Medical equipment service state intelligent monitoring system and method based on big data

Abstract

The invention relates to the technical field of medical equipment state monitoring, and particularly discloses a medical equipment service state intelligent monitoring system and method based on big data, and the method comprises the steps: collecting medical data through mother medical equipment, transmitting the medical data to child medical equipment, and presetting the to-be-transmitted time of the mother medical equipment corresponding to the to-be-transmitted medical data; predicting receiving time when the child medical equipment receives the corresponding to-be-sent medical data from the mother medical equipment; whether data transmission delay exists between the child medical equipment and the mother medical equipment is judged; and if the data transmission delay exists, the equipment operation parameters of the child medical equipment and the mother medical equipment are dynamically adjusted respectively, so that continuously changing network conditions and equipment loads can be adapted, the maximum stability and reliability of the system are kept, the service life of the equipment is prolonged, and the maintenance cost is reduced. The efficiency and quality of medical services are improved, and the trust and satisfaction of patients to medical institutions are enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical equipment status monitoring, and relates to a medical equipment service status intelligent monitoring system and method based on big data. Background Art

[0002] In modern medical equipment, data transmission between parent and child medical devices is essential to ensure the coordinated operation of the devices. This coordinated operation not only improves the efficiency of medical services, but also directly affects the quality of diagnosis and treatment.

[0003] However, in the prior art, there is often a long delay problem in the process of transmitting data from a parent medical device to a child medical device. The delay problem that often occurs when a parent medical device transmits data to a child medical device will have a serious negative impact on the performance of the medical device and patient safety, resulting in untimely data processing, affecting the normal operation of the medical device and the accurate processing of patient data.

[0004] In addition, when receiving data, sub-medical devices are usually unable to dynamically adjust according to the data type and their own status, resulting in low data reception efficiency. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention provides a medical equipment service status intelligent monitoring system and method based on big data, which are used to solve the above technical problems.

[0006] In order to achieve the above purpose and other purposes, the technical solution adopted by the present invention is as follows: On one hand, the present invention provides a medical equipment service status intelligent monitoring system based on big data, including a sending time setting module, a receiving time estimation module, a transmission delay judgment module and a device delay adjustment module, wherein the above modules are connected by wired and / or wireless connection to realize data transmission between the modules; A sending time setting module, which collects medical data through the parent medical device and sends it to the child medical device, and presets the waiting time for the parent medical device to send the medical data; A receiving time prediction module, used to predict the receiving time of the child medical device receiving the corresponding medical data to be sent from the parent medical device; A transmission delay judgment module, based on the preset waiting time of the mother medical device corresponding to the medical data to be sent and the reception time of the child medical device receiving the medical data to be sent from the mother medical device, judges whether there is a data transmission delay between the child and the mother medical device; if so, executes the device delay adjustment module; The equipment delay adjustment module dynamically adjusts the equipment operating parameters of the parent and child medical devices respectively.

[0007] In the preferred technical solution of the present application, the reception time of the child medical device receiving the corresponding medical data sent by the parent medical device is predicted, and the specific prediction process is: Obtain the delayed reception time of each medical data received by the sub-medical device from the device database, calculate it, and obtain the average delayed time of the sub-medical device corresponding to the previous data transmission ; Using the calculation formula , predict the time it takes for the child medical device to receive the corresponding medical data sent by the parent medical device , The waiting time for sending medical data corresponding to the mother medical device. is the predicted transmission delay duration, where It is calculated by the following prediction model: The prediction model is , thereby predicting the predicted transmission delay time , are the weight factors set respectively, Used to adjust the impact of the average delay time of previous data transmission on the prediction. Used to adjust the impact of data volume and bandwidth on transmission time, Used to adjust the impact of network status factors on prediction, is the random noise term, The total amount of medical data that the parent medical device wants to send. is the network bandwidth, is the network status factor.

[0008] In a preferred technical solution of the present application, the operation logic of the transmission delay judgment module is: Get the floor numbers of the parent medical equipment and the child medical equipment, and record them as and ; Calculate the vertical distance between parent and child medical devices , n' is the actual vertical distance between each floor; Based on the horizontal distance between parent and child medical devices , analyze and obtain the actual distance between the parent and child medical devices ; Then calculate the theoretical transmission time between parent and child medical devices , T' is the standard speed of signal propagation, is the preset transmission time error correction factor; The actual transmission time between the parent medical device and the child medical device is obtained by performing a difference operation on the waiting time for sending the medical data to be sent by the parent medical device and the receiving time of the medical data to be sent by the child medical device from the parent medical device; Synchronously calculating the difference between the actual transmission time between the parent and child medical devices and the theoretical transmission time between the parent and child medical devices, and recording the difference result as the transmission time difference between the parent and child medical devices; The transmission time difference between the parent and child medical devices is compared with the preset maximum allowable transmission time upper limit threshold. If the former is greater than the latter, it is determined that there is a data transmission delay between the parent and child medical devices; otherwise, there is no data transmission delay between the parent and child medical devices.

