CN115883670A - Medical data analysis and acquisition method and device - Google Patents
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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 monitoring medical data of the current period; executing a target data transmission mode to match with a corresponding target compression algorithm, and compressing the monitored medical data to obtain an acquisition data packet; merging the acquired data packet and the compressed identifier and sending the merged data packet to a cloud medical server; and matching a target decompression algorithm corresponding to the target compression algorithm according to 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 the monitored medical data, the efficient compression algorithm is matched in a self-adaptive mode, so that the occupation of redundant information on network resources and storage resources is reduced, and the effectiveness of medical data acquisition is guaranteed.
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
In the information age, 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. Today, biological signals, particularly electrocardiogram signals, are collected periodically for screening and may also be accessed online to facilitate diagnosis and improve healthcare quality.
However, the existing acquisition method usually only acquires and uploads 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 present invention is directed to solve the above problems of the background art, and provides a method and an apparatus for analyzing and collecting medical data.
The purpose of the invention can be realized 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 monitoring medical data of the current period according to the average medical data;
executing the target data transmission mode to match with a corresponding target compression algorithm, and compressing the monitored medical data to obtain an acquisition data packet;
merging the acquired data packet and the compressed identification and sending the merged 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 identification at the cloud medical server, decompressing the acquisition data packet by using the target decompression algorithm to obtain medical acquisition data corresponding to the monitoring medical data, and monitoring the current state of the target person according to the medical acquisition data.
Optionally, 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 for a current period of the monitored medical data according to the average medical data comprises:
calculating the similarity of the average medical data and the monitored medical data; the higher the similarity is, the smaller the risk of the monitored medical data is;
and determining the target data transmission mode according to the similarity.
Optionally, the target compression algorithm comprises a lossless compression algorithm, a lossy compression algorithm and a lossless and lossy mixed 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 and lossy hybrid compression algorithm extracts a signal through a lossy compression algorithm, and then encodes the extracted signal through a lossless compression algorithm.
Optionally, the compression process of the lossless and lossy hybrid compression algorithm is as follows:
performing 3-fold extraction on the monitored medical data, namely x [ n ] = x [3n ], then performing self-differentiation, enabling x [ n ] = x [ n +1] -x [ n ] and storing x [0] in a variable F;
determining and adding a minimum negative value to the signal such that the sequence is positive, i.e., x [ n ] = x [ n ] + min, min = | min (x [ n ]) |, and appending a minimum value, i.e., x [0] = min, at the 0 th position of the signal;
determining the mean of the signal, left-shifting the mean M times, where M =2 K K is valued between 1 and 7, Q is a quotient obtained by dividing the average value by M, R is the rest number, Q is represented by a unary, R is represented by a binary system, and a K bit value of a cascade array QR is obtained;
determining that the K value is in the range of 1-7 and the length of QR is shortest, fixing the K value, and leftwards shifting each data of the signal by 2 K Obtaining a two-dimensional array of QR, and connecting the two-dimensional array to obtain a one-dimensional range;
and splitting the array into 8 bits, converting the data into a decimal system, 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, a decompression flow of the lossless and lossy hybrid compression algorithm is as follows:
removing the 0 th bit element from the collected data packet, converting each data element into 8-bit binary data and connecting the 8 th bit binary data to obtain a one-dimensional array;
that is, the first Q value determines the first 1, the following K-bit is R, by equation 2 K * Q + R determines the decoded data, and the execution is repeated 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 successive elements such that x [ n ] = x [ n-1] + x [ n ] wherein n takes on 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 target person in a historical period and monitoring medical data of a current period, and determining a target data transmission mode of the monitoring medical data of the current period according to the average medical data;
the compression module is used for executing the target data transmission mode to match with a corresponding target compression algorithm and compressing the monitored medical data to obtain an acquisition data packet;
the sending module is used for combining the acquired data packet and the compressed identifier and sending the 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 at the cloud medical server according to the compression identification, decompressing the acquisition data packet by using the target decompression algorithm to obtain medical acquisition data corresponding to the monitoring medical data, and monitoring the current state of the target personnel according to the medical acquisition data.
