CN115883670B - Medical data analysis and acquisition method and device - Google Patents
Medical data analysis and acquisition method and device Download PDFInfo
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- CN115883670B CN115883670B CN202310138633.8A CN202310138633A CN115883670B CN 115883670 B CN115883670 B CN 115883670B CN 202310138633 A CN202310138633 A CN 202310138633A CN 115883670 B CN115883670 B CN 115883670B
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Abstract
The invention discloses a medical data analysis and acquisition method and device, and relates to the technical field of data processing. Acquiring average medical data of a target person in a historical period and monitoring medical data of a current period, and determining a target data transmission mode of the current period of the monitoring medical data; executing a target data transmission mode matching corresponding target compression algorithm, and compressing the monitoring medical data to obtain an acquisition data packet; combining the acquired data packet with the compression identifier and sending the combined acquired data packet to a cloud medical server; and according to a target decompression algorithm corresponding to the target compression algorithm matched with the compression identification, decompressing the acquired data packet by using the target decompression algorithm to obtain medical acquired data corresponding to the monitored medical data, and monitoring the current state of the target personnel according to the medical acquired data. By analyzing the average medical data and monitoring the medical data, the efficient compression algorithm is adaptively matched, so that occupation of redundant information on network resources and storage resources is reduced, and the effectiveness of medical data acquisition is ensured.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a medical data analysis and acquisition method and device.
Background
Under the information age, the scientific technologies such as big data technology, internet of things technology and the like are rapidly developed and widely applied, and particularly applied to remote health monitoring. Nowadays, biological signals, in particular electrocardiographic signals, are collected periodically for screening, but also can be accessed online to facilitate diagnosis and to improve healthcare quality.
However, the existing acquisition method is generally only used for acquiring and uploading medical data, and a large amount of data in daily monitoring is redundant information, so that the acquisition amount of the medical data is huge, and a large amount of network resources and storage resources are occupied.
Disclosure of Invention
The invention aims to solve the problems of the background technology and provides a medical data analysis and acquisition method and a device.
The aim of the invention can be achieved by the following technical scheme:
the embodiment of the invention provides a medical data analysis and acquisition method, which comprises the following steps:
acquiring average medical data of a target person in a historical period and monitoring medical data of a current period, and determining a target data transmission mode of the current period of the monitoring medical data according to the average medical data;
executing a target compression algorithm corresponding to the target data transmission mode matching, and compressing the monitoring medical data to obtain an acquisition data packet;
combining the acquired data packet with the compression identifier and sending the combined acquired data packet to a cloud medical server; the compression identifier is used for uniquely identifying the target compression algorithm;
and matching a target decompression algorithm corresponding to the target compression algorithm according to the compression identifier by the cloud medical server, decompressing the acquired data packet by using the target decompression algorithm to obtain medical acquired data corresponding to the monitoring medical data, and monitoring the current state of the target personnel according to the medical acquired data.
Optionally, the target data transmission mode includes low risk data transmission, medium risk data transmission, and high risk data transmission; determining a target data transmission mode of the current period of the monitoring medical data according to the average medical data comprises:
calculating the similarity of the average medical data and the monitoring medical data; the higher the similarity, the less risk the medical data is monitored;
and determining the target data transmission mode according to the similarity.
Optionally, the target compression algorithm includes a lossless compression algorithm, a lossy compression algorithm, and a lossless lossy hybrid compression algorithm; the low-risk data transmission corresponds to a lossy compression algorithm, the medium-risk data transmission corresponds to a lossless lossy hybrid compression algorithm, and the high-risk data transmission corresponds to a lossless compression algorithm; the lossless compression algorithm comprises at least one of a run-length coding algorithm, a Huffman coding algorithm, a partial matching prediction algorithm and a Rice Golomb coding algorithm; the lossy compression algorithm comprises at least one of a vector quantization algorithm, a transform coding algorithm and a wavelet compression algorithm; the lossless lossy hybrid compression algorithm extracts a signal by a lossy compression algorithm, and then the lossless compression algorithm encodes the extracted signal.