[0009] In a preferred technical solution of the present application, the device delay adjustment module is divided into a parent medical device adjustment sub-unit and a child medical device adjustment sub-unit.

[0010] In the preferred technical solution of the present application, the operation logic of the parent medical device adjusting the sub-unit is: Obtain the data volume, data importance and data urgency of each data type corresponding to the medical data to be sent by the parent medical device; synchronously obtain the parameter values ​​of the current network bandwidth and network delay respectively; Compare the data volume, data importance and data urgency of each data type corresponding to the medical data to be sent by the parent medical device with the numerical ranges corresponding to the preset weight levels, thereby determining the weight level of the factors affecting data compression, such as the data volume, data importance and data urgency of each data type corresponding to the medical data to be sent by the parent medical device; The weight levels of factors affecting data compression corresponding to network bandwidth and network delay are obtained in the same manner according to the method of obtaining the weight levels of factors affecting data compression corresponding to the data volume, data importance and data urgency of each data type of medical data to be sent by the parent medical device; The compression evaluation value of each data type corresponding to the medical data to be sent by the mother medical device is calculated accordingly. , C5 and C6 represent the weight levels of factors affecting data compression, such as network bandwidth and network delay. are the weight levels of factors affecting data compression corresponding to the data volume, data importance and data urgency of the y-th data type, and y is the number of each data type; The compression evaluation value of each data type corresponding to the medical data to be sent by the parent medical device And set the compression interval value Compare and find out if there is a mother medical device whose compression evaluation value of a certain data type corresponding to the medical data to be sent is less than or equal to , then the mother medical device corresponding to the medical data to be sent uses a low compression rate to compress the data type; if there is a mother medical device corresponding to the medical data to be sent corresponding to a certain data type compression evaluation value greater than and less than or equal to , then the mother medical device corresponding to the medical data to be sent is compressed using the compression rate corresponding to the data type; if there is a mother medical device corresponding to the medical data to be sent corresponding to a certain data type compression evaluation value greater than , the medical data to be sent by the mother medical device is compressed using a high compression rate corresponding to the data type.

[0011] In a preferred technical solution of the present application, the operation process of the parent medical device adjusting the sub-unit further includes the following steps: Based on the amount of data generated per second by the parent medical device, the sampling rate of the medical data to be sent, and the proportion of each data type in the medical data to be sent; The amount of raw data corresponding to each data type is equal to the amount of data generated per second by the parent medical device multiplied by the proportion of each data type in the medical data to be sent; Then, the amount of data after feature extraction corresponding to each data type is equal to the original data amount corresponding to each data type multiplied by the feature extraction efficiency of each data type, and the multiplication result is divided by the number 100 to calculate the amount of data after feature extraction corresponding to each data type; Obtain the compression ratio of each data type corresponding to the medical data to be sent by the parent medical device, multiply the amount of data corresponding to each data type after feature extraction by the compression ratio of each data type corresponding to the medical data to be sent by the parent medical device, and calculate the compressed data amount corresponding to each data type by dividing the multiplication result by the number 100. ; The set transmission data volume per second corresponding to the medical data to be sent by the mother medical device is calculated in this way ,y is the number of each data type, y=1,2,..Y,t is the period of aggregation of medical data in the time window, in seconds.

[0012] In a preferred technical solution of the present application, the operation process of the parent medical device adjusting the sub-unit also includes: Obtain each network status parameter value corresponding to each selectable transmission path, wherein each network status parameter value is divided into available bandwidth, packet loss rate and delay, and number each selectable transmission path in sequence as 1, 2, ..f, ...F; The adaptive score of each data type corresponding to each selectable transmission path is calculated , f is the number of each selectable transmission path, f = 1,2,...F; They are preset equalization factors, which are used to adjust the influence weight of each parameter. The available bandwidth, packet loss rate and delay of the selectable transmission path for the fth item; are the maximum possible values ​​of available bandwidth, packet loss rate, delay and priority score, used for normalization. Score the priority of the f-th selectable transmission path for the y-th data type; In this way, the selectable transmission path with the highest adaptive score corresponding to each data type is selected as the transmission path corresponding to each data type.