Optionally, 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 monitored medical data; the higher the similarity, the smaller the risk of the monitored medical data;
and the matching module is used for determining the target data transmission mode according to the similarity.
Optionally, the target compression algorithm comprises a lossless compression algorithm, a lossy compression algorithm and a lossless and lossy mixed 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 and lossy hybrid compression algorithm extracts a signal through a lossy compression algorithm, and then encodes the extracted signal through a lossless compression algorithm.
Optionally, the compression process of the lossless and lossy hybrid compression algorithm executed by the compression module is as follows:
performing 3-fold extraction on the monitored medical data, namely x [ n ] = x [3n ], then performing self-differentiation, enabling x [ n ] = x [ n +1] -x [ n ] and storing x [0] in a variable F;
determining and adding a minimum negative value to the signal such that the sequence is positive, i.e., x [ n ] = x [ n ] + min, min = | min (x [ n ]) |, and appending a minimum value, i.e., x [0] = min, at the 0 th position of the signal;
determining the mean of the signal, left-shifting the mean M times, where M =2 K K is a value between 1 and 7, Q is a quotient obtained by dividing the average value by M, R is the rest number, Q is represented by a unary, R is represented by a binary system, and a K bit value of the QR is obtained;
determining that the K value is in the range of 1-7, the length of QR is shortest, fixing the K value, and leftwards shifting each data of the signal by 2 K Obtaining a two-dimensional array of QR, and connecting the two-dimensional array to obtain a one-dimensional range;
and splitting the array into 8 bits, converting the data into a decimal system, 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 process of the lossless and lossy hybrid compression algorithm executed by the compression module is as follows:
removing the 0 th bit element from the collected data packet, converting each data element into 8-bit binary data and connecting the 8 th bit binary data to obtain a one-dimensional array;
that is, the first Q value determines the first 1, the following K-bit is R, by equation 2 K * Q + R determines the decoded data, and the execution is repeated until all elements are determined;
the element at index 0 is stored as min, and min is subtracted from the element in the array;
adding successive elements such that x [ n ] = x [ n-1] + x [ n ] wherein n takes on 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 comprises the steps of obtaining 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 monitoring medical data of the current period according to the average medical data; executing a target data transmission mode to match with a corresponding target compression algorithm, and compressing the monitored medical data to obtain an acquisition data packet; merging the acquired data packet and the compressed identifier and sending the merged 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 at the cloud medical server according to the compression identification, decompressing the acquisition data packet by using the target decompression algorithm to obtain medical acquisition data corresponding to the monitored medical data, and monitoring the current state of the target personnel according to the medical acquisition data. By analyzing the average medical data and the monitored medical data, the efficient compression algorithm is matched in a self-adaptive mode, so that the occupation of redundant information on network resources and storage resources is reduced, and the effectiveness of medical data acquisition is guaranteed.
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The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a medical data analysis and collection method according to an embodiment of the present invention;
fig. 2 is a structural diagram of a medical data analysis and acquisition device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a medical data analysis and acquisition method. Referring to fig. 1, fig. 1 is a method for analyzing and collecting medical data according to an embodiment of the present invention. The method comprises the following steps:
s101, average medical data of a target person in a historical period and monitoring medical data of a current period are obtained, and a target data transmission mode of the monitoring medical data of the current period is determined according to the average medical data.
And S102, executing a target data transmission mode matched with a corresponding target compression algorithm, and compressing the monitored medical data to obtain an acquisition data packet.
S103, combining the acquired data packet with the compressed identifier and sending the combined data packet to a cloud medical server; the compression identifier is used to uniquely identify the target compression algorithm.
And S104, matching a target decompression algorithm corresponding to the target compression algorithm according to the compression identification at the cloud medical server, decompressing the acquisition data packet by using the target decompression algorithm to obtain medical acquisition data corresponding to the monitored medical data, and monitoring the current state of the target person according to the medical acquisition 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, and the high-efficiency compression algorithm is self-adaptively matched, so that 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 average medical data is mostly redundant information, and can reflect data characteristics of the redundant information in daily collection, and whether the monitored medical data carries more useful data can be determined by comparing the monitored medical data of the current period with the average medical data.