Optionally, the compression flow of the lossless lossy hybrid compression algorithm is:
extracting the monitored medical data 3 times, namely x [ n ] =x [3n ], then performing self-differentiation to ensure that x [ n ] =x [ n+1] -x [ n ] and storing x [0] in a variable F;
determining the minimum negative value and adding the minimum negative value to the signal, so that the sequence is positive, namely x [ n ] = x [ n ] +min, and min= |min (x [ n ])|, and adding the minimum value at the 0 th position of the signal, namely x [0] = min;
determining the mean of the signal, shifting the mean left M times, where m=2 K K is valued between 1 and 7, Q is the quotient of the average value divided by M, R is the rest number, Q is represented by unary, R is represented by binary system, and the K bit value of the cascade array QR is obtained;
determining that the K value is in the range of 1-7, the QR length is shortest, fixing the K value, and shifting each data of the signal to the left by 2 K Obtaining a two-dimensional array of the QR, and connecting the two-dimensional array to obtain a one-dimensional range;
splitting the array into 8 bits, converting the data into decimal, and adding F to the decimal array at the 0 th bit to obtain a final compressed signal serving as the acquisition data packet.
Optionally, the decompression flow of the lossless lossy hybrid compression algorithm is as follows:
removing bit 0 elements from the acquired data packet, converting each data element into 8-bit binary data and concatenating them to obtain a one-dimensional array;
the first Q value is the first 1, the following K bit is R, through formula 2 K * Q+R determines the decoded data, repeatedly executing until all elements are determined;
the element at the 0 th index is stored as min, and min is subtracted from the element of the array;
adding consecutive elements such that x [ n ] = x [ n-1] + x [ n ] where n takes a value from 1 to n and x [0] = F;
the signal is interpolated by a factor of 3 by inserting the same previous value, where x [ n ] = x [ n/3], to obtain a reconstructed signal.
The embodiment of the invention also provides a medical data analysis and acquisition device, which comprises:
the analysis module is used for acquiring average medical data of a historical period of a target person and monitoring medical data of a current period, and determining a target data transmission mode of the current period of the monitoring medical data according to the average medical data;
the compression module is used for executing a target compression algorithm corresponding to the target data transmission mode matching, and compressing the monitoring medical data to obtain an acquisition data packet;
the sending module is used for combining the acquired data packet with the compression identifier and sending the combined acquired data packet to the cloud medical server; the compression identifier is used for uniquely identifying the target compression algorithm;
and the decompression analysis module is used for matching a target decompression algorithm corresponding to the target compression algorithm according to the compression identifier at the cloud medical server, decompressing the acquired data packet by using the target decompression algorithm to obtain medical acquired data corresponding to the monitored medical data, and monitoring the current state of the target personnel according to the medical acquired data.
Optionally, the target data transmission mode includes low risk data transmission, medium risk data transmission, and high risk data transmission; the analysis module comprises a calculation module and a matching module:
the calculation module is used for calculating the similarity between the average medical data and the monitoring medical data; the higher the similarity, the less risk the medical data is monitored;
and the matching module is used for determining the target data transmission mode according to the similarity.
Optionally, the target compression algorithm includes a lossless compression algorithm, a lossy compression algorithm, and a lossless lossy hybrid compression algorithm; the low-risk data transmission corresponds to a lossy compression algorithm, the medium-risk data transmission corresponds to a lossless lossy hybrid compression algorithm, and the high-risk data transmission corresponds to a lossless compression algorithm; the lossless compression algorithm comprises at least one of a run-length coding algorithm, a Huffman coding algorithm, a partial matching prediction algorithm and a Rice Golomb coding algorithm; the lossy compression algorithm comprises at least one of a vector quantization algorithm, a transform coding algorithm and a wavelet compression algorithm; the lossless lossy hybrid compression algorithm extracts a signal by a lossy compression algorithm, and then the lossless compression algorithm encodes the extracted signal.
