技术领域technical field
本发明涉及神经重症监测，尤其是一种神经重症监测装置。The invention relates to neurocritical monitoring, in particular to a neurocritical monitoring device.
背景技术Background technique
在神经重症监测领域的技术，比如，中国实用新型CN201621229538.0公开的神经重症数据采集设备；其包括一机架，所述机架上具有一机箱，所述机箱内安装有一数据处理装置，所述数据处理装置连接有一插件式接入装置；所述机架上还安装有一显示装置，所述显示装置与所述数据处理装置连接。所述机架包括：一立杆安装在一支撑脚上，所述显示装置安装在所述立杆的顶部；所述机箱固定在所述立杆的侧壁。所述插件式接入装置包括：接入口、多个插件槽、接出口。多个所述插件槽内分别固定有：颅内压监测仪信号转换模块，床旁监护仪信号转换模块。所述机箱侧壁开设有插入口，所述多个插件槽安装在所述插入口内面向所述插入口。上述技术方案，通过设置插件式接入装置，实现多种传感器的连接。中国实用新型CN201621229680.5公开的神经重症监控系统，其包括一机体，所述机体内安装有数据处理装置、连接装置，所述数据处理装置与所述连接装置连接，所述连接装置具有多个接口；所述数据处理装置与显示装置、联网设备连接，所述数据处理装置还具有扩展接口；所述数据处理装置通过联网设备与服务器通信。多个接口有：颅内压监测仪信号接口、床旁监护仪信号接口、经颅多普勒信号接口、脑电图信号接口、脑温模块接口、脑氧模块接口、脑部微透析模块接口、脑血流模块接口。所述数据处理装置还连接有摄像头。所述扩展接口连接有一打印机。所述机体包括：一显示器通过一立杆安装在一底座上，所述立杆的侧壁安装有一机箱，所述数据处理装置、所述连接装置均安装在所述机箱内，所述数据处理装置与所述显示器连接。所述立杆的一侧还安装有操作台，所述操作台上具有一手柄。所述显示装置为触摸屏。The technology in the field of neurological critical monitoring, for example, the neurological critical data acquisition equipment disclosed by the Chinese utility model CN201621229538.0; it includes a rack, the rack has a chassis, and a data processing device is installed in the chassis, so the A plug-in access device is connected to the data processing device; a display device is also installed on the rack, and the display device is connected to the data processing device. The rack includes: a vertical pole is installed on a support foot, the display device is installed on the top of the vertical pole; the chassis is fixed on the side wall of the vertical pole. The plug-in access device includes: an access port, a plurality of plug-in slots, and an access port. A plurality of the plug-in slots are respectively fixed with: an intracranial pressure monitor signal conversion module and a bedside monitor signal conversion module. The side wall of the chassis is provided with an insertion opening, and the plurality of plug-in slots are installed in the insertion opening and face the insertion opening. In the above technical solution, the connection of various sensors is realized by setting the plug-in access device. The neurological intensive care monitoring system disclosed by Chinese utility model CN201621229680.5 includes a body, a data processing device and a connecting device are installed in the body, the data processing device is connected with the connecting device, and the connecting device has a plurality of an interface; the data processing device is connected to a display device and a networking device, and the data processing device also has an expansion interface; the data processing device communicates with the server through the networking device. Multiple interfaces include: intracranial pressure monitor signal interface, bedside monitor signal interface, transcranial Doppler signal interface, EEG signal interface, brain temperature module interface, cerebral oxygen module interface, brain microdialysis module interface , Cerebral blood flow module interface. The data processing device is also connected with a camera. The expansion interface is connected with a printer. The body includes: a display is mounted on a base through a pole, a chassis is installed on the side wall of the pole, the data processing device and the connecting device are installed in the chassis, and the data processing device is installed in the chassis. A device is connected to the display. An operating table is also installed on one side of the vertical pole, and the operating table is provided with a handle. The display device is a touch screen.
上述技术方案，目的是解决了原有技术中监控系统获取数据不全面的问题，通过具有多个传感器接口的连接装置，实现多种传感器数据向数据处理装置导入。中国发明CN200610061231.9公开的神经重症监护系统及实现人体多参数信号同步监护的方法，神经重症监护系统，包括信号采集系统、以及将采集到的信号进行处理和显示的处理/显示系统，所述信号采集系统通过接口与处理/显示系统相连；所述信号采集系统包括：用于采集TCD信号的TCD模块，以及，至少一用于采集EEG信号的EEG模块和/或用于采集多参数生命体征信号的MP模块；所述TCD模块、EEG模块和MP模块各包括至少一信号采集通路；分别与各信号采集通路输出端相连的多个采样保持电路，该采样保持电路对各采集通路输出的的信号进行同步采样；多通道或多片A/D转换器，所述A/D转换器入端分别与采样保持电路输出端相连，用于将采样输出的模拟信号转换成数字信号；与接口通讯连接的控制模块，该模块用于产生用于控制A/D转换器进行模数转换的A/D转换控制信号，还产生与同一时钟脉冲同步的采样控制信号，所述采样控制信号用于控制每一采样保持电路同步获取TCD信号、EEG信号和/或MP信号的一个采样点。实现人体多参数信号同步监护的方法，包括如下步骤：采集TCD信号以及EEG信号和/或MP信号，设置与一时钟脉冲同步的采样控制信号；用所述采样控制信号控制各信号通道对TCD信号、EEG信号和MP信号进行同步采样，使各信号通道的采样点相同；将同步采样获取的模拟信号样点进行A/D转换，得到数字信号后缓存输出；由处理/显示系统对该数字信号进行处理和同屏显示。该技术目的是对TCD血流信号和EEG电生理信号及MP生命体征信号的同步采集和同屏显示，实现对TCD血流信号和EEG电生理信号及MP生命体征信号的同步监护，为临床神经重症病人提供了全面而有价值的监护诊断信息，希望提高对重症患者的监护质量和治疗水平，降低了死亡率。The purpose of the above technical solution is to solve the problem of incomplete data acquisition by the monitoring system in the prior art, and to import various sensor data into the data processing device through the connection device with multiple sensor interfaces. Chinese invention CN200610061231.9 discloses a neurointensive care system and a method for realizing synchronous monitoring of human body multi-parameter signals. The neurointensive care system includes a signal acquisition system and a processing/display system for processing and displaying the acquired signals. The said The signal acquisition system is connected to the processing/display system through an interface; the signal acquisition system includes: a TCD module for acquiring TCD signals, and at least one EEG module for acquiring EEG signals and/or for acquiring multi-parameter vital signs The MP module of the signal; the TCD module, the EEG module and the MP module each include at least one signal acquisition path; a plurality of sample and hold circuits respectively connected with the output ends of the signal acquisition paths, the sample and hold circuits are used for the output of each acquisition path. Simultaneous