter, thereby enhancing the accuracy of data calculation.

[0098] The calculation results of the regional parameters and the boundary parameters are saved to obtain the data range of the monitoring data;

[0099] It should be noted that: by calculating the data range of environmental parameters, the numerical fluctuation value of the environmental parameters is obtained. The early warning module is set according to the numerical fluctuation value, and the fluctuation value is used as the buffer zone for early warning; so as to enable timely and accurate early warning; the environmental parameters are differentiated according to the fluctuation value, so that the differences in data values ​​can be intuitively represented.

[0100] Acquire layout image information, construct a 3D space based on the layout image information to obtain a 3D model; acquire equipment image information, connect the operating status of each device with the 3D model based on the equipment image information, and optimize the 3D model.

[0101] Based on the layout image information, the facility layout of the static compounding center is obtained, the facility layout of the static compounding center is converted into three-dimensional coordinate points, and the static compounding center is represented in three-dimensional coordinates based on the three-dimensional coordinate points.

[0102] Obtain device image information and perform image analysis on each device based on the device image information; obtain device images and obtain image pixels from the device images, and represent each pixel by (i, j); obtain the number of pixels in the horizontal direction of the image, denoted as is; obtain the number of pixels in the vertical direction of the image, denoted as js; then i∈[1, is], j∈[1, js];

[0103] For each pixel, obtain its pixel value, denoted as xsz(i,j). Starting from pixel (1,1), traverse the image, using xsz(1,1) as a comparison value. If the pixel value xsz(i,j) is equal to xsz(1,1), the pixel value remains unchanged; otherwise, the pixel value is changed to 0. Based on the portion of the pixel value that is equal to xsz(1,1), determine the continuity of the image and divide it into multiple sub-images. Obtain the vertices of the pixels in each sub-image and determine the pixel range of the sub-image based on the vertices, denoted as (xi,xj) to (di,dj).

[0104] The sub-image pixels are evaluated based on the pixel range to obtain the pixel evaluation value szz;

[0105] ;

[0106] It should be noted that multiplying the image pixel with its position (i, j) enhances the uniqueness of the pixel evaluation value and improves the accuracy of image recognition.

[0107] For each sub-image, traverse the sub-image starting from (xi, xj) to determine the continuity of the images within the sub-image; and calculate the image pixel evaluation value based on the continuity.

[0108] The three-dimensional coordinates of the static matching center are mapped based on the image pixel evaluation values, and the static matching center is identified and divided using the pixel evaluation values; the details are as follows:

[0109] The number of sub-images is obtained, denoted as zs; the pixel evaluation value of each sub-image is obtained, denoted as szz(z); the corresponding part of the static matching center is extracted based on the sub-image, and the corresponding part of the static matching center is identified through the pixel evaluation value of the sub-image;

[0110] It should be noted that by constructing a 3D model of the static mixing center and digitizing the data, the status of each device in the static mixing center can be displayed intuitively.

[0111] Data Analysis Module: Obtain environmental standards for the static compounding center, analyze the environmental parameters of the static compounding center by combining monitoring data and data range, and construct an environmental parameter model; integrate the equipment operation status of the static compounding center with the environmental parameter model to construct a static compounding safety model;

[0112] The specific workflow of the data analysis module is as follows:

[0113] Based on the environmental standards of the static compounding center, the monitoring data and data range of environmental parameters, a multi-level early warning system for environmental parameters is constructed. The monitoring data and data range of environmental parameters are analyzed, the environmental standards of the static compounding center are analyzed, and an environmental parameter model is constructed.

[0114] The equipment operation status of the static mixing center is analyzed, and the equipment operation status is combined with the environmental parameter model to construct a static mixing safety model;

[0115] Please see Figure 3 Obtain the mean and fluctuation values ​​of the regional parameters, determine the standard for the regional parameter values, and calculate the consistency value of the regional parameters based on the mean, fluctuation values, and standard values ​​to obtain the consistency value qxf. c ;

[0116] ;

[0117] Among them: jsz c Preset parameters for the region, which refer to real-time measurement data; qjz c qfd represents the mean of the region parameters for the c-th region. c This represents the floating value of the region parameter for the c-th type of region parameter; bzz c This represents the baseline value of the c-th region parameter; bdz c This represents the fluctuation value of the c-th type of regional parameter.

[0118] Obtain the limit values ​​and mean values ​​of the limit parameters; calculate the consistency value of the limit parameters based on the limit values ​​and the mean value of the limit parameters, and obtain the limit consistency value jxf. d ;

[0119] ;

[0120] Among them: jjsz d Preset parameters for the region, which refer to real-time measurement data; jjz d The mean of the boundary parameters for the d-th type of boundary parameter; jxz d Let d be the boundary value of the boundary parameter.