Optionally, the compression module executes a compression flow of the lossless lossy hybrid compression algorithm as follows:
extracting the monitored medical data 3 times, namely x [ n ] =x [3n ], then performing self-differentiation to ensure that x [ n ] =x [ n+1] -x [ n ] and storing x [0] in a variable F;
determining the minimum negative value and adding the minimum negative value to the signal, so that the sequence is positive, namely x [ n ] = x [ n ] +min, and min= |min (x [ n ])|, and adding the minimum value at the 0 th position of the signal, namely x [0] = min;
determining the mean of the signal, shifting the mean left M times, where m=2 K K is valued between 1 and 7, Q is the quotient of the average value divided by M, R is the rest number, Q is represented by unary, R is represented by binary system, and the K bit value of the cascade array QR is obtained;
determining that the K value is in the range of 1-7, the QR length is shortest, fixing the K value, and shifting each data of the signal to the left by 2 K Obtaining a two-dimensional array of the QR, and connecting the two-dimensional array to obtain a one-dimensional range;
splitting the array into 8 bits, converting the data into decimal, and adding F to the decimal array at the 0 th bit to obtain a final compressed signal serving as the acquisition data packet.
Optionally, the decompression flow of the compression module executing the lossless lossy hybrid compression algorithm is:
removing bit 0 elements from the acquired data packet, converting each data element into 8-bit binary data and concatenating them to obtain a one-dimensional array;
the first Q value is the first 1, the following K bit is R, through formula 2 K * Q+R determines the decoded data, repeatedly executing until all elements are determined;
the element at the 0 th index is stored as min, and min is subtracted from the element of the array;
adding consecutive elements such that x [ n ] = x [ n-1] + x [ n ] where n takes a value from 1 to n and x [0] = F;
the signal is interpolated by a factor of 3 by inserting the same previous value, where x [ n ] = x [ n/3], to obtain a reconstructed signal.
The invention has the beneficial effects that:
the embodiment of the invention provides a medical data analysis and acquisition method, which is used for acquiring average medical data of a target person in a historical period and monitoring medical data of a current period, and determining a target data transmission mode of the current period of the monitoring medical data according to the average medical data; executing a target data transmission mode matching corresponding target compression algorithm, and compressing the monitoring medical data to obtain an acquisition data packet; combining the acquired data packet with the compression identifier and sending the combined acquired data packet to a cloud medical server; the compression identifier is used for uniquely identifying a target compression algorithm; and matching a target decompression algorithm corresponding to the target compression algorithm according to the compression identifier at the cloud medical server, decompressing the acquired data packet by using the target decompression algorithm to obtain medical acquired data corresponding to the monitored medical data, and monitoring the current state of the target personnel according to the medical acquired data. By analyzing the average medical data and monitoring the medical data, the efficient compression algorithm is adaptively matched, so that occupation of redundant information on network resources and storage resources is reduced, and the effectiveness of medical data acquisition is ensured.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a diagram of a medical data analysis and collection method according to an embodiment of the present invention;
fig. 2 is a block diagram of a medical data analysis and acquisition device according to an embodiment of the present invention.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a medical data analysis and acquisition method. Referring to fig. 1, fig. 1 is a schematic diagram of a medical data analysis and acquisition method according to an embodiment of the present invention. The method comprises the following steps:
s101, acquiring average medical data of a historical period of a target person and monitoring medical data of a current period, and determining a target data transmission mode of the current period of the monitoring medical data according to the average medical data.
S102, executing a target compression algorithm corresponding to the target data transmission mode matching, and compressing the monitoring medical data to obtain an acquisition data packet.
S103, combining the acquired data packet with the compression identifier and sending the combined acquired data packet to a cloud medical server; the compression identifier is used to uniquely identify the target compression algorithm.
S104, matching a target decompression algorithm corresponding to the target compression algorithm according to the compression identification at the cloud medical server, decompressing the acquired data packet by using the target decompression algorithm to obtain medical acquired data corresponding to the monitored medical data, and monitoring the current state of the target personnel according to the medical acquired data.
According to the medical data analysis and acquisition method provided by the embodiment of the invention, the average medical data and the monitored medical data are analyzed, the compression algorithm with high efficiency is adaptively matched, and further, the occupation of redundant information on network resources and storage resources is reduced, and the effectiveness of medical data acquisition is ensured.