sampling of signals; multi-channel or multi-chip A/D converters, the input ends of the A/D converters are respectively connected with the output ends of the sampling and hold circuit, and are used to convert the sampled and output analog signals into digital signals; communicate with the interface A connected control module, the module is used to generate an A/D conversion control signal for controlling the A/D converter to perform analog-to-digital conversion, and also generate a sampling control signal synchronized with the same clock pulse, and the sampling control signal is used to control Each sample-and-hold circuit simultaneously acquires one sample point of the TCD signal, EEG signal and/or MP signal. A method for realizing synchronous monitoring of human body multi-parameter signals, comprising the following steps: collecting TCD signals, EEG signals and/or MP signals, and setting a sampling control signal synchronized with a clock pulse; using the sampling control signal to control each signal channel to the TCD signal , EEG signal and MP signal are synchronously sampled, so that the sampling points of each signal channel are the same; A/D conversion is performed on the analog signal sample points obtained by synchronous sampling, and the digital signal is obtained and then buffered and output; the digital signal is processed/displayed by the system. Process and display on the same screen. The purpose of this technology is to synchronously collect and display the TCD blood flow signal, EEG electrophysiological signal and MP vital sign signal on the same screen, realize the synchronous monitoring of TCD blood flow signal, EEG electrophysiological signal and MP vital sign signal, and provide clinical neurological Critically ill patients provide comprehensive and valuable monitoring and diagnostic information, and we hope to improve the quality of care and treatment for critically ill patients and reduce mortality.
上述的现有技术中的涉及对神经重症的监测装置基本都是简单的扩充了传感器的类型和传感器的数据采集端口或者是将多种传感器的信息采集同步进行，其实质全部都是对神经重症的监测数据的简单采集和再现；实质上现有技术中对传感器的数据的处理甚至是对多类型传感数据之间的联系获取更加上位的数据处理需求更大，根本目的还是为了提高监测的智能化和高效化，然而目前还没有技术可以满足这种需要。The above-mentioned monitoring devices for neurological diseases in the prior art basically simply expand the types of sensors and the data collection ports of the sensors or synchronize the information collection of various sensors, and all of them are in essence for neurological diseases. In fact, the processing of sensor data in the prior art even requires more advanced data processing to obtain the connection between multiple types of sensor data, and the fundamental purpose is to improve monitoring. Intelligent and efficient, however, there is currently no technology to meet this need.
发明内容SUMMARY OF THE INVENTION
为了克服现有的技术存在的不足,本发明提供一种神经重症监测装置，该神经重症监测装置本申请解决了，现有技术中对传感器的数据的处理甚至是对多类型传感数据之间的联系获取更加上位的数据处理需求，提高了监测的智能化和高效化。In order to overcome the deficiencies of the existing technology, the present invention provides a neurological critical monitoring device, which solves the problem in the present application that the processing of sensor data in the prior art is even between multiple types of sensor data. contact to obtain more high-level data processing requirements, which improves the intelligence and efficiency of monitoring.
本发明解决其技术问题所采用的技术方案是：The technical scheme adopted by the present invention to solve its technical problems is:
一种神经重症监测装置包括，监测输入通道，用于获取原始的多类型传感数据并提供给监测分析处理单元作为监测的依据，还用于获取原始的多类型传感数据并提供给监测分析处理单元作为监测分析单元更新的依据；监测分析处理单元，用于从监测输入通道获取原始的多类型传感数据并且分析多类型传感数据之间的联系，由多类型传感数据之间的联系获取更加上位的数据，并且将该更加上位的数据以中间量的形式发送给监测反馈单元；还用于获取原始的多类型传感数据完成更新；监测分析处理单元至少包括模拟数据库，模拟数据库用于支持由多类型传感数据之间的联系获取更加上位的数据；监测反馈输出单元，用于将中间量解析为可理解的数据，并展示多类型传感数据之间的联系的结果；监测分析处理单元分别与监测输入通道、监测反馈输出单元相连接；所述的中间参量具体是特定形式的数据表征量。A neurocritical monitoring device includes a monitoring input channel for acquiring original multi-type sensing data and providing it to a monitoring analysis processing unit as a basis for monitoring, and also for acquiring original multi-type sensing data and providing it for monitoring and analysis. The processing unit is used as the basis for updating the monitoring and analysis unit; the monitoring and analysis processing unit is used to obtain the original multi-type sensing data from the monitoring input channel and analyze the connection between the multi-type sensing data. Contact to obtain higher-level data, and send the higher-level data to the monitoring feedback unit in the form of an intermediate quantity; it is also used to obtain the original multi-type sensor data to complete the update; the monitoring and analysis processing unit at least includes a simulation database, a simulation database It is used to support the acquisition of higher-level data from the connection between multiple types of sensor data; the monitoring feedback output unit is used to parse the intermediate quantity into understandable data, and to display the results of the connection between multiple types of sensor data; The monitoring analysis processing unit is respectively connected with the monitoring input channel and the monitoring feedback output unit; the intermediate parameter is specifically a data representation quantity in a specific form.
在一个优选或可选地实施例中，所述的多类型传感包括颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感。In a preferred or optional embodiment, the multi-type sensing includes intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, electroencephalogram electrical signal sensing, brain temperature sensing , Cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, brain EMG signal sensing.