[0013] In a preferred technical solution of the present application, the operation process of the sub-medical device adjustment sub-unit includes the following steps: The equipment operating parameter of the sub-medical equipment is the buffer capacity; Based on the total amount of medical data to be sent by the parent medical device , and parameter values ​​of network delay; Calculate the amount of data that a medical device can process within the network latency , is the parameter value of network delay, v is the data processing speed of the sub-medical device; Determine the buffer capacity of the sub-medical device from this , B is the buffer capacity of the sub-medical equipment, The buffer capacity correction factor is set.

[0014] Another aspect of the present invention provides a method for intelligently monitoring the service status of medical equipment based on big data, the method comprising the following steps: Step 1: Collect medical data through the parent medical device and send it to the child medical device, and preset the waiting time for the parent medical device to send the medical data; Step 2: predicting the time at which the child medical device receives the medical data to be sent from the parent medical device; Step 3: based on the waiting time of the medical data to be sent by the parent medical device and the receiving time of the medical data to be sent by the child medical device from the parent medical device, determine whether there is a data transmission delay between the parent and child medical devices; if so, execute step 4, otherwise, exit the method loop; Step 4: Dynamically adjust the equipment operating parameters of the parent and child medical devices respectively.

[0015] Another aspect of the present invention provides a medical equipment service status intelligent monitoring device based on big data, including a processor, a memory and a communication bus; The memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, it is executed to implement a medical equipment service status intelligent monitoring system based on big data as described in the present invention.

[0016] As described above, the present invention provides a medical equipment service status intelligent monitoring system and method based on big data, which has at least the following beneficial effects: (1) The present invention provides a medical equipment service status intelligent monitoring system and method based on big data. By collecting medical data from the parent medical device and presetting its sending time, and predicting the data receiving time of the child medical device, the system can effectively monitor and manage the delay in the data transmission process. This is of great significance for ensuring efficient collaboration between medical devices and the real-time nature of data. On the one hand, it can identify and respond to delays in data transmission in real time, and optimize data flow and processing speed by adjusting device operating parameters, thereby reducing the occurrence of delays. Secondly, it can improve the overall operating efficiency and reliability of medical equipment, and adaptively adjust according to the actual data transmission performance to maintain the optimal working state. In addition, through real-time adjustment, medical equipment can better cope with the challenges of network fluctuations and ensure stability and continuity during data transmission. Ultimately, this strategy not only improves the quality of medical services, but also enhances patients' trust and satisfaction with medical services, and has a positive impact on the reputation and operational results of medical institutions.

[0017] (2) When the embodiment of the present invention detects that there is a data transmission delay, it dynamically adjusts the operating parameters of the parent and child medical devices, which can optimize the device performance in real time and ensure the efficiency and accuracy of data transmission, thereby improving the response speed and treatment effect of the entire medical system. The accurate transmission of real-time data is crucial for doctors to make timely and correct decisions. By dynamically adjusting the operating parameters, the communication delay between devices can be reduced and unnecessary delays in the treatment process can be avoided, which plays a decisive role in improving the success rate and safety of patient treatment. In addition, this adjustment can adapt to the ever-changing network conditions and device loads, maintain the maximum stability and reliability of the system, thereby extending the service life of the equipment and reducing maintenance costs; this not only improves the efficiency and quality of medical services, but also enhances patients' trust and satisfaction with medical institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the connection of various modules of the system of the present invention. Figure 2 It is a schematic diagram of the connection of each step of the method of the present invention. DETAILED DESCRIPTION

[0020] The above contents in combination with the implementation of the present invention are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

[0021] Example 1 See also Figure 1 As shown, a medical equipment service status intelligent monitoring system based on big data includes a sending time setting module, a receiving time estimation module, a transmission delay judgment module and a device delay adjustment module, wherein the above modules are connected by wired and / or wireless connection to realize data transmission between the modules; A sending time setting module, which collects medical data through the parent medical device and sends it to the child medical device, and presets the waiting time for the parent medical device to send the medical data; The sending and receiving times must be accurate to milliseconds; A receiving time prediction module, used to predict the receiving time of the child medical device receiving the corresponding medical data to be sent from the parent medical device; In a possible design, the reception time of the child medical device receiving the corresponding medical data sent by the parent medical device is predicted. The specific prediction process is: Obtain the delayed reception time of each medical data received by the sub-medical device from the device database, calculate it, and obtain the average delayed time of the sub-medical device corresponding to the previous data transmission ; , is the delayed receiving time of the medical data received by the medical device for the i-th time, i is the number of each medical data received in history, i=1,2,...N, is the weight of the proportion of medical data received for the i-th time, where , the newer data has a higher weight, are the weights of receiving medical data for the Nth, N-1th and first time respectively; Using the calculation formula , predict the time it takes for the child medical device to receive the corresponding medical data sent by the parent medical device , The waiting time for sending medical data corresponding to the mother medical device. is the predicted transmission delay duration, where It is calculated by the following prediction model: The prediction model is , thereby predicting the predicted transmission delay time , are the weight factors set respectively, Used to adjust the impact of the average delay time of previous data transmission on the prediction. Used to adjust the impact of data volume and bandwidth on transmission time, Used to adjust the impact of network status factors on prediction, is the random noise term, The total amount of medical data that the parent medical device wants to send. The network bandwidth. The larger the data volume, the smaller the bandwidth and the longer the transmission time. The bandwidth can be obtained in real time through network monitoring tools. It is the network status factor, which reflects the real-time status of the network.