In one implementation, the redundant information in the average medical data is a majority, which can reflect the data characteristics of the redundant information during daily collection, and by comparing the monitored medical data in the current period with the average medical data, it can be determined whether the monitored medical data carries more useful data.
In one embodiment, the target data transmission mode includes low risk data transmission, medium risk data transmission, and high risk data transmission; determining a target data transmission mode for monitoring a current period of medical data based on the average medical data includes:
calculating the similarity between the average medical data and the monitored medical data; the higher the similarity, the smaller the risk of monitoring medical data;
and determining the target data transmission mode according to the similarity.
In one implementation, the higher the similarity, the less risk the medical data is monitored, which is more likely to be redundant information for daily monitoring, so low risk data transmission can be employed; the lower the similarity, the greater the risk of monitoring medical data, the more likely it is to burst monitoring information, so high risk data transmission can be employed; the remainder may be divided into risk data transmissions.
In one embodiment, the target compression algorithm includes a lossless compression algorithm, a lossy compression algorithm, and a lossless lossy hybrid compression algorithm; the low risk data transmission corresponds to a lossy compression algorithm, the medium risk data transmission corresponds to a lossless lossy hybrid compression algorithm, and the high risk data transmission corresponds to a lossless compression algorithm; the lossless compression algorithm comprises at least one of a run-length coding algorithm, a Huffman coding algorithm, a partial matching prediction algorithm and a Rice Golomb coding algorithm; the lossy compression algorithm comprises at least one of a vector quantization algorithm, a transform coding algorithm and a wavelet compression algorithm; the lossless lossy hybrid compression algorithm extracts a signal by the lossy compression algorithm, and then the extracted signal is encoded by the lossless compression algorithm.
In one implementation, a lossy compression algorithm is used for low risk data transmission to reduce the amount of data transmission and storage; for the transmission of the risk data, a lossless and lossy hybrid compression algorithm is adopted to reduce the data transmission quantity and the memory quantity as much as possible; and adopting a lossless compression algorithm for high-risk data transmission, and storing all acquired information so as to carry out data analysis subsequently.
In one embodiment, the compression flow of the lossless lossy hybrid compression algorithm is:
extracting the monitored medical data 3 times, namely x [ n ] =x [3n ], then performing self-differentiation to ensure that x [ n ] =x [ n+1] -x [ n ] and storing x [0] in a variable F;
determining the minimum negative value and adding the minimum negative value to the signal, so that the sequence is positive, namely x [ n ] = x [ n ] +min, and min= |min (x [ n ])|, and adding the minimum value at the 0 th position of the signal, namely x [0] = min;
determining the mean of the signal, shifting the mean left M times, where m=2 K K is valued between 1 and 7, Q is the quotient of the average value divided by M, R is the rest number, Q is represented by unary, R is represented by binary system, and the K bit value of the cascade array QR is obtained;
determining that the K value is in the range of 1-7, the QR length is shortest, fixing the K value, and shifting each data of the signal to the left by 2 K Obtaining a two-dimensional array of the QR, and connecting the two-dimensional array to obtain a one-dimensional range;
splitting the array into 8 bits, converting the data into decimal, and adding F into the decimal array at the 0 th bit to obtain a final compressed signal as an acquisition data packet.
In one embodiment, the decompression flow of the lossless lossy hybrid compression algorithm is:
removing bit 0 elements from the collected data packet, converting each data element into 8-bit binary data and concatenating them to obtain a one-dimensional array;
the first Q value is the first 1, the following K bit is R, through formula 2 K * Q+R determines the decoded data, repeatedly executing until all elements are determined;
the element at the 0 th index is stored as min, and min is subtracted from the element of the array;
adding consecutive elements such that x [ n ] = x [ n-1] + x [ n ] where n takes a value from 1 to n and x [0] = F;
the signal is interpolated by a factor of 3 by inserting the same previous value, where x [ n ] = x [ n/3], to obtain a reconstructed signal.