[0121] The environmental parameter model is constructed based on the boundary matching value and the regional matching value; thus, the environmental parameter model hjc is obtained.

[0122] ;

[0123] It should be noted that by dividing environmental parameters into boundary parameters and regional parameters, and calculating data for different environmental parameters, the accuracy of data calculation is enhanced; by integrating the calculation results through numerical values, the calculation process is simplified, and a comprehensive assessment of environmental parameters is achieved.

[0124] Image recognition is used to assess the operational status of the equipment in the sterile compounding center. Image pixels of the equipment are acquired, and these pixels are compared with their corresponding pixel evaluation values. If a discrepancy is found, the affected area is identified as abnormal. Abnormal areas are extracted and analyzed, including whether water stains are present, whether the equipment has been reset after use, and whether medical waste remains on the worktable. This analysis yields a safety value (SBA). If no abnormalities are found, the safety value is set to 0; otherwise, it is set to 1.

[0125] It should be noted that abnormal issues refer to problems such as water stains on the equipment, failure to reset the equipment, or the presence of medical waste on the equipment.

[0126] By combining environmental parameter models with equipment safety values, a static configuration safety model is constructed. The static configuration safety model JPA is as follows:

[0127] ;

[0128] It should be noted that the data is differentiated by squaring the environmental parameter model and the equipment safety value to prevent numerical confusion in the calculation results and to enhance the accuracy of the calculation of the environmental parameter model and the equipment safety value.

[0129] It should be noted that by combining environmental parameter models with equipment safety values, a comprehensive safety analysis of the static mixing center can be conducted, thereby enhancing the accuracy of the static mixing center analysis.

[0130] Early warning module: Acquires real-time data from the static compounding center, and makes early warning judgments based on the real-time data and the static compounding safety model;

[0131] The specific workflow of the early warning module is as follows:

[0132] The real-time data of the static mixing center is acquired, and the real-time data of the static mixing center is substituted into the static mixing safety model for calculation to obtain the static mixing safety value jpaq;

[0133] If jpaq = 0, then the static mixing center is considered to be operating normally.

[0134] If 0 < jpaq ≤ 2, the operation status of the static mixing center will be monitored again immediately. The monitoring results will be substituted into the static mixing safety model. If the static mixing safety value does not increase, a safety warning will be issued to the static mixing center. If the static mixing safety value increases, a safety warning will be issued to the static mixing center, and the data will be transmitted to the management personnel.

[0135] If jpaq > 2, issue a safety warning to the static compounding center and transmit the data to the management personnel to urge them to handle the static compounding center.

[0136] Visualization module: Based on the 3D model of the static mixing center, the static mixing center is visualized, and data annotation of the static mixing center is performed by combining the monitoring data of equipment operation status and environmental parameters;

[0137] By combining a 3D model of the static compounding center with BIM technology, the 3D model of the static compounding center is visualized to obtain a visual model. Monitoring data on equipment operating status and environmental parameters are acquired and directly connected to the visual model. For each piece of equipment, its safety value is obtained and marked at the corresponding location on the visual model. Based on the environmental parameter monitoring data, the calculation results of various environmental parameters are obtained and highlighted in the visual model. Static compounding center managers can comprehensively monitor the operation of the static compounding center through the calculation results of environmental parameters and equipment safety values.

[0138] It should be noted that by constructing a visualization model to intuitively display the environmental parameters and equipment status of each area, the management difficulty of the static mixing center is simplified, enabling managers to accurately detect and handle abnormal problems in the static mixing center.

[0139] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A centralized monitoring system for static distribution centers based on the Internet of Things, characterized in that, include: Data acquisition module: Monitors the environmental parameters of the static compounding center and obtains the monitoring data of the environmental parameters; Record the layout image information and equipment images of the static assembly center; Data storage module: Performs error analysis on environmental parameter monitoring data, filters the monitoring data based on the analysis results, evaluates the data range based on the filtered data, and stores the monitoring data and the data range; The spatial layout of the static assembly center is analyzed based on the layout image information to construct a 3D model; the equipment images are analyzed to determine the operating status of the equipment; and the 3D model of the static assembly center and the equipment operating status are stored. Data Analysis Module: Obtain environmental standards for the static compounding center; analyze the environmental parameters of the static compounding center by combining monitoring data and data range, and construct an environmental parameter model; integrate the equipment operation status of the static compounding center with the environmental parameter model to construct a static compounding safety model; Early warning module: Acquires real-time data from the static compounding center, and makes early warning judgments based on the real-time data and the static compounding safety model; Visualization module: Based on the 3D model of the static mixing center, the static mixing center is visualized, and data annotation of the static mixing center is performed by combining the monitoring data of equipment operation status and environmental parameters.