In one embodiment, the target data transfer mode includes a low risk data transfer, a medium risk data transfer, and a high risk data transfer; determining a target data transmission mode for monitoring a current period of medical data from the average medical data comprises:
calculating the similarity between the average medical data and the monitored medical data; the higher the similarity is, the smaller the risk of monitoring the medical data is;
and determining a target data transmission mode according to the similarity.
In one implementation, the higher the similarity, the smaller the risk of monitoring medical data, the more likely it is redundant information for routine monitoring, and therefore low risk data transmission may be employed; the lower the similarity, the higher the risk of monitoring medical data, and the more likely it is to be sudden monitoring information, so high-risk data transmission can be adopted; the rest can be divided into medium risk data transmission.
In one embodiment, 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 mixed 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; lossless and lossy hybrid compression algorithms extract signals through a lossy compression algorithm, and then the extracted signals are encoded by a lossless compression algorithm.
In one implementation, a lossy compression algorithm is adopted for low-risk data transmission, so that the data transmission quantity and the storage quantity are reduced; for the middle risk data transmission, a lossless and lossy mixed compression algorithm is adopted to reduce the data transmission amount and the storage amount as much as possible; and a lossless compression algorithm is adopted for high-risk data transmission, and all collected information is stored so as to facilitate subsequent data analysis.
In one embodiment, the compression flow of the lossless and lossy hybrid compression algorithm is as follows:
performing 3-fold extraction on the monitored medical data, namely x [ n ] = x [3n ], then performing self-differentiation, enabling x [ n ] = x [ n +1] -x [ n ] and storing x [0] in a variable F;
determining and adding a minimum negative value to the signal such that the sequence is positive, i.e., x [ n ] = x [ n ] + min, min = | min (x [ n ]) |, and appending a minimum value, i.e., x [0] = min, at the 0 th position of the signal;
determining the mean of the signal, left-shifting the mean M times, where M =2 K K is a value between 1 and 7, Q is a quotient obtained by dividing the average value by M, R is the rest number, Q is represented by a unary, R is represented by a binary system, and a K bit value of the QR is obtained;
determining that the K value is in the range of 1-7 and the length of QR is shortest, fixing the K value, and leftwards shifting each data of the signal by 2 K Obtaining a two-dimensional array of QR, and connecting the two-dimensional array to obtain a one-dimensional range;
and splitting the array into 8 bits, converting the data into a decimal system, and adding F to the decimal array at the 0 th bit to obtain a final compressed signal serving as an acquisition data packet.
In one embodiment, the decompression flow of the lossless and lossy hybrid compression algorithm is as follows:
removing the 0 th bit element from the collected data packet, converting each data element into 8-bit binary data and connecting the 8 th bit binary data to obtain a one-dimensional array;
that is, the first Q value determines the first 1, the following K-bit is R, and the following K-bit is determined by equation 2 K * Q + R determines the decoded data, and the execution is repeated until all elements are determined;
the element at index 0 is stored as min, and min is subtracted from the element in the array;
adding successive elements such that x [ n ] = x [ n-1] + x [ n ] wherein n takes on 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, an embodiment of the present invention further provides a medical data analysis and acquisition apparatus, referring to fig. 2, where fig. 2 is a structural diagram of the medical data analysis and acquisition apparatus provided in the embodiment of the present invention, the apparatus includes:
the analysis module 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 monitoring medical data of the current period according to the average medical data;
the compression module is used for executing a target data transmission mode to match with a corresponding target compression algorithm and compressing the monitored medical data to obtain an acquisition data packet;
the transmitting module is used for combining the acquired data packet and the compressed identifier and transmitting the data packet to the cloud medical server; the compression identifier is used for uniquely identifying a 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 identification at the cloud medical server, decompressing the acquisition data packet by using the target decompression algorithm to obtain medical acquisition data corresponding to the monitored medical data, and monitoring the current state of the target personnel according to the medical acquisition 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, and a high-efficiency compression algorithm is self-adaptively matched, so that 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 transfer mode includes a low risk data transfer, a medium risk data transfer, and a high risk data transfer; 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 is, the smaller the risk of monitoring the medical data is;
and the matching module is used for determining a target data transmission mode according to the similarity.