[0022] A transmission delay judgment module, based on the waiting time of the mother medical device corresponding to the medical data to be sent and the receiving time of the child medical device receiving the medical data to be sent from the mother medical device, judges whether there is a data transmission delay between the child and the mother medical device; if so, executes the device delay adjustment module; In a possible design, the operation logic of the transmission delay determination module is: Get the floor numbers of the parent medical equipment and the child medical equipment, and record them as and ; Calculate the vertical distance between parent and child medical devices , n' is the actual vertical distance between each floor; Based on the plane coordinate positions of the parent medical equipment and the child medical equipment on the corresponding floors, they are imported into the three-dimensional model of the medical center to obtain the horizontal distance between the parent and child medical equipment. , and then analyze the actual distance between the parent and child medical devices ; Then calculate the theoretical transmission time between parent and child medical devices , T' is the standard speed of signal propagation, is the preset transmission time error correction factor; The actual transmission time between the parent medical device and the child medical device is obtained by performing a difference operation on the waiting time for sending the medical data to be sent by the parent medical device and the receiving time of the medical data to be sent by the child medical device from the parent medical device; Synchronously calculating the difference between the actual transmission time between the parent and child medical devices and the theoretical transmission time between the parent and child medical devices, and recording the difference result as the transmission time difference between the parent and child medical devices; The transmission time difference between the parent and child medical devices is compared with the preset maximum allowable transmission time upper limit threshold. If the former is greater than the latter, it is determined that there is a data transmission delay between the parent and child medical devices; otherwise, there is no data transmission delay between the parent and child medical devices.

[0023] The equipment delay adjustment module dynamically adjusts the equipment operating parameters of the parent and child medical devices respectively.

[0024] In one possible design, the device delay adjustment module is divided into a parent medical device adjustment subunit and a child medical device adjustment subunit.

[0025] In one possible design, the operation logic of the parent medical device adjusting the sub-unit is: The device operation parameters of the mother medical device specifically include the set transmission data volume per second corresponding to the medical data to be sent by the mother medical device and the compression rate and transmission path of each data type; Obtain the data volume, data importance and data urgency of each data type corresponding to the medical data to be sent by the parent medical device; synchronously obtain the parameter values ​​of the current network bandwidth and network delay respectively; Compare the data volume, data importance and data urgency of each data type corresponding to the medical data to be sent by the parent medical device with the numerical ranges corresponding to the preset weight levels, thereby determining the weight level of the factors affecting data compression, such as the data volume, data importance and data urgency of each data type corresponding to the medical data to be sent by the parent medical device; The weight levels of factors affecting data compression corresponding to network bandwidth and network delay are obtained in the same manner according to the method of obtaining the weight levels of factors affecting data compression corresponding to the data volume, data importance and data urgency of each data type of medical data to be sent by the parent medical device; The compression evaluation value of each data type corresponding to the medical data to be sent by the mother medical device is calculated accordingly. , C5 and C6 represent the weight levels of factors affecting data compression, such as network bandwidth and network delay. are the weight levels of factors affecting data compression corresponding to the data volume, data importance and data urgency of the y-th data type, and y is the number of each data type; The compression evaluation value of each data type corresponding to the medical data to be sent by the parent medical device And set the compression interval value Compare and find out if there is a mother medical device whose compression evaluation value of a certain data type corresponding to the medical data to be sent is less than or equal to , then the mother medical device corresponding to the medical data to be sent uses a low compression rate to compress the data type; if there is a mother medical device corresponding to the medical data to be sent corresponding to a certain data type compression evaluation value greater than and less than or equal to , then the mother medical device corresponding to the medical data to be sent is compressed using the compression rate corresponding to the data type; if there is a mother medical device corresponding to the medical data to be sent corresponding to a certain data type compression evaluation value greater than , the medical data to be sent by the mother medical device is compressed using a high compression rate corresponding to the data type.