2. The centralized monitoring system for static distribution centers based on the Internet of Things according to claim 1, characterized in that, The data collection process is as follows: Environmental parameters of the static compounding center are monitored; the types of monitored environmental parameters are denoted as 'as'; the monitoring data of environmental parameters are denoted as 'jcs1' to 'jcs'. as ; among which jcs as Let represent the monitoring data for the bs represents the number of monitoring data for each environmental parameter, based on the monitoring time sequence. jcs represents the monitoring data for each type of environmental parameter. a (1) to jcs a (bs); jcs a (bs) represents the monitoring data of the a-th environmental parameter at time bs; Images of the facility layout are acquired to obtain layout image information; the appearance and status of each facility are recorded to obtain equipment image information.

3. The centralized monitoring system for static distribution centers based on the Internet of Things according to claim 1, characterized in that, The monitoring data was filtered as follows: Obtain the operational standard information of the environmental parameters of the static compounding center; classify the environmental parameters according to the operational standard information; extract the monitoring data of the environmental parameters according to the classification results; filter the monitoring data of the environmental parameters according to the operational standard information to obtain the monitoring data that meets the standard; calculate the data range of the static compounding center under the standard limit. The limits of each environmental parameter are obtained based on the operating standard information, and the environmental parameters are divided into regional parameters and boundary parameters based on the limits of the environmental parameters. Let cs denote the types of regional parameters and ds denote the types of boundary parameters; let bs denote the types of environmental parameters, and cs + ds = bs; let bzz ± bdz be the standard for the values ​​of regional parameters, where bzz is the baseline value of the regional parameter and bdz is the fluctuation value of the regional parameter; let jxz be the standard for the values ​​of boundary parameters, where jxz is the boundary value of the boundary parameter.

4. The centralized monitoring system for static distribution centers based on the Internet of Things according to claim 3, characterized in that, The data range for calculating the static compounding center under the standard limitations is as follows: Based on the types of regional parameters (cs), environmental parameter monitoring data are extracted to obtain regional parameter data (qyc). c (b); qyc c (b) represents the monitoring data of the c-th regional parameter at time b; The regional parameter data is filtered according to the criteria for selecting regional parameters to obtain the filtered regional parameter values; Filter the value qsx based on the region parameter. c (b) Combining regional parameter data qyc c (b) Perform calculations to obtain the mean of the regional parameter data, which is denoted as the regional parameter mean; The difference between the regional parameter mean and the regional parameter data is calculated, and the fluctuation of the regional parameter data is calculated based on the difference between the regional parameter mean and the regional parameter data to obtain the regional parameter fluctuation value qfd. c ; The data range of the regional parameter is obtained by considering the mean and fluctuation values ​​of the regional parameter, and is denoted as the regional parameter range qyf. c qyf c =qjz c +qfd c ; Among them: qjz c This represents the mean value of the region parameter for the c-th region. Based on the types of boundary parameters (ds), environmental parameter monitoring data are extracted to obtain boundary parameter data (jxc). d (b); The boundary parameter data is filtered according to the criteria for boundary parameter values ​​to obtain the boundary parameter filter value jsx. d (b); If the boundary parameter data meets the value criteria, the boundary parameter filter value is assigned to 1; otherwise, it is assigned to 0. Filter values ​​based on boundary parameters (jsx) d (b) Combining the boundary parameter data jxc d (b) Perform calculations to obtain the mean of the limit parameter data, denoted as the limit parameter mean jjz; The calculation results of the regional parameters and the boundary parameters are saved to obtain the data range of the monitoring data.