In one embodiment, 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 mixed 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; lossless and lossy hybrid compression algorithms extract signals through a lossy compression algorithm, and then the extracted signals are encoded by a lossless compression algorithm.
In one embodiment, the compression flow of the lossless and lossy hybrid compression algorithm executed by the compression module is as follows:
performing 3-fold extraction on the monitored medical data, namely x [ n ] = x [3n ], then performing self-differentiation, enabling x [ n ] = x [ n +1] -x [ n ] and storing x [0] in a variable F;
determining a minimum negative value and adding it to the signal such that the sequence is positive, i.e. x [ n ] = x [ n ] + min, min = | min (x [ n ]) |, appending the minimum value at the 0 th position of the signal, i.e. x [0] = min;
determining the mean of the signal, left-shifting the mean M times, where M =2 K K is a value between 1 and 7, and Q is an average value divided byUsing the quotient of M and R as the rest number, using a unary to represent Q, and using binary to represent R, so as to obtain the K bit value of the QR of the cascaded array;
determining that the K value is in the range of 1-7 and the length of QR is shortest, fixing the K value, and leftwards shifting each data of the signal by 2 K Obtaining a two-dimensional array of QR, and connecting the two-dimensional array to obtain a one-dimensional range;
and splitting the array into 8 bits, converting the data into a decimal system, adding F to the decimal array at the 0 th bit, and obtaining a final compressed signal as an acquisition data packet.
In one embodiment, the decompression flow of the lossless and lossy hybrid compression algorithm executed by the compression module is as follows:
removing the 0 th bit element from the collected data packet, converting each data element into 8-bit binary data and connecting the data elements to obtain a one-dimensional array;
that is, the first Q value determines the first 1, the following K-bit is R, by equation 2 K * Q + R determines the decoded data, and the execution is repeated until all elements are determined;
the element at index 0 is stored as min, and min is subtracted from the element in the array;
adding successive elements such that x [ n ] = x [ n-1] + x [ n ] wherein n takes on 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.
While one embodiment of the present invention has been described in detail, the description is only a preferred embodiment of the present invention and should not be taken as limiting the scope of the invention; all equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (10)
1. A method of medical data analysis acquisition, 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 monitoring medical data of the current period according to the average medical data;
executing the target data transmission mode to match with a corresponding target compression algorithm, and compressing the monitoring medical data to obtain an acquisition data packet;
merging the acquired data packet and the compressed identification and sending the merged 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 with the cloud medical server according to the compression identifier, decompressing the acquisition data packet by using the target decompression algorithm to obtain medical acquisition data corresponding to the monitored medical data, and monitoring the current state of the target person according to the medical acquisition data.
2. The medical data analysis and collection method of claim 1, wherein 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 for a current period of the monitored medical data according to the average medical data comprises:
calculating the similarity of the average medical data and the monitored medical data; the higher the similarity, the smaller the risk of the monitored medical data;
and determining the target data transmission mode according to the similarity.
3. The medical data analysis and acquisition method according to claim 2, wherein 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 and lossy hybrid compression algorithm extracts a signal through a lossy compression algorithm, and then encodes the extracted signal through a lossless compression algorithm.