[0026] Embodiment 1 Step 1: Define a specific value range and weight for each factor affecting data compression: 1. Data Type: Text: 1; Image: 2; Audio: 3; Video: 4; 2. Data size: (unit: MB): Less than 1MB: 1; 1MB to 10MB: 2; More than 10MB: 3; 3. Data Importance: Low: 1; Medium: 2; High: 3; 4. Urgency: Not urgent: 1; Normal: 2; Urgent: 3; 5. Network bandwidth (unit: Mbps): Less than 1Mbps: 3; 1Mbps to 10Mbps: 2; More than 10Mbps: 1 6. Network delay (unit: milliseconds): Less than 100ms: 1; 100ms to 500ms: 2; More than 500ms: 3 Step 2: Calculate the compression evaluation value; Step 3: Compare the calculated compression evaluation value with the set compression interval value [2,4]: If the compression rating is less than or equal to 2, a lower compression ratio (e.g., 25%) is used; If the compression rating is greater than 2 and less than or equal to 4, use a medium compression ratio (e.g., 50%); If the compression rating is greater than 4, a higher compression ratio (for example, 75%) is used.

[0027] In practical applications, the type of data (such as text, images, audio, video, etc.) has a significant impact on the effectiveness of the compression algorithm. For example, text data usually has a higher compression ratio, while video and image data have already been compressed to a certain extent, so there is less room for further compression. Therefore, there is a practical basis for assigning different weights to these data types. Data size is also a key factor affecting compression decisions. Compressing larger data files before transmission can significantly reduce transmission time and network congestion; The importance and urgency of data are unique considerations in the medical field. For high-importance data, the compression process may need to adopt a conservative strategy to ensure the integrity and accuracy of the data; while high-urgency data needs to be transmitted quickly, and some compression rates may be sacrificed to reduce processing time. Network bandwidth and latency considerations are common factors in network engineering, which directly affect the efficiency and stability of data transmission; This method of dynamically adjusting the compression rate can bring the following benefits: On the one hand, by adjusting the compression rate according to network conditions, available bandwidth can be effectively utilized to reduce data transmission delays and packet loss caused by network congestion, especially in environments with limited bandwidth or unstable network conditions; on the other hand, dynamic compression can intelligently adjust the transmission strategy according to the urgency and importance of the data, ensuring that urgent data arrives quickly while maintaining the quality and integrity of important data; on the other hand, in a medical environment, timely access to medical data is crucial for medical decision-making. Optimizing data transmission can reduce waiting time and improve the response speed and efficiency of medical services.

[0028] In a possible design, the operation process of the mother medical device adjusting the sub-unit further includes the following steps: Based on the amount of data generated per second by the parent medical device, the sampling rate of the medical data to be sent, and the proportion of each data type in the medical data to be sent; The amount of raw data corresponding to each data type is equal to the amount of data generated per second by the parent medical device multiplied by the proportion of each data type in the medical data to be sent; Then, the amount of data after feature extraction corresponding to each data type is equal to the original data amount corresponding to each data type multiplied by the feature extraction efficiency of each data type, and the multiplication result is divided by the number 100 to calculate the amount of data after feature extraction corresponding to each data type; Obtain the compression ratio of each data type corresponding to the medical data to be sent by the parent medical device, multiply the amount of data corresponding to each data type after feature extraction by the compression ratio of each data type corresponding to the medical data to be sent by the parent medical device, and calculate the compressed data amount corresponding to each data type by dividing the multiplication result by the number 100. ; The set transmission data volume per second corresponding to the medical data to be sent by the mother medical device is calculated in this way ,y is the number of each data type, y=1,2,..Y,t is the period of aggregation of medical data in the time window, in seconds.

[0029] In medical equipment and remote monitoring systems, the amount of data is usually very large, especially data from high-resolution medical imaging equipment and continuous monitoring equipment (such as electrocardiogram, electroencephalogram, etc.). The real-time processing and transmission of these data places high demands on network bandwidth and processing power. Without compressing or optimizing these data, directly transmitting raw data will cause significant delays, affecting the quality and efficiency of medical services; By preprocessing the data in an edge computing environment, including feature extraction and compression, the amount of data that needs to be transmitted can be significantly reduced. For example, extracting and transmitting only the key features of the electrocardiogram (such as heart rate, QRS waveform, etc.) instead of the entire original waveform can significantly reduce the amount of data while retaining information that is critical to diagnosis. This approach not only reduces the burden on the network, but also speeds up data processing and analysis, improving response time; In addition, data compression technology can further reduce the size of data without significantly losing the quality of information. In the field of medical imaging, for example, the use of efficient image compression algorithms can significantly reduce the size of data while maintaining image quality. Data aggregation (such as aggregating data within a certain time window) is also an effective data reduction strategy, which reduces the burden on the network by reducing the number of packets sent per second.