5. The centralized monitoring system for static distribution centers based on the Internet of Things according to claim 1, characterized in that, The spatial layout of the static compounding center is analyzed as follows: Based on the layout image information, the facility layout of the static compounding center is obtained, the facility layout of the static compounding center is converted into three-dimensional coordinate points, and the static compounding center is represented in three-dimensional coordinates based on the three-dimensional coordinate points. Obtain device image information and perform image analysis on each device based on the device image information; obtain device images and obtain image pixels from the device images, and represent each pixel by (i, j); obtain the number of pixels in the horizontal direction of the image, denoted as is; obtain the number of pixels in the vertical direction of the image, denoted as js; then i∈[1, is], j∈[1, js]; For each pixel, obtain its pixel value, denoted as xsz(i,j). Starting from pixel (1,1), traverse the image, using xsz(1,1) as a comparison value. If the pixel value xsz(i,j) is equal to xsz(1,1), the pixel value remains unchanged; otherwise, the pixel value is changed to 0. Based on the portion of the pixel value that is equal to xsz(1,1), determine the continuity of the image and divide it into multiple sub-images. Obtain the vertices of the pixels in each sub-image and determine the pixel range of the sub-image based on the vertices, denoted as (xi,xj) to (di,dj). The sub-image pixels are evaluated based on the pixel range to obtain the pixel evaluation value szz; For each sub-image, traverse the sub-image starting from (xi, xj) to determine the continuity of the images within the sub-image; and calculate the image pixel evaluation value based on the continuity. The three-dimensional coordinates of the static matching center are mapped based on the image pixel evaluation values. The static matching center is then identified and divided using the pixel evaluation values: the number of sub-images is obtained, denoted as zs; the pixel evaluation value of each sub-image is obtained, denoted as szz(z); the corresponding parts of the static matching center are extracted based on the sub-images, and the corresponding parts of the static matching center are identified using the pixel evaluation values ​​of the sub-images.

6. The centralized monitoring system for static distribution centers based on the Internet of Things according to claim 1, characterized in that, The static configuration security model is constructed as follows: Based on the environmental standards of the static compounding center, the monitoring data and data range of environmental parameters, a multi-level early warning system for environmental parameters is constructed, the monitoring data and data range of environmental parameters are analyzed, the environmental standards of the static compounding center are analyzed, and an environmental parameter model is constructed. The operating status of the equipment in the static mixing center is analyzed, and the operating status is combined with the environmental parameter model to construct a static mixing safety model.

7. The IoT-based centralized monitoring system for static distribution centers according to claim 6, characterized in that, The environmental parameter model is constructed as follows: Obtain the mean and fluctuation values ​​of the regional parameters, determine the standard values ​​for the regional parameters, and calculate the consistency value qxf based on the mean, fluctuation values, and standard values. c ; Obtain the limit values ​​and mean values ​​of the limit parameters; calculate the consistency value of the limit parameters based on the limit values ​​and the mean value of the limit parameters, and obtain the limit consistency value jxf. d ; An environmental parameter model is constructed based on boundary matching values ​​and regional matching values; The environmental parameter model hjc is obtained.

8. The centralized monitoring system for static distribution centers based on the Internet of Things according to claim 6, characterized in that, The equipment operating status is combined with the environmental parameter model, as follows: Image recognition is performed on the equipment operation status of the static mixing center to obtain the image pixels of the equipment in the static mixing center. The image pixels of the equipment in the static mixing center are compared with the image pixel evaluation value. If the image pixels and the pixel evaluation value do not match, the part is determined to be an abnormal part. The abnormal parts of the equipment are extracted and anomaly analysis is performed on the abnormal parts. If there is no abnormal problem with the equipment in the static mixing center, the equipment safety value is assigned to 0. If there is an abnormal problem with the equipment in the static mixing center, the equipment safety value is assigned to 1. By combining environmental parameter models with equipment safety values, a static configuration safety model is constructed.

9. The centralized monitoring system for static distribution centers based on the Internet of Things according to claim 1, characterized in that, The following are the specific steps for early warning assessment of static compounding centers: The real-time data of the static mixing center is acquired, and the real-time data of the static mixing center is substituted into the static mixing safety model for calculation to obtain the static mixing safety value jpaq; If jpaq = 0, then the static mixing center is considered to be operating normally. If 0 < jpaq ≤ 2, the operation status of the static mixing center will be monitored again immediately. The monitoring results will be substituted into the static mixing safety model. If the static mixing safety value does not increase, a safety warning will be issued to the static mixing center. If the static mixing safety value increases, a safety warning will be issued to the static mixing center, and the data will be transmitted to the management personnel. If jpaq > 2, issue a safety warning to the static compounding center, transmit the data to the management personnel, and process the static compounding center.

10. The IoT-based centralized monitoring system for static distribution centers according to claim 9, characterized in that, The static compounding center is visualized as follows: A 3D model of the static compounding center is used to visualize the center, resulting in a visual model. Monitoring data on equipment operation and environmental parameters are acquired and directly connected to the visual model. For each piece of equipment, its safety value is obtained and marked at the corresponding location on the visual model. Based on the environmental parameter monitoring data, the calculation results for each environmental parameter are obtained and highlighted in the visual model. The static compounding center management personnel can comprehensively monitor the center's operation using the calculated environmental parameters and equipment safety values.

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