在一个优选或可选地实施例中，监测分析处理单元包括更新模块和分析模块，所述的更新模块用于，将原始的多类型传感数据处理为中间参量，将统计获取的多类型传感数据映射的被监控者的意思表达数据处理为第二参量，并且将中间参量的数据格式表征为矩阵数据并具体表征为矩阵参量群；处理中间参量的矩阵参量群，给每一个矩阵参量群中的中间参量分配一个数值且每一个矩阵参量群的所有中间参量总和为一，并且将上述的矩阵参量群分为模拟群和鉴定群；所述的鉴定群标记有第二参量；监测分析处理单元的模拟数据库内至少设置多个模拟分库，分析模块用于，为模拟数据库配置基础的参数，然后将矩阵参量群输入到模拟数据库并获取第二参量；更新模块还用于，使用模拟群在模拟数据库运算并且改变模拟数据库的参数，然后使用鉴定群鉴定模拟数据库的模拟正确系数；然后至少改变一种模拟分库的参数，该模拟分库的参数配置若干种，也即获取若干个新的模拟分库，并且使用鉴定群鉴定每一个新的模拟分库的模拟正确系数，并且选择正确系数最高的一个新的模拟分库的来代替原有的改变参数的模拟分库，然后再使用鉴定群鉴定模拟数据库的模拟正确系数，判断该模拟正确系数是否高于上一次的模拟数据库的模拟正确系数，如果否则终止模拟，如果是则继续循环模拟，直到获取正确系数最高的模拟数据库；所述的使用模拟群在模拟数据库运算具体是使用模拟数据库获取模拟群数据的上位特征并改变模拟数据库参数，模拟数据库参数实际映射模拟群数据的上位特征；分析模块还用于，将原始的多类型传感数据处理为中间参量，将中间参量输入到模拟数据库然后启动模拟获取中间变量对应的第二参量。In a preferred or optional embodiment, the monitoring and analysis processing unit includes an update module and an analysis module, and the update module is used to process the original multi-type sensor data into intermediate parameters, and convert the statistically obtained multi-type transmission data The meaning expression data of the monitored person mapped by the sensory data is processed as the second parameter, and the data format of the intermediate parameter is represented as matrix data and specifically represented as a matrix parameter group; the matrix parameter group of the intermediate parameter is processed, and each matrix parameter group is given The intermediate parameter in is assigned a value and the sum of all intermediate parameters of each matrix parameter group is one, and the above-mentioned matrix parameter group is divided into a simulation group and an identification group; the identification group is marked with a second parameter; monitoring and analysis processing At least a plurality of simulation sub-databases are set in the simulation database of the unit, and the analysis module is used to configure basic parameters for the simulation database, and then input the matrix parameter group into the simulation database to obtain the second parameter; the update module is also used to use the simulation group Calculate and change the parameters of the simulation database, and then use the identification group to identify the simulation correct coefficient of the simulation database; then change the parameters of at least one simulation sub-database, and configure several parameters of the simulation sub-database, that is, obtain several new and use the identification group to identify the simulation correct coefficient of each new simulation sub-library, and select a new simulation sub-library with the highest correct coefficient to replace the original simulation sub-library with changed parameters, and then use The identification group identifies the simulation correct coefficient of the simulation database, and judges whether the simulation correct coefficient is higher than the simulation correct coefficient of the previous simulation database, if not, terminate the simulation, and if so, continue to loop the simulation until the simulation database with the highest correct coefficient is obtained; The above-mentioned operation using the simulation group in the simulation database is to use the simulation database to obtain the upper-level characteristics of the simulation group data and change the simulation database parameters, and the simulation database parameters actually map the upper-level characteristics of the simulation group data; the analysis module is also used to convert the original multi-type data. The sensory data is processed into intermediate parameters, the intermediate parameters are input into the simulation database, and then the simulation is started to obtain the second parameters corresponding to the intermediate variables.
在一个优选或可选地实施例中，所述的模拟数据库采用卷积神经网络模型数据库，所述的模拟分库采用卷积层。In a preferred or optional embodiment, the simulation database adopts a convolutional neural network model database, and the simulation sub-database adopts a convolution layer.
在一个优选或可选地实施例中，将原始的多类型传感数据处理为中间参量，包括：对于每一单位的多类型传感数据，将多类型传感数据数值化，然后给每一种传感数据分配一个权重系数，将所有的数值化的传感数据排列为矩阵数据。In a preferred or optional embodiment, processing the original multi-type sensory data into intermediate parameters includes: for each unit of multi-type sensory data, digitizing the multi-type sensory data, and then for each unit All kinds of sensor data are assigned a weight coefficient, and all the numerical sensor data are arranged as matrix data.
在一个优选或可选地实施例中，将原始的多类型传感数据处理为中间参量还包括为每一种传感数据分配一个动态的权重系数。In a preferred or optional embodiment, processing the original multi-type sensing data into intermediate parameters further includes assigning a dynamic weight coefficient to each type of sensing data.
在一个优选或可选地实施例中，将原始的多类型传感数据处理为中间参量还包括改变数值化的传感数据在矩阵数据中的排列顺序。In a preferred or optional embodiment, the processing of the original multi-type sensor data into intermediate parameters further includes changing the arrangement order of the digitized sensor data in the matrix data.
在一个优选或可选地实施例中，统计获取的多类型传感数据映射的被监控者的意思表达数据处理为第二参量，其中的第二参量表征有被监控者的意念需要。In a preferred or optional embodiment, the meaning expression data of the monitored person mapped from the multi-type sensory data obtained by statistics is processed as a second parameter, wherein the second parameter represents the need of the monitored person's mind.
在一个优选或可选地实施例中，第二参量还表征有被监控者的身体病理变化和急救需要。In a preferred or optional embodiment, the second parameter also characterizes the physical pathological changes and emergency needs of the monitored person.