4. The medical data analysis and collection method according to claim 3, wherein the compression process of the lossless and lossy hybrid compression algorithm is as follows:
performing 3-fold extraction on the monitored medical data, namely x [ n ] = x [3n ], then performing self-differentiation, enabling x [ n ] = x [ n +1] -x [ n ] and storing x [0] in a variable F;
determining and adding a minimum negative value to the signal such that the sequence is positive, i.e., x [ n ] = x [ n ] + min, min = | min (x [ n ]) |, and appending a minimum value, i.e., x [0] = min, at the 0 th position of the signal;
determining the mean of the signal, left-shifting the mean M times, where M =2 K K is a value between 1 and 7, Q is a quotient obtained by dividing the average value by M, R is the rest number, Q is represented by a unary, R is represented by a binary system, and a K bit value of the QR is obtained;
determining that the K value is in the range of 1-7 and the length of QR is shortest, fixing the K value, and leftwards shifting each data of the signal by 2 K Obtaining a two-dimensional array of QR, and connecting the two-dimensional array to obtain a one-dimensional range;
and splitting the array into 8 bits, converting the data into a decimal system, and adding F to the decimal array at the 0 th bit to obtain a final compressed signal serving as the acquisition data packet.
5. The medical data analysis and collection method according to claim 4, wherein the decompression process of the lossless and lossy hybrid compression algorithm is as follows:
removing the 0 th bit element from the acquisition data packet, converting each data element into 8-bit binary data and connecting the data elements to obtain a one-dimensional array;
that is, the first Q value determines the first 1, the following K-bit is R, by equation 2 K * Q + R determines the decoded data, and the execution is repeated 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 successive elements such that x [ n ] = x [ n-1] + x [ n ] wherein n takes on 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.
6. A medical data analysis acquisition device, the device comprising:
the analysis module 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 monitoring medical data of the current period according to the average medical data;
the compression module is used for executing the target data transmission mode to match with a 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 and the compressed identifier and sending the 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 at the cloud medical server according to the compression identifier, decompressing the acquisition data packet by using the target decompression algorithm to obtain medical acquisition data corresponding to the monitoring medical data, and monitoring the current state of the target personnel according to the medical acquisition data.
7. The medical data analysis and acquisition device according to claim 6, wherein 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 monitored medical data; the higher the similarity, the smaller the risk of the monitored medical data;
and the matching module is used for determining the target data transmission mode according to the similarity.
8. The medical data analysis and acquisition device according to claim 7, wherein 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 and lossy hybrid compression algorithm extracts a signal through a lossy compression algorithm, and then encodes the extracted signal through a lossless compression algorithm.
9. The medical data analysis and acquisition device according to claim 8, wherein the compression module executes the lossless and lossy hybrid compression algorithm by a compression process comprising:
performing 3-fold extraction on the monitored medical data, namely x [ n ] = x [3n ], then performing self-differentiation, enabling x [ n ] = x [ n +1] -x [ n ] and storing x [0] in a variable F;
determining a minimum negative value and adding it to the signal such that the sequence is positive, i.e. x [ n ] = x [ n ] + min, min = | min (x [ n ]) |, appending the minimum value at the 0 th position of the signal, i.e. x [0] = min;
determining the mean of the signal, left-shifting the mean M times, where M =2 K K is a value between 1 and 7, Q is a quotient obtained by dividing the average value by M, R is the rest number, Q is represented by a unary, R is represented by a binary system, and a K bit value of the QR is obtained;
determining that the K value is in the range of 1-7, the length of QR is shortest, fixing the K value, and leftwards shifting each data of the signal by 2 K Obtaining a two-dimensional array of QR, and connecting the two-dimensional array to obtain a one-dimensional range;
and splitting the array into 8 bits, converting the data into a decimal system, adding F to the decimal array at the 0 th bit, and obtaining a final compressed signal serving as the acquisition data packet.
10. The medical data analysis and acquisition device according to claim 9, wherein the decompression process of the lossless and lossy hybrid compression algorithm executed by the compression module is as follows:
removing the 0 th bit element from the collected data packet, converting each data element into 8-bit binary data and connecting the 8 th bit binary data to obtain a one-dimensional array;
that is, the first Q value determines the first 1, the following K-bit is R, by equation 2 K * Q + R determines the decoded data, and the execution is repeated until all elements are determined;
the element at index 0 is stored as min, and min is subtracted from the element in the array;
adding successive elements such that x [ n ] = x [ n-1] + x [ n ] wherein n takes on 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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