[0030] In a possible design, the operation process of the parent medical device adjusting the sub-unit also includes: Obtain each network status parameter value corresponding to each selectable transmission path, wherein each network status parameter value is divided into available bandwidth, packet loss rate and delay, and number each selectable transmission path in sequence as 1, 2, ..f, ...F; The adaptive score of each data type corresponding to each selectable transmission path is calculated , f is the number of each selectable transmission path, f = 1,2,...F; They are preset equalization factors, which are used to adjust the influence weight of each parameter. The available bandwidth, packet loss rate and delay of the selectable transmission path for the fth item; are the maximum possible values ​​of available bandwidth, packet loss rate, delay and priority score, used for normalization. Score the priority of the f-th selectable transmission path for the y-th data type; Bandwidth: High bandwidth generally means faster data transmission speed, which is especially important for large data volumes or medical data that requires high-speed transmission.

[0031] Latency: Low latency is critical for real-time medical applications, such as remote surgery or real-time monitoring.

[0032] Packet loss rate: Low packet loss rate is critical to ensure the integrity and reliability of data transmission, especially when transmitting critical medical information.

[0033] Priority: Some data may have a higher priority than other data, for example, data about medical emergencies should be transmitted first.

[0034] Normalization is necessary because it can convert various parameters of different magnitudes to the same scale, so that the weight distribution is more reasonable. For example, bandwidth may be measured in Mbps, latency in ms, and packet loss rate in a percentage. In this way, the selectable transmission path with the highest adaptive score corresponding to each data type is selected as the transmission path corresponding to each data type.

[0035] Based on the needs of network performance optimization, the use of multi-path transmission technology can significantly improve the reliability and efficiency of data transmission; in practical applications, different types of data (such as text, images, videos, etc.) may have different sensitivities to bandwidth, delay, and packet loss rate. For example, video streaming requires higher bandwidth and lower delay, while file transfer may have a higher tolerance for delay but is more sensitive to packet loss rate. By evaluating the performance of different transmission paths, such as bandwidth capacity, delay time, error rate, etc., and taking these parameters into account in an adaptive score, the optimal transmission path can be selected for each data type; It can dynamically adapt to changes in network conditions and optimize the data transmission process, thereby enhancing the robustness and flexibility of the entire system. For example, when the network is congested or a path fails, the system can automatically switch to other paths with better performance to avoid interruptions or significant delays in data transmission. In addition, this multi-path selection strategy can also balance the network load and avoid over-reliance on a single path, thereby reducing the occurrence of bottlenecks and improving the overall network utilization and performance.

[0036] In one possible design, the operation process of the sub-medical device adjustment sub-unit includes the following steps: The equipment operating parameter of the sub-medical equipment is the buffer capacity; Based on the total amount of medical data to be sent by the parent medical device , and parameter values ​​of network delay; Calculate the amount of data that a medical device can process within the network latency , is the parameter value of network delay, v is the data processing speed of the sub-medical device; Determine the buffer capacity of the sub-medical device from this , B is the buffer capacity of the sub-medical equipment, The buffer capacity correction factor is set.

[0037] Example 2 See also Figure 2 As shown, a method for intelligent monitoring of medical equipment service status based on big data includes the following steps: Step 1: Collect medical data through the parent medical device and send it to the child medical device, and preset the waiting time for the parent medical device to send the medical data; Step 2: predicting the time at which the child medical device receives the medical data to be sent from the parent medical device; Step 3: based on the waiting time of the medical data to be sent by the parent medical device and the receiving time of the medical data to be sent by the child medical device from the parent medical device, determine whether there is a data transmission delay between the parent and child medical devices; if so, execute step 4, otherwise, exit the method loop; Step 4: Dynamically adjust the equipment operating parameters of the parent and child medical devices respectively.

[0038] Example 3 A big data-based intelligent monitoring device for medical equipment service status, comprising a processor, a memory and a communication bus; The memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, it is executed to implement a medical equipment service status intelligent monitoring system based on big data as described in the present invention.