本发明的有益效果是，本申请解决了“现有技术中对传感器的数据的处理甚至是对多类型传感数据之间的联系获取更加上位的数据处理需求”提高了监测的智能化和高效化。在具体实现上，通过建立的模拟数据库就可以实际用于对多类型传感数据之间的联系获取更加上位的数据，并具体通过将原始的多类型传感数据处理为中间参量，将统计获取的多类型传感数据映射的被监控者的意思表达数据处理为第二参量，并且将中间参量的数据格式表征为矩阵数据，然后通过对中间参量的处理尤其是通过模拟过程获取上位特征进而获取多类型传感数据之间的联系更加上位的数据。其中使用模拟数据库获取模拟群数据的上位特征并改变模拟数据库参数，更加可以不断优化和迭代模拟数据库，使得数据处理更加精准；模拟数据库采用卷积神经网络模型数据库，模拟分库采用卷积层以提高数据的处理的效率、成熟性并且降低成本。The beneficial effect of the present invention is that the present application solves "the processing of sensor data in the prior art and even the higher-level data processing requirements for obtaining the connection between multiple types of sensor data" and improves the intelligence and efficiency of monitoring. change. In terms of specific implementation, the established simulation database can actually be used to obtain more high-level data for the connection between multi-type sensor data, and specifically by processing the original multi-type sensor data as intermediate parameters, the statistical acquisition The meaning expression data of the monitored person mapped by the multi-type sensor data is processed as the second parameter, and the data format of the intermediate parameter is represented as matrix data, and then the upper feature is obtained by processing the intermediate parameter, especially through the simulation process. The connection between multi-type sensor data is more high-level data. Among them, using the simulation database to obtain the upper characteristics of the simulation group data and changing the parameters of the simulation database can continuously optimize and iterate the simulation database, making the data processing more accurate; the simulation database adopts the convolutional neural network model database, and the simulation sub-database adopts the convolution layer to Improve the efficiency, sophistication and cost reduction of data processing.
具体实施方式Detailed ways
具体实施中，本申请的实施例包括：In specific implementation, the embodiments of the present application include:
监测输入通道，其用于获取原始的多类型传感数据并提供给监测分析处理单元作为监测的依据，还用于获取原始的多类型传感数据并提供给监测分析处理单元作为监测分析单元更新的依据；The monitoring input channel is used to obtain the original multi-type sensing data and provide it to the monitoring analysis and processing unit as the basis for monitoring, and is also used to obtain the original multi-type sensor data and provide it to the monitoring analysis and processing unit as the monitoring and analysis unit update. basis;
监测分析处理单元，其用于从监测输入通道获取原始的多类型传感数据并且分析多类型传感数据之间的联系，由多类型传感数据之间的联系获取更加上位的数据，并且将该更加上位的数据以中间量的形式发送给监测反馈单元；还用于获取原始的多类型传感数据完成更新；The monitoring analysis processing unit is used to obtain the original multi-type sensing data from the monitoring input channel and analyze the connection between the multi-type sensor data, obtain higher-level data from the connection between the multi-type sensor data, and convert the multi-type sensor data. The higher-level data is sent to the monitoring feedback unit in the form of an intermediate quantity; it is also used to obtain the original multi-type sensing data to complete the update;
监测分析处理单元至少包括模拟数据库，模拟数据库用于支持由多类型传感数据之间的联系获取更加上位的数据；The monitoring analysis and processing unit includes at least an analog database, and the analog database is used to support the acquisition of higher-level data by the connection between multiple types of sensor data;
监测反馈输出单元，其用于将中间量解析为可理解的数据，并展示多类型传感数据之间的联系的结果；Monitoring feedback output units, which are used to parse intermediate quantities into understandable data, and demonstrate the results of connections between multiple types of sensory data;
监测分析处理单元分别与监测输入通道、监测反馈输出单元相连接；所述的中间参量具体是特定形式的数据表征量。The monitoring analysis processing unit is respectively connected with the monitoring input channel and the monitoring feedback output unit; the intermediate parameter is specifically a data representation quantity in a specific form.
在实施中，监测分析单元的更新具体是，所述的监测输入通道获取原始的多类型传感数据并提供给监测分析处理单元作为监测分析单元更新的依据，然后，监测分析处理单元获取原始的多类型传感数据完成更新。In implementation, the update of the monitoring and analysis unit is specifically that the monitoring input channel obtains the original multi-type sensing data and provides it to the monitoring and analysis processing unit as the basis for updating the monitoring and analysis unit, and then the monitoring and analysis processing unit obtains the original Multiple types of sensor data are updated.
监测分析处理单元的监测具体是，所述的监测输入通道获取原始的多类型传感数据并提供给监测分析处理单元作为监测的依据，然后，监测分析处理单元，从监测输入通道获取原始的多类型传感数据并且分析多类型传感数据之间的联系，由多类型传感数据之间的联系获取更加上位的数据(监测分析处理单元的模拟数据库由多类型传感数据之间的联系获取更加上位的数据)，并且将该更加上位的数据以中间量的形式发送给监测反馈单元；监测反馈输出单元将中间量解析为可理解的数据，并展示多类型传感数据之间的联系的结果。The monitoring of the monitoring analysis processing unit is specifically that the monitoring input channel obtains the original multi-type sensing data and provides it to the monitoring analysis processing unit as the basis for monitoring, and then the monitoring analysis processing unit obtains the original multi-type sensor data from the monitoring input channel. Type sensor data and analyze the connection between the multi-type sensor data, and obtain higher-level data from the connection between the multi-type sensor data (the analog database of the monitoring and analysis processing unit is obtained by the connection between the multi-type sensor data The higher-level data), and the higher-level data is sent to the monitoring feedback unit in the form of an intermediate quantity; the monitoring feedback output unit parses the intermediate quantity into understandable data, and shows the relationship between multiple types of sensor data. result.
所述的多类型传感包括颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感；所以在具体实施中，监测分析单元的更新具体是，所述的监测输入通道获取原始的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据并提供给监测分析处理单元作为监测分析单元更新的依据，然后，监测分析处理单元获取原始的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据完成更新。The multi-type sensing includes intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, electroencephalogram electrical signal sensing brain temperature sensing, brain oxygen sensing, brain microdialysis transmission. Therefore, in the specific implementation, the update of the monitoring and analysis unit is specifically, the monitoring input channel obtains the original intracranial pressure monitor electrical signal sensing, transcranial pressure monitor Doppler electrical signal sensing, EEG electrical signal sensing, brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, brain EMG signal sensing data and provide monitoring and analysis The processing unit serves as the basis for updating the monitoring and analysis unit, and then the monitoring and analysis processing unit obtains the original intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, and EEG electrical signal sensing and brain temperature sensing. , Cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, and brain EMG signal sensing data have been updated.