Based on the same inventive concept, the embodiment of the present invention further provides a medical data analysis and acquisition device, referring to fig. 2, fig. 2 is a structural diagram of the medical data analysis and acquisition device provided by the embodiment of the present invention, where the device includes:
the analysis module is used for acquiring average medical data of the historical period of the target personnel and monitoring medical data of the current period, and determining a target data transmission mode of the current period of the monitoring medical data according to the average medical data;
the compression module is used for executing a target data transmission mode matching corresponding target compression algorithm and compressing the monitoring medical data to obtain an acquisition data packet;
the sending module is used for combining the acquired data packet with the compression identifier and sending the acquired data packet to the cloud medical server; the compression identifier is used for uniquely identifying a target compression algorithm;
the decompression analysis module is used for matching a target decompression algorithm corresponding to the target compression algorithm according to the compression identification at the cloud medical server, decompressing the acquired data packet by using the target decompression algorithm to obtain medical acquired data corresponding to the monitored medical data, and monitoring the current state of the target personnel according to the medical acquired data.
According to the medical data analysis and acquisition device provided by the embodiment of the invention, the average medical data and the monitored medical data are analyzed, the compression algorithm with high efficiency is adaptively matched, and further, the occupation of redundant information on network resources and storage resources is reduced, and the effectiveness of medical data acquisition is ensured.
In one embodiment, the target data transmission mode includes low risk data transmission, medium risk data transmission, and high risk data transmission; the analysis module comprises a calculation module and a matching module:
the calculation module is used for calculating the similarity between the average medical data and the monitored medical data; the higher the similarity, the smaller the risk of monitoring medical data;
and the matching module is used for determining a target data transmission mode according to the similarity.
In one embodiment, the target compression algorithm includes a lossless compression algorithm, a lossy compression algorithm, and a lossless lossy hybrid compression algorithm; the low risk data transmission corresponds to a lossy compression algorithm, the medium risk data transmission corresponds to a lossless lossy hybrid compression algorithm, and the high risk data transmission corresponds to a lossless compression algorithm; the lossless compression algorithm comprises at least one of a run-length coding algorithm, a Huffman coding algorithm, a partial matching prediction algorithm and a Rice Golomb coding algorithm; the lossy compression algorithm comprises at least one of a vector quantization algorithm, a transform coding algorithm and a wavelet compression algorithm; the lossless lossy hybrid compression algorithm extracts a signal by the lossy compression algorithm, and then the extracted signal is encoded by the lossless compression algorithm.
In one embodiment, the compression module performs a lossless lossy hybrid compression algorithm with a compression flow of:
extracting the monitored medical data 3 times, namely x [ n ] =x [3n ], then performing self-differentiation to ensure that x [ n ] =x [ n+1] -x [ n ] and storing x [0] in a variable F;
determining the minimum negative value and adding the minimum negative value to the signal, so that the sequence is positive, namely x [ n ] = x [ n ] +min, and min= |min (x [ n ])|, and adding the minimum value at the 0 th position of the signal, namely x [0] = min;
determining the mean of the signal, shifting the mean left M times, where m=2 K K is valued between 1 and 7, Q is the quotient of the average value divided by M, R is the rest number, Q is represented by unary, R is represented by binary system, and the K bit value of the cascade array QR is obtained;
determining that the K value is in the range of 1-7, the QR length is shortest, fixing the K value, and shifting each data of the signal to the left by 2 K Obtaining a two-dimensional array of the QR, and connecting the two-dimensional array to obtain a one-dimensional range;
splitting the array into 8 bits, converting the data into decimal, and adding F into the decimal array at the 0 th bit to obtain a final compressed signal as an acquisition data packet.