[0039] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0040] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0041] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A medical equipment service status intelligent monitoring system based on big data, characterized in that: It includes a sending time setting module, a receiving time estimation module, a transmission delay judgment module and a device delay adjustment module, and the above modules are connected by wired and / or wireless connection to realize data transmission between the modules; A sending time setting module, which collects medical data through the parent medical device and sends it to the child medical device, and presets the waiting time for the parent medical device to send the medical data; A receiving time prediction module, used to predict the receiving time of the child medical device receiving the corresponding medical data to be sent from the parent medical device; A transmission delay judgment module, based on the waiting time of the medical data to be sent by the parent medical device and the receiving time of the medical data to be sent by the child medical device from the parent medical device, judges whether there is a data transmission delay between the child and parent medical devices; if so, executes the device delay adjustment module; The equipment delay adjustment module dynamically adjusts the equipment operating parameters of the parent and child medical devices respectively.

2. According to the big data-based intelligent monitoring system for medical equipment service status according to claim 1, it is characterized in that: Predict the time when the child medical device receives the corresponding medical data sent by the parent medical device. The specific prediction process is as follows: Obtain the delayed reception time of each medical data received by the sub-medical device from the device database, calculate it, and obtain the average delayed time of the sub-medical device corresponding to the previous data transmission ; Using the calculation formula , predict the time it takes for the child medical device to receive the corresponding medical data sent by the parent medical device , The waiting time for sending medical data corresponding to the mother medical device. is the predicted transmission delay duration, where It is calculated by the following prediction model: The prediction model is , thereby predicting the predicted transmission delay time , are the weight factors set respectively, Used to adjust the impact of the average delay time of previous data transmission on the prediction. Used to adjust the impact of data volume and bandwidth on transmission time, Used to adjust the impact of network status factors on prediction, is the random noise term, The total amount of medical data that the parent medical device wants to send. is the network bandwidth, is the network status factor.

3. According to the big data-based intelligent monitoring system for medical equipment service status according to claim 1, it is characterized in that: The operation logic of the transmission delay judgment module is: Get the floor numbers of the parent medical equipment and the child medical equipment respectively, and record them as and ; Calculate the vertical distance between parent and child medical devices , n' is the actual vertical distance between each floor; Based on the horizontal distance between parent and child medical devices , analyze and obtain the actual distance between the parent and child medical devices ; Then calculate the theoretical transmission time between parent and child medical devices , T' is the standard speed of signal propagation, is the preset transmission time error correction factor; The actual transmission time between the parent medical device and the child medical device is obtained by performing a difference operation on the waiting time for sending the medical data to be sent by the parent medical device and the receiving time of the medical data to be sent by the child medical device from the parent medical device; Synchronously calculating the difference between the actual transmission time between the parent and child medical devices and the theoretical transmission time between the parent and child medical devices, and recording the difference result as the transmission time difference between the parent and child medical devices; The transmission time difference between the parent and child medical devices is compared with the preset maximum allowable transmission time upper limit threshold. If the former is greater than the latter, it is determined that there is a data transmission delay between the parent and child medical devices; otherwise, there is no data transmission delay between the parent and child medical devices.

4. According to the big data-based intelligent monitoring system for medical equipment service status according to claim 1, it is characterized in that: The equipment delay adjustment module is divided into a parent medical equipment adjustment subunit and a child medical equipment adjustment subunit.

5. According to claim 4, a medical equipment service status intelligent monitoring system based on big data is characterized in that: The operation logic of the parent medical device adjustment sub-unit is: Obtain the data volume, data importance and data urgency of each data type corresponding to the medical data to be sent by the parent medical device; synchronously obtain the parameter values ​​of the current network bandwidth and network delay respectively; Compare the data volume, data importance and data urgency of each data type corresponding to the medical data to be sent by the parent medical device with the numerical ranges corresponding to the preset weight levels, thereby determining the weight level of the factors affecting data compression, such as the data volume, data importance and data urgency of each data type corresponding to the medical data to be sent by the parent medical device; The weight levels of factors affecting data compression corresponding to network bandwidth and network delay are obtained in the same manner according to the method of obtaining the weight levels of factors affecting data compression corresponding to the data volume, data importance and data urgency of each data type of medical data to be sent by the parent medical device; The compression evaluation value of each data type corresponding to the medical data to be sent by the mother medical device is calculated accordingly. , C5 and C6 represent the weight levels of factors affecting data compression, such as network bandwidth and network delay. are the weight levels of factors affecting data compression corresponding to the data volume, data importance and data urgency of the y-th data type, and y is the number of each data type; The compression evaluation value of each data type corresponding to the medical data to be sent by the parent medical device And set the compression interval value Compare and find out if there is a mother medical device whose compression evaluation value of a certain data type corresponding to the medical data to be sent is less than or equal to , then the mother medical device corresponding to the medical data to be sent uses a low compression rate to compress the data type; if there is a mother medical device corresponding to the medical data to be sent corresponding to a certain data type compression evaluation value greater than and less than or equal to , then the mother medical device corresponding to the medical data to be sent is compressed using the compression rate corresponding to the data type; if there is a mother medical device corresponding to the medical data to be sent corresponding to a certain data type compression evaluation value greater than , the medical data to be sent by the mother medical device is compressed using a high compression rate corresponding to the data type.