监测分析处理单元的监测具体是，所述的监测输入通道获取原始的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据并提供给监测分析处理单元作为监测的依据，然后，监测分析处理单元，从监测输入通道获取原始的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据并且分析颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据之间的联系，由颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据之间的联系获取更加上位的数据(监测分析处理单元的模拟数据库由颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据之间的联系获取更加上位的数据)，并且将该更加上位的数据以中间量的形式发送给监测反馈单元；监测反馈输出单元将中间量解析为可理解的数据，并展示颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据之间的联系的结果。解决了“现有技术中对传感器的数据的处理甚至是对多类型传感数据之间的联系获取更加上位的数据处理需求”提高了监测的智能化和高效化。The monitoring of the monitoring analysis and processing unit is specifically, the monitoring input channel obtains the original intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, electroencephalogram electrical signal sensing, brain temperature sensing, Brain oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, brain EMG signal sensing data are provided to the monitoring analysis and processing unit as the basis for monitoring, and then the monitoring analysis and processing unit obtains the raw data from the monitoring input channel. Intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, EEG electrical signal sensing, brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing , Brain EMG signal sensing data and analysis of intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, EEG electrical signal sensing, brain temperature sensing, cerebral oxygen sensing, brain microscopic The connection between dialysis sensing, cerebral blood flow sensing, and brain electromyographic signal sensing data is determined by the electrical signal sensing of intracranial pressure monitor, transcranial Doppler electrical signal sensing, and electroencephalogram electrical signal sensing. The connection between brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, and brain electromyographic signal sensing data can obtain more upper-level data (the analog database of the monitoring analysis and processing unit is composed of intracranial Pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, EEG electrical signal sensing, brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, brain muscle The connection between the electrical signal sensing data obtains higher-level data), and sends the higher-level data to the monitoring feedback unit in the form of an intermediate quantity; the monitoring feedback output unit parses the intermediate quantity into understandable data, and displays Intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, EEG electrical signal sensing, brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, Results of the link between EMG sensor data. It solves "the processing needs of the sensor data in the prior art and even the more advanced data processing requirements for the connection between multiple types of sensor data", which improves the intelligence and efficiency of monitoring.
在具体实施中，监测分析处理单元包括更新模块和分析模块，所述的更新模块用于，将原始的多类型传感数据处理为中间参量，将统计获取的多类型传感数据映射的被监控者的意思表达数据处理为第二参量，并且将中间参量的数据格式表征为矩阵数据并具体表征为矩阵参量群；处理中间参量的矩阵参量群，给每一个矩阵参量群中的中间参量分配一个数值且每一个矩阵参量群的所有中间参量总和为1，并且将上述的矩阵参量群分为模拟群和鉴定群；所述的鉴定群标记有第二参量；监测分析处理单元的模拟数据库内至少设置多个模拟分库，分析模块用于，为模拟数据库配置基础的参数，然后将矩阵参量群输入到模拟数据库并获取第二参量；更新模块还用于，使用模拟群在模拟数据库运算并且改变模拟数据库的参数，然后使用鉴定群鉴定模拟数据库的模拟正确系数；然后至少改变一种模拟分库的参数，该模拟分库的参数配置若干种，也即获取若干个新的模拟分库，并且使用鉴定群鉴定每一个新的模拟分库的模拟正确系数，并且选择正确系数最高的一个新的模拟分库的来代替原有的改变参数的模拟分库，然后再使用鉴定群鉴定模拟数据库的模拟正确系数，判断该模拟正确系数是否高于上一次的模拟数据库的模拟正确系数，如果否则终止模拟，如果是则继续循环模拟，直到获取正确系数最高的模拟数据库；所述的使用模拟群在模拟数据库运算具体是使用模拟数据库获取模拟群数据的上位特征并改变模拟数据库参数，模拟数据库参数实际映射模拟群数据的上位特征；分析模块还用于，将原始的多类型传感数据处理为中间参量，将中间参量输入到模拟数据库然后启动模拟获取中间变量对应的第二参量；In a specific implementation, the monitoring and analysis processing unit includes an update module and an analysis module, and the update module is used to process the original multi-type sensor data into intermediate parameters, and map the statistically acquired multi-type sensor data to the monitored data. The meaning of the operator is to process the data as the second parameter, and characterize the data format of the intermediate parameter as matrix data and specifically represent it as a matrix parameter group; process the matrix parameter group of the intermediate parameter, and assign a and the sum of all intermediate parameters of each matrix parameter group is 1, and the above-mentioned matrix parameter group is divided into a simulation group and an identification group; the identification group is marked with a second parameter; the simulation database of the monitoring analysis processing unit contains at least one Set up multiple simulation sub-databases, the analysis module is used to configure basic parameters for the simulation database, and then input the matrix parameter group into the simulation database and obtain the second parameter; the update module is also used to use the simulation group to operate in the simulation database and change Simulate the parameters of the database, and then use the identification group to identify the simulation correct coefficients of the simulation database; then change the parameters of at least one simulation sub-database, and configure several parameters of the simulation sub-database, that is, obtain several new simulation sub-databases, and Use the identification group to identify the simulation correct coefficient of each new simulation sub-library, and select a new simulation sub-library with the highest correct coefficient to replace the original simulation sub-library with changed parameters, and then use the identification group to identify the simulation database. Simulation correct coefficient, judge whether the simulation correct coefficient is higher than the simulation correct coefficient of the previous simulation database, if not, terminate the simulation, if so, continue to loop the simulation until the simulation database with the highest correct coefficient is obtained; The simulation database operation is to use the simulation database to obtain the upper-level characteristics of the simulation group data and change the parameters of the simulation database, and the simulation database parameters actually map the upper-level characteristics of the simulation group data; the analysis module is also used to process the original multi-type sensor data into intermediate parameters, input the intermediate parameters into the simulation database and then start the simulation to obtain the second parameters corresponding to the intermediate variables;
监测分析处理单元的模拟数据库建立，至少如下步骤：The establishment of the simulation database of the monitoring analysis processing unit, at least the following steps:
更新模块将原始的多类型传感数据处理为中间参量，将统计获取的多类型传感数据映射的被监控者的意思表达数据处理为第二参量，并且将中间参量的数据格式表征为矩阵数据并具体表征为矩阵参量群；The update module processes the original multi-type sensor data as intermediate parameters, processes the monitored person's meaning expression data mapped from the multi-type sensor data obtained by statistics as the second parameter, and characterizes the data format of the intermediate parameters as matrix data And it is specifically characterized as a matrix parameter group;