In one embodiment, the decompression flow of the lossless lossy hybrid compression algorithm performed by the compression module is:
removing bit 0 elements from the collected data packet, converting each data element into 8-bit binary data and concatenating them to obtain a one-dimensional array;
the first Q value is the first 1, the following K bit is R, through formula 2 K * Q+R determines the decoded data, repeatedly executing until all elements are determined;
the element at the 0 th index is stored as min, and min is subtracted from the element of the array;
adding consecutive elements such that x [ n ] = x [ n-1] + x [ n ] where n takes a value from 1 to n and x [0] = F;
the signal is interpolated by a factor of 3 by inserting the same previous value, where x [ n ] = x [ n/3], to obtain a reconstructed signal.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (4)
1. A medical data analysis and acquisition method, the method comprising:
acquiring average medical data of a target person in a historical period and monitoring medical data of a current period, and determining a target data transmission mode of the current period of the monitoring medical data according to the average medical data;
executing a target compression algorithm corresponding to the target data transmission mode matching, and compressing the monitoring medical data to obtain an acquisition data packet;
combining the acquired data packet with the compression identifier and sending the combined acquired data packet to a cloud medical server; the compression identifier is used for uniquely identifying the target compression algorithm;
matching a target decompression algorithm corresponding to the target compression algorithm according to the compression identifier at the cloud medical server, decompressing the acquired data packet by using the target decompression algorithm to obtain medical acquired data corresponding to the monitoring medical data, and monitoring the current state of the target personnel according to the medical acquired data;
the target data transmission mode comprises low risk data transmission, medium risk data transmission and high risk data transmission; determining a target data transmission mode of the current period of the monitoring medical data according to the average medical data comprises:
calculating the similarity of the average medical data and the monitoring medical data; the higher the similarity, the less risk the medical data is monitored;
determining the target data transmission mode according to the similarity;
the target compression algorithm comprises a lossless compression algorithm, a lossy compression algorithm and a lossless and lossy hybrid compression algorithm; the low-risk data transmission corresponds to a lossy compression algorithm, the medium-risk data transmission corresponds to a lossless lossy hybrid compression algorithm, and the high-risk data transmission corresponds to a lossless compression algorithm; the lossless compression algorithm comprises at least one of a run-length coding algorithm, a Huffman coding algorithm, a partial matching prediction algorithm and a Rice Golomb coding algorithm; the lossy compression algorithm comprises at least one of a vector quantization algorithm, a transform coding algorithm and a wavelet compression algorithm; the lossless lossy hybrid compression algorithm extracts signals through a lossy compression algorithm, and then the lossless compression algorithm encodes the extracted signals;
the compression flow of the lossless lossy hybrid compression algorithm is as follows:
extracting the monitored medical data 3 times, namely x [ n ] =x [3n ], then performing self-differentiation to ensure that x [ n ] =x [ n+1] -x [ n ] and storing x [0] in a variable F;
determining the minimum negative value and adding the minimum negative value to the signal, so that the sequence is positive, namely x [ n ] = x [ n ] +min, and min= |min (x [ n ])|, and adding the minimum value at the 0 th position of the signal, namely x [0] = min;
determining the mean of the signal, shifting the mean left M times, where m=2 K K is valued between 1 and 7, Q is the quotient of the average value divided by M, R is the rest number, Q is represented by unary, R is represented by binary system, and the K bit value of the cascade array QR is obtained;
determining that the K value is in the range of 1-7, enabling the length of the QR to be shortest, fixing the K value, and enabling each data of the signals to beLeft shift 2 K Obtaining a two-dimensional array of the QR, and connecting the two-dimensional array to obtain a one-dimensional range;
splitting the array into 8 bits, converting the data into decimal, and adding F to the decimal array at the 0 th bit to obtain a final compressed signal serving as the acquisition data packet.
2. The medical data analysis and acquisition method according to claim 1, wherein the decompression flow of the lossless lossy hybrid compression algorithm is as follows:
removing bit 0 elements from the acquired data packet, converting each data element into 8-bit binary data and concatenating them to obtain a one-dimensional array;
the first Q value is the first 1, the following K bit is R, through formula 2 K * Q+R determines the decoded data, repeatedly executing until all elements are determined;
the element at the 0 th index is stored as min, and min is subtracted from the element of the array;
adding consecutive elements such that x [ n ] = x [ n-1] + x [ n ] where n takes a value from 1 to n and x [0] = F;
the signal is interpolated by a factor of 3 by inserting the same previous value, where x [ n ] = x [ n/3], to obtain a reconstructed signal.