6. The big data-based intelligent monitoring system for medical equipment service status according to claim 5 is characterized in that: The operation process of adjusting the sub-unit of the parent medical device also includes the following steps: Based on the amount of data generated per second by the parent medical device, the sampling rate of the medical data to be sent, and the proportion of each data type in the medical data to be sent; The amount of raw data corresponding to each data type is equal to the amount of data generated per second by the parent medical device multiplied by the proportion of each data type in the medical data to be sent; Then, the amount of data after feature extraction corresponding to each data type is equal to the original data amount corresponding to each data type multiplied by the feature extraction efficiency of each data type, and the multiplication result is divided by the number 100 to calculate the amount of data after feature extraction corresponding to each data type; Obtain the compression ratio of each data type corresponding to the medical data to be sent by the parent medical device, multiply the amount of data corresponding to each data type after feature extraction by the compression ratio of each data type corresponding to the medical data to be sent by the parent medical device, and calculate the compressed data amount corresponding to each data type by dividing the multiplication result by the number 100. ; The set transmission data volume per second corresponding to the medical data to be sent by the mother medical device is calculated in this way ,y is the number of each data type, y=1,2,..Y,t is the period of aggregation of medical data in the time window, in seconds.

7. The big data-based intelligent monitoring system for medical equipment service status according to claim 6 is characterized in that: The operation process of adjusting the sub-unit of the parent medical device also includes: Obtain each network status parameter value corresponding to each selectable transmission path, wherein each network status parameter value is divided into available bandwidth, packet loss rate and delay, and number each selectable transmission path in sequence as 1, 2, ..f, ...F; The adaptive score of each data type corresponding to each selectable transmission path is calculated , f is the number of each selectable transmission path, f = 1,2,...F; They are preset equalization factors, which are used to adjust the influence weight of each parameter. The available bandwidth, packet loss rate and delay of the selectable transmission path for item f; are the maximum possible values ​​of available bandwidth, packet loss rate, delay and priority score, used for normalization. Score the priority of the f-th selectable transmission path for the y-th data type; In this way, the selectable transmission path with the highest adaptive score corresponding to each data type is selected as the transmission path corresponding to each data type.

8. The big data-based intelligent monitoring system for medical equipment service status according to claim 7, characterized in that: The operation process of the sub-medical equipment adjustment sub-unit includes the following steps: The equipment operating parameter of the sub-medical equipment is the buffer capacity; Based on the total amount of medical data to be sent by the parent medical device , and parameter values ​​of network delay; Calculate the amount of data that a medical device can process within the network latency , is the parameter value of network delay, v is the data processing speed of the sub-medical device; Determine the buffer capacity of the sub-medical device from this , B is the buffer capacity of the sub-medical equipment, The buffer capacity correction factor is set.

9. A method for intelligent monitoring of medical equipment service status based on big data, characterized in that: The intelligent monitoring system for medical equipment service status based on big data is implemented based on any one of claims 1 to 8, and includes: Step 1: Collect medical data through the parent medical device and send it to the child medical device, and preset the waiting time for the parent medical device to send the medical data; Step 2: predicting the time at which the child medical device receives the medical data to be sent from the parent medical device; Step 3: based on the waiting time of the medical data to be sent by the parent medical device and the receiving time of the medical data to be sent by the child medical device from the parent medical device, determine whether there is a data transmission delay between the parent and child medical devices; if so, execute step 4, otherwise, exit the method loop; Step 4: Dynamically adjust the equipment operating parameters of the parent and child medical devices respectively.

10. An intelligent monitoring device for medical equipment service status based on big data, characterized in that: It is implemented based on a big data-based intelligent monitoring system for medical equipment service status as described in any one of claims 1 to 8, comprising a processor, a memory and a communication bus; The memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, it is executed to implement a medical equipment service status intelligent monitoring system based on big data as described in any one of claims 1-8.

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