更新模块处理中间参量的矩阵参量群，给每一个矩阵参量群中的中间参量分配一个数值且每一个矩阵参量群的所有中间参量总和为1，并且将上述的矩阵参量群分为模拟群和鉴定群：The update module processes the matrix parameter groups of the intermediate parameters, assigns a value to the intermediate parameters in each matrix parameter group and the sum of all intermediate parameters in each matrix parameter group is 1, and divides the above matrix parameter groups into simulation groups and identification groups. group:
所述的鉴定群标记有第二参量；The identification group is marked with a second parameter;
建立的模拟数据库内至少设置多个模拟分库，为模拟数据库配置基础的参数，然后将矩阵参量群输入到模拟数据库并获取第二参量；Set at least a plurality of simulation sub-databases in the established simulation database, configure basic parameters for the simulation database, and then input the matrix parameter group into the simulation database and obtain the second parameter;
更新模块使用模拟群在模拟数据库运算并且改变模拟数据库的参数，然后使用鉴定群鉴定模拟数据库的模拟正确系数；然后至少改变一种模拟分库的参数，该模拟分库的参数配置若干种，也即获取若干个新的模拟分库，并且使用鉴定群鉴定每一个新的模拟分库的模拟正确系数，并且选择正确系数最高的一个新的模拟分库的来代替原有的改变参数的模拟分库，然后再使用鉴定群鉴定模拟数据库的模拟正确系数，判断该模拟正确系数是否高于上一次的模拟数据库的模拟正确系数，如果否则终止模拟，如果是则继续循环模拟，直到获取正确系数最高的模拟数据库；所述的使用模拟群在模拟数据库运算具体是使用模拟数据库获取模拟群数据的上位特征并改变模拟数据库参数，模拟数据库参数实际映射模拟群数据的上位特征；具体的，所述的模拟数据库采用卷积神经网络模型数据库，所述的模拟分库采用卷积层。The update module uses the simulation group to operate in the simulation database and changes the parameters of the simulation database, and then uses the identification group to identify the simulation correct coefficient of the simulation database; then changes the parameters of at least one simulation sub-database, and the parameters of the simulation sub-database are configured in several kinds, and also That is, several new simulation sub-libraries are obtained, and the identification group is used to identify the simulation correct coefficient of each new simulation sub-library, and a new simulation sub-library with the highest correct coefficient is selected to replace the original simulation sub-library with changed parameters. Then use the identification group to identify the simulation correct coefficient of the simulation database, and judge whether the simulation correct coefficient is higher than the simulation correct coefficient of the previous simulation database. The simulation database; the described use of simulation group in simulation database operation is to use the simulation database to obtain the upper-level characteristics of simulation group data and change simulation database parameters, and the simulation database parameters actually map the upper-level characteristics of simulation group data; The simulation database adopts a convolutional neural network model database, and the simulation sub-database adopts a convolution layer.
所以，监测分析处理单元的卷积神经网络模型数据库建立，至少如下步骤：Therefore, to monitor the establishment of the convolutional neural network model database of the analysis and processing unit, at least the following steps are required:
更新模块将原始的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据处理为中间参量，将统计获取的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据映射的被监控者的意思表达数据处理为第二参量，并且将中间参量的数据格式表征为矩阵数据并具体表征为矩阵参量群；The update module combines the original intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, EEG electrical signal sensing, brain temperature sensing, brain oxygen sensing, brain microdialysis sensing, brain The blood flow sensing and EMG signal sensing data are processed as intermediate parameters, and the statistically obtained electrical signal sensing of intracranial pressure monitor, transcranial Doppler electrical signal sensing, and EEG electrical signal sensing brain temperature Sensing, cerebral oxygen sensing, cerebral microdialysis sensing, cerebral blood flow sensing, and brain electromyographic signal sensing data mapping of the meaning expression data of the monitored person is processed as the second parameter, and the data format of the intermediate parameter is processed Characterized as matrix data and specifically characterized as a matrix parameter group;
更新模块处理中间参量的矩阵参量群，给每一个矩阵参量群中的中间参量分配一个数值且每一个矩阵参量群的所有中间参量总和为1，并且将上述的矩阵参量群分为模拟群和鉴定群：The update module processes the matrix parameter groups of the intermediate parameters, assigns a value to the intermediate parameters in each matrix parameter group and the sum of all intermediate parameters in each matrix parameter group is 1, and divides the above matrix parameter groups into simulation groups and identification groups. group:
所述的鉴定群标记有第二参量；The identification group is marked with a second parameter;
建立的卷积神经网络模型数据库内至少设置多个卷积层，为卷积神经网络模型数据库配置基础的参数，然后将矩阵参量群输入到卷积神经网络模型数据库并获取第二参量；At least a plurality of convolution layers are set in the established convolutional neural network model database, basic parameters are configured for the convolutional neural network model database, and then the matrix parameter group is input into the convolutional neural network model database and the second parameter is obtained;
更新模块使用模拟群在卷积神经网络模型数据库运算并且改变卷积神经网络模型数据库的参数，然后使用鉴定群鉴定卷积神经网络模型数据库的模拟正确系数；然后至少改变一种卷积层的参数，该卷积层的参数配置若干种，也即获取若干个新的卷积层，并且使用鉴定群鉴定每一个新的卷积层的模拟正确系数，并且选择正确系数最高的一个新的卷积层的来代替原有的改变参数的卷积层，然后再使用鉴定群鉴定卷积神经网络模型数据库的模拟正确系数，判断该模拟正确系数是否高于上一次的卷积神经网络模型数据库的模拟正确系数，如果否则终止模拟，如果是则继续循环模拟，直到获取正确系数最高的卷积神经网络模型数据库；所述的使用模拟群在卷积神经网络模型数据库运算具体是使用卷积神经网络模型数据库获取模拟群数据的上位特征并改变卷积神经网络模型数据库参数，卷积神经网络模型数据库参数实际映射模拟群数据的上位特征。The update module uses the simulation group to operate on the convolutional neural network model database and changes the parameters of the convolutional neural network model database, and then uses the identification group to identify the simulation correct coefficients of the convolutional neural network model database; and then changes the parameters of at least one convolutional layer , the parameters of the convolutional layer are configured in several ways, that is, several new convolutional layers are obtained, and the identification group is used to identify the simulated correct coefficients of each new convolutional layer, and a new convolutional convolution with the highest correct coefficient is selected Then use the identification group to identify the simulation correct coefficient of the convolutional neural network model database, and judge whether the simulation correct coefficient is higher than the previous simulation of the convolutional neural network model database. Correct coefficient, if otherwise, terminate the simulation, if so, continue to loop the simulation until the convolutional neural network model database with the highest correct coefficient is obtained; the described use of the simulation group to operate in the convolutional neural network model database is to use the convolutional neural network model. The database acquires the upper-level features of the simulated group data and changes the parameters of the convolutional neural network model database, and the convolutional neural network model database parameters actually map the upper-level features of the simulated group data.