3. A medical data analysis acquisition device, the device comprising:
the analysis module is used for acquiring average medical data of a historical period of a target person and monitoring medical data of a current period, and determining a target data transmission mode of the current period of the monitoring medical data according to the average medical data;
the compression module is used for executing a target compression algorithm corresponding to the target data transmission mode matching, and compressing the monitoring medical data to obtain an acquisition data packet;
the sending module is used for combining the acquired data packet with the compression identifier and sending the combined acquired data packet to the cloud medical server; the compression identifier is used for uniquely identifying the target compression algorithm;
the decompression analysis module is used for matching a target decompression algorithm corresponding to the target compression algorithm according to the compression identifier at the cloud medical server, decompressing the acquired data packet by using the target decompression algorithm to obtain medical acquired data corresponding to the monitoring medical data, and monitoring the current state of the target personnel according to the medical acquired data;
the target data transmission mode comprises low risk data transmission, medium risk data transmission and high risk data transmission; the analysis module comprises a calculation module and a matching module:
the calculation module is used for calculating the similarity between the average medical data and the monitoring medical data; the higher the similarity, the less risk the medical data is monitored;
the matching module is used for determining the target data transmission mode according to the similarity;
the target compression algorithm comprises a lossless compression algorithm, a lossy compression algorithm and a lossless and lossy hybrid compression algorithm; the low-risk data transmission corresponds to a lossy compression algorithm, the medium-risk data transmission corresponds to a lossless lossy hybrid compression algorithm, and the high-risk data transmission corresponds to a lossless compression algorithm; the lossless compression algorithm comprises at least one of a run-length coding algorithm, a Huffman coding algorithm, a partial matching prediction algorithm and a Rice Golomb coding algorithm; the lossy compression algorithm comprises at least one of a vector quantization algorithm, a transform coding algorithm and a wavelet compression algorithm; the lossless lossy hybrid compression algorithm extracts signals through a lossy compression algorithm, and then the lossless compression algorithm encodes the extracted signals;
the compression flow of the compression module executing the lossless lossy hybrid compression algorithm is as follows:
extracting the monitored medical data 3 times, namely x [ n ] =x [3n ], then performing self-differentiation to ensure that x [ n ] =x [ n+1] -x [ n ] and storing x [0] in a variable F;
determining the minimum negative value and adding the minimum negative value to the signal, so that the sequence is positive, namely x [ n ] = x [ n ] +min, and min= |min (x [ n ])|, and adding the minimum value at the 0 th position of the signal, namely x [0] = min;
determining the mean of the signal, shifting the mean left M times, where m=2 K K is valued between 1 and 7, Q is the quotient of the average value divided by M, R is the rest number, Q is represented by unary, R is represented by binary system, and the K bit value of the cascade array QR is obtained;
determining that the K value is in the range of 1-7, the QR length is shortest, fixing the K value, and shifting each data of the signal to the left by 2 K Obtaining a two-dimensional array of the QR, and connecting the two-dimensional array to obtain a one-dimensional range;
splitting the array into 8 bits, converting the data into decimal, and adding F to the decimal array at the 0 th bit to obtain a final compressed signal serving as the acquisition data packet.
4. A medical data analysis and acquisition device according to claim 3, wherein the decompression process performed by the compression module to perform the lossless lossy hybrid compression algorithm is:
removing bit 0 elements from the acquired data packet, converting each data element into 8-bit binary data and concatenating them to obtain a one-dimensional array;
the first Q value is the first 1, the following K bit is R, through formula 2 K * Q+R determines the decoded data, repeatedly executing until all elements are determined;
the element at the 0 th index is stored as min, and min is subtracted from the element of the array;
adding consecutive elements such that x [ n ] = x [ n-1] + x [ n ] where n takes a value from 1 to n and x [0] = F;
the signal is interpolated by a factor of 3 by inserting the same previous value, where x [ n ] = x [ n/3], to obtain a reconstructed signal.
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