上述的模拟过程配置为卷积神经网络模型数据库的训练学习。The above-mentioned simulation process is configured as the training and learning of the convolutional neural network model database.
所以更加具体的，更新模块使用模拟群在卷积神经网络模型数据库运算并且改变卷积神经网络模型数据库的参数，然后使用鉴定群鉴定卷积神经网络模型数据库的训练学习正确系数；然后至少改变一种卷积层的参数，该卷积层的参数配置若干种，也即获取若干个新的卷积层，并且使用鉴定群鉴定每一个新的卷积层的训练学习正确系数，并且选择正确系数最高的一个新的卷积层的来代替原有的改变参数的卷积层，然后再使用鉴定群鉴定卷积神经网络模型数据库的训练学习正确系数，判断该训练学习正确系数是否高于上一次的卷积神经网络模型数据库的训练学习正确系数，如果否则终止模拟，如果是则继续循环训练学习，直到获取正确系数最高的卷积神经网络模型数据库；所述的使用模拟群在卷积神经网络模型数据库运算具体是使用卷积神经网络模型数据库获取模拟群数据的上位特征并改变卷积神经网络模型数据库参数，卷积神经网络模型数据库参数实际映射模拟群数据的上位特征。So more specifically, the update module uses the simulation group to operate on the convolutional neural network model database and changes the parameters of the convolutional neural network model database, and then uses the identification group to identify the training of the convolutional neural network model database to learn the correct coefficients; then change at least one The parameters of the convolutional layer are configured in several ways, that is, several new convolutional layers are obtained, and the training of each new convolutional layer is identified by the identification group to learn the correct coefficients, and the correct coefficients are selected The highest new convolutional layer replaces the original convolutional layer that changes parameters, and then uses the identification group to identify the training and learning correct coefficients of the convolutional neural network model database, and judges whether the training and learning correct coefficients are higher than the previous one. The training of the convolutional neural network model database learns the correct coefficients, if otherwise, the simulation is terminated, and if so, the cyclic training and learning are continued until the convolutional neural network model database with the highest correct coefficient is obtained; the use of the simulation group in the convolutional neural network The model database operation is to use the convolutional neural network model database to obtain the upper-level features of the simulated group data and change the parameters of the convolutional neural network model database. The parameters of the convolutional neural network model database actually map the upper-level features of the simulated group data.
通过上述方式建立的模拟数据库就可以实际用于对多类型传感数据之间的联系获取更加上位的数据并具体通过将原始的多类型传感数据处理为中间参量，将统计获取的多类型传感数据映射的被监控者的意思表达数据处理为第二参量，并且将中间参量的数据格式表征为矩阵数据，然后通过上述的对中间参量的处理尤其是通过模拟过程获取上位特征进而获取多类型传感数据之间的联系更加上位的数据。The simulation database established in the above way can actually be used to obtain more high-level data for the connection between the multi-type sensor data. Specifically, by processing the original multi-type sensor data as intermediate parameters, the statistical acquisition of The meaning expression data of the monitored person in the sensory data mapping is processed as the second parameter, and the data format of the intermediate parameter is represented as matrix data, and then the upper feature is obtained through the above-mentioned processing of the intermediate parameter, especially through the simulation process, and then multi-type The connection between the sensor data is more high-level data.
使用模拟数据库获取模拟群数据的上位特征并改变模拟数据库参数，模拟数据库参数实际映射模拟群数据的上位特征；模拟数据库采用卷积神经网络模型数据库，模拟分库采用卷积层以提高数据的处理的效率、成熟性并且降低成本。Use the simulation database to obtain the upper-level characteristics of the simulation group data and change the parameters of the simulation database, and the simulation database parameters actually map the upper-level characteristics of the simulation group data; the simulation database adopts the convolutional neural network model database, and the simulation sub-database adopts the convolution layer to improve data processing. efficiency, maturity and cost reduction.
实施中，将原始的多类型传感数据处理为中间参量，包括：对于每一单位的多类型传感数据，将多类型传感数据数值化，然后给每一种传感数据分配一个权重系数，将所有的数值化的传感数据排列为矩阵数据；即将原始的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感处理为中间参量，包括：对于每一单位的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感，将颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数值化，然后给每一种传感数据分配一个权重系数，将所有的数值化的传感数据排列为矩阵数据，比如按照颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感的重要程度以及其与上位数据的关系程度的大小为颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感分配由大到小的权重系数，该权重系数用于将上述传感数据数值化的依据。In the implementation, the original multi-type sensing data is processed as an intermediate parameter, including: for each unit of the multi-type sensing data, digitizing the multi-type sensing data, and then assigning a weight coefficient to each type of sensing data , arrange all the numerical sensing data into matrix data; namely, the original intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, EEG electrical signal sensing, brain temperature sensing, Cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, and brain EMG signal sensing processing are intermediate parameters, including: for each unit of intracranial pressure monitor electrical signal sensing, transcranial Doppler Ler electrical signal sensing, EEG electrical signal sensing, brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, brain electromyography signal sensing, the intracranial pressure monitor electrical Signal Sensing, Transcranial Doppler Electrical Signal Sensing, EEG Electrical Signal Sensing, Brain Temperature Sensing, Brain Oxygen Sensing, Brain Microdialysis Sensing, Cerebral Blood Flow Sensing, Brain EMG Signal Sensing Numericalization, and then assign a weight coefficient to each sensor data, and arrange all the digitized sensor data into matrix data. Sensory, EEG electrical signal sensing, brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, brain EMG signal sensing importance and their relationship with upper data The size is intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, EEG electrical signal sensing brain temperature sensing, brain oxygen sensing, brain microdialysis sensing, cerebral blood flow Sensing and brain EMG signal sensing are assigned weight coefficients from large to small, and the weight coefficients are used as the basis for digitizing the above-mentioned sensing data.
实施中，还包括为每一种传感数据分配一个动态的权重系数；即为每一个颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据分配一个动态的权重系数，通过配置上述的权重系数为动态变化可以不断优化中间参量。In the implementation, it also includes assigning a dynamic weight coefficient to each type of sensing data; that is, for each intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, and EEG electrical signal sensing. Brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, and EMG signal sensing data are assigned a dynamic weight coefficient. By configuring the above weight coefficient as dynamic changes, the intermediate Parameter.
实施中，还包括改变数值化的传感数据在矩阵数据中的排列顺序；即改变数值化的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据在矩阵数据中的排列顺序，也可以优化中间参量。In the implementation, it also includes changing the arrangement order of the digitized sensing data in the matrix data; that is, changing the digitized intracranial pressure monitor electrical signal sensing, transcranial Doppler electrical signal sensing, electroencephalogram electrical signal sensing Sensing the order of brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, and brain electromyographic signal sensing data in the matrix data can also optimize the intermediate parameters.
实施中，统计获取的多类型传感数据映射的被监控者的意思表达数据处理为第二参量，其中的第二参量表征有被监控者的意念需要；即统计获取的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据分别映射的被监控者的意思表达数据，并且将上述的意思表达数据处理为第二参量，其中的第二参量表征有被监控者的意念需要，比如说某一确定的多类型传感数据表达了被监控者的想要躺起，相应的，被监控者的意思表达数据就是对应的意念需求是想要躺起，那么第二参量实质就是对应标记“被监控者想要躺起”的数据。In the implementation, the meaning expression data of the monitored person mapped by the multi-type sensory data obtained by statistics is processed as the second parameter, wherein the second parameter represents the mental needs of the monitored person; that is, the statistically obtained intracranial pressure monitor electrical Signal Sensing, Transcranial Doppler Electrical Signal Sensing, EEG Electrical Signal Sensing, Brain Temperature Sensing, Brain Oxygen Sensing, Brain Microdialysis Sensing, Cerebral Blood Flow Sensing, Brain EMG Signal Sensing The data is mapped to the meaning expression data of the monitored person, and the above meaning expression data is processed as a second parameter, wherein the second parameter represents the intentional needs of the monitored person, such as a certain multi-type sensor data. It expresses that the monitored person wants to lie down. Correspondingly, the expressed data of the monitored person's meaning is that the corresponding idea demand is to lie down, so the second parameter is actually the corresponding mark "the monitored person wants to lie down". data.
更加具体的，实施中，第二参量还表征有被监控者的身体病理变化和急救需要，那么第二参量实质就是对应标记“被监控者具体有什么的身体病理变化”的数据/“被监控者具体需要什么的急救”的数据，More specifically, in the implementation, the second parameter also represents the physical pathological changes and emergency needs of the monitored person, so the second parameter is essentially the data corresponding to the label "what specific physical pathological changes does the monitored person have"/"monitored" data on what first aid the patient needs”,
因为分析模块用于将原始的多类型传感数据处理为中间参量，将中间参量输入到模拟数据库然后启动模拟获取中间变量对应的第二参量，所以实施中分析模块将原始的颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据处理为中间参量，将中间参量输入到模拟数据库然后启动模拟就可以获取中间变量对应的第二参量，第二参量实质就是对应标记被对监控的“被监控者具体有什么的身体病理变化”的数据/“被监控者具体需要什么的急救”的数据/“被监控者具体需要什么”的数据，所以通过这种方式就可以根据颅内压监测仪电信号传感、经颅多普勒电信号传感、脑电图电信号传感脑温传感、脑氧传感、脑部微透析传感、脑血流传感、脑肌电信号传感数据获取上位的数据。Because the analysis module is used to process the original multi-type sensory data into intermediate parameters, input the intermediate parameters into the simulation database, and then start the simulation to obtain the second parameters corresponding to the intermediate variables, the analysis module in the implementation uses the original intracranial pressure monitor Electrical Signal Sensing, Transcranial Doppler Electrical Signal Sensing, EEG Electrical Signal Sensing, Brain Temperature Sensing, Cerebral Oxygen Sensing, Brain Microdialysis Sensing, Cerebral Blood Flow Sensing, Brain EMG Signal Transmission The sensory data is processed as an intermediate parameter. Input the intermediate parameter into the simulation database and then start the simulation to obtain the second parameter corresponding to the intermediate variable. The second parameter is in essence corresponding to the marked “monitored person’s specific physical pathological changes”. ” data / “What is the first aid the monitored person needs” / “What is the specific need of the monitored person”, so in this way, we can sense the electrical signal of the intracranial pressure monitor, and the transcranial doppler Ler electrical signal sensing, EEG electrical signal sensing, brain temperature sensing, cerebral oxygen sensing, brain microdialysis sensing, cerebral blood flow sensing, and brain EMG signal sensing data to obtain upper-level data.
最后应当说明的是:以上实施例仅用以说明本发明的技术方案而非对其限制；尽管参照较佳实施例对本发明进行了详细的说明，所属领域的普通技术人员应当理解：依然可以对本发明的具体实施方式进行修改或者对部分技术特征进行等同替换；而不脱离本发明技术方案的精神，其均应涵盖在本发明请求保护的技术方案范围当中。Finally it should be noted that: the above embodiment is only used to illustrate the technical scheme of the present invention and not to limit it; Although the present invention has been described in detail with reference to the preferred embodiment, those of ordinary skill in the art should understand: The specific embodiments of the invention are modified or some technical features are equivalently replaced; without departing from the spirit of the technical solutions of the present invention, all of them should be included in the scope of the technical solutions claimed in the present invention.
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| CN202010838503.1ACN111951948A (en) | 2020-08-19 | 2020-08-19 | A neurocritical monitoring device |
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